Search is not available for this dataset
article
stringlengths 4.36k
149k
| summary
stringlengths 32
3.35k
| section_headings
listlengths 1
91
| keywords
listlengths 0
141
| year
stringclasses 13
values | title
stringlengths 20
281
|
|---|---|---|---|---|---|
The classical Hodgkin-Huxley ( HH ) model neglects the time-dependence of ion concentrations in spiking dynamics . The dynamics is therefore limited to a time scale of milliseconds , which is determined by the membrane capacitance multiplied by the resistance of the ion channels , and by the gating time constants . We study slow dynamics in an extended HH framework that includes time-dependent ion concentrations , pumps , and buffers . Fluxes across the neuronal membrane change intra- and extracellular ion concentrations , whereby the latter can also change through contact to reservoirs in the surroundings . Ion gain and loss of the system is identified as a bifurcation parameter whose essential importance was not realized in earlier studies . Our systematic study of the bifurcation structure and thus the phase space structure helps to understand activation and inhibition of a new excitability in ion homeostasis which emerges in such extended models . Also modulatory mechanisms that regulate the spiking rate can be explained by bifurcations . The dynamics on three distinct slow times scales is determined by the cell volume-to-surface-area ratio and the membrane permeability ( seconds ) , the buffer time constants ( tens of seconds ) , and the slower backward buffering ( minutes to hours ) . The modulatory dynamics and the newly emerging excitable dynamics corresponds to pathological conditions observed in epileptiform burst activity , and spreading depression in migraine aura and stroke , respectively .
In this paper we study ion dynamics in ion-based neuron models . In comparison to classical HH type membrane models this introduces dynamics on much slower time scales . While spiking activity is in the order of milliseconds , the time scales of ion dynamics range from seconds to minutes and even hours depending on the process ( transmembrane fluxes , glial buffering , backward buffering ) . The slow dynamics leads to new phenomena . Slow burst modulation as in seizure-like activity ( SLA ) emerges from moderate changes in the ion concentrations . Phase space excursions with large changes in the ionic variables establish a new type of ionic excitability as observed in cortical spreading depression ( SD ) during stroke and in migraine with aura [1] , [2] . Such newly emerging dynamics can be understood from the phase space structure of the ion-based models . Mathematical models of neural ion dynamics can be divided into two classes . On the one hand the discovery of SD by Leão in 1944 [3]—a severe perturbation of neural ion homeostasis associated with huge changes in the potassium , sodium and chloride ion concentrations in the extracellular space ( ECS ) [4] that spreads through the tissue—has attracted many modelling approaches dealing with the propagation of large ion concentration variations in tissue . In 1963 Grafstein described spatial potassium dynamics during SD in a reaction-diffusion framework with a phenomenological cubic rate function for the local potassium release by the neurons [5] . Reshodko and Burés proposed an even simpler cellular automata model for SD propagation [6] . In 1978 Tuckwell and Miura developed a SD model that is amenable to a more direct interpretation in terms of biophysical quantities [7] . It contains ion movements across the neural membrane and ion diffusion in the ECS . In more recent studies Dahlem et al . suggested certain refinements of the spatial coupling mechanisms , e . g . , the inclusion of nonlocal and time-delayed feedback terms to explain very specific patterns of SD propagation in pathological situations like migraine with aura and stroke [8] , [9] . On the other hand single cell ion dynamics were studied in HH-like membrane models that were extended to include ion changes in the intracellular space ( ICS ) and the ECS since the 1980s . While the first extensions of this type were developed for cardiac cells by DiFranceso and Noble [10] , [11] , the first cortical model in this spirit was developed by Kager , Wadman and Somjen ( KWS ) [12] only in 2000 . Their model contains abundant physiological detail in terms of morphology and ion channels , and was in fact designed for seizure-like activity ( SLA ) and local SD dynamics . It succeeded spectacularly in reproducing the experimentally known phenomenology . An even more detailed model was proposed by Shapiro at the same time [13] who—like Yao , Huang and Miura for KWS [14]—also investigated SD propagation with a spatial continuum ansatz . Another model of SD investigated Ca transmission along an astrocyte lane [15] , where glutamate released from neurons that acts on metabotropic receptors of astrocytes determines the characteristics . HH-like models of intermediate complexity were developed by Fröhlich , Bazhenov et al . to describe potassium dynamics during epileptiform bursting [16]–[18] . The simplest HH-like model of cortical ion dynamics was developed by Barreto , Cressman et al . [19]–[21] who describe the effect of ion dynamics in epileptiform bursting modulation in a single compartment model that is based on the classical HH ion channels . Interestingly , in none of these models , which are considerably simpler than , for example , Shapiro's model and the KWS model , extreme ion dynamics like in SD or stroke was studied . To our knowledge the only exception is a study by Zandt et al . who describe in the framework of Cressman et al . what they call the “wave of death” that follows the anoxic depolarization after decapitation as measured in experiments with rats [22] . In this study we systematically analyze the entire phase space of such local ion-based neuron models containing the full dynamical repertoire ranging from fast action potentials to slow changes in ion concentrations . We start with the simplest possible model for SD dynamics—a variation of the Barreto , Cressman et al . model—and reproduce most of the results for the KWS model . Our analysis covers SLA and SD . Three situations should be distinguished: isolated , closed , and open systems , which is reminiscent of a thermodynamic viewpoint ( see Fig . 1 ) . An isolated system without transfer of metabolic energy for the ATPase-driven pumps will attain its thermodynamic equilibrium , i . e . , its Donnan equilibrium . A closed neuron system with functioning pumps but without ion regulation by glial cells or the vascular system is generally bistable [23] . There is a stable state of free energy-starvation ( FES ) that is close to the Donnan equilibrium and coexists with the physiological resting state . The ion pumps cannot recover the physiological resting state from FES . We will now develop a novel phase space perspective on the dynamics in open neuron systems . We describe the first slow-fast decomposition of local SD dynamics , in which the ion gain and loss through external reservoirs is identified as the crucial quantity whose essential importance was not realized in earlier studies . Treating this slow variable as a parameter allows us to derive thresholds for SD ignition and the abrupt , subsequent repolarization of the membrane in a bifurcation analysis for the first time . Moreover we analyze oscillatory dynamics in open systems and thereby relate SLA and SD to different so-called torus bifurcations . This categorizes SLA and SD as genuinely different though they are ‘sibling’ dynamics as they both bifurcate from the same ‘parent’ limit cycle in a supercritical and subcritical manner , respectively , which also explains the all-or-none nature of SD . In contrast , SLA is gradual .
Local ion dynamics of neurons has been studied in models of various complexity . Reduced model types consist of an electrically excitable membrane containing gated ion channels and ion concentrations in an intra- and an extracellular compartment [19]–[22] . Transmembrane currents must be converted to ion fluxes that lead to changes in the compartmental ion concentrations . Such an extension requires ion pumps to prevent the differences between ICS and ECS ion concentrations that are present under physiological resting conditions from depleting . We consider a model containing sodium , potassium and chloride ions . The simulation code is available from ModelDB [24] , the accession number is 167714 . The HH-like membrane dynamics is described by the membrane potential and the potassium activation variable . The sodium activation is approximated adiabatically and the sodium inactivation follows from an assumed functional relation between and . The ICS and ECS concentrations of sodium , potassium and chloride ions are denoted by , and , respectively . In a closed system mass conservation holds , i . e . , ( 1 ) with and the ICS/ECS volumes . Together with the electroneutrality of ion fluxes across the membrane , i . e . , ( 2 ) only two of the six ion concentrations are independent dynamical variables . The full list of rate equations then reads ( 3 ) ( 4 ) ( 5 ) ( 6 ) They are complemented by six constraints on gating variables and ion concentrations: ( 7 ) ( 8 ) ( 9 ) ( 10 ) ( 11 ) ( 12 ) Superscript 0 indicates ion concentrations in the physiological resting state . Unless otherwise stated and are used as initial conditions in the simulations . Constrained ion concentrations ( Eqs . ( 7 ) – ( 10 ) ) then also take their physiological resting state values . These ion concentrations , the membrane capacitance , the gating time scale parameter , the conversion factor from currents to ion fluxes , and the ICS and ECS volumes are listed in Tab . 1 . The conversion factor is an expression of the membrane surface area and Faraday's constant ( both given in Tab . 1 , too ) : ( 13 ) We remark that all parameters in Tab . 1 are given in typical units of the respective quantities . The numerical values in these units can directly be used for simulations . Time is then given in msec , the membrane potential in mV and ion concentrations in . The electroneutrality of the total transmembrane ion flux as expressed in Eqs . ( 2 ) and ( 7 ) is a consequence of the large time scale separation between the membrane dynamics and the ion dynamics ( cf . Ref . [23] and the below discussion of time scales ) . This constraint is the reason why the thermodynamic equilibrium of the system must be understood as a Donnan equilibrium . This is the electrochemical equilibrium of a system with a membrane that is impermeable to some charged particles , which can be reached in an electroneutral fashion , i . e . , without separating charges . We do not include this impermeant matter explicitly , because it does not influence the dynamics as long as osmosis is not considered . One should however keep in mind that the initial ion concentrations in Tab . 1 do not imply zero charge in the ICS or ECS and hence impermeant matter to compensate for this must be present . The gating functions , and are given by ( 14 ) ( 15 ) ( 16 ) Here and are the asymptotic values and is potassium activation time scale . They are expressed in terms of the Hodgkin-Huxley exponential functions [19]–[21] ( 17 ) ( 18 ) ( 19 ) ( 20 ) The three ion currents are ( 21 ) ( 22 ) ( 23 ) They are given in terms of the leak and gated conductances ( with ) and the Nernst potentials which are computed from the ( dynamical ) ion concentrations : ( 24 ) denotes the valence of the particular ion species . The pump current modelling the ATPase-driven exchange of intracellular sodium with extracellular potassium at a -ratio is given by ( 25 ) where is the maximal pump rate [21] . The pump current increases with and . The values for the conductances and pump rate are also given in Tab . 1 . Let us remark that in comparisons with Ref . [23] , we have mildly increased the maximal pump rate and decreased the chloride conductance to obtain a SD threshold in agreement with experiments ( see Sect . Results ) . Eqs . ( 3 ) – ( 12 ) describe a closed system in which ion pumps are the only mechanism maintaining ion homeostasis and in which mass conservation holds for each ion species . A remark on terminology is due at this point: a ‘closed’ system refers exclusively to the conservation of the ion species that we model . We do not directly model other mass transfer that occurs in real neural systems . Yet it is indirectly included . The ion pumps use energy released by hydrolysis of ATP , a molecule whose components ( glucose and oxygen or lactate ) therefore have to pass the system boundaries . In thermodynamics , it is customary to call systems that exchange energy but not matter with their environment closed . Since ATP is in this framework only considered as an energy source , we can describe the system as closed , if ions cannot be transferred across its boundaries . As mentioned above the closed system is bistable . Superthreshold stimulations cause a transition from physiological resting conditions to FES . To resolve this and change the behaviour to local SD dynamics it is necessary to include further regulation mechanisms [23] . Since SD is in particular characterized by an extreme elevation of potassium in the ECS we will only discuss potassium regulation . If ECS potassium ions are subject to a regulation mechanism which is independent of the membrane dynamics , then the symmetry between ICS and ECS potassium dynamics is broken and Eq . ( 9 ) for the potassium conservation does not hold . Let us represent changes of the potassium content of the system by a variable which is defined by the following relation: ( 26 ) Changes of the potassium content , i . e . , changes of , can be of different physiological origin . If glial buffering is at work the potassium content will be reduced by the amount of buffered potassium . An initial potassium elevation simply leads to an accordingly increased : ( 27 ) For the coupling to an extracellular potassium bath or to the vasculature is a measure for the amount of potassium that has diffused into ( positive ) or out of ( negative ) the system . We are going to discuss two regulation schemes—coupling to an extracellular bath and glial buffering . They could be implemented simultaneously , but for our purpose it will suffice to apply only one scheme at a time . In the second subsection of Sect . Results , the dynamics of is given by glial buffering , while in the third subsection we will discuss the oscillatory regimes one finds for bath coupling with elevated bath concentrations . To implement glial buffering we assume a phenomenological chemical reaction of the following type [12] , [25]: ( 28 ) The buffer concentration is denoted by . We are using the buffer model from Ref . [12] in which the potassium-dependent buffering rate is given as ( 29 ) The parameter is normally assumed to have the same numerical value as the constant backward buffering rate which is hence an overall parameter for the buffering strength . However , the parameters should be denoted differently as they have different units ( cf . Tab . 1 ) . This chemical reaction scheme together with the mass conservation constraint ( 30 ) where is the initial buffer concentration , leads to the following differential equation for : ( 31 ) Eq . ( 27 ) the implies the following rate equation for ( 32 ) where and are given by Eqs . ( 27 ) and ( 26 ) , respectively . To model the coupling to a potassium bath one normally includes an explicit rate equation for the ECS potassium concentration ( 33 ) where the diffusive coupling flux ( 34 ) is defined by its coupling strength and the potassium bath concentration . Eq . ( 26 ) implies that Eq . ( 33 ) can be rewritten in terms of as follows: ( 35 ) Note that we have chosen to formulate ion regulation in terms of rather than which would be completely equivalent . This is crucial , because the dynamics of happens on a time scale that is only defined by the buffering or the diffusive process , while dynamics involves transmembrane fluxes and reservoir coupling dynamics at different time scales ( cf . the last paragraph of this section ) . This can be seen from Eq . ( 33 ) . Both regulation schemes—glial buffering given by Eq . ( 32 ) and coupling to a bath with a physiological bath concentration as in Eq . ( 35 ) —can be used to change the system dynamics from bistable to ionically excitable , i . e . , excitable with large excursions in the ionic variables . Like all other system parameters the regulation parameters and are given in Tab . 1 . They have been adjusted so that the duration of the depolarized phase is in agreement with experimental data on spreading depression . Note that the parameters we have chosen are up to almost one order of magnitude lower than intact brain values like the ones used in Refs . [12] , [25]–[27] . While this does not affect the general time scale separation between glial or vascular ion regulation and ion fluxes across the cellular membrane , the duration of SD depends crucially on these parameters . However , during SD oxygen deprivation will weaken glial buffering , and the swelling of glial cells and blood vessel constriction will restrict diffusion to the vasculature . Such processes can be included to ion-based neuron models and make ion regulation during SD much slower [12] , [25]–[27] . For our purpose it is however sufficient to assume smaller values from the beginning . We remark that the ion regulation schemes in our model only refer to vascular coupling and glial buffering . Lateral ion movement between the ECS of nearby neurons is a different diffusive process that determines the velocity of a travelling SD wave in tissue . This is not described in our framework . In the following section we will demonstrate in detail how can be understood as the inhibitory variable of this excitation process . The above presented model is indeed the simplest ion-based neuron model that exhibits local SD dynamics . Model simplicity is an appealing feature in its own right , but one might doubt the physiological relevance of such a reduced model . Our hypothesis is that it captures very general dynamical features of neuronal ion dynamics , and to confirm this we will compare the results obtained with the reduced model to the physiologically much more detailed KWS model [12] . This detailed model contains five different gated ion channels ( transient and persistent sodium , delayed rectifier and transient potassium , and NMDA receptor gated currents ) and has been used intensively to study SD and SLA . In fact , one modification is required so that we can replicate the results obtained from the reduced model . The KWS model contains an unphysical so-called ‘fixed leak’ current ( 36 ) that has a constant reversal potential of mV and no associated ion species . This current only enters the rate equation for the membrane potential . The effect on the model dynamics is dramatic . To see this note that the electroneutrality constraint Eq . ( 8 ) reflects a model degeneracy ( 37 ) that occurs when is modelled explicitly with ( for details see Ref . [23] ) . With a fixed leak current Eq . ( 37 ) becomes ( 38 ) which implies that mV is a necessary fixed point condition for the system . In other words , the type of bistability with a second depolarized fixed point that we normally find in closed systems is ruled out by this unphysical current . If we , however , replace it with a chloride leak current as in our model ( cf . Eqs . ( 6 ) and ( 23 ) ) , i . e . , a current with a dynamically adjusting reversal potential by virtue of Eq . ( 24 ) , we find the same type of bistability for the closed system and monostability for the system that is buffered or coupled to a potassium bath . The morphological parameters ( compartmental volumes and membrane surface area ) are the same as for the reduced model . In fact in Ref . [14] the KWS model was used without additional ion regulation for a reaction-diffusion study of SD , and the only recovery mechanism of the local system seems to be this unphysical current . Theoretically SD could be a travelling wave in a reaction-diffusion system with bistable local dynamics , but unpublished results show that the propagation properties in the bistable system are dramatically different from standard SD dynamics with wave fronts and backs travelling at different velocities . We hence suppose that a local potassium clearing mechanism is crucially involved in SD . We conclude this section with a discussion of the time scales of the model . To this end , it is helpful to keep in mind that the phenomenon of excitability requires a separation of time scales . We have electrical and ionic excitability and these dynamics themselves are separated by no fewer than three orders of magnitude . Dynamics of happens on a scale that is faster than milliseconds . This follows from the gating time scale which is given explicitly in Eq . ( 15 ) and the time scale of of which can be computed from the membrane capacitance ( given in Tab . 1 ) and the resistance of the ion channels ( for details see Ref . [28] ) : ( 39 ) with ( 40 ) If we approximate the products of gating variables in the above expression with 0 . 1 this gives . Dynamics of happens on a scale in the order of milliseconds . The time scale of ion dynamics is more explicit in the Goldman-Hodgkin-Katz ( GHK ) formalism than in the Nernst formalism used in this paper . The Nernst currents in Eqs . ( 21 ) – ( 23 ) are an approximation of the physically more accurate GHK currents , but in Ref . [23] we have shown that ion dynamics of GHK models and Nernst models are very similar . That is why the latter may be used for studies like this . For time scale considerations , however , we will now switch to the GHK description . The GHK current of ions with concentrations across a membrane is given by ( 41 ) where is the permeability of the membrane to the considered ion species and is the dimensionless membrane potential with ( 42 ) This expression contains the ideal gas constant , the temperature , ion valence and Faraday's constant . If we now write down the GHK analogue of the ion rate Eqs . ( 5 ) and ( 6 ) we obtain ( 43 ) For the conversion factor we have inserted the expression Eq . ( 13 ) . The fraction term is of the order of the ion concentrations , is a dimensionless quantity and hence of order one . With the ion dynamics time scale ( 44 ) we can thus group the parameters as follows ( 45 ) Permeabilities of ion channels can be found in Refs [14] , [23] , [29] . Similar as for the resistance the permeability of a gated channel involves a product of gating variables . Approximating such terms again with 0 . 1 a typical value for the permeability is . Together with the values for the membrane surface area and the cell volume from Tab . 1 the time scale of transmembrane ion dynamics is . The slowest time scales are related to potassium regulation , i . e . , to dynamics . The glia scheme from Eq . ( 28 ) and Eq . ( 32 ) contains a forward buffering process that reduces at a time scale ( 46 ) and a backward buffering process with time scale ( 47 ) With the parameters from Tab . 1 this leads to and . So backward buffering is much slower . This is an important property , because in the following section we will see that recovery from FES requires a strong reduction of the potassium content . If buffering and backward buffering would happen on the same time scale the required potassium reduction would not be possible . Backward buffering could well happen at a considerably faster scale than Eq . ( 47 ) , but as soon as is comparable to the buffer cannot re-establish physiological conditions after FES . The glia scheme here is phenomenological . A more biophysically detailed model would describe a glial cell as a third compartment . An elevation of ECS potassium leads to glial uptake . Spatial buffering , i . e . , the fast transfer of potassium ions between glial cells with elevated concentrations to regions of lower concentrations maintains an almost constant potassium level in the glial cells . In SD potassium in the ECS is strongly elevated during an about 80 sec lasting phase of FES and is continuously cleared during this time . After 80 sec the concentration quickly recoveres to even slightly less than the normal physiological level . Still there is a huge potassium deficit in the system and what we call backward buffering , i . e . , the release of potassium from the glial cells , sets in . It is much slower than the uptake , because it is driven by a far smaller deviation of the potassium concentration from normal physiological resting level . Similar to the above explanation of slow backward buffering in the glia scheme , an extremely slow backward time scale follows naturally in diffusive coupling . For diffusion the potassium content is reduced at a time scale ( 48 ) if extracellular potassium is greater than . Backward diffusion , however , only occurs in the final recovery phase that sets in after the neuron has returned from the transient FES state and is repolarized . While is still far from the resting state level , is comparable to normal physiological conditions ( see the below bifurcation diagrams in Figs . 2b and 3b ) and hence the driving force during the final recovery phase is very small for a bath concentration close to the resting state level . Consequently backward diffusion is much slower than forward diffusion . Note that this argument for different slow regulation time scales relies exclusively on the almost constant values of the ECS potassium concentration along the physiological fixed point branch ( see Figs . 2b and 3b ) . It is not a feature of the particular regulation scheme we apply .
At first we will not treat the change of the potassium content as a dynamical variable , but as a parameter whose influence on the system's stability we investigate . So the model we consider is defined by the rate Eqs . ( 3 ) – ( 6 ) and the constraint Eqs . ( 7 ) , ( 8 ) , ( 10 ) – ( 12 ) and ( 26 ) . Its stability will be important for the full system with dynamical ion exchange between the neuron and a bath or glial reservoir to be discussed in the next two subsections . The phenomenon of ionic excitability as in SD only occurs for dynamical . We will see that a slow-fast decomposition of ionic excitability is possible . The fast ion dynamics is governed by the transmembrane dynamics that we discuss now and happens at the time scale . The dynamics of is much slower ( and ) . Fast ion dynamics of the full system can hence be understood by assuming as a parameter that determines the level at which fast ( transmembrane ) ion dynamics occurs . This implies a direct physiological relevance of the closed system bifurcation structure with respect to potassium content variation for transition thresholds in the full ( open ) system . The bifurcation diagram of the reduced model is presented in Fig . 2 . It is shown in the -plane ( Fig . 2a ) and in the -plane ( Fig . 2b ) to display membrane and ion dynamics , respectively . A pair of arrows pointing in the direction of extracellular potassium changes only due to fluxes across the membrane ( vertical ‘m’ direction ) and only due to exchange with a reservoir ( diagonal ‘r’ direction ) is added to Fig . 2b . The fixed point continuation yields a branch ( black line ) where fully stable sections are solid and unstable sections are dashed . Stability changes occur in saddle-node bifurcations ( also called limit point bifurcation , LP ) and Hopf bifurcations ( HB ) . In a LP the stability changes in one direction ( zero-eigenvalue bifurcation ) , in a HB it changes in two directions and a limit cycle is created ( complex eigenvalue bifurcation ) . A limit cycle is usually represented by the maximal and minimal value of the dynamical variables . However , the oscillation amplitude of the ionic variables is almost zero for the limit cycles in our model . Maximal and minimal values cannot be distinguished on the figure scale . Hence in the -plane the limit cycle continuation appears only as a single line ( green ) . Stability changes of limit cycles occur in saddle-node bifurcations of limit cycles ( LP ) . The limit cycles in our model disappear in homoclinic bifurcations . In this bifurcation a limit cycle collides with a saddle . When it reaches the saddle it becomes a homoclinic cycle of infinite period . As a reference point the initial physiological condition is marked by a black square . We will call the entire stable fixed point branch that contains this point the physiological branch , because the conditions are comparable to the normal functioning physiological state—in particular , action potential dynamics is possible when the system is on this branch . Let us discuss the bifurcation diagram starting from this reference point and follow the fixed point curve in the right direction , i . e . , for increasing . The physiological fixed point loses its stability in the first ( supercritical ) Hopf bifurcation ( HB1 ) at mM . The extracellular potassium concentration is then at mM . In other word , much of the added potassium has been taken up by the cell . The limit cycle associated with HB1 loses its stability in a period-doubling bifurcation ( PD ) and remains unstable . Finally it disappears in a homoclinic bifurcation shortly after its creation ( cf . right inset in Fig . 2a ) . The stable limit cycle emanating from the PD point becomes unstable in a and vanishes in a homoclinic bifurcation , too . The parameter range of these bifurcations is extremely small ( ) . Such fine parameter scales will not play a role for the interpretation of ion dynamics . Ion concentrations are stationary and physiological up to , but for practical purposes it is irrelevant if we identify or as the end of the physiological branch . The first HB is followed by four more bifurcations ( LP1 , HB2 , LP2 , HB3 ) that all neither restore the fixed point stability nor create any stable limit cycles . The limit cycles for HB2 and HB3 are hence not plotted either . It is only the fourth Hopf bifurcation ( HB4 ) at mM in which the fixed point becomes stable again and in which a stable limit cycle is created . The limit cycle branch loses its stability in LP1 and regains it in LP2 . It becomes unstable again and even more unstable in LP3 and LP4 . Shortly after that ( not resolved on the scales in Fig . 2 ) it ends in a homoclinic bifurcation with the saddle between HB1 and LP2 . At HB4 the stable free energy-starved branch begins . It is generally characterized by a strong increase in the ECS potassium compared to physiological resting conditions ( Fig . 2b ) , and a significant membrane depolarization ( Fig . 2a ) . Corresponding to the extracellular elevation intracellular potassium is significantly lowered . This goes along with inverse changes of the compartmental sodium concentrations ( all not shown ) . is hence characterized by largely reduced ion gradients and strong membrane depolarization . In fact , at this membrane potential the sodium channels are inactivated which is normally called depolarization block in HH-like membrane models without ion dynamics . Depolarization block is , however , only one feature of FES . The closeness of FES to the thermodynamic equilibrium of the system is more importantly manifested in the reduced ion gradients . On no more bifurcations occur and it remains stable for increasing . The interpretation of this bifurcation diagram should be as follows . The end of defines the maximal potassium content compatible with a physiological state of a neuron . For larger it will be inevitably driven to the FES . In other words the end of marks the threshold value for a slow , gradual elevation of the potassium content to cause the transition from physiological resting conditions to FES . In a buffered system it is the threshold for SD ignition . On the other hand stable FES-like conditions require a minimal potassium content which marks the end of . It is given by mM . Below this value the only stable fixed point is physiological . Again there is a narrow range , namely between and mM , in which stable oscillations can occur . When glial buffering is at work the end of defines the threshold for potassium buffering , i . e . , for the potassium reduction that is required to return from FES to physiological conditions ( cf . Eq . ( 27 ) ) . In the second subsection of Sect . Results , we will see that this is exactly how ion regulation facilitates recovery in SD models . There is another way the bifurcation diagram in Fig . 2b can be read . As we have remarked above the limit cycles of the model are characterized by large oscillation amplitudes in the membrane variables ( not shown ) and , but almost constant ionic variables , and ( only shown ) . So Fig . 2b tells us which extracellular potassium concentrations can possibly be stable and which ones cannot . Values below the end of at mM , values between mM and mM and finally concentrations in the range of starting at mM can be stable . Any other extracellular potassium concentration is unstable and the system will evolve towards a stable ion configuration that is present in the phase space . The highest stable potassium concentration below FES values is . If potassium in the ECS is increased instantaneously , this value indicates the threshold for SD ignition or the transition to FES . Performing the same type of bifurcation analysis with the physiologically more detailed model from Kager et al . [12] , [14] ( cf . last paragraph of Sect . Models ) leads to the diagram in Fig . 3 . It has been shown before that also in this model there is stable FES [23] . We do not find the same bifurcations as in the reduced model , but only two LPs and one HB . However , the physiological implications are very similar . Like in the reduced model there is an upper limit of the potassium content for stable physiological conditions ( mM ) and a lower limit for stable FES ( mM ) . Also the downward snaking and the stability changes of the limit cycle that starts from HB1 are very similar to Fig . 2 . This leads to the same type of conclusion concerning possible stable extracellular potassium concentrations . While numerical values of the stability limits in terms of are specific to each model , the topological similarity of the bifurcation diagrams suggests a generality of results: there is a stable physiological branch that ends at some maximal value of the potassium content . Beyond this point the neuron cannot maintain physiological conditions , but will face FES . On the other hand the stable FES branch ends for a sufficiently reduced potassium content the neuron will return to physiological conditions . The new bifurcation diagrams presented in this section confirm our results from Ref . [23]: Neuron models whose ionic homeostasis is only provided by ATPase-driven pumps , but without diffusive coupling or glial buffering , will have a highly unphysiological fixed point that is characterized by free energy-starvation and membrane depolarization . However , the presented bifurcation diagrams here contain additional information of great importance . Using the new bifurcation parameter crucially extends our results from Ref . [23] by uncovering the threshold concentrations in extracellular potassium concentration . These are completely novel insights . In the next subsection the bifurcation diagrams of the unbuffered ( closed ) systems shall facilitate a phase space understanding of the activation and inhibition process of ionic excitability as observed in SD in the buffered ( open ) systems . We are aiming for an interpretation of ionic excitability where neuronal discharge and recovery are fast dynamics that are governed by the bistable structure discussed above , whereas additional ion regulation takes the role of slowly changing . However , only the gated ion dynamics , i . e . , dynamics of sodium and potassium is fast compared to that of , chloride is similarly slow . Due to the enforcement of electroneutrality this means that the overall concentration of positively charged ions in the ICS , i . e . , the sum of sodium and potassium ion concentrations changes on the same slow time scale as the chloride concentration . To describe this slow process not dynamically but—like —in terms of a parameter we simply investigate the stability for a given distribution of non-dynamic , i . e . , impermeant chloride . To determine this stability we set the chloride current to zero and vary in a certain range ( from 8 to 32 mM for the reduced model , and from 9 to 33 mM for the detailed model ) . This affects the system only through the electroneutrality constraint Eq . ( 7 ) which sets the intracellular charge concentration to be shared by sodium and potassium . For each value of we perform a fixed point continuation as in Figs . 2 and 3 which yields similarly folded s-shaped curves . The result is shown in Fig . 4 . For our analysis of SD it is only relevant where ends . That is why the plot does not contain the whole fixed point curve , but only and a part of the unstable branch for a selection of values . As a reference the diagrams also contain the fixed point curves from Figs . 2 and 3 which include chloride dynamics . The FES branches in Fig . 4 end in Hopf bifurcations . The bifurcation points for different chloride concentrations yield the blue Hopf line . It marks the threshold for recovery from FES when dynamics of chloride and is slow . In the previous subsection we have analyzed the phase space structure of ion-based neuron models without contact to a reservoir , i . e . , without glial buffering or diffusive coupling . These models have only transmembrane ion dynamics and obey mass conservation of each ion species . Hence they describe a closed system . The bistability of a physiological state and FES that we found in these closed models is not experimentally observed , because real neurons are always open systems not merely in the sense that they consume energy—a necessary prerequisite for being far from thermodynamic equilibrium—but they also can lose or gain ions through reservoirs or buffers . We will now include glial buffering and show how it facilitates recovery from FES , a condition which in contrast to the physiological state is close to a thermodynamic equilibrium , namely the Donnan equilibrium ( cf . Ref . [23] ) . When glial buffering is at work , becomes a dynamical variable whose dynamics is given by the buffering rate Eq . ( 32 ) . In a previous subsection we have explained that the bifurcation diagrams in Figs . 2 and 3 imply thresholds for an elevation of extracellular potassium to trigger the transition from physiological resting conditions to FES . This is in agreement with computational and experimental SD studies in which high extracellular potassium concentrations are often used to trigger SD . Another physiologically relevant way of SD ignition is the disturbance or temporary interruption of ion pump activity . As we have shown in Ref . [23] there is a minimal pump rate required for normal physiological conditions in a neuron . Below this rate the neuron will go into a FES state and remain in that state even when the pump activity is back to normal . For the simulations in Fig . 5 we have interrupted the pump activity for about 10 sec in the reduced model , and we have elevated the extracellular potassium concentration by mM in the detailed model to trigger SD . Both stimulation types work for both models , but only the two examples are shown . The phase of pump interruption ( Fig . 5a and 5c ) is indicated by the shaded region in the plots , the time of potassium elevation is marked by the vertical grey line . The dynamics of the two models is very similar: in response to the stimulation the neuron strongly depolarizes and remains in that depolarized state for about 70 sec ( Fig . 5a and 5b ) . After that the neurons repolarize abruptly and asymptotically return to their initial state . In addition to the membrane potential ( black curve ) the potential plots also contain the Nernst potentials for sodium ( red line ) , potassium ( blue line ) and chloride ( green line ) that change with the ion concentrations according to the definition of the Nernst potentials in Eq . ( 24 ) . In Fig . 5c and 4d we see that the potential dynamics goes along with great changes in the ion concentrations . In particular , extracellular potassium is strongly increased in the depolarized phase . These conditions are very similar to the type of FES states discussed in the previous subsection . The recovery of ion concentrations sets in with the abrupt repolarization , but it is a very slow asymptotic process that is not shown in Fig . 5 . In both models the neuron is capable of producing spiking activity again right after the repolarization . All these aspects of ion dynamics during SD are well-known from several studies [12] , [14] . We remark that the time series are almost identical if glial buffering is replaced by the coupling to a potassium bath . Both , the strength of glial buffering and of diffusive coupling have been adjusted so that the depolarized phase lasts about 70 sec which is the experimentally determined time . We will focus on bath coupling in last subsection of Sec . Results . If neither buffering nor a potassium bath is included the neuron does not repolarize ( for time series plots of terminal transitions to FES see Ref . [23] ) . The time series in Fig . 5 are useful to confirm that the neuron models we investigate have the desired phenomenology and indeed show SD-like dynamics . Yet the nature of the different phases of this ionic excitation process—the fast depolarization , the prolonged FES phase and the abrupt repolarization—remains enigmatic [12] , [14] , [29] , [30] . In a phase space plot the picture becomes much clearer and the entire process can be directly related to the two stable branches , and , that we found for the closed and therefore pure transmembrane models in the previous subsection . In Fig . 6 the time series from Fig . 5 for a simulation time of 50 min are shown in the - and the -plane . The parts of the trajectories during the stimulation ( pump interruption and potassium elevation ) are dashed . In the chosen planes vertical lines belong to dynamics of constant potassium contents that can be understood in terms of the models we analyzed in the previous subsection . That is why Fig . 6 contains the fixed point curves from Fig . 4 as shaded lines as a guide to the eye . In Fig . 6c and 6d buffering dynamics is diagonal as indicated by the pair of arrows added to the plot . For both trajectories the stimulation is followed by a vertical activation process that leads to the transition from to . The verticality means that this is a process almost purely due to transmembrane dynamics . It is governed by the bistable phase space structure that we discussed in the previous section and also in Ref . [23] . Buffering dynamics is too slow to inhibit the activation . The types of stimulation we applied are related to bifurcations of the transmembrane system: the potassium elevation is beyond the end of which is marked by the first Hopf bifurcation ( HB1 ) in Fig . 2 . The interruption of pump activity means that we go below a pump rate threshold that is defined by a saddle-node bifurcation ( cf . Ref . [23] ) . More generally , to initiate an ionic excitation it is necessary to stimulate the system until it enters the basin of attraction—derived in the unbuffered system—of the FES state . The activation is followed by a phase of both , slow transient transmembrane dynamics mostly due to chloride , and potassium buffering . It is the latter that bends the trajectories in the diagonal direction so that they go along the FES branches from Fig . 4 . The trajectories slowly approach the repolarization threshold given by the Hopf line . The duration of this FES phase is determined by how long it takes the system to reach the Hopf line . This process is a mixture of buffering and transient transmembrane dynamics for the reduced model and more buffering-dominated in the detailed model . The duration of the FES phase is consequently a result of both types of dynamics . However , the main insight we gain from this plot is: glial buffering is the necessary inhibitory mechanism that takes the system to the Hopf line so that it can repolarize . We remark that the time series and phase space plots for bath coupling instead of buffering are almost identical and the same interpretation holds . The more general conclusion is then: ion dynamics beyond transmembrane processes is necessary to take the system to the Hopf line so that it can repolarize . This can , of course , be a combination of bath coupling and buffering . When the Hopf line is reached that neuron repolarizes abruptly which is the second almost purely vertical process . The repolarization is followed by slow asymptotic recovery dynamics of ion concentrations that takes the neuron back to the initial state which is at mM . The neuron regains the electrical excitability that is lost during FES already right after the repolarization . So the system is back to physiological function long before the ion gradients are fully restored . Let us summarize the results from this subsection . By relating the SD time series from Fig . 5 to the bifurcation structure of the unbuffered models from the first subsection of Sect . Results and in particular to the two stable branches and we have succeeded to understand ionic excitability as a sequence of different dynamical phases . The initial depolarization and the later repolarization are membrane-mediated fast processes that obey the bistable dynamics of unbuffered systems . The FES phase is buffering-dominated and lasts until buffering has taken the system to a well-defined repolarization threshold . The recovery phase is dominated by backward buffering . The full excursion time is the sum of the durations of each phase . For the de- and repolarization process this duration mainly depends on the time scale of the transmembrane dynamics and is hence comparably short . The duration of the FES phase is a result of both , the transient transmembrane dynamics and glial ion regulation at a much slower time scale . The final recovery phase is mainly backward buffering dominated which is the slowest process . Hence the duration of an SD excursion is mainly determined by the slow buffering and backward buffering time scales . This conclusion that relies on our novel understanding of the different thresholds involved in SD is in fact in agreement with recent experimental data suggesting vascular clearance of extracellular potassium as the central recovery mechanism in SD [31] , [32] . The dynamics of excitable systems can often be changed to self-sustained oscillations by a suitable parameter variation . The type of bifurcation that leads to the oscillations and the shape of the limit cycle in the oscillatory regime determine excitation properties like threshold sharpness and latency [28] . The oscillatory dynamics that is related to ionic excitability can be obtained for bath coupling with an elevated bath concentration . So in this section we replace the buffering dynamics for with the diffusive coupling given by Eq . ( 35 ) . This coupling is used in experimental in-vitro studies of SD [33] and has also been applied in computational models that are very similar to our reduced one [19]–[21] . Depending on the level of the bath concentration , we find three qualitatively different types of oscillatory dynamics that are shown in Fig . 7 . The top row ( a ) shows the time series of seizure-like activity for . It is characterized by repetitive bursting and low amplitude ion oscillations . The other types of oscillatory dynamics are tonic firing at with almost constant ion concentrations ( Fig . 7b ) and periodic SD at with large ionic amplitudes ( Fig . 7c ) . We see that SLA and periodic SD exhibit slow oscillations of the ion concentrations and fast spiking activity , which hints at the toroidal nature of these dynamics . Below we will relate SLA and periodic SD to torus bifurcations of the tonic firing limit cycle . The examples in Fig . 7 show that our model contains a variety of physiologically distinct and clinically important dynamical regimes . A great richness of oscillatory dynamics , in fact , under the simultaneous variation of and the glial buffering strength has already been reported in Refs . [19]–[21] for a very similar model . In Ref . [19] , [20] the authors even give a bifurcation analysis of ionic oscillations for elevation . To investigate dynamical changes and the transitions between the dynamical regimes in our model we perform a similar bifurcation analysis and vary , too . Two important differences should be noted though . First , Ref . [19] , [20] uses an approximation of the multi-time scale model in which the fast spiking dynamics is averaged over time , while our analysis does not rely on such an approximation . Second , our analysis covers a bigger range of values which allows us to compare SLA and SD , while Ref . [19] , [20] exclusively deals with SLA . Fig . 8 shows the bifurcation diagram for variation in the -plane and in the -plane . In addition to fixed points ( black ) and limit cycles ( green ) also quasiperiodic torus solutions ( blue ) are contained in the diagram . In comparison to Fig . 2 this model contains a new type of bifurcation , namely a torus bifurcation ( TR ) . A torus bifurcation is a secondary Hopf bifurcation of the radius of a limit cycle in which an invariant torus is created . If this torus is stable , nearby trajectories will be asymptotically bound to its surface . However , we cannot follow such solutions with standard continuation techniques , because these require an algebraic formulation in terms of the oscillation period . This is not possible for torus solutions , because on a torus the motion is quasiperiodic , i . e . , characterized by two incommensurate frequencies . We can hence only track the stable solutions by integrating the equations of motion and slowly varying . It is due to this numerically expensive method that in this section we will only analyze oscillatory dynamics of the reduced HH model with time-dependent ion concentrations . The result of this bifurcation analysis in Fig . 8 shows us that there is a maximal level of the bath concentration compatible with physiological conditions . It is identified with the subcritical Hopf bifurcation HB1 in which the fixed point loses its stability . The related limit cycle is omitted , because it stays unstable and terminates in a homoclinic bifurcation with the unstable fixed point branch . The fixed point undergoes further bifurcations ( LP1 , LP2 , HB2 , HB3 ) which all leave it unstable and do not create stable limit cycles . It is in HB4 that the fixed point becomes stable again and also a stable limit cycle is created . This is the last fixed point bifurcation of the model . The limit cycle that is created in HB4 changes its stability in several bifurcations . The physiologically most relevant ones are the four torus bifurcations . The bifurcation labels indicate the order of detection for the continuation that starts at HB4 . Initially the limit cycle is characterized by fast low-amplitude oscillations . It becomes unstable in the subcritical torus bifurcation TR1 . It regains and again loses its stability in the subcritical torus bifurcations TR2 and TR3 . The last torus bifurcation , the restabilizing supercritical TR4 , is directly followed by a PD after which no stable limit cycles exist any more . Again we have omitted in the diagram the unstable branch after PD and the limit cycle that is created in PD , which remains unstable . Physiologically it is more intuitive to discuss the diagram for increasing starting from the initial physiological conditions marked by the black square . Normal physiological conditions become unstable at and above this value the neuron spikes continuously according to the stable limit cycle branch between PD and TR4 . When is reached the dynamics changes from stationary spiking to seizure-like activity on an invariant torus . The beginning of SLA is hence due to a supercritical torus bifurcation and the related ionic oscillation sets in with finite period and zero amplitude . From on tonic spiking activity is stable again and there is a small -range of bistability between SLA and this tonic firing . As we mentioned above solutions on an invariant torus cannot be followed with normal continuation tools like AUTO , so only stable branches are detected . The details of the bifurcation scenario at TR3 are hence not totally clear , but we suspect that the unstable invariant torus that must exist near TR3 collides with the right end of the stable torus SLA-branch in a saddle-node bifurcation of tori . Tonic spiking then remains stable until TR2 . This bifurcation is related to the period SD that already exist well below . In fact , the threshold value is in agreement with experiments [33] . Again the unstable torus near TR2 is not detected , but we suppose that a similar scenario as in TR3 occurs . The dynamics on the torus branch related to TR2 ( and TR1 where it seems to end ) is very different from the first torus branch . While the periods of the slow oscillations during SLA are 16–45 sec the ion oscillations of periodic SDs are much slower with periods of 350–550 sec . Another crucial difference is obvious from Fig . 8b which shows the bifurcation diagram in the -plane . The fixed point is just a straight line , because the diffusive coupling Eq . ( 35 ) makes a necessary fixed point condition . The limit cycle is always extremely close to this line . On the chosen scale it cannot be distinguished from the fixed point and is hence not contained in the plot . Only the torus solutions of SD and SLA attain values that differ significantly from the regulation level . The ionic amplitudes of SD are one order of magnitude larger than those of SLA . This has to do with the fact that the peak of SD—as described above—must be understood as a metastable FES state that exists due to the bistability of the transmembrane dynamics . The dynamics of SLA is clearly of a different nature . Note that the bifurcation diagram reveals a bistability of tonic firing and full-blown SD between the left end of the SD branch at about 11 mM and TR2 . This means that there is no gradual increase in the ionic amplitudes that slowly leads to SD , but instead it implies that SD is a manifest all-or-none process . In Fig . 9 we look at the same bifurcation diagram in the - and the -plane . While in Fig . 8 most of the ionic phase space structure is hidden , because for fixed points and limit cycles , the -presentation in Fig . 9a provides further insights into the ion dynamics . We see that the stable fixed point branch before HB1 has extracellular sodium concentrations close to the physiological value . The stable branch after HB4 , however , has an extremely reduced extracellular sodium level and is indeed FES-like . The stable limit cycles between PD and TR4 and between TR3 and TR2 , and also SLA are rather close to the physiological sodium level . On the other hand , periodic SD is an oscillation between FES and normal physiological conditions , which is an expected confirmation of the findings from the previous section . Fig . 9b is useful in connecting the phase space structure of the bath coupled system to that of the transmembrane model of the first subsection of Sect . Results . If we interchange the - and the -axis in the diagram it looks very similar to Fig . 2b . The torus bifurcations TR1 , TR2 and TR3 are very close to the limit point bifurcations , and of the transmembrane model . The fixed point curves are topologically identical . This striking similarity has to do with the fact that the limit cycle in Fig . 2 has almost constant ion concentrations . We have pointed out in the first subsection of Sect . Results that Fig . 2 tells us which extracellular potassium concentrations are stable for pure transmembrane dynamics . Diffusive coupling with bath concentrations at such potassium levels leads to negligibly small values of ( cf . Eq . ( 35 ) ) . Therefore the limit cycle is still present in the bath coupled model and also the stability changes can be related to those in the transmembrane model . Again this can be seen as a confirmation of the results from the previous section: the transmembrane phase space plays a central role for models that are coupled to external reservoirs . We can interpret the ionic oscillations from Fig . 7 and the bifurcations leading to them with respect to this phase space . Last we consider the dynamics of SLA and periodic SD in a phase space projection . In Fig . 10 the trajectories for SLA and periodic SD are plotted in the -plane together with the underlying fixed point and limit cycles from the transmembrane model ( cf . Fig . 4 ) . The periodic SD trajectory has a very similar shape to the single SD excursion from Fig . 6 and is clearly guided by the stable fixed point branches and . On the other hand SLA is a qualitatively very different phenomenon . Rather than relating to the FES branch , it is an oscillation between physiological conditions and those stable limit cycles that exist for moderately elevated extracellular potassium concentrations . The ion concentrations remain far from FES . So SLA and SD are not only related to distinct bifurcations , though of similar toroidal nature and branching from the same limit cycle , but they are also located far from each other in the phase space . This completes our phase space analysis of local ion dynamics in open neuron systems .
In this paper we have analyzed dynamics at different time scales in a HH model that includes time-dependent ion concentrations . Such models are also called second generation Hodgkin-Huxley models . They exhibit two types of excitability , electrical and ionic excitability , which are based on fast and slow dynamics . The time scales of these types of excitability are themselves separated by four to five orders of magnitude . The dynamics ranges from high-frequency bursts of about 100 Hz with short interburst periods of the order of 10 msec ( Fig . 7a ) to the slow periodic SD with frequencies of about Hz and periods of about 7:30 min ( Fig . 7c ) . The slow SD dynamics in our model is classified as ultra-slow or near-DC ( direct current ) activity and cannot normally be observed by electroencephalography ( EEG ) recordings , because of artifacts due to the resistance of the dura ( thick outermost layer of the meninges that surrounds the brain ) . However , recently subdural EEG recordings provided evidence that SDs occur in abundance in people with structural brain damage [1] . Indirect evidence was already provided earlier by functional magnetic resonance imaging ( fMRI ) [34] and a patient's symptom reports combined with fMRI [35] that SD also occurs in migraine with aura [2] . The slowest dynamics that can be accurately measured by EEG , i . e . , the delta band , with frequencies about 0 . 5 to 4 Hz , has attracted modelling approaches much more than SD , which was doubted to occur in human brain until the first direct measurements were reported . It is interesting to compare the origin of slow time scales in such delta band models to our slow dynamics . Models of the delta band essentially come in two types . On the one hand thalamo-cortical network and mean field models of HH neurons with fixed ion concentrations have been studied [36] . In this case , a slow time scale emerges because the cells are interconnected via synaptic connections using metabotropic receptors that are slow , because they act through second messengers . On the other hand , single neuron models with currents that are not contained in HH , namely a hyperpolarization-activated depolarizing current , -dependent sodium and potassium currents , and a persistent sodium current , were suggested . The interplay between these currents gives rise to oscillations at a frequency of about 2–3 Hz [37] . It is therefore hardly surprising that these currents , in particular the persistent sodium and the -dependent sodium and potassium currents , have also been proposed to play an essential role in SD [30] , [38] . Furthermore , bursting as another example of slow modulating dynamics was studied in a pure conductance-based model with a dendritic and an axo-somatic compartment [16] . Also metabotropic receptors as modeled by Bennett et al . [15] and other cellular processes at appropriately slow time scales may play a role and contribute to the repolarization . In contrast to those approaches our results show that dynamics in a HH framework with time-dependent ion concentrations and buffer reservoirs already range from seconds to hours even with the original set of voltage-gated ion currents . Time scales from milliseconds ( membrane dynamics ) to seconds ( ion dynamics ) and even minutes to hours ( ion exchange with reservoirs ) can be directly computed from the model parameters ( cf . Sect . Models ) . The interplay of membrane dynamics , ion dynamics and coupling to external reservoirs ( glia or vasculature ) naturally leads to dynamics typical of SLA and SD . In particular SD is explained by a bistability of neuronal ion dynamics that occurs in the absence of external reservoirs . The potassium gain or loss through reservoirs provided by an extracellular bath , the vasculature or the glial cells is identified as a bifurcation parameter whose essential importance was not realized in earlier studies ( see Fig . 11 ) . Using this bifurcation parameter and the extracellular potassium concentration as the order parameter , we obtain a folded fixed point curve with the two outer stable branches corresponding to states with normal physiological function , hence named physiological branch , and to states being free-energy starved ( ) . The definition of the bifurcation parameter implies that exchange with ion reservoirs happens along the diagonal direction labelled by ‘r’ . Membrane-mediated dynamics is in the vertical ‘m’ direction . In the full system where the ion exchange is a dynamical variable our unconventional choice of variables , i . e . modelling instead of , makes it obvious that the time scales of diagonal and vertical dynamics is separated by at least two orders of magnitude . Slow dynamics is along and , and the fast dynamics describes the jumps between these branches . We remark that dynamics along is slower than along , because the branch is almost horizontal which leads to a very small gradient driving the diffusive coupling . Similarly the release of buffered potassium from the glial cells is only weakly driven ( cf . the discussion of buffering time scales in Sect . Model ) . In the closed system sufficiently strong stimulations lead to the transition from the physiological resting state located on to FES . In the full system with dynamical ion exchange with the reservoirs , physiological conditions are restored after a large phase space excursion to the the before stable FES state . We refer to this process as ionic excitability . In contrast to the electrical excitability of the membrane potential this process involves large changes in the ion concentrations . The entire phase space excursion of this excitation process can be explained through the specific transits between and along and . We observe ion changes on three slow time scales . ( i ) Vertical transits between and caused by transmembrane dynamics in the order of seconds . The time scale is determined by the volume-surface-area ratio and the membrane permeability to the ions . ( ii ) Diagonal dynamics along in the order of tens of seconds caused by contact to ion reservoirs . This time scale is determined by buffer time constants or vascular coupling strength . ( iii ) Dynamics on again caused by contact to ion reservoirs , but at the slower backward buffering time scale in the order of minutes to hours determined by the slower backward rate of the buffer [12] . During this long refractory phase of ionic excitability the spiking dynamics based on electrical excitability—separated by seven orders of magnitude—seems fully functional . The right end of and the left end of are marked by bifurcations that occur for an accordingly elevated or reduced potassium content . This is the first explanation of thresholds for local SD dynamics in terms of bifurcations . We remark , however , that for SD ignition the important question is not where ends , but instead where the basin of attraction of begins . This new understanding of SD dynamics suggests a method to investigate the SD susceptibility of a given neuron model . One should consider the closed model without coupling to external reservoirs and check if shows the typical bistability between a physiological resting state and FES . We remark that unphysical so-called ‘fixed leak’ currents must be replaced by proper leak currents with associated leaking ions . Thresholds for the transition between and translate to thresholds for SD ignition and repolarization , i . e . , recovery from FES in the full open model . Knowledge of the potassium reduction needed to reach the repolarization threshold and knowledge about the buffer capacity could then tell us if recovery from FES can be expected ( such as in migraine with aura ) or if the depolarization is terminal ( such as in stroke ) . Although our model does not contain all important processes involved in SD , our phase space explanation appears to be valid also for certain model extensions . For example , considering only diffusive regulation of potassium is physically inconsistent , but adding an analoguous regulation term for sodium turns out not to alter the dynamics qualitatively . Moreover osmosis-driven cell swelling—normally regarded as a key indicator of SD—is not included in our model , but can be added easily [13] , [30] , [39] . Unpublished results confirm that also with such cell swelling dynamics the fundamental bifurcation structure of Fig . 11 is preserved . As a clinical application of our framework , we have linked a genetic defect , which affects the inactivation gate and which is present in a rare subtype of migraine with aura , to SD . Our simulations show that such mutations render neurons more vulnerable to SD [40] . The interesting point , however , is that on the level of the fast time scale the firing rate is decreased , which in a mean field approach ( as done for the delta band ) translates to decreased activity . This effect seemingly contradicts the increased SD susceptibility and hence illustrates the pitfalls in trying to neglect ion dynamics in the brain and to bridge the gap in time scales by population models .
|
The classical theory by Hodgkin and Huxley ( HH ) describes nerve impulses ( spikes ) that manifest communication between nerve cells . The underlying mechanism of a single spike is excitability , i . e . , a small disturbance triggers a large excursion that reverts without further input to the original state . A spike lasts a 1/1000 second and , even though during this period ions are exchanged across the nerve cell membrane , the change in the corresponding ion concentrations can become significant only in series of such spikes . Under certain pathological conditions , changes in ion concentrations become massive and last minutes to hours before they recover . This establishes a new type of excitability underlying communication failure between nerve cells during migraine and stroke . To clarify this mechanism and to recognize the relevant factors that determine the slow time scales of ion changes , we use an extended version of the classical HH theory . We identify one variable of particular importance , the potassium ion gain or loss through some reservoirs provided by the nerve cell surroundings . We suggest to describe the new excitability as a sequence of two fast processes with constant total ion content separated by two slow processes of ion clearance ( loss ) and re-uptake ( gain ) .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"nonlinear",
"dynamics",
"computer",
"and",
"information",
"sciences",
"systems",
"science",
"mathematics",
"neural",
"homeostasis",
"computational",
"neuroscience",
"single",
"neuron",
"function",
"physiology",
"biology",
"and",
"life",
"sciences",
"brain",
"electrophysiology",
"computational",
"biology",
"electrophysiology",
"physical",
"sciences",
"neuroscience",
"neurophysiology",
"dynamical",
"systems"
] |
2014
|
Dynamics from Seconds to Hours in Hodgkin-Huxley Model with Time-Dependent Ion Concentrations and Buffer Reservoirs
|
Plasmodium parasites , the causal agents of malaria , result in more than 1 million deaths annually . Plasmodium are unicellular eukaryotes with small ∼23 Mb genomes encoding ∼5200 protein-coding genes . The protein-coding genes comprise about half of these genomes . Although evolutionary processes have a significant impact on malaria control , the selective pressures within Plasmodium genomes are poorly understood , particularly in the non-protein-coding portion of the genome . We use evolutionary methods to describe selective processes in both the coding and non-coding regions of these genomes . Based on genome alignments of seven Plasmodium species , we show that protein-coding , intergenic and intronic regions are all subject to purifying selection and we identify 670 conserved non-genic elements . We then use genome-wide polymorphism data from P . falciparum to describe short-term selective processes in this species and identify some candidate genes for balancing ( diversifying ) selection . Our analyses suggest that there are many functional elements in the non-genic regions of these genomes and that adaptive evolution has occurred more frequently in the protein-coding regions of the genome .
Half of the world's population is at risk of contracting malaria from Plasmodium species [1] , so an understanding of their biology has considerable potential to influence human health . An understanding of evolution and natural selection are particularly important , because malaria control is limited by the evolution of resistance to anti-malarial drugs [2]–[5] and high levels of genetic variation in parasite surface proteins , which hinder natural and vaccine-induced immunity [6] , [7] . An understanding of selection in these genomes can also contribute to our understanding of their function . For example , intronic and intergenic regions have been shown to be more conserved than neutrally evolving sites [8] , [9] , suggesting that purifying selection has been acting to conserve functional elements in non-genic regions of the genomes . In this study we describe selection in Plasmodium genomes over the long term using alignments of the genomes of seven Plasmodium species , showing that there is considerable constraint outside protein-coding regions and identifying 670 non-genic conserved elements . We describe selection in the short term using genome-wide polymorphism data from 13 strains of P . falciparum . This analysis suggested that protein-coding exons were more likely to be subject to non-neutral ( adaptive ) than non-genic regions .
The analysis of the divergence between distantly related species facilitates analysis of purifying selection ( constraint ) . However , there are several types of selection acting simultaneously on genomes that are less well described from this data , particularly in non-genic regions that are difficult to align between highly divergent species . Firstly , adaptive ( or directional ) evolution , which acts to change the nucleotide sequence from its ancestral state [23] . There is also evidence that a few genes are subject to balancing selection , which acts to maintain multiple alleles ( different versions of a gene ) in a population [24] . Frequency-dependent balancing selection , where rare alleles have a selective advantage , is thought to be particularly important in Plasmodium genes encoding surface-exposed proteins that are targets of acquired immunity [25]–[29] . To examine these types of selection , we generated genome-wide genetic diversity ( polymorphism ) data from thirteen P . falciparum isolates ( strains ) and the divergence between P . falciparum and the chimpanzee parasite P . reichenowi , using publicly available ABI capillary reads ( Table S5 , NCBI dbSNP accessions: ss# 159747249–159815961 ) . We called 69 , 805 SNPs within P . falciparum and 190 , 631 fixed differences between P . falciparum and P . reichenowi . We were able to obtain a coarse minor allele frequency ( MAF ) estimate for 54 , 641 SNPs and a derived allele frequency estimate for 24 , 573 SNPs ( see Materials and Methods ) . See Table S6 for further summary statistics .
In summary , we show that there is considerable constraint in intergenic regions and introns . We identify 670 conserved non-genic elements and our analysis suggest that only a minority of these are un-annotated protein-coding exons , or structured ncRNAs . We suspect that many more functional non-genic elements remain undiscovered . Our analysis is consistent with the majority of non-neutral ( directional or balancing ) selection events having occurred in P . falciparum exons . Genetic diversity data collected from within populations and divergence data from more closely related Plasmodium species , both of which will soon be available , will be required to confirm this prediction .
The following genome versions were used for the alignment: P . falciparum version 2 . 1 . 4 , July 2007 , from ftp://ftp . sanger . ac . uk/pub/pathogens/Plasmodium/falciparum/3D7/3D7 . version2 . 1 . 4 Plasmodium knowlesi version PK4 , October 2007 , from ftp://ftp . sanger . ac . uk/pub/pathogens/P_knowlesi/Archive/PK4 . annotation/ Plasmodium vivax is as published in Carlton et al . [60] , with ordered and orientated contigs in pseudo-chromosomes . Because many of the subtelomeric contigs could not be assigned to the pseudo-chromosomes , they are not present . However , these contigs are extremely AT-rich and contain mainly Vir genes , and so they do not align with chromosome regions of other Plasmodium species . Pseudo-chromosomes and annotation are available upon request . Plasmodium berghei , obtained April 2007 , from ftp://ftp . sanger . ac . uk/pub/pathogens/Plasmodium/berghei/ Plasmodium chabaudi , obtained April 2007 , from ftp://ftp . sanger . ac . uk/pub/pathogens/Plasmodium/chabaudi/ Plasmodium yoelii , PlasmoDB version 5 . 4 , from http://www . plasmodb . org/common/downloads/release-5 . 4/Pyoelii/ Plasmodium reichenowi A predicted Plasmodium reichenowi genome was created by aligning 78 , 442 P . reichenowi ABI capillary reads to the P . falciparum genome with SSAHA2 , as described previously [9] . Fixed differences were discovered with a minimum phred score of 25 . Deletions in the P . reichenowi sequence were identified , requiring at least two read alignments identifying an identical deletion before we accepted it . Insertions in P . reichenowi could not be included without manipulating the overall alignment . We then assumed that P . reichenowi was identical to P . falciparum except in locations of fixed differences or deletions in the P . reichenowi sequence . We excluded regions of the genome that were non-unique ( as described below for SNPs ) or lacked read coverage . The six assembled genomes were repeat masked using Repeat Masker Open-3 . 0 , from http://www . repeatmasker . org , then aligned in two phases . The first phase was synteny mapping . Sub-sequences of the genomes were grouped into syntenic blocks . Each of the six genomes contributed at most one subsequence to a given block , and each block contained sequence from at least two species . Then , in the second phase , a nucleotide-level multiple alignment was constructed within each block . The synteny map was generated using Mercator [61] . Mercator requires anchor sequences along each genome . In addition , for each anchor , one must specify which anchors on other genomes are strongly similar . We chose as anchors the known and predicted exons in each genome , with the annotations obtained as described . Two anchors , each from a different genome , were deemed similar if the BLAT [62] score of the pair was below 1×10−50 . The BLAT scores were computed in protein space . Mercator used the anchors and BLAT-similar pairs in a modified k-way reciprocal best hit algorithm [63] . The non-draft genome sequences ( P . falciparum , P . knowlesi , and P . vivax ) served as scaffolds for the draft species . The result was 170 syntenic blocks . The largest blocks covered parts of all six species and contained entire chromosomes from some of them , while the smallest contained small fragments of just two species . Mercator also produced alignment constraints for each block . A nucleotide-level multiple alignment within each block was generated with MAVID [64] , using the alignment constraints as well as a phylogenetic tree relating the six species . Branch lengths for the tree were estimated with PAML [65] , fixing the known topology for these Plasmodium species . Working upwards from the leaves , MAVID associates to each branch node a maximum-likelihood alignment of the sequences in the subtree rooted at that node . The alignment at the root of the full tree is a multiple alignment of all the input sequences . The accuracy and coverage of both the syntenic map and the block alignments were validated manually according to various descriptive statistics . The predicted P . reichenowi genome was then added to the alignment assuming complete synteny with P . falciparum . Alignment sites corresponding to exon , intron , and intergenic categories were extracted based on the annotation for P . falciparum , P . knowlesi and P . yoelii ( one species from each of the three main clades ) , requiring identical annotation in all three species . For calculations of constraint we used two different models: The HKY85 model [13] , with transition and transversion rates estimated globally for the whole tree , and a non-time reversible 12 parameter model ( Nonrev ) , with all substitution parameters estimated individually for each of the three main branches in the tree ( for a total of 36 substitution rates ) . To further ensure robustness we estimated the model parameters from three different data sets: A ) The full alignment ( representing overall selective pressures ) , B ) introns , intergenic , and FFD sites ( high divergence and AT content ) , and C ) exons ( lower divergence and AT content ) . Model parameters and branch lengths for the topology shown in Figure 1A were estimated with the maximum likelihood based package Hyphy [66] . The resulting six parameterized models were then used to estimate branch lengths based on the full alignment . Branch lengths for the different genomic regions ( exon , intergenic , intron , FFD ) were then estimated with each model as scalings of the full alignment tree ( the relative divergence over all branches of the tree ) , by keeping the relative branch lengths within the tree fixed . Relative constraint for each region was calculated as , as described in the text . Variance of the constraint estimates was evaluated by 200 bootstrap replicates of alignment columns , with replacement . This variance is shown as error bars in Figure 2A , for each of the six models , and for each of the four categories ( full alignment , exon , intergenic , intron ) . This measures the statistical variation of the constraint estimates , but does not address the biological variation . The latter is difficult to assess in a meaningful way , due to variation in neutral rate across the genome ( Figure S4 ) . The precision of estimates of neutral rate in specific regions of the alignment is limited by the relative scarcity of FFD sites , meaning that for smaller , biologically relevant window sizes , e . g . 20 kb , the neutral rate estimates will be highly variant ( or non-existent ) due to small FFD sample size , leading to uninformative constraint estimates . To estimate the constraint near gene and exon boundaries , we extracted alignment columns upstream of start codons ( termed promoters ) , downstream of stop codons ( terminators ) and between exons ( introns ) . In each case , we extracted a region corresponding to one third of the observed median length of the given feature . Introns were then further divided into donors ( 5′ ) and acceptors ( 3′ ) . To reduce the effect of mis-annotations , we required all species to be present in the alignment , and identical , overlapping annotation features in all the six species for which annotation exists . When the exact boundaries for a feature on the alignment varied in different species' annotation , we chose the maximum start and minimum stop positions ( shortest consensus ) . Results were very similar when we used the longest consensus ( minimum start , maximum stop ) , a comparison can be seen in Figure S5 . To examine whether divergence estimates from Plasmodium species alignments was negatively correlated with DAF , we located each SNP position in the alignment of Plasmodium species . We then calculated the median number of rejected substitutions estimated by GERP in both the 5nt and 11nt window around the SNP . Estimates of constraint within each main clade were calculated in three ways: First by the same procedure as described above for the full tree , but with each of the three main clades ( Rodent , Primate , Ape ) scaling independently . Second by estimating the models independently for each of these clades , excluding the long branches to the root . These clade-specific models were then used to estimate the sum of branch lengths ( again excluding the long branches ) for each of the categories ( exon , intron , intergenic , FFD ) . Both these measures were calculated using regions that were consistently annotated ( as exon , intron etc . ) in P . falciparum , P . yoelii and P . knowlesi . Constraint was then calculated as described above . Third , we calculated constraint within clades as above using regions of the genome that were consistently annotated in all species within the clade ( P . knowlesi and P . vivax for the primate clade , P . yoelii , P . berghei and P . chabaudi for the rodent clade , and only P . falciparum for the ape clade ) . CEs were identified with gerpcol and gerpelem programs of GERP [17] , version 2 . 1b ( from http://mendel . stanford . edu/sidowlab/downloads/GERP/index . html ) . Parameters for running gerpcol were estimated using Hyphy using a HKY85 model , fitted to the full alignment from FFD sites . These parameters were; a neutral rate of 3 . 067 , and transition/transversion rate of 2 . 63 , and the following phylogenetic tree ( ( ( PlaBer:0 . 0839783 , PlaYoe:0 . 127063 ) :0 . 126016 , PlaCha:0 . 147872 ) :0 . 59425 , ( PlaViv:0 . 295231 , PlaKno:0 . 213155 ) :0 . 74449 , ( PlaFal:0 . 0141867 , PlaRei:0 . 0420219 ) :0 . 678922 ) ; . The gerpelem program of GERP was run using 10% false discovery rate and default parameters . The average proportion of substitutions that were separated by a multiple of 3 bases ( PMOT ) was calculated by comparing each sequence in the CE alignment slice to each other sequence . In each pairwise comparison we record the distance between each substitution , and then the proportion of these distances that were multiples of 3 . The PMOT score for a CE is the average of these proportions from all pairwise comparisons . We determined the average tBLASTx bit score for each CE as follows . These BLAST searches are designed to detect protein-coding exons , and the median exon length for P . falciparum is 192 nt . So for each Plasmodium species represent in the CE alignment slice , we extract 192 nt of sequence extending out from the midpoint of the CE . We then used this 192 nt sequence to search the Alveolate genomes ( Table S4 ) with NCBI tBLASTx ( searched translated nucleotide databases using a translated nucleotide query ) , accepting only the best hit ( irrespective of E-value ) . For each CE , we record the average bit score for each Plasmodium species represented in the alignment slice . Because all query sequences are the same length bit scores should be comparable between CEs . CEs were scored for RNA structure using RNAz version 1 . 0 ( http://www . tbi . univie . ac . at/~wash/RNAz ) [21] . Since some CEs were longer than the maximum length that RNAz can process , alignments were pre-processed with rnazWindow . pl , ( default parameters except no reference sequence ) , before running RNAz . RNAz was run with default parameters , except predictions were done for both strands ) . Since rnazWindow . pl has a minimum length cut-off of 50 nt , predictions were not produced for the smaller elements . To give an estimate of the false discovery rate in our data , one simulated alignment was produced for each long CE with SISSIz ( Version 0 . 1 ) [22] . There were 8726 long exonic CEs where we could produce an RNAz prediction from both the native and the simulated alignment . From these , 98 of the SISSIz alignments had a support vector probability from RNAz >0 . 95 , and 527 of the native alignments had a SV probability >0 . 95 . So false discovery rate ( FDR ) is 98/527 = 0 . 19 . Similarly non-exonic FDR is 13/33 = 0 . 4 . Control elements for RNASeq expression analysis were chosen to match the length and GC content of each intergenic CE , but otherwise at random . This produced a set of 406 intergenic ‘non-conserved’ element controls whose length and GC content distributions did not differ significantly from a set of 489 P . falciparum intergenic CE elements ( Mann-Whitney tests P>0 . 1 ) . We located a set of 120 intronic ‘non-conserved’ element controls in the same way to compare to the 80 intronic CEs in P . falciparum . There was no significant difference between the RNAseq expression levels of intergenic controls and intergenic CE elements , or between intronic controls and intronic CE elements ( Mann-Whitney tests , both P>0 . 05 ) . Reads from 13 P . falciparum isolates and P . reichenowi were used to identify single nucleotide polymorphisms ( SNPs ) and fixed differences , as follows . See Table S5 for detailed information on SNP calls . All non-WTSI derived reads were downloaded from the NCBI Trace Archive ftp server ( ftp . ncbi . nih . gov/pub/TraceDB/plasmodium_falciparum ) . Fastq files were mapped to the 3D7 version 2 . 1 . 4 reference sequence ( ftp://ftp . sanger . ac . uk/pub/pathogens/Plasmodium/falciparum/3D7/3D7 . version2 . 1 . 4/ ) using SSAHA2 , with the parameters ( -seeds 15 , -score 250 , -tags 1 , -diff 15 , -output cigar , -memory 300 , -cut 5000 ) and ssaha_pileup code . Ssaha2 , ssaha_pileup and associated documentation are available from http://www . sanger . ac . uk/Software/analysis/SSAHA2/ . We excluded non-unique and tandem repeat regions of the genome . Non-unique regions were identified using the SSAHA2 read mapping score ( mapping scores range from 0–50 ) . We calculated the mean mapping score for each 2kb window of the genome ( with a 100 nt step ) , using all reads aligned from all isolates ( Smean ) . The 10th percentile of all Smean values was 49 . 5 ( Smean values were frequently 50 ) . We excluded any region within 2kb of a window with Smean <49 . 5 . We identified tandem repeats using the Emboss application etandem ( flags -minrepeat 2 -maxrepeat 10 ) , and excluded all tandem repeats with ≥70% identity ( to the other repeat ) . Excluding non-unique regions and tandem repeats left a remainder of 19 , 664 , 344 nt of unique non-repeat sequence , 84% of the genome . As an aid to determining suitable thresholds at which to accept SNP calls , we identified a set of 788 ‘reliable SNPs’ that were called in ≥6 isolates ( of the 13 that we examined ) . Since it is unlikely that we call a false positive 6 times in an identical position , with an identical base , this set of SNPs is enriched for true calls . We then examined these reliable SNPs in each isolate ( i ) in turn . Each isolate will call a subset of these SNPs . Each SNP call may be from one or more reads , we determine the average phred score ( for the SNP base ) from all reads , PSNP ( = total phred score/number of reads calling this base ) for each reliable SNP in isolate i . We then examine the distribution of reliable SNP PSNP scores for isolate i , comparing it to the distribution of PSNP scores from all other SNPs called in isolate i . The distribution of PSNP scores from reliable SNPs were always significantly higher than the distribution of PSNP scores from all non-reliable SNPs ( all other SNPs ) due to more true calls in the reliable SNP set . Assuming that 95% of reliable calls are correct , we set the minimum phred scores required to call a SNP in isolate i as the 5th percentile of the distribution of reliable SNP PSNP scores . We refer to this value as min ( PSNP , i ) . At each site in the genome where ssaha_pileup calls a SNP ( in any isolate ) we accept all SNP or reference base calls from each isolates that is supported by ≥2 reads with a PSNP ≥min ( PSNP , i ) . When two alleles satisfied these criteria in an isolate , we accept both alleles , since some samples may contain >1 clone . In practice all min ( PSNP , i ) scores were ∼25 , so SNPs are supported by at least 2 reads with cumulative phred ≥∼50 . This method identifies reliable SNPs without a bias towards common SNPs . Fixed differences in P . reichenowi were accepted if supported by ≥2 reads with an average phred score for an allele ≥25 . Derived allele frequencies were calculated from polymorphic sites with a P . reichenowi base call and ≥three isolate base calls , minor allele frequencies from polymorphic sites with ≥four isolate base calls ( 24 , 573 DAF calls , 54 , 641 MAF calls ) . For gene-specific analysis SNPs were assigned to a gene if they lay within the exons , introns or the half of adjacent intergenic region closer to the gene . We estimate that the false discovery rate for SNP calling is 1–2% , as follows . We aligned 30 , 840 reference 3D7 isolate reads from chromosome 12 onto the assembled 3D7 genome and called SNPs as above . Using the thresholds and filters we described above ( including only unique regions and excluding tandem repeats ) , we accept 47 SNPs from 3D7 in chromosome 12 ( Table S5 ) . With a similar number of aligned reads , we accept 2 , 160 SNPs from the PFCLIN isolate and 2 , 583 SNPs from the IT isolate . If we assume that 47 of the 2160 SNPs from the PFCLIN isolate are false discoveries , then the false discovery rate ( FDR ) is 47/2160 = 2 . 1% . With the same reasoning the IT isolate has 1 . 8% FDR ( Table S5 ) . This is probably an overestimate of the FDR because a ) some of the 3D7 calls may be correct ( i . e . : errors in the reference sequence ) , and b ) 3D7 reads are calling SNPs from a larger proportion of chromosome 12 ( 95% coverage at ≥2 read depth vs . ∼75% for IT and PFCLIN isolates , see Table S5 ) . We also estimated the error rate of SNP allele calling using some Illumina data that was available for the IT and PFCLIN isolates ( D . Jeffares , unpublished data ) . Briefly , 90 genes were chosen from primarily polymorphic but unique regions of the genome , and PCR-amplified from various isolates including PFCLIN and IT . Amplicons were sequenced to high depth with Illumina technology , and mapped to the same reference genome with MAQ . We examined how many of the PFCLIN and IT calls from the SSAHA2-mapped ABI capillary reads matched the alleles called ( this study ) matched those from the MAQ-mapped Illumina reads , using only sites covered by either 10 or 20 Illumina reads . Differences , which may be false SNP calls in either data set , were of the order of 1–2% , as predicted above . In general , error rates in different regions of the genome ( exon , intron , intergenic , FFD , non-synonymous sites ) were not significantly different . The exception was that for both isolates intergenic sites had significantly higher error rates than exonic regions ( see Table S5 ) . We expect this to result in an artifactual shift of intergenic sites to a lower allele frequencies , because artifactual alleles will be rare . We take this into account by comparing only intergenic vs . other intergenic sites . For the comparison of DAF/MAF this would be expected to diminish any affect of lower MAF distribution in exons vs . intergenic sites . Intronic , exonic , FFD and non-synonymous sites did not differ in error rates . For each isolate ( i ) , a predicted genome was created , for each site in the genome we accept all SNP or reference base calls that were supported by ≥2 reads with a PSNP ≥min ( PSNP , i ) . Sites without sufficient quantity coverage were denoted ‘N’ and not used in the analysis . Tajima's D was calculated using Variscan ( Version 2 . 0 , [67] ) , using a fixed number of alleles ( 4 ) for each SNP ( Variscan chooses a random selection if >4 are available at a site ) , using only polymorphic sites ( Variscan parameters FixNum = 1 , NumNuc = 4 , UseMuts = 0 ) . The McDonald-Kreitman test neutrality index was calculated as NI = ( Pn/Ps ) / ( Dn/Ds ) , where Pn and Ps are non-synonymous and synonymous SNPs and Dn and Ds are non-synonymous and synonymous fixed differences ( between P . reichenowi and P . falciparum ) . It has been shown that the McDonald-Kreitman test can be used to estimate the average proportion of non-synonymous substitutions ( α ) that have been fixed by adaptive evolution [32] , according to the formula , where Ds and Dn are the average number of synonymous and non-synonymous substitutions per gene and Pns is the average of per gene where Pn and Ps are the numbers of synonymous and non-synonymous polymorphisms respectively . This test can be generalised to use other classes of sites as the selected test , in place of non-synonymous sites in the original MK test [33] . We calculated a using four-fold degenerate sites as the neutral control and either non-synonymous sites ( for exons ) , intronic sites , or intergenic sites , bootstrapping ( by gene ) 1000 times to determine the 5th and 95th percentiles . Intergenic SNPs and fixed differences were assigned to a gene if they fell in the half of the intergenic region closest to the gene . All statistics were performed in R ( Version 2 . 6 . 0 ) ( Ref . [68] ) . Tests for differences in DAF or MAF used Mann-Whitney U tests . Tests for differences in selective constraint between exon , intron , and intergenic sites within a gene used paired Mann-Whitney U tests .
|
Malaria causes debilitating ill-health in millions of people and kills about one million people annually , mostly young children . It is caused by a single-cell Plasmodium parasite transmitted to humans via mosquito bites . It is difficult to control this parasite because variable genetic make-up enables it to evade detection by vaccines and because drug resistance has repeatedly evolved . Therefore any progress in our understanding of the evolution and genetic variation of the parasite will be central to controlling the parasite . Genic regions that encode proteins are comparatively easy to characterize , whereas non-genic regions are poorly understood . We compare the genomes of seven distantly-related Plasmodium species and find that some of the non-genic regions are very similar between species . The absence of significant evolutionary differences between these non-genic regions implies that they play an important role in the survival of the organism . We then compare the genomes of thirteen different strains of Plasmodium falciparum . It is currently accepted that several families of antigenic parasite genes evolve rapidly . However , using two methods we demonstrate that many other genes have also undergone adaptive evolution .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/population",
"genetics",
"infectious",
"diseases",
"genetics",
"and",
"genomics/comparative",
"genomics",
"evolutionary",
"biology/genomics"
] |
2010
|
Long- and Short-Term Selective Forces on Malaria Parasite Genomes
|
The importance of the large number of thin-diameter and unmyelinated axons that connect different cortical areas is unknown . The pronounced propagation delays in these axons may prevent synchronization of cortical networks and therefore hinder efficient information integration and processing . Yet , such global information integration across cortical areas is vital for higher cognitive function . We hypothesized that delays in communication between cortical areas can disrupt synchronization and therefore enhance the set of activity trajectories and computations interconnected networks can perform . To evaluate this hypothesis , we studied the effect of long-range cortical projections with propagation delays in interconnected large-scale cortical networks that exhibited spontaneous rhythmic activity . Long-range connections with delays caused the emergence of metastable , spatio-temporally distinct activity states between which the networks spontaneously transitioned . Interestingly , the observed activity patterns correspond to macroscopic network dynamics such as globally synchronized activity , propagating wave fronts , and spiral waves that have been previously observed in neurophysiological recordings from humans and animal models . Transient perturbations with simulated transcranial alternating current stimulation ( tACS ) confirmed the multistability of the interconnected networks by switching the networks between these metastable states . Our model thus proposes that slower long-range connections enrich the landscape of activity states and represent a parsimonious mechanism for the emergence of multistability in cortical networks . These results further provide a mechanistic link between the known deficits in connectivity and cortical state dynamics in neuropsychiatric illnesses such as schizophrenia and autism , as well as suggest non-invasive brain stimulation as an effective treatment for these illnesses .
Cognition emerges from the organized temporal structure of electric activity in large , interconnected cortical networks [1]–[3] . The network topology is a key determinant of the types of macroscopic activity patterns a network can generate [4]–[11] . Understanding this structure-function relationship provides important insight not only into normal brain function but also into the mechanistic basis of psychiatric illnesses such as schizophrenia and autism that likely represent “connectivity disorders” [12]–[15] . These connectivity disorders are associated with both structural and functional impairments in connectivity [16]–[19] . Consequently , an understanding of the relationship between network topology and dynamics will facilitate the development of new treatment modalities that counteract dysfunctional network connectivity in psychiatric illnesses . Systematic parameterization of network topology in computational models has demonstrated that connections between random pairs of distant , excitatory neurons within a network enhance temporal synchronization , whereas predominantly local connectivity between neighboring excitatory neurons facilitates macroscopic activity patterns such as oscillations and planar and spiral waves that propagate through the network [20]–[22] . However , individual cortical networks seldom act in isolation because of their interconnectivity with other networks by means of long-range projections ( LRPs ) . Most studies of interconnected networks have focused on how networks synchronize via fast LRPs , with the exception of recent theoretical work that highlights the additional complexity and computational abilities of networks that include physiological delays [23]–[25] . Mathematical studies of the effects of delays on coupled oscillators have predicted diverse results as a consequence of delays . Foundational papers have found that delays between coupled systems produce stability under certain parameters [26] , including stability of synchronization in systems of coupled neurons [27] . Delays have also been shown to generate bifurcations and multistability in coupled oscillator systems [28] , [29] and neural loops [30] , and to give rise to bifurcations and instability in neural field models [31] , [32] . Recently , multistability as a result of delays was found in a Hopfield neural network model [33] . This presence of multistability in such abstract models of neurons and networks of neurons suggests that propagation delays promote multistability . In order to bridge the gap between abstract , theoretical models and biology , we built a large-scale , detailed model of two interconnected cortical networks . The spiking neuron models used in our study accurately reflected the subthreshold dynamics of real neurons and were subject to noise injections that mimicked the stochastic nature of neuronal signaling . With this model , we examined the functional role of the estimated fifty percent of connecting axons with long propagation delays as a consequence of small axonal diameter or a lack of myelination [34]–[36] . We hypothesized that slower long-range projections may enrich overall network activity by counteracting and disrupting the intrinsic , spontaneous dynamics of individual networks . According to our hypothesis , slower projections provide perturbations that are ill-timed to synchronize networks and therefore enable different activity trajectories that individual networks are unable to generate . To test this hypothesis , we used large-scale computer simulations to ask what role long-range projections with propagation delays may play in organizing the overall dynamics of two interconnected cortical networks with intrinsic spontaneous dynamics similar to isolated cortical networks in vivo [37] , [38] . We found that such projections greatly enlarge the repertoire of macroscopic activity patterns in comparison to the networks without propagation delays and that these patterns corresponded to metastable activity states . The interconnected networks spontaneously transitioned between these states . We then evaluated non-invasive brain stimulation ( transcranial Alternating Current stimulation , tACS ) [39]–[41] as a tool to manipulate these dynamics and found that both in-phase and anti-phase tACS induced and guided state transitions . These findings are of broad translational importance since transitions between metastable macroscopic activity states have recently emerged as a fundamental organizational principle of cortical activity , the dynamics of which are impaired in neuropsychiatric disorders [42] , [43] . Our results therefore suggest a novel mechanism of multistability in cortex and a therapeutic modality with which to manipulate cortical dynamics .
To understand the effect of long-range projections ( LRPs ) on the dynamics of two interconnected cortical networks , we built a large-scale computational model of two networks connected by LRPs ( Fig . 1A ) where each network consisted of a two-dimensional sheet of excitatory pyramidal cells ( 400×400 PYs ) and a matched sheet of inhibitory interneurons ( 200×200 INs ) . The synaptic connectivity within the two excitatory-inhibitory networks was chosen to generate slow rhythmic activity in the absence of LRPs ( Fig . S1A ) , a hallmark activity pattern of isolated cortex [37] , [38] , that was structured by alternating epochs of activity ( UP states ) and quiescence ( DOWN states ) . As expected , adding sparse instantaneous ( zero-delay ) LRPs at the same synaptic strength as the local PY-PY excitation ( G ( LRP ) = 0 . 06 , P ( local ) = 99% ) synchronized the activity pattern of PYs across networks ( Fig . 1B: Fraction of PYs active as a function of time; left: no LRPs; right: with LRPs; see also Fig . S1A , sample PY membrane voltage traces ) . Both with and without LRPs , UP states emerged as initially localized “regions of initiation” that then expanded through the local excitatory connectivity ( circular patterns in Fig . 1C , time snap-shots of firing rates ) . In the presence of LRPs , the UP states synchronized their occurrence across the two networks ( Fig . 1C , bottom; Fig . 1D: phase-plane representation , left: no LRPs; right: with LRPs ) , which increased the correlation of individual neurons with their homologous partner in the other network ( Fig . 1E , correlation coefficients for PY membrane voltages; left: no LRPs; right: with LRPs ) . The region of initiation in Network 2 ( arrow in 1C ) corresponded to the area of low correlation ( arrow in 1E ) since the local connections within that network mostly contributed to that region's activity when Network 1 was in a DOWN state . The endogenous network oscillation of the two unconnected networks was only minimally altered by LRPs ( spectral peak at 3 . 2 and 3 . 3 Hz for the two unconnected networks with peak power of 3 . 39e7 and 3 . 33e7 , respectively; with LRPs: 3 . 3 Hz for both networks with 3 . 79e7 and 3 . 57e7 peak power , in arbitrary units , Fig . S1B ) . Therefore , LRP without propagation delays enabled the synchronization of the intrinsic network activity states without pronounced changes to the overall dynamics of the individual networks . Changing the LRP from a random pattern to a homologous configuration further enhanced inter-network synchronization ( Fig . S2 ) . To mimic realistic delays in action potential propagation along low-diameter and unmyelinated fibers that connect different networks , we next added physiologically plausible delays [36] to the LRPs such that presynaptic action potentials in one network led to delayed postsynaptic activity in the other network ( 1 , 2 , 5 , 10 , 30 , and 50 msec ) . We ran simulations for parameterized number and strength of LRPs ( P ( local ) : 0 . 95 , 0 . 97 , 0 . 99 , 0 . 999; G ( LRP ) = 0 . 015 , 0 . 03 , 0 . 06 , 0 . 09 , 0 . 12; 100 simulations per delay value ) to evaluate the effect of delays on the overall dynamics ( five simulations per parameter set with different noise values ) . Because the number and strength of LRPs in the human cortex is not well-characterized , we used a range of parameters to explore the spatio-temporal activity patterns that result from different LRP parameter sets . In our model , P ( local ) values of 99% and 99 . 9% resulted in approximately 30% and 3 . 6% of neurons having LRPs , respectively . These numbers are similar to the LRP numbers reported for murine cortex [44] . We clustered the simulation outputs with linkage analysis using the peak cross-correlation value , which measures the overall synchronization of the two PY networks ( dendrograms in Fig . 2A: 0 msec and Fig . 2B: 50 msec delays , respectively ) . In the absence of propagation delays , simulations were tightly linked , showing similarity of behavior across simulations . The majority of simulations ( 82% ) fell into a single cluster with close to maximum synchronization index ( Fig . 2A , dark blue ) with only a small fraction exhibiting different behavior ( 8% , cyan ) . Thus , without delays in the LRPs , the overall network behavior was very consistent and robust . For increased delays , the relative branch lengths within each cluster became longer and fewer simulations were grouped with the most-synchronized cluster ( Fig . 2B , 55% dark blue , 42% cyan , 2% green , 1% black ) . Therefore , in agreement with our initial hypothesis , these results demonstrate that propagation delays increase the number of different configurations the connected networks can occupy as a function of the LRP parameters . We then examined how these different synchronization patterns impacted the intrinsic dynamics within the individual networks . Indeed , inspection of the spatio-temporal activity profiles revealed the occurrence of three distinct patterns , which can be classified as network states . Typically , networks were in a rapid fire ( RF ) state , with most PYs in the network firing almost simultaneously and the network as a whole demonstrating slow oscillatory behavior ( Fig . 3A , top: pronounced peaks correspond to network-wide UP states in PY activity pattern; bottom: consecutive time snapshots of PY firing activity; see also Movie S1 ) . However , the addition of delays to the LRPs also supported two alternate forms of spatio-temporal dynamics: slow propagating ( SP ) state , with regional UP states originating in one or a few areas and slowly traversing through the local network ( Fig . 3B , top: rhythmic structure is less apparent in network-wide activity profile due to lack of zero-lag synchrony within the network , bottom: initial onset of UP state morphs into a propagating , expanding wave front; see also Movie S2 ) ; and spiral wave ( SW ) state , with a wave originating from single ( or occasionally multiple ) rotor in a spiral pattern ( Fig . 3C; see also Movie S3 ) . Next , we asked how the occurrence of these three different macroscopic network states depended on LRP delays . We found that most interconnected networks followed an RF pattern , especially for short LRP delays ( Fig . 3D , left , relative percent of time spent in RF: 99 . 31±0 . 24% for 0 msec delay versus 76 . 88±2 . 26% for 50 msec , mean±s . e . m . , Table S1 ) . For longer delays , the percentage of time spent in RF decreased and SP became more prominent ( Fig . 3D , middle , SP for 0 msec delay: 0 . 69±0 . 24%; 21 . 94±2 . 15% for 50 msec delay ) . Also , SW , which never occurred in the absence of delays , increased its relative presence with larger delays ( Fig . 3D , right , 1 . 19±0 . 37% , note different scales ) . We then further examined if the interconnected networks stayed in one state for the entire simulation or whether they exhibited spontaneous transitions between these states . We found that , in general , the networks only remained in the same state without transitioning for short LRP delays ( Fig . 3E , average transition frequencies , 0 . 0088±0 . 0031 Hz for 0 msec delay; 0 . 135±0 . 0134 Hz for 50 msec delay , see also Table S1 ) . Therefore , longer ( and thus more realistic ) propagation delays increased not only the presence of other , non-RF states but also the number of transitions between states . In order to further evaluate the robustness of this result , we also tested the effects of a distribution of delays . We ran two sets of simulations , the first with delays uniformly distributed ±20% of the mean and the second with delays uniformly distributed ±100% of the mean . Our results indicate that wider distributions resulted in fewer state transitions ( Fig . S3A , top: narrow distribution , 0 . 0981±0 . 0104 Hz for 50 msec delay , bottom: wide distribution , 0 . 0694±0 . 0091 Hz for 50 msec delay , see also Table S2 ) . Additionally , a broader distribution of delays resulted in less time spent in SP ( Fig . S3B ) . Consequently , a wider distribution of LRP delays , which entails a greater number of shorter delays , seems to stabilize network behavior yet does not abolish multistability . We then analyzed the transitions of individual simulations through these metastable spatio-temporal activity patterns over time ( Fig . 4A: LRP delay = 50 msec , P ( local ) = 0 . 97 , G ( LRP ) = 0 . 06 , example snapshots of PY activity from a single simulation , time of occurrence indicated in color , SP at 0 . 61 and 2 . 25 sec , RF at 2 . 98 sec , SW at 4 . 62 sec , SP at 5 . 44 sec , RF at 7 . 65 sec; Fig . 4B: PY activity profile with times of example snapshots indicated with arrows ) . Averaged across time , the spectral power of the network exhibited a peak at the intrinsic oscillation frequency at ∼3 Hz ( Fig . 4C , left ) . However , the spectrogram demonstrated a slow yet pronounced modulation of power at that intrinsic frequency over time ( Fig . 4C , middle , epochs with high power in red , dashed lines denote intrinsic network frequency , Fig . 4C , right , power at 3 Hz over time ) . These fluctuations corresponded to the occurrence of different network states , with RF states being linked to higher power at the intrinsic frequency ( Fig . 4C , right ) . Correspondingly , power at the intrinsic frequency was lower when the system was in SP and SW states . Synaptic depression of the local excitatory coupling played a key role in determining the effect of incoming synaptic activity from the other network ( Fig . S4 ) . To further understand these different network states , we next applied perturbations to probe the stability of each state . Specifically , we simulated transcranial alternating current stimulation ( tACS ) , which has recently emerged as a promising treatment for psychiatric and neurological illnesses because of its hypothesized ability to selectively manipulate temporal structure of cortical network activity [40] , [41] , [45] , [46] . TACS causes a weak global perturbation of targeted cortical networks due to the low amplitude and broad spatial spread of the weak electric field generated by the scalp stimulation electrodes [47] , [48] . Therefore , tACS may be an ideal approach to bias the overall temporal activity structure of interconnected cortical networks . We here used this stimulation modality to probe the dynamic properties of the different activity states that emerged from LRPs with propagation delays . We found that tACS at 3 Hz ( close to the endogenous frequency of the individual networks ) not only enhanced the synchronization between the two networks but switched the two networks to the fully synchronized , RF state ( Fig . 5A , representative simulation , LRP delay 30 msec , P ( local ) = 0 . 99 , G ( LRP ) = 0 . 12; top: activity profiles; middle: stimulation waveform; bottom: spectrograms; see also Movie S4 ) . Network 1 was in RF fire state before tACS onset ( distinct peak in the spectrogram at ∼3 Hz ) and Network 2 was in SW state ( no peak in the spectrogram due to the lack of synchrony within the individual PY network ) . Importantly , the enhanced , synchronized rhythmic RF activity during stimulation was not limited to the duration of the stimulation but rather outlasted the stimulation . Therefore , the effect of tACS was not just a reflection of the shared input to all PYs but rather represented an outlasting change in activity structure . This “memory” of network activity , in this case during stimulation , is the main feature of a multistable system . The simulated tACS was an effective perturbation , enabling the network to switch to another state ( shorter , 1 sec stimulation had the same effect , data not shown ) . Interestingly , a small fraction of the simulations did not show this enhancing effect of tACS . Rather , in these cases , tACS switched the networks from RF to either SW or SP states ( Fig . 5B , plots same as in Fig . 5A , delay 10 msec , P ( local ) = 0 . 97 , G ( LRP ) = 0 . 06; see also Movie S5 ) . In this simulation , the networks were in synchronized RF state that was switched to SP by tACS and then followed by SW at stimulation removal . To further demonstrate that the state switching by tACS is indeed a consequence of the LRPs , we evaluated models with no LRPs and therefore no communication between the two networks . We found little multistability before and during tACS confirming that LRPs are important for inducing multistable states in cortical networks ( Fig . S5 ) . Given these distinct effects of the same stimulation protocol in different simulations , we determined the relative occurrence of the different states and the state transition probabilities for all simulations ( including all propagation delays , fraction of LRPs , and strength of LRPs ) as a function of tACS . In the control condition before onset of stimulation ( Fig . 5C , top row , Table S3 ) , the majority of simulations exhibited RF behavior with a small fraction demonstrating SP and SW . With increasing propagation delays , the percentage of simulations with SP behavior markedly increased ( from 0 . 2% for 0 msec delay to 24 . 2% for 50 msec delay ) . Interestingly , during stimulation ( Fig . 5C , middle row ) , we found the highest fraction of non-RF , and in particular SP , activity patterns in simulations with low LRP propagation delays . As a result , tACS increased the occurrence of the SP state for short propagation delays and decreased the occurrence of SP for longer propagation delays . In further support that such stimulation has a complex effect pattern , we found an increased presence of SW for all delay values after tACS ( Fig . 5C , bottom row ) . Overall , the state-dependent transition probabilities in the absence of tACS , at tACS onset , and at tACS removal ( Fig . 5D ) demonstrated that tACS effectively switched activity state , with the most prominent effects being elimination of SP ( 86 . 89% transition probability from SP to RF at onset compared to 43 . 22% in the absence of stimulation ) and yet the same stimulation induced a switch from RF to SP in a subset of simulations ( 17 . 53% transition probability from RF to SP , compared to 1 . 8% in the absence of tACS ) . In turn , if the stimulation succeeded in inducing a transition to RF , the removal of stimulation failed to introduce a state transition back . Specifically , the transition probabilities out of the RF state closely matched the overall transition probabilities in the absence of stimulation ( 0 . 09% for RF to SW and 1 . 74% for RF to SP at stimulation removal in comparison to 0 . 05% for RF to SW and 1 . 85% for RF to SP ) . We then compared how networks behaved together and found that in the absence of stimulation , both networks were in the RF state for the majority of simulations ( Fig . S6 , left , 99 . 66±0 . 33% for 0 msec delay , 78 . 68±3 . 70% for 50 msec delay ) . With stimulation , there was a small decrease in the percent of time where both PY networks were in RF ( 81 . 56±3 . 27% for 0 msec delay ) with the exception of the 50 msec delay simulations ( 88 . 81±2 . 16% , see also Table S4 ) , where the stimulation increased the likelihood of both networks being in RF . In contrast , both SP states were often only found in one of the two networks at a time for delays up to 10 msec ( Fig . S6 , middle , 0 . 00±0 . 00% for 0 msec delay , see also Table S4 ) . For longer delays , simultaneous SP in both networks became much more prominent ( 17 . 94±3 . 50% and 42 . 74±4 . 59% for 30 and 50 msec delays , respectively ) . Similarly , SW never occurred in both PY networks simultaneously before stimulation ( 0 . 00%±0 . 00% for all delay values ) . Interestingly , during and after stimulation , a subset of simulations exhibited simultaneous SW in both networks , a pattern that never occurred without stimulation ( Fig . S6 , right ) . We further examined two simulations that represented peculiarities in our dataset due to their sustained anti-phase locking . Both simulations responded to tACS by switching to ( near ) zero-lag synchronization that was maintained after stimulation removal ( Fig . S7 ) . Having established that tACS affects the spatio-temporal activity of two interconnected networks , we next quantified the effect of tACS on the power of the network activity at the stimulation frequency ( 3 Hz ) . First , we looked at the effectiveness of tACS to entrain two networks during stimulation by comparing the power during stimulation to the power before stimulation . We found that tACS enhanced the power at 3 Hz of both PY networks during stimulation for most simulations , indicating its ability to entrain networks ( Fig . 6A , logarithmic enhancement ratio , 88 . 1% of all simulations in top right quadrant ) . The correlation between the enhancement in each of the two networks varied with LRP delay , but with no monotonic relationship between delays ( Fig . 6D , left ) . Next , we analyzed the outlasting effect of tACS after stimulation had stopped . After tACS , the outlasting enhancement was significantly correlated between the two networks , and again with no monotonic relationship between correlation and propagation delay ( Fig . 6B , 58 . 6% of all simulation in top right quadrant and 6D , middle ) . Thus , tACS can enhance the power of networks at their intrinsic frequencies , an effect that lasts beyond the duration of stimulation . In addition , this enhancement lacks a direct relationship with the values of the propagation delays between the two networks . To investigate how this outlasting effect of tACS related to the entrainment during stimulation , we compared the enhancement of power at 3 Hz during stimulation to the enhancement of power after stimulation ( Fig . 6C , 66 . 9% of all simulations exhibited enhancement both during and after stimulation ) . We found that the instantaneous and outlasting effects were tightly correlated , showing that tACS directly increased 3 Hz power ( Fig . 6D , right ) . The cross-correlation peak amplitude and offset , which indicate similarity of behavior and simultaneity of behavior respectively , confirmed these outlasting effects ( Fig . 6E , before: 2 sec window before stimulation onset; during: 4 sec of stimulation; after: 2 sec window after stimulation , normalized cross-correlation ) . With stimulation , the cross-correlation peak was increased ( bright yellow ) , showing that the two networks demonstrate similar behavior during tACS . The phase offset between the two networks was reduced by tACS , an effect that persisted after tACS ended . These effects , together with the outlasting increase in power , show that the two networks were able to sustain a modified network state after tACS . Thus , tACS has an enduring effect on connected networks by entraining the two networks together and increasing their power at the stimulation frequency . Although tACS typically entrained networks to a 3 Hz RF state , occasionally it had an opposite effect by disrupting RF during tACS and causing it to enter SW after tACS . We examined these network dynamics to determine which factors influenced such disruption . Networks that ended in SW after tACS were most often in SP or SW during tACS and only very rarely in RF ( Fig . 7A ) . Consequently , we considered networks in SW and SP during tACS to both be indicators of stimulation-induced state disruption . When looking at PY activity before tACS , networks in RF during tACS had no specific pattern of activity while networks in SP or SW had a clear temporal structure in their PY activity prior to the onset of tACS ( Fig . 7B , left ) , indicating that the excitatory state of the network was a factor in the response to tACS . The mean PY activity at tACS onset ( t = 2 . 0 ) showed that networks in SP and SW during tACS had activity levels at onset compared to networks that entered or remained in RF ( Fig . 7B , right ) . This trend suggests that networks in an excited state are more likely to break from RF upon external stimulation . To verify this conjecture , we measured the depression coefficient of each network upon tACS onset ( D; lower values indicate greater synaptic depression ) . The depression coefficient was indeed lower for networks that entered SW during tACS , and the normalized variance of D was greater for networks in SP or SW during tACS ( Fig . S8 ) . Thus , increased synaptic depression , along with a wider variance of depression across the network , predisposed networks towards non-RF behavior , indicating a difficulty in responding to incoming excitation from the other network during a currently- or recently-excited state . Along with the above described network excitation , however , other factors also facilitated switching to a non-RF state during tACS . Higher LRP connectivity ( i . e . lower P ( local ) ) and lower LRP conductance ( G ( LRP ) ) both made networks more likely to enter a non-RF state , and these effects were increased with lower delays ( Fig . 7C ) . After tACS , however , networks were more likely to enter SW with lower LRP connectivity and lower conductance , with no clear effect of delay ( Fig . 7D ) . Consequently , lower levels of LRP conductance were more likely to disrupt the RF state while higher levels of LRP conductance generally promoted entrainment and the presence of the RF state . The paradoxical effect of connectivity parameter P ( local ) indicates that the effect of network topology was altered by stimulation . The relative prominence of SW after removal of tACS led us to measure the stability of the SW state . We first examined stability of SW in the absence of tACS and found that SW was a metastable state ( Fig . S9A ) . Then we examined longer runs of simulations where at least one network switched to SW after tACS ( Fig . S9B ) . As networks remained in SW for longer periods of time after removal of tACS , the likelihood of them switching from SW decreased , with 28 . 95% of networks remaining in SW for the entire extended simulation time . Simulations with lower LRP connectivity had longer SW persistence , while LRP conductance and delay had no effect on persistence ( Fig . S9C ) . This effect of connectivity corresponds to that found in Fig . 7D , where less-connected networks are more likely to demonstrate SW behavior . These findings further confirm that SW is a metastable state whose stability is affected by network structure . To further probe the mechanisms behind state disruption by tACS , we next simulated antiphase tACS using the same parameters but with the stimulation signal for the two networks phase-shifted by 180 degrees ( Fig . 8A ) . Such stimulation has recently been used in a human tACS study to disrupt phase synchronization yet without direct experimental demonstration of a network effect of out-of-phase tACS [46] . During stimulation , correlation between the activity of the two networks was disrupted , an effect that persisted after removal of tACS ( Fig . 8B , top ) . However , while the networks were out of phase during stimulation , they returned to their original , reduced phase offset after tACS removal ( Fig . 8B , bottom ) . Consequently , antiphase tACS disrupted the dynamics of two interconnected networks but the temporal lag induced by tACS did not persist after tACS removal . When examining the spatio-temporal activity patterns , we found that networks demonstrated three behaviors during antiphase tACS , which were grouped by k-means clustering of their cross-correlograms . “Strong antiphase” behavior occurred when the two networks were individually entrained by their respective stimulation ( Fig . 8C; Delay = 10 msec , P ( local ) = 0 . 99; G ( LRP ) = 0 . 06 ) . “Interspersed weak firing” was a result of networks firing in response to both their stimulation as well as the excitation from the other network , resulting in a series of strong and weak UP states ( Fig . 8D; Delay = 10 msec; P ( local ) = 0 . 99; G ( LRP ) = 0 . 09 ) . The third behavior , “breaking from RF” , occurred also with in-phase tACS in the form of SP and SW states ( Fig . 8E; Delay = 1 msec , P ( local ) = 0 . 99 , G ( LRP ) = 0 . 09; see Fig . 5B ) . In this case , one or both of the networks is no longer in RF in response to stimulation . By examining the effects of parameters on behavior during antiphase tACS , the causes of RF disruption can be more thoroughly uncovered . Higher LRP connectivity ( i . e . low P ( local ) ) and higher LRP conductance made interspersed weak firing more likely ( Fig . 8F; see Table S5 for all values ) . This pattern is most likely mediated by the synaptic input from the other network during its UP state . Delays had a minimal effect on behavior during antiphase tACS . Low LRP connectivity most strongly predisposed the networks to break from RF , the converse of what we found during in-phase tACS . Interestingly , the lower LRP connectivity also promoted the persistence of SW after in-phase tACS . Finally , an interesting behavior arose during antiphase stimulation where the two networks entered a high-frequency ( >8 Hz ) antiphase state ( Fig . S10 ) . This state occurred for all simulations with parameters of Delay = 50 msec , P ( local ) = 0 . 95 , G ( LRP ) = 0 . 12 and for 40% of simulations with Delay = 50 msec , P ( local ) = 0 . 95 , G ( LRP ) = 0 . 09 , but no others , and persisted beyond the removal of tACS . This unique behavior further demonstrates the multistability of interconnected cortical networks and the ability of tACS to change network state with outlasting effects .
Brain stimulation , whether through implanted electrodes such as in deep brain stimulation [50] or through non-invasive application of electric [51] or magnetic fields [52] , has established itself as a promising approach for the treatment of a large and growing number of neurological and psychiatric disorders for which only limited pharmacological treatments exist . However , the underlying mechanisms of most of the stimulation paradigms remain hotly debated and little clarity exists with regard to the interaction dynamics between stimulation-induced perturbations and intrinsic network dynamics . We here used simulated transcranial Alternating Current stimulation ( tACS ) to test if a shared common input to both networks in the form of a weak global perturbation of the PY membrane voltages can synchronize the networks . Based on previous modeling and in vitro work [48] , [53] , [54] , we used stimulation waveforms that were matched in frequency to the intrinsic oscillation frequency of the unconnected networks . Interestingly , not only did such 3 Hz sine-wave transcranial current stimulation ( tACS ) switch the network to a synchronized , rapid fire state , but also—and perhaps more importantly—the network remained in that state at the removal of stimulation in a majority of the simulations . These results suggest that tACS can affect cortical networks by inducing a switch to a qualitatively different , more synchronized network state , which is stable and therefore outlasts the application of the brain stimulation . The amount of time this synchronized state persists after stimulation was not comprehensively mapped . Future work should address which parameters contribute to the persistence of synchronization between two networks; such work can then help to improve the design of non-invasive brain stimulation as a clinical treatment for disorders with impaired synchronization . Our study suggests that rather than reorganizing synaptic strength , tACS can induce a switch between different macroscopic activity states that are part of a repertoire of cortical states mediated by LRPs with propagation delays . Interestingly , we also found that the same stimulation paradigm had the opposite effect in a ( small ) subset of simulations where the stimulation reduced the synchronization; these results demonstrate that ( 1 ) the ongoing network dynamics ( i . e . network state ) and the underlying network topology determine the response to brain stimulation and ( 2 ) a global stimulus does not necessarily enhance synchronization . Antiphase tACS , a stimulus designed to disrupt synchronization , caused a set of new behaviors during stimulation , but in most cases failed to create antiphase structure between the networks as an outlasting effect . Consequently , the outlasting effects of stimulation are dependent on the phase of stimulation as well as the intrinsic network structure . As part of a computational model , conclusions drawn from our simulations of tACS are limited by the size of our networks and the fact that each PY receives the same magnitude of stimulation; however , simulated variance of tACS current amplitude has previously been found to have no effect on network response [55] . While it may be necessary to vary the strength of tACS current and electrode size to produce the same effects with patients , our simulations reveal that tACS has the ability to affect network dynamics by introducing periodic excitability into a system . The dependence of the overall effect on current network state at stimulation onset further demonstrates the potential of adaptive , feedback brain stimulation [56] , [57] where the stimulation waveform is dynamically adjusted to the ongoing brain activity . Pathological changes in connectivity in the central nervous system ( CNS ) are a hallmark of many neurological and psychiatric illnesses . For example , schizophrenia is often called a connectivity disorder due to the findings of aberrations in white matter and lack of functional connectivity in both functional MRI and electroencephalogram ( EEG ) studies [13] , [15] , [43] , [58]–[69] . We here tested a range of physiologically plausible propagation delays and coupling strengths and found that the occurrence of macroscopic dynamics which lacked synchrony depended on the LRP propagation delays in the presence of slow endogenous rhythmic activity in the individual networks . Therefore , our results predict that disease state and progression can be assayed by determining the structure of global state transitions during awake resting or sleeping , two behavioral states where slow rhythmic activity dominates the spontaneous activity patterns [70] , [71] . Furthermore , CNS disorders such as multiple sclerosis [72] , where the integrity of the white matter tracts are affected , and epilepsy , which is associated with abnormal cortical oscillations [73]–[75] , may lead to similar changes to the landscape of cortical activity states . A spatio-temporal pattern similar to our SP state was recently found to occur in human seizures [76] , suggesting that the states in our simulations have biological correlates with the potential to be pathological . Accordingly , these cortical activity states represent a promising target for rational design of ( non- ) invasive brain stimulation as evaluated in this study . We used computer simulations of large-scale , interconnected cortical networks in this study and found that long-range projections with physiological delays can play an unanticipated role in generating multistable network dynamics in cortex . Therefore , the so far neglected slow connecting fibers between cortical areas may not be a “flawed design” that prevents large-scale synchronization of cortical areas but rather enables the emergence of additional , qualitatively different network states that likely serve different neural computations . The ability of non-invasive brain stimulation to change these network states points to a promising treatment option for neuropsychiatric disorders involving abnormal connectivity and network dynamics .
We used the Izhikevich model [6] , [77] , [78] of pyramidal cells ( PYs ) and inhibitory interneurons ( INs ) for the computational simulations in this study . The Izhikevich model provides a very good compromise between biological plausibility and computational efficiency . Each model neuron consists of only two coupled differential equations with four parameters a , b , c , and d that determine the intrinsic dynamics . We used an Euler solver with a step width of Δt = 0 . 1 msec such that the update rule at every time-step of the stimulation to compute the new value of the membrane potential V′ is:where V is the membrane voltage at the previous time-step , EAMPA = 0 mV is the excitatory reversal potential ( AMPA ) , EGABA = −80 mV is the inhibitory reversal potential ( GABAA ) , GEX and GIN represent the sums of all afferent excitatory ( gPY ) and inhibitory ( gIN ) conductances , ItACS and INoise are current injections to model transcranial alternating current stimulation ( tACS ) and to cause spontaneous background noise , and u is the slow recovery variable . For PYs , parameters a ( recovery time-scale ) and b ( recovery sensitivity ) were set to 0 . 02 and 0 . 2 , respectively . We modeled regular spiking , intrinsically bursting , and chattering PY cells by setting the reset potential parameter , c , to values from −65 to −50 mV , and the recovery after an action potential , d , to values from 6 to 8 . All values were drawn from generalized Pareto distributions ( μ = −50 , σ = −30 , ξ = −2 , median = −61 . 26 mV for parameter c; μ = 6 , σ = 4 , ξ = −2 , median = 7 . 50 for parameter d ) . These distributions helped to bias the parameter values such that regular spiking cells were the most frequent PY cell type . For the INs , the parameters c and d were set to −65 mV and 2 , respectively . To model both fast and low-threshold spiking neurons , parameters a and b were drawn from uniform distributions ( 0 . 02 to 0 . 1 and 0 . 2 to 0 . 25 , respectively ) . Synapses were model by conductances that were updated with a step in case of a presynaptic action potential and that were subject to exponential decay otherwise . All synapses of a given type were lumped together into a single synapse to increase computational efficiency of the simulations [79] . The respective update rules for the conductances were:where GEX and GIN were the corresponding total conductances at the occurrence of the last presynaptic action potential , τEX = 2 msec and τIN = 3 msec were the synaptic decay time-constants , and Δtpsp was the time elapsed since the last presynaptic action potential . PY-PY connections exhibited short-term synaptic depression [80] with a single depression variable D ( D = 1: no depression , D = 0: complete depression ) that exhibited an exponential recovery time-course ( τD = 300 msec ) . PY-PY synaptic gPY-PY strength was calculated as:where GPY-PY denoted the synaptic strength and D was updated for each presynaptic action potential for all PY-PY synapses:where r = 0 . 6 represented the fraction of synaptic resources available immediately after vesicle release caused by an action potential . All simulations in this study consisted of two connected networks . Each network consisted of two layers , a PY network ( 400×400 model neurons arranged on a two-dimensional grid ) and an IN network ( 200×200 model neurons arranged on a grid ) . The large number of neurons was motivated by the fact that tACS is likely to act as a global weak perturbation similar to the endogenous electric field [81] . Each PY network exhibited sparse local connectivity where each PY cell connected to a random 30%-subset of 120 cells in its surrounding 11×11 grid of PY cells ( GPY-PY = 0 . 06 , no autapses ) . Synaptic inhibition had global random connectivity both for PY-IN excitation ( GPY-IN = 0 . 0001 , 25 PY-IN connections per PY ) and feedback IN-PY inhibition ( GPY-IN = 0 . 0002 , 49 IN-PY per IN ) . The global connectivity scheme for synaptic inhibition was chosen such that inhibition provided an overall activity-dependent reduction of PY firing rate without any extra spatial structure . The synaptic connectivity was chosen such that a 3 Hz endogenous oscillation occurred in the absence of long-range projections ( LRPs ) . LRPs were configured by replacing a defined ( small ) fraction of local PY-PY connections with excitatory projections to random PYs in the other network ( 0 . 1 , 1 , 3 , or 5% of local PY-PY connections ) . We evaluated the effect of a range of propagation delays for these LRPs ( 0 , 1 , 2 , 5 , 10 , 30 , and 50 msec ) . All cells received a current injection INoise that was the sum of ( 1 ) a constant current injection ranging from 0 to 1 . 5 ( generalized Pareto distribution with ì = 1 , ó = −3 , î = −3 , median = 0 . 1895 ) to create spontaneously firing PYs and ( 2 ) a variable current with a random value drawn at every time-step ( uniform distribution from 0 to 2 and 0 to 1 . 5 for PYs and INs , respectively ) . Non-invasive brain stimulation with transcranial Alternating Current stimulation ( tACS ) was modeled with a small current injection ( ItACS , amplitude 1 . 0 corresponding to 10 pA , resulting in average in a membrane voltage depolarization of about 100 µV ) into PY cells that are susceptible to applied electric fields because of their elongated somato-dendritic axes [82]–[84] . The effect of the electric field resulting from tACS was modeled by injecting a small current into all PYs [81] . The amplitude ( 10 pA ) was chosen such that the corresponding change of the membrane voltage was about 100 µV . INs were not stimulated since they hardly respond to weak electric fields due to their morphology [82] . Stimulation frequency was 3 Hz to match endogenous oscillation frequency of networks . Network activity profiles were determined by the fraction of PY neurons that were firing over time . Both normalized cross-correlations and spectrograms were based on these activity profiles by network . Spectrograms were computed by Wavelet transformation with Morlet wavelets ( 0 . 5 to 10 Hz in 0 . 5 Hz step-width ) . Macroscopic spatio-temporal activity states were distinguished by the median PY activity peaks ( percent PYs firing ) in 1 sec bins . Peaks ( UP states ) were extracted with the Matlab findpeaks function ( threshold: 1% of maximum , dead time 50 msec , Mathworks , Natwick , MA ) . Rapid fire ( RF ) was assigned to peak values >60% of total number of PYs in the network , slow propagating ( SP ) was assigned to values 15–60% , and spiral wave ( SW ) was assigned to values <15% . Relative time spent in different states was determined over all simulations with the two networks considered together . State-dependent transition probabilities were determined for a 1 sec window before stimulation onset , 1 sec after stimulation onset , and last 1 sec window of simulation after stimulation . Data are reported as mean±s . e . m . Significance of correlations was determined by corrcoef function in Matlab with 0 . 05 as significance cut-off .
|
The brain mediates behavior by orchestrating the activity of billions of neurons that communicate with each other through electric impulses . The transmission of these action potentials is surprisingly slow for a large fraction of these connections . Given the importance of precise timing of neuronal activity , the function of these slow connections has remained a puzzle . We here used computer simulations to investigate how slow connection speeds alter the overall activity patterns of two brain networks . We found that these connections enable the interconnected networks to generate distinct activity patterns such as different types of waves of electric activity . Our results therefore suggest that the slow transmission of electric impulses in the brain is not a “design flaw” but rather plays an important role in enabling the brain to generate a richer set of activity patterns . The ability of the brain to switch between different activity states is crucial to normal cognition , and abnormalities in switching behavior are associated with cognitive symptoms in psychiatric disorders such as schizophrenia and autism . It is therefore promising that we were able to control transitions between different activity states with non-invasive brain stimulation in our simulations , suggesting a novel approach to the treatment of these illnesses .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
|
Emergence of Metastable State Dynamics in Interconnected Cortical Networks with Propagation Delays
|
Normal mode analysis ( NMA ) methods are widely used to study dynamic aspects of protein structures . Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form . There is a trade-off between speed and accuracy in different choices . In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials . On the other extreme , fast elastic network model ( ENM ) methods with Cα−only representations and simplified potentials that based on geometry alone , thus oblivious to protein sequence . Here we present ENCoM , an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA . ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles . Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability . While existing methods are based on machine learning or enthalpic considerations , the use of ENCoM , based on vibrational normal modes , is based on entropic considerations . This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations . We compare ENCoM to a large number of methods in terms of accuracy and self-consistency . We show that the accuracy of ENCoM is comparable to that of the best existing methods . We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations .
Biological macromolecules are dynamic objects . In the case of proteins , such movements form a continuum ranging from bond and angle vibrations , sub-rotameric and rotameric side-chain rearrangements [1] , loop or domain movements through to folding . Such movements are closely related to function and play important roles in most processes such as enzyme catalysis [2] , signal transduction [3] and molecular recognition [4] among others . While the number of proteins with known structure is vast with around 85K structures for over 35K protein chains ( at 90% sequence identity ) in the PDB database [5] , our view of protein structure tends to be somewhat biased , even if unconsciously , towards considering such macromolecules as rigid objects . This is due in part to the static nature of images used in publications to guide our interpretations of how structural details influence protein function . However , the main reason is that most known structures were solved using X-ray crystallography [6] where dynamic proprieties are limited to b-factors and the observation of alternative locations . Despite this , it is common to analyze larger conformational changes using X-ray structures with the comparison of different crystal structures for the same protein obtained in different conditions or bound to different partners ( protein , ligand , nucleic acid ) . It is particularly necessary to consider the potential effect of crystal packing [7] , [8] when studying dynamic properties using X-ray structures . Nuclear magnetic resonance ( NMR ) is a powerful technique that gives more direct information regarding protein dynamics [9] , [10] . Different NMR methodologies probe distinct timescales covering 15 orders of magnitude from 10−12 s side chain rotations via nuclear spin relaxation to 103 s using real time NMR [10] . In practice , there is a limitation on the size of proteins that can be studied ( between 50–100 kDa ) although this boundary is being continuously pushed [11] providing at least partial dynamic information on extremely large systems [12] . However , only a small portion ( around 10% ) of the available proteins structures in the Protein Data Bank ( PDB ) are the result of NMR experiments [5] . Molecular dynamic simulations numerically solve the classical equations of motion for an ensemble of atoms whose interactions are modeled using empirical potential energy functions [13]–[15] . At each time step the positions and velocities of each atom are calculated based on their current position and velocity as a result of the forces exerted by the rest of the system . The first MD simulation of a protein ( Bovine Pancreatic Trypsin Inhibitor , BPTI ) ran for a total 8 . 8 ps [16] followed by slightly longer simulations ( up to 56 ps ) [17] . A film of the latter can be seen online ( http://youtu . be/_hMa6G0ZoPQ ) . Despite using simplified potentials and structure representations ( implicit hydrogen atoms ) as well as ignoring the solvent , these first simulations showed large oscillations around the equilibrium structure , concerted loop motions and hydrogen bond fluctuations that correlate with experimental observations . Nowadays , the latest breakthroughs in molecular dynamics simulations deal with biological processes that take place over longer timescales . For example , protein folding [18] , transmembrane receptor activation [19] , [20] and ligand binding [21] . These simulations require substantial computer power or purpose built hardware such as Anton that pushes the current limit of MD simulations to the millisecond range [22] . Despite powerful freely available programs like NAMD [23] and GROMACS [24] and the raise of computational power over the last decade , longer simulations reaching timescales where most biological processes take place are still state-of-the-art . Normal modes are long established in the analysis of the vibrational properties of molecules in physical chemistry [25] . Their application to the study of proteins dates back to just over 30 years [26]–[30] . These earlier Normal Mode Analysis ( NMA ) methods utilized either internal or Cartesian coordinates and complex potentials ( at times the same ones used in MD ) . As with earlier MD methods their application was restricted to relatively small proteins . Size limitations notwithstanding , these early studies were sufficient to demonstrate the existence of modes representing concerted delocalized motions , showing a facet of protein dynamics that is difficult to access with MD methods . Some simplifications were later introduced and shown to have little effect on the slowest vibrational modes and their utility to predict certain molecular properties such as crystallographic b-factors . These simplifications included the use of a single-parameter potential [31] , blocks of consecutive amino acids considered as units ( nodes ) [32] and the assumptions of isotropic [33] fluctuations in the Gaussian Network Model ( GNM ) or anisotropic fluctuations [34] . These approximations have drastically reduced the computational time required , thus permitting a much broader exploration of conformational space using conventional desktop computers in a matter of minutes . Of these , the most amply used method is the Anisotropic Network Model ( ANM ) [35] , [36] . ANM is often referred simply as an elastic network model; one should however bear in mind that all normal mode analysis methods are examples of elastic network models . ANM uses a simple Hook potential that connects every node ( a point mass defined at the position of an alpha carbon ) , within a predetermined cut-off distance ( usually 18 Å ) . More recently , a simplified model , called Spring generalized Tensor Model ( STeM ) , that uses a potential function with four terms ( covalent bond stretching , angle bending , dihedral angle torsion and non-bonded interaction ) has been proposed [37] . The normal mode analysis of a macromolecule produces a set of modes ( eigenvectors and their respective eigenvalues ) that represent possible movements . Any conformation of the macromolecule can in principle be reached from any other using a linear combination of amplitudes associated to eigenvectors . It is essential however to not loose sight of the limitations of normal mode analysis methods . Namely , normal modes tell us absolutely nothing about the actual dynamics of a protein in the sense of the evolution in time of atomic coordinates . Plainly speaking , normal mode analysis is informative about the possible movements but not actual movements . Additionally , normal modes tell us of the possible movements around equilibrium . These two caveats clearly place normal mode analysis and molecular dynamics apart . First , molecular dynamics gives an actual dynamics ( insofar as the potential is realistic and quantum effects can be ignored ) . Second , while the equilibrium state ( or the starting conformation ) affects the dynamics , one can explore biologically relevant timescales given sufficient computational resources to perform long simulations . The vast majority of coarse-grained NMA models only use the geometry of the protein backbone ( via Cα Cartesian position ) disregarding the nature of the corresponding amino acid , in doing so a lot of information is lost . To our knowledge , there have been three independent attempts at expanding coarse-grained NMA models over the years to include extra information based on backbone and side-chain atoms . Micheletti et al . [38] developed the βGM model in which the protein is represented by Cβ atoms for all residues except Glycine in addition to the Cα atoms . The Hamiltonian is a function exclusively of Cα and Cβ distances . As a Gaussian model , βGM does not give information about directions of movement but only their magnitude and can be used solely to predict b-factors . As Cβ atoms do not change position , this model cannot be used by definition to predict the effect of mutations , either on dynamics or stability . The authors report results on b-factor prediction comparable to GNM . Lopéz-Blanco et al . [39] developed a NMA model in internal coordinates with three different levels of representation: 1 . Heavy-atoms , 2 . five pseudo-atoms ( backbone: NH , Cα , CO and side-chain: Cβ and one at the center of mass of the remaining side chain atoms ) and 3 . Cα representation . While the potential is customizable , the default potential uses a force constant that is distance dependent but atom type independent . The method is validated through overlap analysis on a dataset of 23 cases . The authors report no significant differences in overlap for the different representations . Lastly , Kurkcuoglu et al . [40] developed a method that mixes different levels of coarse-graining . The authors test the method on a single protein , Triosephosphate Isomerase with higher atomic representation for a loop and achieve better overlap values compared to Cα only representation . All the methods above , while adding more detail to the representation utilize force constants that are not atom-type dependent . Therefore , while less coarse-grained , all the methods above are still atom-type and amino-acid type agnostic . By definition , irrespective of the level of coarse-graining , such models cannot account for the effect of mutations on protein dynamics or stability . It has been shown that different amino acids interact differently and that single mutations can have a high impact on protein function and stability [41]–[43] . Mutations on non-catalytic residues that participate into concerted ( correlated ) movements have been shown to disrupt protein function in NMR relaxation experiments [44]–[46] . Several cases have been documented of mutations that don't affect the global fold of the protein , but affect protein dynamics and disrupt enzyme function [47] . To overcome this limitation of coarse-grained NMA methods while maintaining the advantages of simplified elastic network models , we developed a new mixed coarse-grained NMA model called Elastic Network Contact Model ( ENCoM ) . ENCoM employs a potential function based on the four bodies potential of STeM with an addition to take in consideration the nature and the orientation of side chains . Side-chain atomic contacts are used to modulate the long range interaction term with a factor based on the surface area in contact [48] and the type of each atom in contact . Additionally , we introduce a non-specific version of ENCoM ( ENCoMns ) where all interactions between atom types are the same . ENCoM and ENCoMns were validated trough comparison to ANM , GNM and STeM with respect to the prediction of crystallographic b-factors and conformational changes , two properties conventionally used to test ENM methods . Moreover , we test the ability of ENCoM and ENCoMns to predict the effect of mutations with respect to protein stability and compare the ability of ENCoM and ENCoMns to a large number of existing methods specifically designed for the prediction of the effect of single point mutations on protein stability . Finally , we use ENCoM to predict the effect of mutations on protein function in the absence of any effects on protein stability .
We utilized a dataset of 113 non-redundant high-resolution crystal structures [49] to predict b-factors using the calculated ENCoM eigenvectors and eigenvalues as described previously [35] ( Equation 4 ) . We compared the predicted b-factors using ENCoM , ENCoMns , ANM , STeM and GNM to the experimental Cα b-factors for the above dataset ( Supplementary Table S1 ) . For each protein we calculate the Pearson correlation between experimental and predicted values . The results in Figure 1 represent the bootstrapping average of 10000 iterations . We observe that while comparable , ENCoM ( median = 0 . 54 ) and ENCoMns ( median = 0 . 56 ) have lower median values than STeM ( median = 0 . 60 ) and GNM ( median = 0 . 59 ) but similar or higher than ANM ( median = 0 . 54 ) . It should be noted that it is possible to find specific parameter sets that maximize b-factor correlations beyond the values obtained with STeM and GNM ( see methods ) . However we observe a trade-off between the prediction of b-factors on one side and overlap and the effect of mutations on the other ( see methods ) . Ultimately we opted for a parameter set that maximizes overlap and the prediction of mutations with complete disregard to b-factor predictions . Nonetheless , as shown below , even the lower correlations obtained with ENCoM are sufficiently high to detect functionally relevant local variations in b-factors as a result of mutations . As GNM does not provide information on the direction of movements or the effect of mutations , it is not considered further in the present study . By definition , any conformation of a protein can be described as a linear combination of amplitudes associated to the eigenvectors representing normal modes . It should be stressed that such conformations are as precise as the choice of structure representation used and correct within the quadratic approximation of the potential around equilibrium . Those limitations notwithstanding , one application of NMA is to explore the conformational space of macromolecules using such linear combinations of amplitudes . Pairs of distinct protein conformations , often obtained by X-ray crystallography are used to assess the extent to which the eigenvectors calculated from a starting conformation could generate movements that could lead to conformational changes in the direction of a target conformation . Rather than an optimization to determine the amplitudes for a linear combination of eigenvectors , this is often simplified to the analysis of the overlap ( Equation 5 ) , i . e . , the determination of the single largest contribution from a single eigenvector towards the target conformation . In a sense the overlap represents a lower bound on the ability to predict conformational changes without requiring the use of an optimization process . The analysis of overlap for ANM , STeM , ENCoM and ENCoMns was performed using the Protein Structural Change Database ( PSCDB ) [50] , which contains 839 pairs of protein structures undergoing conformational change upon ligand binding . The authors classify those changes into seven types: coupled domain motions ( 59 entries ) , independent domain motions ( 70 entries ) , coupled local motions ( 125 entries ) , independent local motions ( 135 entries ) , burying ligand motions ( 104 entries ) , no significant motion ( 311 entries ) and other type of motions ( 35 entries ) . The independent movements are movements that don't affect the binding pocket , while dependent movements are necessary to accommodate ligands in the pose found in the bound ( holo ) form . Burying movements are associated with a significant change of the solvent accessible surfaces of the ligand , but with small structural changes ( backbone RMSD variation lower than 1 Å ) . Despite differentiating between types of movements based on the ligands , the ligands were not used as part of the normal mode analysis . Since side-chain movements associated to the burying movements cannot be predicted with coarse-grained NMA methods , we restrict the analysis to domain and loop movements [51] as these involve backbone movements amenable to analysis using coarse grained NMA methods . For practical purposes , in order to simplify the calculations in this large-scale analysis , NMR structures were not considered . It is worth stressing however that all NMA methods presented here don't have any restriction with respect to the structure determination method and can also be used with modeled structures . A total of 736 conformational changes , half representing apo to holo changes and the other half holo to apo ( in total 368 entries from PSCDB ) are used in this study ( Supplementary Table S2 ) . Overlap calculations were performed from the unbound ( apo ) form to the bound form ( holo ) and from the bound form to the unbound form . Bootstrapped results based onto the best overlap found within the first 10 slowest modes [52] , [53] for the different types of conformational changes , domain or loop are shown in figures 2 and 3 respectively . In each case a set of box-plots represent the performance of the four methods being compared , namely STeM , ANM , ENCoM and ENCoMns . The left-most set of box-plots represents the average over all data while subsequent sets represent distinct subsets of the dataset as labeled . The first observation ( comparing Figures 2 and 3 ) is that all tested NMA models show higher average overlaps for domain movements ( Figure 2 ) than loop movements ( Figure 3 ) . This confirms earlier observations that NMA methods capture essential cooperative global ( delocalized ) movements associated with domain movements [51] . Loop movements on the other hand are likely to come about from a more fine tuned combination of normal mode amplitudes than what can be adequately described with a single eigenvector as measured by the overlap . The second observation is that STeM performs quite poorly compared to other methods irrespective of the type of movement ( domain or loop ) . This is somewhat surprising when one compares with ENCoM or ENCoMns considering how similar the potentials are . This suggests that the modulation of interactions by the surface area in contact ( the βij terms in Equation 1 ) of the corresponding side-chains as well as the specific parameters used are crucial . Focusing for a moment on domain movements ( Figure 2 ) , ENCoM/ENCoMns outperform all other methods for domain movements in general as well as for every sub category of types of motions therein . Independent movements show lower overlaps than coupled ones , a fraction of those movements may not be biologically relevant due to crystal packing . Interestingly , while there are no differences between the overlap for independent movements starting from the apo or holo forms , this is not the case for coupled movements . In this case ( right-most two sets in Figure 2 ) , it is easier to use the apo ( unbound ) form to predict the holo ( bound ) form , suggesting that the lower packing in the apo form ( as this are frequently more open ) generates eigenvectors that favor a more comprehensive exploration of conformational space . Lastly , with respect to loop movements ( Figure 3 ) , while it is more difficult to obtain good overlaps irrespective of the method or type of structure used , overall ENCoM/ENCoMns again outperforms ANM . Some of the same patterns observed for domain movements are repeated here . For example , the higher overlap for coupled apo versus holo movements . We observe that ENCoMns consistently performs almost as well as ENCoM irrespective of the type of motion used ( all sets in Figures 2 and 3 ) . As side-chain conformations in crystal structures tent to minimize unfavourable interactions , the modulation of interactions by atom types that differentiate ENCoM from ENCoMns plays as minor but still positive role . Normal mode resonance frequencies ( eigenvalues ) are related to vibrational entropy [54] , [55] ( see methods ) . Therefore , it is reasonable to assume that the information contained in the eigenvectors can be used to infer differences in protein stability between two structures differing by a mutation under the assumption that the mutation does not drastically affect the equilibrium structure . A mutation may affect stability due to an increase in the entropy of the folded state by lowering its resonance frequencies , thus making more microstates accessible around the equilibrium . We utilize experimental data from the ProTherm database [56] on the thermodynamic effect of mutations to validate the use of ENCoM to predict protein stability . Here we benefit from the manual curation efforts previously performed to generate a non-redundant subset of ProTherm comprising 303 mutations used for the validation of the PoPMuSiC-2 . 0 [57] . The dataset contains 45 stabilizing mutations ( ΔΔG<−0 . 5 kcal/mol ) , 84 neutral mutations ( ΔΔG [−0 . 5 , 0 . 5] kcal/mol ) and 174 destabilizing mutations ( ΔΔG>0 . 5 kcal/mol ) ( Supplementary Table S2 ) . Each protein in the dataset have at least one structure in the PDB database [58] . As we calculate the eigenvectors in the mutated form we require model structures of the mutants . We generate such models using Modeller [59] and are thus assume that the mutation does not drastically affect the structure . Mutations were generated using the mutated . py script from the standard Modeller software distribution . Modeller utilizes a two-pass minimization . The first one optimizes only the mutated residue , with the rest of the protein fixed . The second pass optimizes the non-mutated neighboring atoms . It is important to stress that our goal is to model the mutated protein as accurately as possible and thus using any method that unrealistically holds the backbone fixed to model the mutant form would be an unnecessary simplification . We observe a backbone RMSD for the whole protein of 0 . 01+/−0 . 01 Å on average . Considering that RMSD is a global measure that could mask more drastic local backbone rearrangements , we also calculated the average maximum Cα displacement but with a value of 0 . 13+/−0 . 12 Å we are confident that while not fixed , backbone rearrangements are indeed minimal . In the present work we predict the effect of mutations ( Equation 6 ) for ENCoM , ENCoMns , ANM and STeM and compare the results to existing methods for the prediction of the effect of mutations using the PoPMuSiC-2 . 0 dataset above . We compare our results to those reported by Dehouck et al . [57] for different existing techniques: CUPSAT , a Boltzman mean-force potential [60]; DMutant , an atom-based distance potential [61]; PoPMuSiC-2 . 0 , a neural network based method [57]; Eris , a force field based approach [62]; I-Mutant 2 . 0 , a support vector machine method [63]; and AUTO-MUTE , a knowledge based four body potential [64] . We used the same dataset to generate the data for FoldX 3 . 0 , an empirical full atom force-field [65] and Rosetta [66] , based on the knowledge based Rosetta energy function . A negative control model was build with a randomized reshuffling of the experimental data . Figure 4 presents RMSE results for each model . The raw data for the 303 mutations is available in Supplementary Table S3 . ANM and STeM are as good as the random model when considering all types of mutations together ( Figure 4 ) . This is not surprising as the potentials used ANM and STeM are exclusively geometry-based and are thus agnostic to sequence . ENCoMns , ENCoM , Eris , CUPSAT , DMutant , I-Mutant 2 . 0 and give similar results and predict significantly better than the random model . AUTO-MUTE , FoldX 3 . 0 , Rosetta and in particular PoPMuSiC-2 . 0 outperform all of the other models . The RMSE values for the subset of 174 destabilizing mutations ( Figure 5 ) shows similar trends as the whole dataset with the exceptions of DMutant losing performance and PoPMuSiC-2 . 0 as well as AUTO-MUTE gaining performance compared to the others . It is important to stress that the low RMSE of PoPMuSiC-2 . 0 on the overall dataset is to a great extent due to its ability to predict destabilizing mutations . The subset of 45 stabilizing mutations ( Figure 6 ) gives completely different results as those obtained for destabilizing mutations . AUTO-MUTE , Rosetta , FoldX 3 . 0 and PoPMuSiC-2 . 0 that outperformed all of the models on the whole dataset or the destabilizing mutations dataset cannot predict better than the random model . This is also true for CUPSAT , I-Mutant 2 . 0 and Eris . ENCoM and DMutant are the only models with significantly better than random RMSE values for the prediction of stabilizing mutations . ANM and STeM outperform all models on the neutral mutations ( Figure 7 ) . All other models fail to predict neutral mutations any better than random . While the accuracy of ANM and STeM to predict neutral mutations may seem surprising at first , it is in fact an artifact of the methodology . As the wild type or mutated structures are assumed to maintain the same general backbone structure , the eigenvectors/eigenvalues calculated with ANM or STeM will always be extremely similar for wild type and mutant forms . Any differences will arise as a result of small variations in backbone conformation produced by Modeller . As such , ANM and STeM predict almost every mutation as neutral , explaining their high success in this case . At first glance , the comparison of ENCoMns and ENCoM could suggest that a large part of the effect observed come from a consideration of the total area in contact and not the specific types of amino acids in contact . However , the side chains in contact are already in conformations that minimize unfavourable contacts to the extent that is acceptable in reality ( in the experimental structure ) or as a result of the energy minimization performed by Modeller for the mutant form given the local environments . The fact that ENCoM is able to improve on ENCoMns is the actual surprising result and points to the existence of frustration in molecular interactions [67] . Considering that none of the existing models can reasonably predict neutral mutations , the only models that achieve a certain balance in predicting both destabilizing as well as stabilizing mutations better than random and with low bias are ENCoM and DMutant . The analysis of the performance of ENCoM in the prediction of different types of mutations in terms of amino acid properties shows that mutations from small ( ANDCGPSV ) to big ( others ) residues are the most accurately predicted followed by mutations between non-polar or aromatic residues ( ACGILMFPWV ) . ENCoM performs poorly on exposed residues ( defined as having more than 30% of the surface area exposed to solvent ) ( Figure 8 ) . It may in principle be possible to find particular linear combinations of ENCoM and other methods that further improve predictions given the widely different ( and potentially complementary ) nature of the various approaches with respect to ENCoM . We performed linear regressions to find parameters involving ENCoM and each of the other methods in turn that maximize the RMSE difference between the combined models and random predictions ( Eq . 8 ) . When considering all types of mutations together , all mixed models perform better than either model individually ( left-most column in Figure 8 ) . By definition , a mixed model cannot perform worst than the better of the two models individually . The contribution of ENCoM to the improved performance of the combined model varies according to the model . The ratio of the relative contributions ( in parenthesis ) , broadly classifies the methods into three categories: 1 . Methods where ENCoM contributes highly , including I-Mutant ( 1 . 18±0 . 25 ) , DMutant ( 1 . 07±0 . 31 ) , CUPSAT ( 1 . 02±0 . 09 ) and Eris ( 1 . 02±0 . 11 ) ; 2 . Methods where the contribution of ENCoM is smaller than that of the other method but still significant , including Rosetta ( 0 . 90±0 . 11 ) and FoldX3 ( 0 . 89±0 . 08 ) ; and finally methods where the addition of ENCoM have a small beneficial effect , including in this class Automute ( 0 . 69±0 . 13 ) and especially PoPMuSIC ( 0 . 18±0 . 10 ) . The left-hand side dendrogram in Figure 8 clusters the methods according to their overall accuracy relative to random based on the entire profile of predictions ( Eq . 8 ) for different subsets of the data ( columns ) according to the type of mutations being predicted . This clustering of methods shows that the relative position of the methods is maintained throughout except for a small rearrangement due to changes in the predictions for CUPSAT . This result suggests that the contribution from the combination of ENCoM to other methods is uniform irrespective of the type of mutations studied . One basic requirement for a system that predicts the effect of mutations on stability is that it should be self-consistent , both unbiased and with small error with respect to the prediction of the forward or back mutations as reported by Thiltgen et al . [68] . The authors built a non-redundant set of 65 pairs of PDB structures containing single mutations ( called form A and form B ) and utilized different models to predict the effect of each mutation going from the form A to form B and back . From a thermodynamic point of view , the predicted variation in free energy variation should be of the same magnitude for the forward or back mutations , ΔΔGA→B = −ΔΔGB→A . Using the Thiltgen dataset we performed a similar analysis for ENCoM , ENCoMns , ANM , STeM , CUPSAT , DMutant , PoPMuSiC-2 . 0 and a random model ( Gaussian prediction with unitary standard deviation ) . For the remaining methods ( Rosetta , Eris and I-Mutant ) we utilize the data provided by Thiltgen . We removed three cases involving prolines as such cases produce backbone alterations . Furthermore , PoPMuSiC-2 . 0 failed to return results for five cases . The final dataset therefore contains 57 pairs ( Supplementary Table S4 ) . The CUPSAT and AUTO-MUTE servers failed to predict 25 and 32 cases respectively . As these failure rates are significant considering the size of the dataset , we prefer to not include these two methods in figures 9 and 10 ( the remaining cases appear however in Supplementary Table S4 ) . The results in figure 9 show that compared to the random model ( a positive control in this case ) , Rosetta and FoldX 3 . 0 show moderate bias while PoPMuSiC-2 . 0 and I-Mutant show significant bias . All biased methods are biased toward the prediction of destabilizing mutations ( data not shown ) in agreement with the results in Figure 3 . DMutant , Eris , ENCoM and ENCoMns are the only models with bias comparable to that of the random model ( the positive control in this experiment ) . ENCoM , ENCoMns , and to a lesser extent Rosetta and DMutant have lower errors than the random model ( Figure 10 ) . All other methods display an error equal or higher than that of the random model . ENCoM and ENCoMns vastly outperform all the others models in terms of error . Lastly , STeM and ANM show low and moderate biases respectively and errors equivalent to random ( data not shown ) but as mentioned , these methods cannot be used for the prediction of mutations ( other than neutral mutations as an artefact ) . Mutations may not only affect protein stability but also protein function . While experimental data is less abundant , one protein in particular , dihydrofolate reductase ( DHFR ) from E . coli , has been widely used experimentally to understand this relationship [69] , [70] . Recently , Boher et al . [47] have analyzed the effect of the G121V mutation on protein dynamics in DHFR by NMR spectroscopy . This mutation is located 15 Å away from the binding site but reduces enzyme catalysis by 200 fold with negligible effect on protein stability ( 0 . 70 kcal/mol ) . The authors evaluated , among many other parameters , the S2 parameter of the folic acid bound form for the wild type and mutated forms and identify the regions where the mutation affects flexibility . We calculated b-factor differences ( Equation 4 ) between the folate-bound wild type ( PDB ID 1RX7 ) and the G121V mutant ( modeled with Modeller ) forms of DHFR ( Supplementary Table S5 ) . We obtain a good agreement ( Pearson correlation = 0 . 61 ) between our predicted b-factor difference and S2 differences ( Figure 11 ) . As mentioned earlier , the overall correlation of 0 . 54 in the prediction of b-factors ( Figure 1 ) appears at least in this case to be sufficient to capture essential functional information .
Our results show that a small modification of the long-range interaction term in the potential energy function of STeM had an important positive impact on the model . This small change improves the method in comparison to existing NMA methods in the traditional areas such as the prediction of b-factors and conformational sampling ( overlap ) where coarse-grained normal mode analysis are applied . More importantly however , it opens an entire new area of application to coarse-grained normal mode analysis methods . Specifically ENCoM is the first coarse-grained normal-mode analysis method that permits to take in consideration the specific sequence of the protein in addition to the geometry . This is introduced through a modification in the long-range interactions to account for types of atoms in contact modulated by their surface in contact . As a validation of the approach we explored the ability of the method to predict the effect of mutations in protein stability . In doing so we created the first entropy-based methodology to predict the effect of mutations on the thermodynamic stability of proteins . This methodology is entirely orthogonal to existing methods that are either machine learning or enthalpy based . Not only the approach is novel but also the method performs extremely favourably compared to other methods when viewed in terms of both error and bias . As the approach taken in ENCoM is completely different from existing methods for the prediction of the effect of mutations on protein stability , a new opportunity arises to combine ENCoM with enthalpy and machine-learning methods . Unfortunately , we tried to create a naïve method based on linear combinations of the predictions of ENCoM and the different methods presented without success , perhaps due to the large bias characteristic to the different methods . To assess the relative importance of contact area and the modulation of interactions with atom types , we tested a model that has non-specific atom-type interactions ( ENCoMns ) , this model is atom type insensitive , but is sensitive the orientation of side-chain atoms . While a large fraction of the observed effect can be attributed to surfaces in contact only , ENCoM is consistently better than ENCoMns , particularly at predicting destabilizing mutations where the possibility to accommodate unfavourable interactions is more restricted . We cannot however exclude the effect of the intrinsic difficulty in modeling destabilizing mutations . For stabilizing mutations , the near equivalence of ENCoM and ENCoMns may be explained in part by the successful energy minimization of the mutated side-chain performed by Modeller . ANM and STeM failed to predict the effect of mutations on the whole dataset . They were not expected to perform well because their respective potentials only take in account the position of alpha carbons ( backbone geometry ) . As such ANM and STeM tend to predict mutations as neutral , explaining their excellent performance onto the neutral subset and failure otherwise . Our results suggest that surfaces in contact are essential in a coarse-grained NMA model to predict the effect of mutation and that the specific interactions between atom types is necessary to get more subtle results , particularly stabilizing mutations . ENCoM is consistently better than ENCoMns in the prediction of loop or domain movements irrespective of the dependency of the coupling of this movement to ligand binding or the starting structure ( apo or holo form ) and both outperform ANM and STeM . Our results corroborate previous work on a mix coarse-grained method adding a atomistic resolution to loops capable of improving the prediction of loop movements [40] . ENCoM performs considerably better than STeM throughout despite having very similar potentials , showing the importance of surfaces in contact in the prediction of movements . There is little difference between ENCoMns and ENCoM in the prediction of b-factors , but both perform worst than ANM , STeM and GNM . At least in the case of DHFR b-factor differences capture some essential characteristics of the system as calculated by NMR . However , one should be careful in placing too much emphasis on the validation of b-factor predictions using experimental data derived from crystals as these are affected to a great extent by rigid body motions within the crystal [71] . PoPMuSiC-2 . 0 , AUTO-MUTE , FoldX 3 . 0 and Rosetta perform better than other models in the whole test dataset of mutations . However , the dataset consists of 15% stabilizing mutation , 57% of destabilizing and 28% of neutral mutations . When looking at each subset , machine learning or enthalpy based models failed to predict better than random on the stabilizing mutations subset . Biases in the dataset may have affected the training of machine-learning methods . For example the training set of PoPMuSiC-2 . 0 contains 2648 mutations in proportions that are similar to those in the testing set with 60% , 29% and 11% destabilizing , neutral and stabilizing mutations respectively . While it is true that most mutations tend to be destabilizing , if one is interested in detecting stabilizing mutations , a method over trained on destabilizing mutations will not meet expectations . Indeed , PoPMuSiC-2 . 0 and I-mutant the two machine learning based methods , have larger biases and errors than other methods in their predictions . Our method relies on a model structure of the mutant . As the modeling may fail to find the most stable side-chain conformation , it could have a bias toward giving slightly higher energies to the mutant . Notwithstanding this potential bias , ENCoM have the lowest error and bias . This may be a case where less is more as the coarse-grained nature of the method makes it also less sensitive to errors in modeling that may affect enthalpy-based methods to a greater extent . Finally , there is one more advantage in the approach taken in ENCoM . As the network model is a global connected model it considers indirectly the entire protein , while in existing enthalpy or machine-learning methods the effect of a mutation is calculated mostly from a local point of view . The prediction of the thermodynamic effect of mutations is very important to understand disease-causing mutations as well as in protein engineering . With respect to human diseases , and particularly speaking of cancer mutations , one of the factors that may lead to tumour suppressor or oncogenic mutations is their effect on stability ( the authors thank Gaddy Getz from the Broad Institute for first introducing us to this hypothesis ) . Specifically , destabilizing mutations in tumour suppressor genes or alternatively stabilizing mutations in oncogenes may be driver mutations in cancer . Therefore the prediction of stabilizing mutations may be very important to predict driver mutations in oncogenes . Likewise , in protein engineering , one major goal is that of improving protein stability with the prediction of stabilizing mutations . Such mutations may be useful not as the final goal ( for purification or industrial purposes ) but also to create a ‘stability buffer’ that permits the introduction of potentially destabilizing additional mutations that may be relevant to create the intended new function . The work presented here is to our knowledge also the most extensive test of existing methods for the prediction of the effect of mutations in protein stability . The majority of methods tested in the present work fail to predict stabilizing mutations . However , we are aware that the random reshuffled model used may be too stringent given the excessive number of destabilizing mutations in the dataset . The only models that predict stabilizing as well as destabilizing mutations are ENCoM and DMutant , however ENCoM is the only method with low self-consistency bias and error . While the contribution of side chain entropy to stability is well established [72] , [73] , here we use backbone normal modes to predict stability . As a consequence of the relationship between normal modes and entropy , our results attest to the importance of backbone entropy to stability and increase our understanding of the overall importance of entropy to stability . The strong trend observed on the behaviour of different parameters sets with respect to the α4 parameter is very interesting . Lower values are associated with better predictions of conformational changes while higher values are associated with better b-factor predictions . One way to rationalize this observation is to consider that higher α4 values lead to a rigidification of the structure , adding constraints and restricting overall motion . Likewise , lower α4 values remove constraints and thus lead to higher overlap . We used ENCoM to predict the functional effects of the G121V mutant of the E . coli DHFR compared to NMR data . This position is part of a correlated network of residues that play a role in enzyme catalysis but with little effect on stability . The mutation affects this network by disrupting the movement of residues that are far from the binding site . We can predict the local changes in S2 with ENCoM . As these predictions are based on b-factor calculations , this result shows that at least in this case , even with b-factor prediction correlation lower than ANM , STeM and GNM we can detect functionally relevant variations . Clearly , despite the greater performance of GNM , ANM or STeM in the calculation of b-factors , these methods cannot predict b-factor differences as a consequence of mutations , as their predictions are the same for the two forms . While a more extensive study is necessary involving S2 NMR parameters , our results serve as an example against relying too heavily on crystallographic b-factors for the evaluation of normal mode analysis methods .
In order to obtain a set of parameters to be used with ENCoM we performed a sparse exhaustive integer search of the logarithm of parameters with for to maximize the prediction ability of the algorithm in terms of overlap and prediction of mutations . In other words , we searched all combinations of 13 distinct relative orders of magnitude for the set of 4 parameters . For each parameter set , we calculated the bootstrapped median RMSE ( see below ) Z-score sum for the prediction of stabilizing and destabilizing mutations , . Keeping in mind that lower RMSE values represent better predictions , the 2000 parameter sets ( out of 28561 combinations ) with highest were then used to calculate Z-scores for overlap in domain and loop movements . As our goal is to obtain a parameter set that combines low RMSE and high overlap , we ranked the 1000 parameter sets according to . The parameter set with highest is ( solid black line in Figure 13 ) . The optimization of the bootstrapped median is equivalent to a training procedure with leave-many-out testing . Given the dichotomy in predicting the effect of mutations and overlap on the one hand and b-factors on the other , we provide , the following is the best parameter set observed for the prediction of b-factors with average b-factor correlation of . The exploration of parameter space shows that there is a clear trade-off between the prediction of mutations ( low RMSE ) , conformational sampling ( high overlap ) and b-factors ( high correlations ) . Parameter sets that improve the prediction of b-factors are invariably associated with poor conformational changes ( low overlap ) associated to both domain and loop movements and variable RMSE for the prediction of mutations ( red lines in Figure 13 ) . On the other hand , parameter sets that predict poorly b-factors , perform better in the prediction of conformational changes and the effect of mutations ( blue lines in Figure 13 ) . The parameters used in STeM , are arbitrary , taken without modifications from a previous study focusing on folding [77] . As expected , this set of parameters can be considerably improved upon as can be observed in Figure 13 ( dashed line ) . The four right-most variables in the parallel coordinates plot in Figure 13 show the logarithm of the α parameters for each parameter set . Either class of parameter sets , better for b-factors ( in red ) or better for overlap/RMSE ( in blue ) come about from widely diverging values for each parameter across several orders of magnitude . There are however some patterns . Most notably for α4 , where there is an almost perfect separation of parameter sets around α4 = 1 . Interestingly , higher values of α1 and α2 , associated with stronger constraints on distances and angles tend also to be associated to better overlap values . While it is likely that a better-performing set of parameters can be found , the wide variation of values across many orders of magnitude show that within certain limits , the method is robust with respect to the choice of parameters . This result justifies the sparse search employed . Bootstrapping is a simple and general statistical technique to estimate standard errors , p-values , and other quantities associated with finite samples of unknown distributions . In particular , bootstrapping help mitigate the effect of outliers and offers better estimates in small samples . Bootstrapping is a process by which the replicates ( here 10000 replicates ) of the sample points are stochastically generated ( with repetitions ) and used to measure statistical quantities . In particular , bootstrapping allows the quantification of error of the mean [78]–[82] . Explained in simple terms , two extreme bootstrapping samples would be one in which the estimations of the real distribution of values is entirely made of replicates of the best case and another entirely of the worst case . Some more realistic combination of cases in fact better describes the real distribution . Thus , bootstrapping , while still affected by any biases present in the sample of cases , helps alleviate them to some extent . One of the most common types of experimental data used to validate normal mode models is the calculation of predicted b-factors and their correlation to experimentally determined b-factors . B-factors measure how much each atom oscillates around its equilibrium position [6] . Predicted b-factors are calculated as previously described [35] . Namely , for a given Cα node ( i ) , one calculates the sum over all eigenvectors representing internal movements ( n = 7 to 3N ) of the sum of the squared ith component of each eigenvector in the spatial coordinates x , y and z normalized by the corresponding eigenvalues: ( 4 ) We calculate the Pearson correlation between predicted and experimental b-factors for each protein and average random samples according to the bootstrapping protocol described above . The overlap is a measure that quantifies the similarity between the direction of movements described by eigenvectors calculated from a starting structure and the variations in coordinates observed between that conformation and a target conformation [52] , [53] . In other words , the goal of overlap is to quantify to what extent movements based on particular eigenvectors can describe another conformation . The overlap between the nth mode , , described by the eigenvector is given by ( 5 ) where represent the vector of displacements of coordinates between the starting and target conformations . The larger the overlap , the closer one can get to the target conformation from the starting one though the movements defined entirely and by the nth eigenvector . We calculate the best overlap among the first 10 slowest modes representing internal motions . Insofar as the simplified elastic network model captures essential characteristics of the dynamics of proteins around their equilibrium structures , the eigenvalues obtained from the normal mode analysis can be directly used to define entropy differences around equilibrium . Following earlier work [54] , [55] , the vibrational entropy difference between two conformations in terms of their respective sets of eigenvalues is given by: ( 6 ) In the present work the enthalpic contributions to the free energy are completely ignored . Therefore , in the present work we directly compare experimental values of ΔG to predicted ΔS values . In order to use the same nomenclature as the existing published methods , we utilize ΔΔG to calculate the variation of free energy variation as a measure of conferred stability of a mutation . A linear regression going through the origin is build between predicted ΔΔG and experimental ΔΔG values to evaluate the prediction ability of the different models . The use of this type of regression is justified by the fact that a comparison of a protein to itself ( in the absence of any mutation ) should not have any impact on the energy of the model and the model should always predict an experimental variation of zero . However , a linear regression that is not going through the origin would predict a value different from zero equal to the intercept term . In other words , the effect of two consecutive mutations , going from the wild type to a mutated form back to the wild type form ( WT→M→WT ) would not end with the expected net null change . The accuracy of the different methods was evaluated using a bootstrapped average root mean square error of a linear regression going through the origin between the predicted and experimental values . We refer to this as RMSE for short and use it to describe the strength of the relationship between experimental and predicted data . If one was to plot the predicted energies variation of ΔΔGA→B versus ΔΔGB→A and trace a line y = −x , the bias would represent a tendency of a model to have points not equally distributed above or below that line while the error would represent how far away a point is from this line . In other words , considering a dataset of forward and back predictions , the error is a measure of how the predicted ΔΔG differ and the bias how skewed the predictions are towards the forward or back predictions [68] . A perfect model , both self-consistent and unbiased , would have all the points in the line . Statistically , the measures of bias and error are positively correlated . The higher the error for a particular method , the higher the chance of bias . We determined the efficacy of linear combinations involving ENCoM and any of the other models for the prediction of the effect of mutations on thermostability as follows . For a given bootstrap sample of the data , we rescaled the predictions of each model as follows: ( 7 ) where the vector notation signifies all data points in the particular bootstrap sample and the index represents each model as well as the random model . We then use singular value decomposition to determine the best parameter the normalized predicted values to calculate the parameters that maximize the RMSE difference between the linear combination model and the random predictions as follows ( 8 ) whereand ( 9 ) The bootstrapped average is then calculated from the 10000 bootstrap iterations . The relative contribution of ENCoM and the model under consideration is given by the ratio of the parameters . It is interesting to note that this ratio could be seen as an effective temperature factor , particularly considering that predicted values are primarily enthalpic in nature for certain methods and entropy based in ENCoM .
|
Normal mode analysis ( NMA ) methods can be used to explore potential movements around an equilibrium conformation by mean of calculating the eigenvectors and eigenvalues associated to different normal modes . Each normal mode represents a global collective , correlated and complex , form of motion of the entire protein . Any conformation around equilibrium can be represented as a weighted combination of normal modes . Differences in the magnitudes of the set of eigenvalues between two structures can be used to calculate differences in entropy . We introduce ENCoM the first coarse-grained NMA method to consider atom-specific side-chain interactions and thus account for the effect of mutations on eigenvectors and eigenvalues . ENCoM performs better than existing NMA methods with respect to traditional applications of NMA methods but is the first to predict the effect of mutations on protein stability and function . Comparing ENCoM to a large set of dedicated methods for the prediction of the effect of mutations on protein stability shows that ENCoM performs better than existing methods particularly on stabilizing mutations . ENCoM is the first entropy-based method developed to predict the effect of mutations on protein stability .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biochemistry",
"proteins",
"protein",
"structure",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"biophysics",
"molecular",
"biology",
"macromolecular",
"structure",
"analysis",
"biophysical",
"simulations"
] |
2014
|
A Coarse-Grained Elastic Network Atom Contact Model and Its Use in the Simulation of Protein Dynamics and the Prediction of the Effect of Mutations
|
Despite the ∼1018 αβ T cell receptor ( TCR ) structures that can be randomly manufactured by the human thymus , some surface more frequently than others . The pinnacles of this distortion are public TCRs , which exhibit amino acid-identical structures across different individuals . Public TCRs are thought to result from both recombinatorial bias and antigen-driven selection , but the mechanisms that underlie inter-individual TCR sharing are still largely theoretical . To examine this phenomenon at the atomic level , we solved the co-complex structure of one of the most widespread and numerically frequent public TCRs in the human population . The archetypal AS01 public TCR recognizes an immunodominant BMLF1 peptide , derived from the ubiquitous Epstein-Barr virus , bound to HLA-A*0201 . The AS01 TCR was observed to dock in a diagonal fashion , grasping the solvent exposed peptide crest with two sets of complementarity-determining region ( CDR ) loops , and was fastened to the peptide and HLA-A*0201 platform with residue sets found only within TCR genes biased in the public response . Computer simulations of a random V ( D ) J recombination process demonstrated that both TCRα and TCRβ amino acid sequences could be manufactured easily , thereby explaining the prevalence of this receptor across different individuals . Interestingly , the AS01 TCR was encoded largely by germline DNA , indicating that the TCR loci already comprise gene segments that specifically recognize this ancient pathogen . Such pattern recognition receptor-like traits within the αβ TCR system further blur the boundaries between the adaptive and innate immune systems .
Epstein-Barr virus ( EBV ) , also called human herpesvirus 4 ( HHV-4 ) , is a genetically stable agent that has slowly co-evolved with our species and its antecedents for millions of years . EBV is typically transmitted orally during childhood , propagates in B cells and epithelia , and is shed for the lifetime of the host . More than 90% of the world's population is infected with EBV . This mutual coexistence is not without heavy resource cost for the host . Large populations of CD8+ αβ T lymphocytes are deployed for the purposes of EBV surveillance and suppression . These populations peak during asymptomatic primary infection [1] , acute infectious mononucleosis ( AIM ) [2] and old age [3] . Across the entire EBV proteome , one of the most immunogenic CD8+ T cell targets is the HLA-A*0201-restricted GLCTLVAML peptide derived from the BMLF1 protein ( residues 280–288; herein referred to as GLC-A2 ) . During primary infection , up to 11% of the total peripheral CD8+ T cell pool can be specific for GLC-A2 [4]; this response contracts to 0 . 5–2 . 2% of the peripheral CD8+ T cell pool during persistent infection [4] , but can swell again to 10% in old age [3] . Given the high in vivo frequencies of this response and the ubiquity of both EBV infection and the HLA-A*0201 allele , it is unsurprising that GLC-A2 is one of the most studied HLA class-I target antigens . Interestingly , initial investigations into the clonotypic nature of the GLC-A2 response revealed that CD8+ T cells are deployed with a biased T cell receptor ( TCR ) repertoire [5] , [6] , [7] , [8] that is stable over time [9] . The TCR is a clonotypic , membrane-bound receptor that binds peptide-MHC ( pMHC ) . Genetically , TCRs are rearranged into α- and β-chains from a selection of 176 variable ( V ) , diversity ( D ) , joining ( J ) , and constant ( C ) genes on chromosomes 7 and 14 . Random recombination of these genes generates only 5–10% of the potential diversity within the TCR repertoire; exonucleolytic activity , random N nucleotide additions at the V ( D ) J junctions [10] and αβ chain pairing contribute the remainder . Theoretical TCR diversity in humans has been placed in the region of 1015–1020 unique structures [11] , [12] , [13] , with direct in vivo estimates greater than 2 . 5×107 unique structures [14] . Structurally , TCR α- and β-chains fold to expose six highly flexible complementary determining region ( CDR ) loops that can contact the pMHC binding face . The germline-encoded CDR1 and CDR2 loops , from the TRAV and TRBV genes , participate heavily in MHC contacts and occasionally peptide contacts . The variable CDR3 loops , which span the V ( D ) J joints , are key to TCR diversity and participate heavily in peptide contacts . TCRs dock with pMHC complexes in a roughly diagonal fashion , such that the CDR3α loops are placed over the peptide N-terminus and the CDR3β loops lie over the peptide C-terminus . In spite of the universe of TCR options available to the immune system , some pMHC antigens provoke the emergence of biased and predictable repertoires ( reviewed in [15] , [16] ) . Accordingly , the CD8+ T cell response to the GLC-A2 antigen is seen to provoke type III and type IV TCR bias . Type III bias is defined by memory T cells bearing identical TCR receptor protein sequences , often encoded by redundant codons , found between individuals presenting a common pMHC antigen . Type IV bias is defined by memory T cells bearing near identical TCR receptor protein sequences , differing by only one or two residues in the CDR3 loop , found between individuals presenting a common pMHC antigen . GLC-A2-specific responses exhibit biased usage of the TRBV20-1 , TRBJ1-2 , TRAV5 , and TRAJ31 genes and conserved CDR3 amino acid usage and length . We recently undertook a large scale ex vivo TCR sequencing analysis ( 754 transcripts ) , as well as a meta-analysis , of the GLC-A2 response and found that the most frequently shared ( public ) receptor comprised the above genes with the CDR3α and CDR3β core sequences CAEDNNARLMF and CSARDGTGNGYTF , respectively [17] . In order to gain insight into the structural basis underlying the emergence of this ubiquitous αβ receptor , we solved the structure of an archetypal GLC-specific TCR , derived from the CD8+ T cell clone AS01 , in complex with the GLC-A2 antigen . In parallel , we performed a detailed thermodynamic dissection of the complex and identified key TCR contact hotspots via a biophysical mutagenesis scan . To investigate the genetic basis behind the dominant selection of the receptor , we performed computer simulations of a random V ( D ) J recombination process to assess the ease and frequency of manufacture . Herein , we describe the structural and genetic basis for the dominance of the AS01 TCR in EBV-infected humans .
The structure of the AS01-GLC-A2 complex was determined to a resolution of 2 . 54 Å ( Table 1 ) . The final model had Rfree of 30 . 7% and Rcryst of 21 . 8% . The ratio , Rcryst/Rfree , falls within the accepted limits shown in the theoretically expected distribution [18] . The AS01 TCR was centrally perched on the GLC-A2 molecule over the exposed residues of the GLC peptide ( Figure 1 ) . As illustrated in Figure 2A , AS01 bound in a canonically diagonal fashion and was observed to dock at an angle of 41 . 7° , as calculated by the proposed TCR/pMHC crossing angle standard [19] . This crossing angle falls within the range of previous human TCR/pMHC class-I ( pMHC-I ) complexes ( 34°–80° , average 52 . 5° ) . The central residues of the GLC peptide bulged out from the MHC surface in the classical fashion . Unusually , however , Leu at residue P5 , typically one of the most exposed regions of pMHC-I 9-mer peptides , was pulled down into the MHC cleft , kinking the backbone and making the adjacent residues , Thr at P4 and Val at P6 , more solvent exposed ( Figure 2B ) . The CDR1α/CDR3α loops and CDR1β/CDR3β loops were positioned on either side of the exposed peptide residues at P4 and P6 , clasping each side of the peptide bulge ( Figure 2C and 2D ) . The CDRα and CDRβ loops , as well as framework ( FW ) residues , shared in fastening the AS01 TCR to the MHC α-1 and α-2 helices ( Figure 2E and 2F ) . A total of 20 contacts were made at the TCR/pMHC interface , comprising 4 peptide contacts and 16 MHC contacts ( Table 2 ) . This is the second lowest number of contacts observed across all human TCR/pMHC complexes to date [19] , [20] , [21] , [22] . However , the total buried surface area ( BSA ) of the AS01-GLC-A2 complex was 2134 Å2 , which falls within the normal range of previous human TCR/pMHC-I complexes ( 1471–2452 Å2 , mean 1992 Å2 ) ( Table 3; Figure 2A ) . The CD8+ T cell response to the GLC-A2 antigen exhibits Type III and Type IV bias in vivo , with identical or near identical TCRs within and between individuals [6] , [7] , [15] , [16] , [17] . The AS01-GLC-A2 structure provides insight into the selection of this public TCR . First , selection of the TRAV5 gene can be accounted for by the presence of the Thr31-Try32 residue pair at the tip of the CDR1α loop ( Figure 2E ) . These residues fix AS01 to the MHC α-2 helix via hydrogen bonds with Thr163 and with the mobile ‘gatekeeper’ Gln155 [23] . Engagement of the MHC α-2 helix is further strengthened through FW residues within the TRAV5 gene-encoded chain . Tyr49 and Lys69 can be seen to form a hydrogen bond and salt bridge with Glu154 and Glu166 , respectively ( Figure 2E ) . In addition , Tyr32 in the CDR1α loop also assists in peptide recognition through a van der Waals interaction with Thr4 ( Figure 2C ) . Of the 47 TRAV genes available for recombination , only the TRAV5 gene encodes a Thr-Tyr pair in the CDR1α loop and only TRAV5 encodes Tyr49 and Lys69 within the FWα region . The germline origins of these peptide contacts , as well as all TCR-peptide contacts , are shown in Figure 2G . Second , selection of TRAJ31 over 56 other TRAJ gene options can be accounted for by the presence of Arg97 and the Asn94-Asn95 pair within the CDR3α core . The two Asn residues form a structural arch that stabilizes the CDR3α loop above the first peptide backbone hump residue at P4 . Arg97 acts as a “hook” to stabilize the arch in a raised manner above Thr4 . The raised CDR3α lip allows Asp93 to lie precisely level with Thr4 to form a hydrogen bond ( Figure 2C ) . Only the TRAJ31 gene encodes the Asn94 , Asn95 and Arg97 pattern in the joining segment . Third , selection of TRBV20-1 over the other 53 TRBV genes can be accounted for by gene-specific interactions . Asn52 and Glu53 at the tip of the CDR2β loop engage Gln72 and Arg75 of the MHC α-1 helix , respectively , through a van der Waals interaction , a hydrogen bond and a salt bridge ( Figure 2F ) . Glu60 , in the FWβ region , forms a salt bridge with Arg65 ( Figure 2F ) . A peptide-specific interaction is achieved via Thr32 in the CDR1β loop , which engages Met8 through a van der Waals bond ( Figure 2D ) . Genetically , a small number of TRBV genes encode an Asn-Glu pair in the CDR2β but only TRBV20-1 encodes both the CDR2β Asn-Glu pair as well as FWβ Glu60 and CDR1β Thr32 . Fourth , the importance of the contribution of the TRBD1 gene is highlighted by the contacts made through Thr101 , which is TRBD1-encoded . Thr101 makes a hydrogen bond with Met8 of the peptide ( Figure 2D ) . Finally , selection of TRBJ1-2 over the other 12 TRBJ gene options can be accounted for by the Tyr105-Thr106 pairing in the CDR3β loop terminus . Tyr105 forms a hydrogen bond with Arg98 , found at the beginning of the CDR3β sequence . This internal brace stabilizes the entire loop structure . It is of particular interest that only the TRBV20-1 gene encodes Arg at this position . Thr106 also has an important internal structural function , stabilizing the β-barrel formation through a methyl interaction with Phe29 of the CDR1β . It is of additional note that only TRBV20-1 encodes Phe at this position when compared to all available 54 CDR1β loops . To conclude , only TRBJ1-2 encodes a Tyr-Thr pair within the joining gene area , underlying its genetic preference associated with TRBV20-1 in the public receptor . To complement information gained from the crystal complex , we dissected in detail the affinity and thermodynamics of the public AS01 TCR . To achieve this , the binding strength of the AS01-GLC-A2 complex was measured at 5 , 12 , 19 , 25 and 37°C using surface plasmon resonance ( SPR ) . At 25°C , the KD of the complex was 8 . 1 µM ( Figure 3; Figure S1 ) . This baseline affinity falls within the range of previously published human TCR/pMHC-I complexes ( 0 . 1–100 µM ) and is typical of human TCR/pMHC-I complexes from viral systems ( 0 . 1–21 µM ) [19] , [24] , [25] ( Table 3 ) . Interestingly , we observed that the affinity of the AS01-GLC-A2 complex interaction decreased with increasing temperatures; thus , KD values gradually decreased from 4 . 7 µM at 5°C to 16 . 7 µM at 37°C ( Figure 3A–G ) . This difference was mainly due to a much faster off-rate ( Koff ) at higher temperatures ( Koff = 1 . 2 sec−1 at 37°C compared to 0 . 15 sec−1 at 5°C ) ( Figure 3H ) . Thus , at physiological temperature ( 37°C ) , the AS01 TCR binds with weaker affinity than at the standard measurement temperature ( 25°C ) . This difference may impact the antigen sensitivity of CD8+ T cells bearing this public receptor in vivo . The affinity of an interaction can be represented as its binding free energy , ΔG° ( ΔG° = -RTlnKD ) . This binding energy is the sum of enthalpic ( ΔH ) and entropic ( −TΔS ) components as calculated using the Gibbs-Helmholtz equation ( ΔG° = ΔH − TΔS° ) , either of which can be favourable ( act to increase the affinity ) or unfavourable ( act to decrease the affinity ) . The binding of proteins is also accompanied by a change in the heat capacity ΔCp° . In order to calculate ΔH° , TΔS° and ΔCp° , the binding constant data were subjected to van't Hoff analysis by plotting the binding ΔG° versus temperature ( K ) using nonlinear regression to fit the three-parameter equation to the curve ( see Materials and Methods ) . The AS01-GLC-A2 interaction was characterized by a binding ΔG° of −6 . 9 kcal/mol at 25°C ( the standard for measuring TCR/pMHC parameters [26] ) , which is within the normal range for TCR/pMHC interactions [27] . The energy of the interaction was probably derived primarily from a net increase in the formation of new noncovalent bonds ( hydrogen bonds , salt bridges and van der Waals contacts ) during complex formation , evident from the favorable enthalpy ( ΔH° = −8 . 8 kcal/mol ) . Notably , this TCR/pMHC interaction is entropically unfavourable ( TΔS° = −1 . 9 kcal/mol ) , although this value lies at the lower end of the scale of published TCR/pMHC entropic values ( −0 . 4 to −29 kcal/mol ) [27] . The relatively small ΔCp° value of −0 . 4 kcal/mol·K is within the range of other TCR/pMHC complexes [27] ( Figure 3G ) , which conforms with the normal SC value for this complex . Next , we investigated the thermodynamic properties of the AS01-GLC-A2 complex at the physiologically relevant temperature of 37°C . At this temperature , the binding free energy ( ΔG° = −6 . 8 kcal/mol ) was very similar to the binding energy observed at 25°C ( ΔG° = −6 . 9 kcal/mol ) . However , at 37°C , the interaction was driven strongly by enthalpy , evident from the decrease in ΔH° to −14 . 1 kcal/mol ( 37°C ) ( Figure S3 ) . This resulted in a larger entropic cost to complex formation ( TΔS° = −7 . 3 kcal/mol ) . Thus , at 37°C , the interaction between the AS01 TCR and GLC-A2 is more enthalpically driven , with a greater entropic penalty , compared to 25°C . The AS01-GLC-A2 structure indicated that Thr4 , Val6 and Met8 of the peptide are important for TCR docking . Indeed , the AS01 TCR contacts only these residues in the peptide . To verify the importance of these contact areas , we performed an Ala mutagenesis scan across the peptide backbone and evaluated the capacity of the pMHC-I mutants to bind the AS01 TCR using SPR ( Figure 4; Figure S2 ) . As peptide positions P1 , P2 and P9 are often buried and/or important for MHC binding , we focused on assessing the solvent exposed positions P3 , P4 , P5 , P6 and P8 . We did not assess P7 , as this residue is Ala in the native sequence . As expected , given that no contacts were made with Cys3 , mutation of P3 had a relatively small effect on TCR binding ( ∼5-fold reduction ) ( Figure 4 ) . Conversely , mutation of Thr4 reduced AS01 TCR binding by more than 60-fold to 685 . 2 µM , presumably through loss of two side-chain contacts provided by the CDR1α and CDR3α loops . Interestingly , mutation at Leu5 resulted in a ∼17-fold reduction in affinity to 177 µM . While Leu5 is not a TCR contact residue , it does affect the total peptide backbone structure by providing a secondary anchor that results in a kink in the centre of the peptide backbone . This kink , made via hydrogen bonds with main chain atoms of Val152 and Leu156 of the MHC α-1 helix , pulls Leu5 into the MHC groove . In this conformation , Thr4 and Val6 form two “humps” on either side of Leu5 . The mutation of Leu5 to Ala would probably result in the loss of this MHC anchoring and allow the peptide backbone to relax in the cleft . This backbone loosening would likely push Thr4 and Val6 into new conformations , resulting in the loss of original contacts . As expected , mutation of TCR contact residue Val6 resulted in a considerable reduction in affinity to 133 . 8 µM ( ∼13-fold ) . This affinity reduction is likely caused by the loss of two non-polar interactions between the Val6 side-chain atoms and the CDR3β loop . Finally , mutation of Met8 reduced the affinity of AS01 TCR binding to 43 . 9 µM . This modest effect can likely be explained by the loss of a single , side-chain-derived van der Waals bond with the CDR1β loop . The original hydrogen bond , formed by the CDR3β and the main-chain atoms of Met8 , would likely remain when Met8 is mutated to Ala . The SC program , from the CCP4 suit [28] , is able to index the binding potential of two molecules between 0 . 0 ( no SC ) and 1 . 0 ( perfect SC ) . As well as overall SC , SC indices can be partitioned to different zones of the contact face . This can help specify where two molecules invest the bulk of their contact energies . We calculated the SC of the AS01 TCR between: ( i ) the whole GLC-A2 molecule; ( ii ) just the HLA A*0201 molecule; and , ( iii ) just the peptide ( Table 3 ) . We also extended this SC assessment to a full meta-analysis of all conventional human TCR/pMHC-I complexes solved to date ( Table 3 ) . Alloreactive structures were omitted from the meta-analysis . This review also included BSA and binding affinities of the complex set , as well as the number of contacts made between TCR and peptide within 3 . 4Å . The AS01-GLC-A2 complex exhibited a SC index of 0 . 640 , which is average for TCR/MHC-I complexes ( mean = 0 . 648 ) . Interestingly , while the total pMHC-I SC appeared typical , the AS01 TCR revealed a SC preference for MHC-I ( SC = 0 . 676 ) over peptide ( SC = 0 . 577 ) . The large majority of TCR/MHC-I complexes studied thus far reliably exhibit a higher SC index for peptide compared with MHC . In fact , only the JM22 TCR joins AS01 in this unusual , large-scale switch of region preference . Along with obvious structural features that promote AS01 selection , underlying genetic factors are likely to exist that elevate the frequency at which this receptor is manufactured . We have previously identified a process of convergent recombination that may enable some TCRs to be produced more efficiently than others [29] . Using computer simulations of a random V ( D ) J recombination process we have demonstrated for numerous systems [17] , [30] , [31] that convergent recombination leads to large differences in TCR production frequencies , even in the case of completely unbiased gene recombination . These simulations account for the various mechanisms that contribute to the production of TCR nucleotide and amino acid sequences , including TRV , TRD and TRJ gene splicing , N nucleotide additions , recurrent CDR3 motifs and codon redundancy . We have previously shown that the public TCR β-chain ( TRBV20-1/CSARDGTGNGYTF/TRBJ1-2 ) is the most common chain used in vivo in the GLC-specific response and is the second most frequent GLC-A2-specific TRBV20-1/TRBJ1-2 chain made in silico by random gene recombination [17] . We have expanded this TCR β-chain analysis to include additional published clonotypes [6] , [7] and confirm our previous results here ( Figure 5C–D ) . In addition , we simulated the production of TCR α-chains using the TRAV5 and TRAJ31 genes to assess the relative production frequencies of GLC-A2-specific TCR α-chain sequences based on previously identified in vivo clonotypes [6] , [7] . The simulation indicated that the public TCR α-chain ( TRAV5/CAEDNNARLMF/TRAJ31 ) is the most efficiently produced TRAV5/TRAJ31 combination in the GLC-A2 response ( Figure 5A–B ) , being generated ∼6 times more frequently in silico than the next most efficiently generated clonotype CAEIHARLMF . Aiding the ease of production , both the public TCR α- and β-chain amino acid sequences can be encoded by nucleotide sequences requiring few N nucleotide insertions The AS01 β-chain contains just 4 N nucleotide insertions , with only Asn103 being randomly encoded ( Figure 5E ) . Furthermore , the AS01 TCR β-chain can been observed in vivo to be manufactured with just two N nucleotide additions ( Figure 5F ) [17] . The AS01 TCR α-chain contains just a single N nucleotide insertion , partially encoding Asp93 .
The archetypal AS01 TCR is likely to be one of the most common and numerically frequent αβ TCRs in humans . This is based on the following considerations: ( i ) the HLA-A*0201 allele is arguably the most common and widespread MHC-I allele in humans , with frequencies above 60% in certain regions [32]; ( ii ) EBV is one of the most successfully disseminated human pathogens , persistently infecting more than 90% of individuals [33]; ( iii ) the GLC-A2 antigen is one of the most immunodominant CD8+ T cell targets across the EBV proteome [4]; ( iv ) GLC-specific CD8+ T cell responses are amongst the largest observed , both in the EBV system and in comparative terms with respect to other human pathogens studied thus far [4]; and , ( v ) the GLC-specific response is dominated by CD8+ T cells that bear a public TRAV5/TRBV20-1 receptor [17] . Structurally , most features exhibited by the AS01 TCR are within the parameters previously seen in the TCR/pMHC-I system [19] . AS01 docks in a roughly diagonal fashion to the pMHC and is positioned centrally above the peptide . The TCR α-chain is positioned above the peptide N-terminus and the TCR β-chain is positioned above peptide C-terminus . The peptide is engaged chiefly by the CDR3 loops with additional support from the CDR1 loops . AS01 interacts with the MHC helices via residues within the CDR1 and CDR2 loops , which include bonds with universal MHC anchors Arg65 and Gln155 . Gln155 is proposed to be a “gatekeeper” residue guiding MHC-I-restricted TCR recognition as it is universally contacted by all αβ TCRs studied to date and often switches conformation between bound and unbound forms [23] , [34] . Interestingly , however , the AS01 TCR does not contact Arg69 , which represents the third member of the classical MHC “restriction triad” [34] . Biophysically , the AS01 TCR binds GLC-A2 with a KD of 8 . 1 µM , which is typical for TCR interactions with viral MHC-I-restricted antigens [19] , [24] . The AS01-GLC-A2 complex also has an average BSA ( of 2134 Å2 ) that is within , if not towards the higher end , of the TCR/pMHC system . One notable structural trait of the AS01 TCR is its SC preference . The large majority of TCR/pMHC-I complexes have a peptide>MHC SC bias . This bias is inverted in the AS01-GLC-A2 complex . Again , interestingly , only the JM22 TCR , which is also public and HLA-A*02-restricted , exhibits this inverted preference . Ultimately though , this particular trait cannot be exclusively assigned to public TCRs since the RA15 and LC13 TCRs show conventional SC preference . Thermodynamic analysis revealed that the interaction between the AS01 TCR and GLC-A2 was within the normal range of other reported TCR/pMHC interactions [27] . Importantly , the interaction was strongly enthalpically driven; thus , stabilization of the TCR/pMHC interface through the formation of noncovalent bonds is likely to be the chief factor driving complex formation . In addition , the complex exhibited unfavourable entropy , indicating that there was a net gain of order during TCR/pMHC binding . This observation indicates that the entropic energy generated by expulsion of solvent ( ordered water molecules ) upon complex formation is countered by the entropic energy penalty attributed to the conformational ordering of the TCR CDR loops and pMHC surface during docking . These data are in agreement with previous thermodynamic analyses of TCR/pMHC interactions , which show that the favorable enthalpic energy generated by the formation of a relatively large number of new contacts at the interface is countered by a large unfavorable entropic cost that results in a relatively weak binding affinity compared to other protein-protein interactions [27] , [35] , [36] , [37] . Lastly , we investigated the thermodynamic properties of the AS01-GLC-A2 complex at the physiologically relevant temperature of 37°C . Notably , at 37°C , the interaction between the AS01 TCR and GLC-A2 was more strongly enthalpically driven , with a greater entropic penalty , compared to 25°C . Importantly , these differences in binding energy almost halved the affinity of AS01 TCR binding at 37°C compared to the standard measurement temperature of 25°C . Kinetic analysis revealed that the reduction in binding affinity was primarily attributed to a much faster off-rate at higher temperatures ( Figure 3H ) . Thus , the AS01 TCR appears to bind less optimally to GLC-A2 at physiological temperatures ( 37°C ) compared with the standard temperature used for SPR measurements in vitro ( 25°C ) . The mutagenesis scan across the GLC peptide highlighted a number of critical zones for TCR recognition . As expected , mutation of the prominent peptide hump residues , Thr4 and Val6 , resulted in 10–70 fold loss in affinity . A relatively surprising finding was the 17-fold loss in affinity following mutation of Leu5 . Leu5 is not involved in TCR contacts and points down into the MHC cleft . Leu5 does , however , act as a secondary anchor for the GLC peptide , reinforcing the peptide backbone . Loss of this reinforcement would likely result in the peptide backbone ‘sinking’ into the MHC cleft , producing a less interactive interface . Recently seen in other systems [38] , this observation highlights the importance of peripheral residues in TCR engagement . A note of broader interest is that every mutation along the GLC peptide resulted in a reduction of affinity ( 40 µM and above ) , whether within a TCR contact zone or not . TCRs specific for class-I-bound antigens of viral origin typically operate in the KD <10 µM range and none have been seen over 30 µM [24] . Thus , a mutation at any of these points could result in suboptimal engagement with the archetypal public TCR . This would likely result in either the public TCR being outcompeted by higher affinity options in vivo or a hole in the TCR repertoire and possibly a reduction in immunogenicity . Interestingly , there is no evidence that EBV attempts to escape from the GLC response , as seen by complete epitope conservation across all known strains and isolates ( GeneBank ) . This is remarkable given the considerable genetic variation between EBV strains [39] , [40] and within some T cell epitopes [41] , [42] , [43] , [44] . It is certainly conceivable that EBV has at least some flexibility to mutate the GLC backbone without a significant loss of viral fitness . Hence , a question presents itself . Why does EBV not try to escape from one of the most potent T cell responses raised against it ? In assessing this question , it is important to note that genetic evidence from EBV studies suggests that evolutionary pressure on T cell epitopes is directed towards their conservation rather than their inactivation [45] . This leads to speculation that epitope conservation may be advantageous to the virus . Thus , some highly immunogenic epitopes could be maintained deliberately to elicit large fleets of CD8+ T cells , perhaps to regulate viral replication , minimize pathology and maintain a peaceful coexistence with the host . Alternatively , the large T cell responses generated by these epitopes may aid the virus as bonus replicative tissue . In addition to the well established tropic tissues , B cells and epithelia , EBV has recently been found in several human tissues including T cells [46] . A related question is whether TCR affinity influences epitope variation . The affinity of the AS01 TCR , at physiological temperature , is in the lower half of the range reported for anti-viral TCRs to date [24] . Could this small decrease in relative affinity place less selection pressure on the GLC epitope ? This is an interesting question . The answer is likely influenced by the intrinsic mutagenic potential of the epitope . Genetically , the public AS01 TCR is primarily assembled from chromosome-derived DNA ( Figure 5E ) . The TCR β-chain and TCR α-chain incorporate just 4 and 1 non-germline nucleotide/s , respectively . Only one residue in the receptor ( Asn103β ) is encoded wholly by non-germline DNA . Interestingly , in many individuals , Asn103β in the AS01 TCR is majority encoded by the TRBJ1-2 gene ( Figure 5F ) [17] . Thus every residue in the AS01 TCR can be wholly or partially encoded by germline DNA . This “germline-rich” feature of the TCR α-chain and TCR β-chain amino acid sequences of AS01 contributes towards the prediction that these are frequently produced by convergent recombination [30] . The public AS01 TCR comprises the TRBV20-1 , TRBD1 , TRBJ1-2 , TRAV5 and TRAJ31 genes . Structural analysis revealed that each of these TR genes encode unique residue patterns that appeared specialized for the GLC-A2 ligand . Critical docking residues , found within the TCR/pMHC interface , were exclusively encoded by the TR genes listed above and were not resident in the other 168 genes available on the TCR α and β loci . Thus , this specific structural architecture required to preserve the AS01 docking mode explains the TRBV , TRBJ , TRAV , and TRAJ bias in the GLC-A2 CD8+ T cell response . The AS01-GLC-A2 complex necessitates comparison with other published public TCR structures including the LC13 TCR [47] and the JM22 TCR [48] . The LC13 TCR is specific for the FLRGRAYGL ( FLR ) peptide from EBV and restricted by the HLA-B*0801 molecule . The LC13 TCR , composed of TRBV7-8/TRBJ2-7 and TRAV26-2/TRAJ52 gene products , is residue-identical between individuals ( Type III bias ) [49] , [50] , [51] . Intriguingly , the LC13 TCR is also heavily comprised of germline DNA . The TCR β-chain can be constructed wholly from germline DNA [49] , [50] , [51] . The TCR α-chain shows a similar degree of germline composition and only a single residue ( Pro93 ) is encoded by DNA of non-germline origin . The TCR/pMHC complex reveals that the LC13 TCR engages its cognate ligand at a crossing angle of 42° [47] , virtually identical to that of AS01 . Also akin to AS01 , the contact footprint was evenly split between the TCR α- and β-chains . The LC13 TCR was seen to adjust conformation during ligation , manoeuvring its CDR3 loops around two central solvent expose residues at P6 and P7 . The AS01 TCR also engaged central residues of the peptide; however , it is unknown whether AS01 also undergoes conformational change upon ligation , as this would require the TCR structure in an unligated state . A structural basis underlying the selection of LC13 TCR was evident upon examination of the complex . Contact residues exclusively encoded by the constituent genes were critical for specific engagement [47] , and mutation of these germline-encoded residues abrogated recognition [50] . Overall , the LC13 and AS01 TCRs exhibit close genetic and structural parallels . However , the investment of germline DNA composition alternates between the receptors . Thus , the LC13 TCR exhibits more germline composition on the β-chain compared with the α-chain . This pattern is inverted in the AS01 TCR . It is also worth noting that the HLA-B*0801 allele , to which the LC13 TCR is restricted , is arguably the most common HLA-B allele in Caucasian populations . The JM22 TCR is specific for the GILGFVFTL ( GIL ) peptide from the influenza virus and is restricted by the HLA-A*0201 molecule . In this response , gene bias is skewed to TRBV19 with a common Arg residue in the CDR3β loop [52] , [53] . However , in contrast to the GLC- and FLR-responses , the GIL-specific repertoire is more variable and identical TCRs are not always apparent across individuals . A number of different TRBJ genes are used in the repertoire along with variation in CDR3β residue composition and CDR3β length [52] , [53] . The paired TCR α-chain is also variable , with fluctuation in TRAV gene usage . In general , the GIL-specific response is an example of Type IV bias . The TCR/pMHC complex revealed that JM22 engages its cognate ligand at a crossing angle of 62° [48] , which is more orthogonal compared with AS01 . As suggested by the above mentioned gene bias , the contact footprint is considerably “β-centric” , with residues within the TRBV19-encoded CDR1 and CDR2 loops dominating pMHC engagement . This docking modality allowed the conserved Arg in the CDR3β loop to peg the TCR between the peptide and MHC groove . A detailed mutational analysis revealed that germline-encoded residues were critical for antigen specificity and , along with CDR3β Arg peg , it was hypothesized that the TRBV19 gene may have been evolutionary useful during recurrent influenza pandemics [54] . The germline-rich composition of the AS01 TCR draws parallels with the TCRs displayed by iNKT cells from the innate immune compartment . Here , the iNKT TCR α-chain ( TRAV10/TRAJ18 ) can be manufactured wholly by germline DNA [55] . Structural analysis of an iNKT TCR bound to its cognate CD1d-α-GalCer ligand revealed that the receptor docks in a parallel fashion to the antigen cleft [56] , a docking modality very different to the diagonal docking of AS01 and the other MHC-restricted receptors [19] . In this parallel docking mode , the CDR1α and CDR3α loops were seen to dominate the contact footprint across both CD1d and the antigen; the residues involved in these contacts were exclusively encoded by the TRAV10 and TRAJ18 genes , providing a structural basis for invariant gene bias . A further mutational study across the iNKT TCR confirmed the critical importance of these germline-encoded CDR1α and CDR3α residues for ligand recognition [57] . These studies reinforce the observation that extreme biases within TCR repertoire formation are likely shaped through highly specific structural requirements of the target ligand . The iNKT TCR differs from the AS01 TCR in that it that has an unprecedentedly small BSA ( of 910Å2 ) , likely as a result of the α-chain binding towards the terminus of the binding cleft . This binding mode pushes the β-chain towards the extreme end of the binding cleft and limits its role during engagement [56] , [58] , providing a structural basis for the highly diverse nature of the iNKT β-chain repertoire in vivo [55] . Conversely , the AS01 TCR α- and β-chains are both public , and the contact footprint is more evenly spread across both chains . EBV has engaged our species and its antecedents for approximately 80 million years [59] . During this entwined co-evolution , countless immune assaults and counter-assaults would have been waged , with many genes formed and lost . Natural selection and hereditary transmission would have progressively bestowed useful genes for host defence and virus offence . It is likely that the 450 million year old combinatorial immune system [60] , and more specifically the highly polymorphic TCR and MHC gene set , would have been intimately involved in this “arms race” . Thus , it is conceivable that public TCRs , such as AS01 , may be very old defence structures , easily formed and found on the chromosome , that provide a naive pre-emptive defence net against almost-certain infection by primordial pathogens . Conversely , it could be argued public TCRs are simple by-products of biases in the V ( D ) J recombination system , according to which some receptor combinations leave the thymus more often than others . In conflict with this straightforward genetic hypothesis is the observation that some public TCRs exist with the same amino acid CDR3 sequences that are redundantly encoded by largely non-germline derived DNA [49] . This indicates that , during competitive antigen-driven selection , there is some structural advantage already encoded in the original germline sequence . That is , we appear to be born with αβ TCR fragments already lying in the chromosome that are exquisitely specific for EBV targets . Given the time scale of this conflict , it is intriguing to consider whether public TCRs have evolved to aid the host or EBV itself; perhaps they could even represent some middle ground that favours a largely peaceful coexistence ? Ultimately though , the role of evolution in guiding this phenomenon is unknown and can only be conjectured . After all , these TRBV and TRAV gene segments are likely useful in defence against other pathogens . In addition , TCR variable genes appear to have an innate preference for MHC [61] , so the idea of a germline receptor having both highly tuned specificity and broad cross-reactive potential is , while not strictly mutually exclusive , an interesting observation . Aside from the origins of the public receptor phenomenon , the AS01-GLC-A2 complex may provide a fascinating glimpse of an ancient immune battle , fought quietly and in the same way , possibly billions of times across the two hundred thousand years of homo sapiens history [62] .
The AS01 CD8+ T cell clone was generated from a healthy EBV+ , HLA-A*0201+ individual as described previously [63] . Briefly , PBMC were stimulated with 1 µM GLCTLVAML peptide and cloned via limiting dilution . The AS01 TCR was identified as described previously [64] . Briefly , total RNA was extracted from 105 T cells using TRIzol reagent and a RT-PCR was performed using Superscript III ( Invitrogen Life Technologies ) . PCR was performed using a panel of TRAV- and TRBV-specific primers and the product was cloned into the pGEM-T vector system ( Promega ) . The TCR product was sequenced using the ABI PRISM Big Dye termination reaction kit ( Applied Biosystems ) and the sequences were defined according to the international ImMunoGeneTics database ( IMGT ) TCR gene nomenclature [65] . The HLA-A*0201 ( A2 ) α-chain and β2m sequences were generated by PCR mutagenesis ( Stratagene ) and PCR cloning . All sequences were confirmed by automated DNA sequencing . A disulphide-linked construct was used to produce the soluble domains ( variable and constant ) for both the TCR α- and β-chains [66] , [67] . The soluble A2 α-chain ( α-1 , α-2 and α-3 domains ) was tagged with a biotinylation sequence . All 4 constructs , TCRα , TCRβ , A2-tagged and β2m , were inserted into separate pGMT7 expression plasmids under the control of the T7 promoter [66] . Competent Rosetta DE3 E . coli cells were used to express the TCRα , TCRβ , A2-tagged and β2m proteins in the form of inclusion bodies ( IBs ) as described previously [66] . For a 1L TCR refold , 30 mg of AS01 α-chain IBs were incubated at 37°C for 15 mins with 10 mM DTT and added to cold refold buffer ( 50 mM TRIS pH 8 . 1 , 2 mM EDTA , 2 . 5 M urea , 6 mM cysteamine hydrochloride and 4 mM cystamine ) . After 15 mins , 30 mg of AS01 β-chain , incubated for 15 mins at 37°C with 10 mM DTT , was added . For a 1 L GLC-A2 refold , 30 mg of A2 α-chain was mixed with 30 mg of β2m and 4 mg of the GLCTLVAML peptide ( or GLC mutants ) for 15 mins at 37°C with 10 mM DTT . This mixture was then added to cold refold buffer ( 50 mM TRIS pH 8 , 2 mM EDTA , 400 mM L-arginine , 6 mM cysteamine hydrochloride and 4 mM cystamine ) . Refolds were mixed at 4°C for 1 hr . Dialysis was carried out against 10 mM TRIS pH 8 . 1 until the conductivity of the refolds was under 2 mS/cm . The refolds were then filtered and purified . Primary purification was conducted using an ion exchange ( Poros50HQTM ) column and secondary purification was conducted using a gel filtration ( Superdex200HRTM ) column . The protein was purified using either BIAcore buffer ( 10 mM HEPES pH 7 . 4 , 150 mM NaCl , 3 mM EDTA and 0 . 005% ( v/v ) Surfactant P20 ) or crystallization buffer ( 10 mM TRIS pH 8 . 1 , 10 mM NaCl ) . Protein quality was analyzed by Coomassie-stained SDS-PAGE . Binding analysis was performed independently using a BIAcore 3000 and a BIAcore T100 equipped with a CM5 sensor chip as reported previously [68] . Between 200 and 400 response units ( RUs ) of biotinylated pMHC was immobilized to streptavidin , which was chemically linked to the chip surface . The pMHC was injected at a slow flow rate ( 10 µl/min ) to ensure uniform distribution on the chip surface . Combined with the small amount of pMHC bound to the chip surface , this reduced the likelihood of off-rate limiting mass transfer effects . AS01 TCR was concentrated to 100 µM on the same day of SPR analysis to reduce the likelihood of TCR aggregation affecting the results . For equilibrium and kinetic analysis , ten serial dilutions were carefully prepared in triplicate for each sample and injected over the relevant sensor chips at 25°C . AS01 was injected over the chip surface using kinetic injections at a flow rate of 45 µl/min . For thermodynamic experiments , this method was repeated at the following temperatures: 12°C , 19°C , 25°C , 32°C , and 37°C . Results were analyzed using BIAevaluation 3 . 1 , Microsoft Excel and Origin 6 . 1 . The equilibrium binding constant ( KD ) values were calculated using a nonlinear curve fit ( y = ( P1x ) / ( P2 + x ) ) . The thermodynamic parameters were calculated according to the Gibbs-Helmholtz equation ( ΔG° = ΔH − TΔS° ) . The binding free energies , ΔG° ( ΔG° = -RTlnKD ) were plotted against temperature ( K ) using nonlinear regression to fit the three-parameter equation , ( y = dH+dCp* ( x-298 ) -x*dS-x*dCp*ln ( x/298 ) ) , as reported previously [69] . The process of convergent recombination encompasses the production of an amino acid sequence by a variety of nucleotide sequences and the production of a nucleotide sequence by a variety of recombination mechanisms ( i . e . different germline gene contributions and nucleotide additions ) [17] , [29] , [30] , [31] . It also accounts for the frequent occurrence of some V ( D ) J recombination events due to the involvement of fewer nucleotide additions . To quantitatively assess the collective contribution of these various elements of convergent recombination in enhancing the production frequency of some TCR amino acid clonotypes relative to others in the absence of recombination biases , we used computer simulations of a random V ( D ) J recombination process [30] to estimate the relative production frequencies of the observed GLC-specific TCR amino acid clonotypes . The maximum number of nucleotide deletions from the ends of the TCR genes and the maximum number of nucleotide additions considered in the simulations were chosen to allow for the production of the majority of observed TCR sequences . For the TRAV5/TRAJ31 clonotypes , the simulated VJ recombination process allowed up to 10 nucleotide deletions from the 3′ end of the TRAV5 gene , up to 16 deletions from the 5′ end of the TRAJ31 gene , and up to 16 nucleotide additions . For each simulated TCR sequence , the number of nucleotide deletions from the 3′ end of the TRAV5 gene , the number of nucleotide deletions from the 5′ end of the TRAJ31 gene , and the number of nucleotide additions were determined from uniform distributions of the numbers of nucleotide deletions and additions . The nucleotide base of each of the nucleotide additions was randomly chosen . A total of 10 million in-frame TRAV5/TRAJ31 sequences were simulated . The computer simulations were performed using Matlab 7 . 9 . 0 ( The Mathworks , Natick , MA ) . AS01-GLC-A2 crystals were grown at 18°C by vapour diffusion via the hanging drop technique . 200 nL of 1∶1 molar ratio TCR and pMHC-I ( at 10 mg/ml ) was added to 200 nL of reservoir solution . Optimal crystals were obtained with 0 . 16 M calcium acetate hydrate , 0 . 08 M sodium cacodylate pH 6 . 0 , 12 . 5% polyethylene glycol ( PEG ) 8000 and 20% glycerol . Data were collected at 100 K on beamline IO3 at the Diamond Light Source ( DLS ) , Oxfordshire , UK . The AS01-GLC-A2 complex dataset was collected at a wavelength of 0 . 976Å using an ADSC Q315 CCD detector . Reflection intensities were estimated with the MOSFLM package [70] and the data were scaled , reduced and analyzed with SCALA and the CCP4 package [28] . The structure was solved with Molecular Replacement using AMORE [71] . The model sequence was adjusted with COOT [72] and the model refined with REFMAC5 [73] . Graphical representations were prepared with PYMOL [74] . Data reduction and refinement statistics are shown in Table 1 . The reflection data and final model coordinates were deposited with the PDB database , assigned accession code 3O4L .
|
The human immune recombination machinery can generate approximately 1018 unique αβ T cell receptor structures . The recombination event , once thought to be random , has now been shown to involve enzymatic biases during chromosomal rearrangement; additional biases occur during thymic selection and antigen-driven expansion in the periphery . The furthest extremes of these collective biases result in public T cell receptors ( TCRs ) , defined as residue-identical receptors found across different individuals who share a common major histocompatibility complex ( MHC ) allele . One of the most prominent public T cell responses found in humans is raised against the GLCTLVAML ( GLC ) peptide from Epstein-Barr virus . We , and others , have previously shown that a public TCR constructed from the TRBV20-1/TRBJ1-2 and TRAV5/TRAJ31 gene segments dominates the GLC-specific repertoire . Here , we investigate the genetic , biophysical and structural forces that drive this public receptor , designated AS01 , with in silico estimates of relative production frequencies during gene recombination , thermodynamic scanning and crystallographic studies of the AS01-GLC-A2 complex . We find that the TCRα and TCRβ amino acid sequences of AS01 are produced efficiently by a process of convergent recombination and employ unique residues , encoded only by the above-mentioned genes , to engage antigen in a highly specific manner .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/antigen",
"processing",
"and",
"recognition",
"immunology/immune",
"response",
"infectious",
"diseases/viral",
"infections",
"immunology/immunity",
"to",
"infections"
] |
2010
|
Genetic and Structural Basis for Selection of a Ubiquitous T Cell Receptor Deployed in Epstein-Barr Virus Infection
|
Mitotic kinesins are essential for faithful chromosome segregation and cell proliferation . Therefore , in humans , kinesin motor proteins have been identified as anti-cancer drug targets and small molecule inhibitors are now tested in clinical studies . Phylogenetic analyses have assigned five of the approximately fifty kinesin motor proteins coded by Trypanosoma brucei genome to the Kinesin-13 family . Kinesins of this family have unusual biochemical properties because they do not transport cargo along microtubules but are able to depolymerise microtubules at their ends , therefore contributing to the regulation of microtubule length . In other eukaryotic genomes sequenced to date , only between one and three Kinesin-13s are present . We have used immunolocalisation , RNAi-mediated protein depletion , biochemical in vitro assays and a mouse model of infection to study the single mitotic Kinesin-13 in T . brucei . Subcellular localisation of all five T . brucei Kinesin-13s revealed distinct distributions , indicating that the expansion of this kinesin family in kinetoplastids is accompanied by functional diversification . Only a single kinesin ( TbKif13-1 ) has a nuclear localisation . Using active , recombinant TbKif13-1 in in vitro assays we experimentally confirm the depolymerising properties of this kinesin . We analyse the biological function of TbKif13-1 by RNAi-mediated protein depletion and show its central role in regulating spindle assembly during mitosis . Absence of the protein leads to abnormally long and bent mitotic spindles , causing chromosome mis-segregation and cell death . RNAi-depletion in a mouse model of infection completely prevents infection with the parasite . Given its essential role in mitosis , proliferation and survival of the parasite and the availability of a simple in vitro activity assay , TbKif13-1 has been identified as an excellent potential drug target .
Although molecular evolutionary analysis places the branching point of kinetoplastids near the root of the eukaryotic tree , many aspects of their cellular architecture and complexity are nevertheless not hugely divergent from metazoan organisms [1] . One of the most conserved elements between kinetoplastids and other eukaryotes are microtubule-based structures [2] . The sequences of α- and β-tubulin are very similar ( ∼94% similarity at protein level ) to their mammalian orthologues and minor tubulin isotypes , such as γ-tubulin , have also been identified [3] . Although defined by only a small subset of conserved proteins , the axonemal structure in the flagellum of kinetoplastids is also virtually identical to that found in cilia and flagella of mammals . Trypanosoma brucei has developed into one of the model organisms to study flagellar assembly and a number of flagellar proteins associated with ciliopathies in humans are conserved in trypanosomes [4] , [5] , [6] , [7] , [8] , [9] . The kinetoplastid genome project has also revealed the presence of a large number of kinesin motor proteins [10] . Recent comprehensive phylogenetic analyses have identified 41 kinesin family proteins in T . brucei [11] , [12] . This is similar to the number of kinesins found in mammals , e . g . 45 in humans [13] . The large kinesin family in kinetoplastids reflects the complexity of the microtubule cytoskeleton in these parasites . In addition to the flagellum they possess an elaborate subpellicular microtubule corset , an intranuclear mitotic spindle and possibly a small number of cytoplasmic microtubules involved in vesicle transport [2] , [14] , [15] , [16] , [17] . Also , the complex karyotype of T . brucei , consisting of three different classes of chromosomes ( megabase- , intermediate- and minichromosomes ) with a total number exceeding one hundred and the associated unusual segregation patterns might require a substantial number of specific mitotic kinesins [reviewed in 18] , [19] . Similar large numbers of kinesins are also found in other protozoa possessing elaborate microtubule structures , such as ciliates or diatoms [12] . Based on comparative sequence analysis , the kinesin superfamily of motor proteins can be subdivided in up to 17 families [11] , [12] , [20] , [21] . Due to the lack of functional analysis of kinesins across a wide range of species it is not easily possible to infer precise function of a kinesin based on its assignment to a particular family . Broadly , families tend to either contain kinesins involved in chromosome segregation , spindle dynamics and transport of membranous vesicles or organelles [22] , although some families contain kinesins that do not all share similar functions [21] . The exclusive presence of some families in a subset of species able to build cilia and flagella indicates shared biological functions in relation to these structures [12] . The Kinesin-13 family is unusual because , in contrast to most other kinesins , they do not transport cargo along microtubules but instead are able to depolymerise microtubules at both the plus and minus ends . The only other kinesins known to have a depolymerising activity are yeast Kinesin-8 Kip3p and yeast Kinesin-14 Kar3p [23] , [24] . However , in contrast to Kinesin-13 , Kip3p and Kar3p only depolymerise microtubules at the plus- or minus-end , respectively , and display a processive , ATP-dependent motility along microtubules . In humans , three Kinesin-13 members have been identified ( Kif2a , Kif2b and Kif2c ) . Kif2c is also known as MCAK ( mitotic centromere associated kinesin ) . All three human Kinesin-13 proteins have mitotic functions [25] , [26] , although Kif2a has been reported to have an additional role in regulating growth cone dynamics in axons [27] . Mitotic kinesins are of particular interest because they are potential drug targets for diseases where cell proliferation is associated with pathogenicity , such as cancer . Several synthetic , small molecule human kinesin inhibitors with antitumour activities have already entered clinical trials and it is hoped that they will lead to the development of novel anticancer drugs [28] , [29] , [30] , [31] . By analogy , a similar strategy is feasible against parasitic diseases where disease progression and pathogenicity is caused by parasite proliferation in the human host , as is the case e . g . for malaria and sleeping sickness . For this reason and to explore their biological functions we have begun to characterise kinesin motor proteins in T . brucei . We were specifically interested in mitotic kinesins . Because most members of the Kinesin-13 family in other organisms have mitotic functions we initially focused on their characterisation . Comparative sequence analysis has identified five Kinesin-13s in kinetoplastids [12] . This surprisingly large number , which exceeds the number of Kinesin-13s in any other organism where a complete genome is available , could either be caused by a mis-assignment using phylogenetic tools or by extended functionality of these depolymerising kinesins in kinetoplastids . A recent report showing that one member of this family in Leishmania major is involved in flagellar length regulation and not in mitosis indicates that functional diversification is the most likely reason for the expansion of this kinesin family in kinetoplastids [32] . Here we report the characterisation of the single mitotic kinesin of the Kinesin-13 family in T . brucei ( termed TbKif13-1 ) . We show that it is important for the regulation of spindle length during mitosis . We also demonstrate that TbKif13-1 is essential for cell viability in procyclic and bloodstream from T . brucei in culture . Importantly for its potential as a drug target , RNAi-mediated protein depletion in a mouse model completely protects from infection .
A phylogenetic analysis of kinesins in T . brucei has assigned five kinesins to the microtubule-depolymerising Kinesin-13 family [12] . This large number is unusual because humans and other eukaryotes have only three or fewer members of this family , most of which are involved in mitotic processes . To investigate whether T . brucei Kinesin-13s have more diverse cellular functions we determined the subcellular localisations of all five Kinesin-13s ( Fig . 1 ) . We generated polyclonal antibodies against recombinant proteins representing specific regions of each kinesin ( Fig . S1 ) . Of the five kinesins only TbKif13-1 localised to the nucleus . Its localisation is similar to the nuclear localisation of the previously identified orthologue LmjKIN13-1 in Leishmania major [33] . The other four T . brucei Kinesin-13s localised to non-nuclear targets . TbKif13-4 is found along the entire flagellum and TbKif13-3 is homogeneously distributed across the cell body , but is excluded from the flagellum and nucleus . We were unable to detect expression of endogenous TbKif13-2 and TbKif13-5 in procyclic and bloodstream trypanosomes by immunofluorescence or western blotting . To discriminate whether this was due to the failure of the antibodies to detect the proteins or due to the absence , below detection levels , of both proteins , we expressed inducible , cmyc-tagged ectopic copies of both kinesins . After induction , overexpressed TbKif13-2 and TbKif13-5 were detectable by immunofluorescence microscopy and blotting using antibodies against the native proteins and also by anti-myc antibodies ( Fig . 1 , Fig . S1 ) . Therefore , the most likely explanation of the inability to detect endogenous protein is that both proteins are either not expressed in these two life cycle stages or at extremely low levels . The ectopically expressed TbKif13-2 is localised to the tip of the flagellum and TbKif13-5 is distributed throughout the cell body . A flagellar tip localisation has also been reported for the GFP-tagged , ectopically expressed kinesin LmjKIN13-2 in promastigote Leishmania major , a protein very similar in sequence to TbKif13-2 [32] . The distinct localisation of TbKif13-2 at the flagellar tip and the same localisation of its L . major homologue suggests that this is the genuine localisation of this kinesin , although in both studies detection was only achieved with an overexpressed , epitope-tagged protein . However , RNAi of this protein in procyclic T . brucei caused flagellar lengthening , indicating that it has role at this life cycle stage [32] . Although the localisation and expression of endogenous TbKif13-5 needs to be further examined , both TbKif13-2 and TbKif13-5 are excluded from the nucleus during the entire cell cycle ( Fig . S2 ) and are therefore unlikely to have functions in chromosome segregation . Therefore , the most plausible scenario is that TbKif13-1 is the only nuclear and mitotic Kinesin-13 in T . brucei , an organism that undergoes a closed mitosis . None of the kinesins , with exception of TbKif13-1 , showed differential expression or localisation during the cell cycle ( Fig . S2 ) . Immunofluorescence microscopy on procyclic 427 cells using anti-TbKif13-1 revealed that the nuclear staining of TbKif13-1 is cell cycle-dependent ( Fig . 2 ) . TbKif13-1 was not detectable in nuclei of non-dividing interphase cells , recognisable by the 1 kinetoplast/1 nucleus configuration ( 1K1N ) . The nuclear staining of TbKif13-1 was detectable in the nuclei from late G2/early M-phase ( 2K1N ) and before the mitotic spindle was visible . Interestingly , the initial signal colocalised with the nucleolus . Upon formation of the mitotic spindle TbKif13-1 colocalised with the spindle structure throughout mitosis . The bulk of the kinesin staining localised to the central , pole-to-pole bundle of spindle microtubules . During late anaphase ( Fig . 2 , bottom panel ) protein could also be detected outside the central spindle , spreading into adjacent chromatin . In addition to the nuclear localisation , the antibodies also revealed two distinct dots inside the cell body , often , but not always , adjacent to the kinetoplast . We observed a duplication of this signal from two dots to four dots in G2-phase or early mitosis ( Fig . 3 ) . This additional pattern of TbKif13-1 staining was due to cross reactivity of the antibody with an unknown protein in immunofluorescence , but not Western blotting , and was , in contrast to the nuclear signal , not affected by RNAi-mediated TbKif13-1 depletion . We also introduced an ectopically overexpressed , epitope-tagged copy of TbKif13-1 and did not observe the structures outside the nucleus ( Fig . S3 ) . To study the function of TbKif13-1 in T . brucei , procyclic and bloodstream cells were stably transfected with an RNA interference plasmid construct . RNAi against TbKif13-1 was induced by the addition of doxycycline and resulted in the depletion of TbKif13-1 to undetectable levels by Western blotting and immunofluorescence within 48 hours ( Fig . 3 ) . As indicated above , only the nuclear staining of TbKif13-1 was depletable while the staining of the two-dot structures of TbKif13-1 remained unaltered ( Fig . 3C ) . In both life cycle stages , the depletion of TbKif13-1 resulted in growth defects 24 hours post-induction ( Fig . 3B ) . After three days ( bloodstream cells ) and four days ( procyclic cells ) cell numbers were reduced by >99% in comparison to the non-induced controls . The depletion of TbKif13-1 resulted in the accumulation of cells with abnormal morphologies ( Fig . 4 ) . In bloodstream cells , depletion resulted in an decrease of 1K1N cells and an increase of 2K1N cells within 9 hours of induction ( from 26% to 43% ) before declining to 17% of total population in 24 hours after induction ( Fig . 4A ) . After 48 hours of doxycycline addition , 24% of the total cell population consisted of cells with more than 2 kinetoplasts and abnormally shaped nuclei ( Fig . 4B ) . Cells containing more than 2 kinetoplasts indicate failure of cytokinesis . This was further supported by flow cytometry analysis ( Fig . 4C ) . After 48 hours of induction both G1 and G2/M peaks had decreased from 56% ( G1 ) and 38% ( G2/M ) to 26% ( G1 ) and 33% ( G2/M ) , respectively , with the appearance of a third peak of higher fluorescence ( 24% of counts , >G2 ) indicating a DNA content of more than 4n corresponding to cells that had undergone at least two rounds of DNA replication in the absence of cytokinesis . In procyclic cells , the depletion of TbKif13-1 also resulted in a decrease of 1K1N cells and the accumulation of 2K1N cells ( Fig . 4A ) . At 24 hours post induction , the proportion of 2K1N cells raised from 27% to 38% before declining to 15% after 48 hours . By 48 hours post induction , 25% of the cells were observed to be 1K0N ( anucleate zoids ) and a further 32% were cells containing abnormal nuclei with either 1 or 2 kinetoplasts . Nuclear abnormalities observed included enlarged and irregular shaped nuclei ( Fig . 4B ) . Flow cytometry analysis showed that after 24 hours of induction , there was a reduction of the G1 peak ( 55% to 44% ) and an increase of the G2/M peak ( 39% to 44% ) ( Fig . 4C ) . This correlates with the observed decrease of 1K1N cells and increase of 2K1N cells . After 48 hours of induction , the G1 and G2/M peaks were further reduced ( 55% to 35% ( G1 ) and 39% to 28% ( G2/M ) ) and a third peak with a DNA content <2n is apparent , corresponding to the accumulation of zoids in the cell population ( 21% of counts ) . Zoid formation was not observed in bloodstream forms , in agreement with other studies showing that defects in mitosis and karyokinesis prevent the completion of cytokinesis only in bloodstream but not in procyclic cells [34] , [35] , [36] . To assess the effect of TbKif13-1 on genome segregation , FISH analysis was performed using two different DNA probes , one specific to the minichromosomal population ( Fig . 5 ) and another specific to the telomeric regions of all chromosomes ( Fig . S4 ) . To demonstrate the correlation of minichromosomal mis-segregation with the appearance of the spindle phenotype , we simultaneously labelled the cells with KMX , an anti-tubulin antibody that preferentially stains the mitotic spindle [37] . During normal mitosis , both the minichromosomal and telomeric signals show the expected symmetrical segregation patterns towards the spindle poles . The depletion of TbKif13-1 and the concurrent appearance of elongated spindle resulted in abnormal segregation patterns of the minichromosomes . Rather than segregating in near-symmetrical patterns to opposite nuclear poles , signals were dispersed and randomly distributed along the length of the mitotic spindle . The severe impact the spindle phenotype has on minichromosomal segregation is also compatible with the unusual mode of microtubule-dependent minichromosomal segregation that was proposed previously [19] . A similar pattern of random segregation as a result of TbKif13-1 depletion was observed for telomeres ( Fig . S4 ) . Given its colocalisation with the mitotic spindle , the predicted functionality of Kinesin-13s and the appearance of abnormal spindles in FISH experiments , we examined the effect of TbKif13-1 depletion on spindle morphology in detail . In procyclic and bloodstream cells we observed the formation of abnormally long mitotic spindles that occasionally span the entire length of the cell body of the trypanosome ( Fig . 5 , 6 ) . Other observed spindle phenotypes include bent spindles and abnormally thick spindles . This phenotype is congruent with the predicted function of TbKif13-1 as a microtubule-depolymeriser , leading to longer microtubules in the absence of the protein . It differs from the phenotype described for two mitotic T . brucei kinesins TbKin-A and TbKin-B that form part of a chromosomal passenger complex and also co-localise with the mitotic spindle [38] , [39] . There , RNAi-mediated depletion prevents the establishment of a mitotic spindle . To test whether the unusually long spindles were still confined within an intact nucleus or actually punctured the nuclear envelope , TbKif13-1 depleted cells were probed with NUP , a monoclonal antibody that recognises a component of the inner nuclear envelope ( Fig . 7A , Fig . S5 ) . Although the spindle caused deformations and protrusions of the nuclear envelope , we never observed a discontinuous NUP staining , indicating that the nuclear envelope remained structurally intact . Using transmission electron microscopy , we investigated the ultrastructural appearance of the mitotic phenotype . The protrusions are filled with bundles of microtubules of the mitotic spindle . Again , we noted that the nuclear membrane was intact at the tip of these protrusions ( Fig . 7B , Fig . S6 ) . Congruent with the role of TbKif13-1 in spindle length regulation was also the observation that ectopic overexpression of this kinesin led to a block of the cell population in early mitosis and failure to form a recognisable spindle ( data not shown ) . To test whether TbKif13-1 is essential for parasite infection in a disease model , ten mice were inoculated with the bloodstream TbKif13-1 RNAi cell line . Five of the mice were fed with water containing doxycycline to induce the depletion of TbKif13-1 whilst the remaining five mice were kept as non-induced controls . Blood samples were taken from all ten mice at daily intervals to chart parasitemia ( Table 1 ) . Within three days of inoculation , all five mice from the control ( i . e . non-depleted ) population developed high levels in parasitemia and had to be culled after five days to terminate the experiment . This was in contrast to the doxycycline-fed mice where all five mice remained parasite-free throughout the duration of the experiment . Parasites were not monitored beyond day 5 , at which point the control ( uninduced ) mice had all either died or had reached a humane end point and the experiment had to be discontinued . We cannot exclude that very small numbers of parasites had survived which could subsequently outgrow but these would be likely to be RNAi escape mutants and therefore not informative . Our cell biological analysis strongly supported the phylogenetic assignment of TbKif13-1 to the Kinesin-13 family . A careful manual alignment also showed the conservation of motifs critical for the depolymerising activity ( Fig . S7 ) [40] , [41] , [42] . To complement the in situ analysis of TbKif13-1 we proceeded to characterise the enzymatical properties of this protein . This analysis was also essential to determine the suitability of this kinesin as a potential drug target because large-scale inhibitor screens are based on in vitro inhibition of the kinesin ATPase activity . It should be noted that the assays described below have been done with tubulin preparations of bovine origin , demonstrating that trypanosome tubulin , which is virtually impossible to purify in large quantities , is dispensable to conduct such tests [also see 43] . To assay the depolymerising properties of TbKif13-1 , purified recombinant full-length kinesin was tested in a microtubule sedimentation assay ( Fig . 8A ) . Taxol-stabilised microtubules were incubated with kinesin in the presence or absence of ATP . The depolymerisation of the microtubules by TbKif13-1 was qualitatively examined by the shift of tubulin from the pellet fraction ( representing polymerised microtubules ) to the supernatant fraction ( representing soluble , depolymerised tubulin ) using SDS gel electrophoresis . In the presence of ATP and TbKif13-1 , more than 95% of total tubulin was found in the supernatant fraction . In the absence of ATP more that 95% of total tubulin was found in the pellet fraction . These data confirm that TbKif13-1 is an ATP-dependent microtubule depolymerising kinesin . To determine the predicted microtubule-stimulated ATPase activity of TbKif13-1 , the steady state ATPase rate was measured at various microtubule concentrations ( Fig . 8B ) . The calculated maximum ATPase activity rate ( kcat ) of TbKf13-1 was 1 . 50±0 . 05 s−1 ( average ± std . dev . ) . This is a similar rate to that observed for the single Kinesin-13 in Plasmodium falciparum ( kcat 1 . 8 s−1 ) , but also in a similar range to depolymerising kinesins in other organisms [44] , [45] , [46] . Initial reactions rates were strongly stimulated by the presence of taxol-stabilised microtubules . The basal ATPase activity in the absence of microtubules was not significantly above background levels with no kinesin added ( Fig . S8 ) .
This report represents the first survey of the entire Kinesin-13 family in the kinetoplastid group and identifies the subcellular localisation of all family members coded by the T . brucei genome [12] . Of the five Kinesin-13s , only one kinesin has a nuclear localisation ( TbKif13-1 ) while the others were associated with the flagellum ( TbKif13-2 and TbKif13-4 ) and the cell body ( TbKif13-3 andTbKif13-5 ) . The presence of five Kinesin-13s is unique to kinetoplastids . Humans and Drosophila possess three Kinesin-13s each and most protozoan genomes known to date contain only a single member of this family [11] . Even in the ciliate Tetrahymena thermophila , which , with 78 kinesin sequences , has more kinesins than any other sequenced organism , only three Kinesin-13s were classified [12] , [47] . In S . cerevisiae and S . pombe , Kinesin-13s are completely absent , although they contain members of families Kinesin-8 and Kinesin-14 which are also able to depolymerise microtubules , albeit in a different manner to Kinesin-13s [23] , [24] , [48] . Kinesin-13s in metazoans have predominantly mitotic functions . In humans , all three Kinesin-13s are involved in mitosis , although one of them ( Kif2A ) has additional cellular functions [26] , [27] . The diverse subcellular localisations of the T . brucei Kinesin-13s indicate that this restricted functionality does not apply in this organism . Rather , the expansion of the number of Kinesin-13s is accompanied by functional divergence . This is in contrast to the situation in the protozoan parasite Giardia lamblia where the single Kinesin-13 has functions both in flagellar dynamics and mitosis [49] . The only Kinesin-13 identified in the algae Chlamydomonas rheinhardii functions in flagellar assembly and disassembly and has no apparent function in mitosis [50] . In Arabidopsis thaliana , at least one of the two Kinesin-13s is also non-mitotic and involved in Golgi-associated functions [51] , [52] . The distinct non-nuclear localisations of TbKif13-2 , TbKif13-3 , TbKif13-4 and TbKif13-5 proteins in T . brucei provide an excellent opportunity to study the functional diversity of the Kinesin-13 family . Immunolocalisation showed that TbKif13-1 has a nuclear localisation which was restricted to the mitotic phase of the cell cycle . A similar distribution has been reported for its functional homologue in Leishmania , LmjKIN13-1 , and in this kinetoplastid the ubiquitin/proteasome pathway is implicated in the cell cycle-dependent regulation of expression [33] . The function of TbKif13-1 was analysed using RNAi-mediated protein depletion in bloodstream and procyclic cell lines . T . brucei undergoes a closed mitosis and the spindle develops inside the nucleus and karyokinesis precedes cytokinesis [reviewed in 53] . In both life cycle stages , the depletion of TbKif13-1 resulted in the formation of extremely long , distorted and bent spindles , leading to chromosome segregation defects . Given that Kinesin-13s are able to depolymerise microtubules at both the plus- and minus- end , a spindle elongation phenotype is expected . Although aberrant spindle dynamics are observed in other organisms as well [54] , in vivo RNAi-depletion of MCAKs in metazoan organisms is associated with less severe phenotypes , in contrast to MCAK depletion done in spindle reconstitution extracts [55] . This has been attributed to observations showing that an increase in the concentration of free tubulin dimers leads to the downregulation of further translation , thereby shifting microtubule dynamics towards depolymerisation and counteracting the lack of enzymatic , kinesin-mediated depolymerising activity [56] , [57] , [58] . Consequently , the severe spindle elongation phenotype indicates that this mechanism of tubulin translational auto-regulation is most likely not operational in trypanosomes . An additional factor for the extreme phenotype observed in T . brucei could be that TbKif13-1 is possibly the only mitotic depolymerising kinesin , whereas in humans and Drosophila several Kinesin-13s and depolymerisers of the Kinesin-8 families have potentially overlapping functions and are , to some extent , able to compensate for the loss of a single kinesin [59] , [60] . T . brucei does not have members of the Kinesin-8 family . Recently , a member of the Kinesin-14 family ( Kar3Cik1 ) in S . cerevisiae has also been identified as a microtubule-depolymeriser [61] . Other members of this family , however , are involved in microtubule-crosslinking and -sliding and do not show depolymerising activity [62] . T . brucei has two kinesins of the Kinesin-14 family [12] . One of these kinesins has been reported to be involved in acidocalcisome maintenance and the second member has not been characterised yet [43] . Biochemical analyses of purified full length TbKif13-1 shows that this protein depolymerises microtubules in an ATP dependent manner similar to Kinesin-13s in other organisms [63] , [64] . We show that enzymatically active , recombinant kinesin can be purified from E . coli and that bovine tubulin can be used both for depolymerising and ATPase assays . In summary , we demonstrate the essentiality of TbKif13-1 for T . brucei and analyse the biological and biochemical basis of its function . It establishes this kinesin as a potential drug target against sleeping sickness .
All animal experiments were carried out in accordance with the ethical rules for animal husbandry of the University of Edinburgh and a UK Home Office license ( held by K . R . M ) granted for this research as covered by the Animals ( Scientific procedures ) Act 1986 . The T . brucei bloodstream Lister 427-derived “single marker” strain , expressing Tet-repressor and T7 RNA polymerase , was grown in HMI-9 medium at 37°C , 5% CO2 in the presence of G418 at 0 . 5 µg ml−1 [65] , [66] . Procyclic strains 427 and 29-13 were maintained in SDM-79 , at 28°C [66] , [67] . The 29-13 cell line , expressing T7 RNA polymerase and Tet repressor , was grown in the presence of 50 µg ml−1 of hygromycin and 15 µg ml−1 of G418 [66] . Cell growth was monitored using a CASY cell counter ( Roche Innovatis AG , Germany ) . The RNAi construct , pFC4-TbKif13-1 was made using the stem-loop pFC4 vector which allows for the tetracycline inducible production of dsRNA with the use of a single T7 promoter [68] . A 597 bp DNA fragment of the TbKif13-1 open reading frame was PCR-amplified using the sense primer 5′-GCGGATCCAAGCTTAAGCAGATGTTCGTGTCTTTCTAC-3′and anti-sense primer 5′-GCCTCGAGTCTAGAACGGCTTTTCTTTAACTCCTTCACA-3′ . The PCR fragment was cloned into the RNAi vector using the restriction sites HindIII , XbaI , XhoI and BamHI ( underlined ) . The resulting construct was linearised with NotI and transfected into procyclic or bloodstream cell by electroporation . Transformants were selected with blasticidin ( 10 µg ml−1 for procyclic and 3 µg ml−1 for bloodstream cells ) . RNAi was induced by the addition of 1 µg ml−1 of doxycycline . Suitable RNAi fragments were selected using RNAit software to avoid off-target effects [69] . The nomenclature of T . brucei Kinesin-13s was adopted from the nomenclature introduced for members of this family in Leishmania [32] , [33] . Fragments of the open reading frames of TbKif13-1 ( residues 486–687 , acc no: Tb09 . 160 . 2260 ) , TbKif13-2 ( amino acid residues 364–683 , TriTrypDB acc no: Tb11 . 02 . 2260 ) , TbKif13-3 ( residues 342–561 , acc no: Tb11 . 02 . 2970 ) , TbKif13-4 ( residues 581–780 , acc no: Tb927 . 4 . 3910 ) and TbKif13-5 ( residues 545–712 , acc no: Tb11 . 02 . 0790 ) were PCR-amplified using a proofreading polymerase ( Accusure , Fermentas ) and primers specified in Table S1 . All fragments were checked against the T . brucei genome sequence database ( http://www . genedb . org ) using Blast to ensure their specificity . The PCR fragments were cloned into the pTrcHisC vector ( Invitrogen ) using restriction sites BamHI and EcoRI , resulting in the generation of expression constructs with an N-terminally 6×histidine tagged kinesin fragments . Recombinant proteins were expressed in E . coli BL21 , cells lysed under native ( TbKif13-4 , TbKif13-3 , Tbif13-1 ) or denaturing ( TbKif13-2 , TbKif13-5 ) buffer conditions using a French Press and affinity-purified on cobalt metal resin ( Talon , BD Biosciences ) . The purified recombinant proteins were used to raise rabbit polyclonal antibodies ( Yorkshire Biosciences ) . Polyclonal antibodies were affinity-purified using the recombinant kinesin fragments , coupled to CNBr-activated Sepharose 4B ( Amersham Biosciences ) . Bound protein was eluted with glycine buffer ( 100 mM glycine-HCl , 100 mM NaCl , pH 2 , 5 ) . The purified antibodies were diluted 1∶25 for immunofluorescence and 1∶1000 for chemiluminescence-based Western blots . Constructs of the C-terminally 2×myc-tagged TbKif13-2myc and TbKif13-5myc kinesins were generated using the tetracycline inducible expression plasmid pHD1484 [70] . The entire open reading frames of TbKif13-2 and TbKif13-5 were PCR-amplified using primers specified in Table S2 . The PCR fragments were cloned into the pHD1484 using the restriction sites Apa1 and BamH1 . The NotI-linearised expression construct was electroporated into the procyclic strain 449 cell line , expressing Tet-repressor . The selection of stable transfectants , integrated into the ribosomal RNA gene locus was done with hygromycin at 50 µg ml−1 . Expression of tagged kinesins was induced by the addition of 1 µg ml−1 of doxycycline . T . brucei cells were fixed in suspension with 3 . 6% formaldehyde and processed as described [71] . In addition to the rabbit anti-kinesin antibodies , other primary antibodies used in this study were mouse monoclonal anti-β-tubulin antibody KMX [37] to visualize the mitotic spindle , mouse monoclonal anti-nuclear envelope antibody NUP [17] and mouse mAb anti-cmyc ( clone 9E10 , ECACC ) . Cells were examined on an Olympus IX71 epifluorescence microscope equipped with a CCD-camera ( F-View , Olympus ) . Images were pseudo-coloured and assembled in Adobe Photoshop CS4 . Procyclic trypanosomes were processed for electron microscopy as described , except that thin sections were not post-stained [72] . Samples embedded in epoxy resin were thin-sectioned and examined on a Jeol 2010 electron microscope operating at 120 KeV . Images were recorded using a Gatan UltraScan 4000 CCD camera . Visualisation of telomeres by FISH and combined immunofluorescence with KMX and minichromosomal FISH was done essentially as described , except that the telomeric oligonucleotide ( TTAGGG ) 5 was synthetically labelled with digoxigenin ( MWG ) [17] , [73] . Cells were processed for flow cytometry analysis exactly as described [71] and analysed on a FACS Calibur flow cytometer using CellQuest software ( Becton Dickson ) . The entire ORF of TbKif13-1 was PCR amplified using primers 5′-GCGGATCCTCGCGAG TGGGAATTAAAGCTGGT-3′ and 5′-CGAAGCTTCTAAATCCCGTTTTGCTCGAGAC-3′and ligated into the pTrcHisC vector ( Invitrogen ) using restriction sites BamHI and EcoRI ( underlined ) , resulting in the generation of an expression construct coding for a N-terminally 6×histidine-tagged TbKif13-1 protein . The recombinant full length TbKif13-1 was expressed in E . coli BL21 ( Promega ) and purified using metal affinity resins by BD biosciences ( BD Talon ) . Purification conditions were specifically adjusted to optimize expression of enzymatically active kinesin [74] . BL21 cells expressing TbKif13-1 at low levels were grown at 37°C in a shaking incubator without additional induction by IPTG until cell density at 600 nm was 1 . 0 . Bacteria were cooled to 4°C and harvested by centrifugation at 2 , 500 g for 10 minutes . Bacterial pellets were then resupended in lysis buffer ( 100 mM PIPES , 100 mM NaCl , 1 mM MgCl2 , 10 mM imidazol , 1 mM ATP , 1mM β-mercaptoethanol , 0 . 2 mM PMSF , 1∶200 dilution of Protease Inhibitor Cocktail ( Sigma-P8340 ) , pH 6 . 9 ) and lysed via two passages at 1000 kPsi through a French press . The lysate was centrifuged at 14 , 000 g for 10 minutes at 4°C and the supernatant was incubated with BD Talon resin at 4°C for 20 minutes before being washed twice with 10 bed volumes of wash buffer ( 100 mM PIPES , 100 mM NaCl , 1 mM MgCl2 , 10 mM imidazol , 0 . 01 mM ATP , 1mM β-mercaptoethanol , 0 . 2 mM PMSF , pH 6 . 9 ) . Protein was eluted in elution buffer ( 100 mM PIPES , 100 mM NaCl , 1 mM MgCl2 , 250 mM imidazol , 0 . 01 mM ATP , 1mM β-mercaptoethanol , pH 6 . 9 ) . Glycerol was added to the peak fractions to a final concentration of 20% ( v/v ) . Aliquots of 50µl were snap-frozen in liquid nitrogen and stored at −80°C . The concentration of the purified TbKif13-1 was determined using the BCA protein determination kit ( Sigma ) . The assay was performed as described [75] . Purified bovine tubulin ( Cytoskeleton Inc . ) was assembled at 37°C for 30 minutes in PME buffer ( 80 mM PIPES , 2 mM MgCl2 , 0 . 5 mM EGTA , 1 mM DTT , 1 mM GTP , pH 6 . 9 ) containing 10 µM taxol ( Sigma ) . Polymerised microtubules were harvested via centrifugation at 250 , 000 g for 15 minutes at 30°C . The polymerised microtubules were subsequently resuspended to a final concentration of 2 . 5 µM in MT buffer ( 80 mM PIPES , 50 mM KCl , 2 mM MgCl2 , 0 . 5 mM EGTA , 1 mM DTT , 0 . 5 mM GTP , 5 µM Taxol , 2% glycerol , pH 6 . 9 ) and then incubated with 1 . 4 µM purified TbKif13-1 in the presence or absence of 1 . 5 mM ATP for 30 minutes at 28°C and centrifuged at 250 , 000 g for 10 minutes at 28°C . The supernatant and pellet were analysed by SDS-PAGE . The coupled pyruvate kinase/lactate dehydrogenase steady-state ATPase assay was performed as described [76] . Purified bovine tubulin was assembled at 37°C for 30 minutes in PME buffer ( 80 mM PIPES , 2 mM MgCl2 , 0 . 5 mM EGTA , 1 mM DTT , 1 mM GTP , pH 6 . 9 ) containing 10 µM taxol . The polymerised tubulin was added to the ATPase assay at concentrations ranging from 0 . 5 µM to 2 . 5 µM . The ATPase assay consisted of 50 mM Tris-acetate , 1 mM MgCl2 , 1 mM DTT , 1 mM ATP , 3 mM PEP , 0 . 2 mM NADH , 6 . 5 µM taxol , 6 mM PIPES , 5 mM NaCl , 12 . 5 mM imidazol , 13 . 59 units ml−1 lactate dehydrogenase , 11 . 2 units ml−1 pyruvate kinase , 0 . 42 µM purified TbKif13-1 , 1% glycerol , pH 7 . 5 . The initial ATPase rates were determined at 25°C using the change in absorbance at 340 nm . Data analysis was done with SigmaPlot V11 . 0 using the Michaelis-Menten equation , where a is the maximum ATPase rate and b is the Km value of TbKif13-1 ( for details see http://www . proweb . org/kinesin/Methods/ATPase_assay . html ) . Ten male age-matched MF1 mice were inoculated i . p . with 35 , 000 trypanosomes in a volume of 200 µl HMI-9 and split into two groups . One group of 5 mice was provided with doxycycline ( 200 µg ml−1 in 5% sucrose ) in their drinking water immediately post-inoculation , whereas the other group of five mice was supplied with drinking water containing 5% sucrose only . Parasite numbers were scored over 5 days by the rapid matching method of Herbert and Lumsden [77] and animals culled using defined humane end points as specified in the relevant UK Home office license .
|
Kinesins represent a class of mechanochemical enzymes that are able to move along microtubule filaments and transport cargo in a directional manner within the cell . Of particular importance are mitotic kinesins , as they ensure the accurate segregation of chromosomes and therefore cell survival . Such kinesins are involved in building and maintaining the mitotic microtubule-based spindle and in chromosome translocation during mitosis . Mitotic kinesins are potentially excellent drug targets because of their roles in an essential process of cell multiplication . Unregulated cell proliferation is associated with diseases such as cancer , but also many infectious diseases . Therefore , the identification of kinesins essential for the proliferation of parasites in the human host offers an attractive prospect for intervention . In our study we present a comprehensive biochemical and cell biological analysis of a mitotic kinesin in the protozoan parasite Trypanosoma brucei , causative agent of sleeping sickness in Africa . We show that this kinesin is essential for parasite survival not only in cultured cells but also in mice infected with this parasite and therefore establish this kinesin as a potential drug target in parasitic infections .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology/cell",
"growth",
"and",
"division",
"microbiology/parasitology",
"cell",
"biology/cytoskeleton"
] |
2010
|
Functional Characterisation and Drug Target Validation of a Mitotic Kinesin-13 in Trypanosoma brucei
|
Syncytins are envelope genes from endogenous retroviruses , “captured” for a role in placentation . They mediate cell-cell fusion , resulting in the formation of a syncytium ( the syncytiotrophoblast ) at the fetomaternal interface . These genes have been found in all placental mammals in which they have been searched for . Cell-cell fusion is also pivotal for muscle fiber formation and repair , where the myotubes are formed from the fusion of mononucleated myoblasts into large multinucleated structures . Here we show , taking advantage of mice knocked out for syncytins , that these captured genes contribute to myoblast fusion , with a >20% reduction in muscle mass , mean muscle fiber area and number of nuclei per fiber in knocked out mice for one of the two murine syncytin genes . Remarkably , this reduction is only observed in males , which subsequently show muscle quantitative traits more similar to those of females . In addition , we show that syncytins also contribute to muscle repair after cardiotoxin-induced injury , with again a male-specific effect on the rate and extent of regeneration . Finally , ex vivo experiments carried out on murine myoblasts demonstrate the direct involvement of syncytins in fusion , with a >40% reduction in fusion index upon addition of siRNA against both syncytins . Importantly , similar effects are observed with primary myoblasts from sheep , dog and human , with a 20–40% reduction upon addition of siRNA against the corresponding syncytins . Altogether , these results show a direct contribution of the fusogenic syncytins to myogenesis , with a demonstrated male-dependence of the effect in mice , suggesting that these captured genes could be responsible for the muscle sexual dimorphism observed in placental mammals .
Syncytins are “captured” genes of retroviral origin that correspond to the envelope gene of ancestrally endogenized retroviruses ( reviewed in [1] ) . These genes encode fusogenic proteins that are involved in the formation by cell-cell fusion of the placental syncytiotrophoblast in eutherian mammals and marsupials [2–5] . Furthermore , genetically modified mice , knocked out for their two syncytin genes–i . e . syncytin-A and syncytin-B [6]—proved to be deficient in placenta development , with altered structures of the materno-fetal interface resulting , in the case of Syncytin-A , in the death of the embryo at mid-gestation [7 , 8] . It thereafter turned out that syncytins can be found in all placental mammals in which they had been searched for , with independently captured syncytins found in all major clades of placental mammals , including the Euarchontoglires ( primates , rodents , lagomorpha ) , Laurasiatherians ( ruminants , carnivores ) , Afrotherians ( tenrec ) , and even the marsupials ( opossum ) . This has led to the hypothesis that these genes , which are absolutely required for placentation , as shown by the knock-out mice experiments , are most likely responsible for the emergence of placental mammals from egg-laying animals . Analysis of the conservation of these “new” genes clearly indicated that they have been subjected to purifying selection in the course of evolution , as expected for any bona fide cellular gene . Of note , cell-cell fusion is a basic phenomenon for all species , from drosophila to humans , where such events are involved in numerous processes including , in addition to placentation in mammals , macrophage fusion for osteoclast formation , and most importantly myoblast fusion for myogenesis during development and for muscle regeneration after injury ( reviewed in ref . [9–13] ) . Although syncytins are essentially expressed in the placenta trophoblast cells , low level expression can be detected in other cells such as fusing macrophages [14] and myoblasts [15] , and we therefore tested , using genetically modified mice , whether syncytins could participate in muscle formation , thus revealing a “collateral” effect of the “primary” capture of these genes for placentation . Myoblast fusion is a complex and tightly controlled process required for the formation of the skeletal muscle fibers ( reviewed in refs . [9 , 10 , 16] ) . The fusion process is highly cell-type specific to ensure that fusogenic myoblasts do not form syncytia with non-muscle cells . The molecular mechanisms that coordinate myoblast fusion remain incompletely understood , although several partners of myoblast fusion have already been identified . These include numerous proteins involved in cell-cell adhesion , as well as proteins involved in actin and lipid dynamics that allow cytoskeletal and membrane remodeling for cell fusion . Furthermore , a muscle-specific membrane protein called myomaker that is transiently expressed during myogenesis and is both necessary and sufficient to promote myoblast fusion in vivo and in vitro has recently been identified [17 , 18] . Although this protein alone is not sufficient to induce the fusion of non-myoblast cells such as fibroblasts , myomaker is likely to be a major player of the membrane fusion process; yet its partner ( either a receptor or a bona fide fusogenic ligand ) remains to be identified . Altogether , the numerous studies carried out on myoblast fusion have led to the conclusion that these processes involve numerous partners and pathways . As syncytins have been shown to be bona fide fusogens , we investigated their involvement in the overall myogenesis process .
As previously reported , whereas deletion of both syncytin genes , i . e . syncytin-A and syncytin-B leads to embryonic lethality , knockout of syncytin-B alone allows for surviving progeny to be obtained . Yet , syncytin-B null ( SynB-/- ) neonates display a sex-independent growth retardation characterized by a reduction of body weight of 18% when compared to wild-type neonates from the same litter [8] . This difference was attributed to associated placental malformations and was still apparent in 6–8 week old male mice but absent in female mice [8] . In order to clarify whether the observed decrease in body weight of male mice could be attributed to a reduction in muscle mass , we measured , in adult 12 week old mice , both the body weight ( Fig 1A and 1B ) and the mass of several skeletal muscles , located in the hind limbs of the mouse: two fast-twitch muscles , i . e . Tibialis Anterior ( TA ) and Extensor Digitorum Longus ( EDL ) , and one slow-twitch muscle the Soleus ( SOL ) ( Fig 1C ) . Measurements were performed on F1 mice , either wild-type or SynB-/- , from at least four different crosses between heterozygous SynB+/- mice , in order to minimize possible lineage effects and compare genetically related individuals . As shown in Fig 1A and 1C , adult SynB-/- and WT female mice show no differences in body weight and muscle mass ( 17 wt and 13 SynB-/- females ) . However , adult SynB-/- male mice show a significant reduction in body mass ( 12%; p<0 . 01 , Mann and Whitney test; 16 wt and 16 SynB-/- males ) . This decrease is associated with a reduction in muscle mass as compared to wild type male mice ( 15%; p<0 . 05 , Mann and Whitney test ) , which is observed independently of the crossing ( S1A Fig ) . We also quantified , in the corresponding animals , as an internal control , the mass of two organs ( heart and kidney ) , and the length of two bones ( tibia and femur ) , with no significant differences seen between SynB-/- and WT mice ( Fig 1D ) . These last results suggest that the reduction in body weight in adult SynB-/- male mice is mainly due to a decrease in muscle mass ( which accounts for 35–40% of the body weight ) . In addition , these decreases appear to affect only skeletal muscles , where fusion processes take place . Altogether these results suggest that Syncytin-B takes part into muscle development in male mice . To better characterize the role of syncytin-B in muscle development , morphological analyses of SynB-/- and WT muscles ( see scheme in S2 Fig ) were performed on cryosections of TA , EDL and SOL muscles , respectively . We first quantified the cross-sectional area ( CSA ) of the entire muscle , which was found to be reduced for the three muscles in SynB-/- males as compared to WT males ( Fig 2A; * p<0 . 05 , ** p<0 . 01 , Mann and Whitney test ) . The alteration in muscle CSA was observed independently of the crossing ( four crosses at least ) ( S1B Fig ) . As expected , in females , which did not show a reduction in muscle mass , no differences in CSA were observed between SynB-/- and wild-type mice . The number of fibers per muscle and the individual myofiber CSA , both parameters that contribute to muscle CSA , were also measured , as illustrated in Fig 2 . Equivalent fiber numbers were observed between SynB-/- and wild-type male mice ( Fig 2B ) , but a significant reduction in myofiber CSA was observed in SynB-/- male mice ( Fig 2C and 2D; * p<0 . 05 , ** p<0 . 01 , Mann and Whitney test ) . The alteration in myofiber mean area was observed independently of the crossing ( three to four crosses ) ( S1C Fig ) . Finally , we quantified the number of myonuclei per fiber , in knockout and wild-type mice ( Fig 3 ) . Transversal sections of the three muscles TA , EDL and SOL were immunolabeled with a dystrophin antibody to outline the sarcolemma , and were stained with 4' , 6'-diamidino-2-phenylindole ( DAPI ) to visualize the nuclei . Nuclei located inside the dystrophin-labeled sarcolemna were identified as myonuclei and counted ( Fig 3B ) . Accordingly , a decrease of approximately 30% in the myonuclei number was observed in muscles from SynB-/- male mice as compared to control males ( Fig 3A; * p<0 . 05 , ** p<0 . 01 , Mann and Whitney test ) , strongly suggestive of a defect in myoblast fusion . This difference was not observed in SynB-/- females in comparison to wild-type . As variations in nuclear size and shape may affect myonuclear count [19] , we measured the nuclear length of myonuclei on longitudinal sections . No differences were observed between either WT or SynB-/- , male or female mice ( S3A and S3B Fig ) . As expected , the resulting number of myonuclei per mm of fiber ( S3C–S3E Fig ) showed a decrease in muscles from SynB-/- males as compared to control males , whereas no significant differences were observed in females . Since myoblast fusion and accumulation of myonuclei contribute to mammalian myofiber development and growth [20–22] the data also strongly suggest that the reduced myofiber CSA observed in SynB-/- male mice , which have an unaltered myofiber number , mainly results from a defect in myoblast fusion to myofibers . Muscle regeneration is a physiological process that recapitulates , through the differentiation and fusion of satellite cell descendants , all the phases of myogenesis [16] . We therefore investigated the requirement for syncytin-B during muscle regeneration . We first analyzed , by in situ hybridization , the expression of syncytin-B upon muscle injury . Muscle injury was induced by a single injection of cardiotoxin into the TA muscle of 8 week old female and male mice . Injection of PBS in the contralateral muscle was used as a control . Muscles were collected after 4 days of regeneration ( see scheme in Fig 4M ) . Hematoxylin , eosin and safran staining ( HES ) of the muscle sections ( Fig 4A ) , shows normal myofibers after PBS injection , with myonuclei located at the myofiber periphery . In contrast , following cardiotoxin injection , similar damage of the muscle section could be observed ( delineated by black arrows , Fig 4D and 4G ) in male and female mice . During muscle regeneration , the damaged myofibers are destroyed and replaced by small newly centro-nucleated fibers , typically representative of muscle regeneration . These new fibers observed in male as in female mice , are illustrated in the enlarged Fig 4J for a male mouse . Specific digoxigenin-labeled antisense probes were then synthetized for the detection of syncytin-B , and the corresponding sense probes were used as negative controls . As further shown in Fig 4E and 4K , labeling was only observed with the antisense probe for syncytin-B , and only in males . Closer examination of the labeling further shows that it is restricted to regions of muscle regeneration ( Fig 4D and 4E ) where myofibers are centro-nucleated ( Fig 4J and 4K ) . In situ hybridization of syncytin-B shows conclusively that this gene is expressed in the regenerative muscle in males , strongly suggesting that the encoded protein could play a role in this process specifically in males . To characterize further the role of the Syncytin-B protein in this process , SynB-/- male and female mice ( as well as control wild-type mice ) were analyzed after cardiotoxin injection as above , and muscle regeneration was quantified at day 14 . Histological sections of the muscle fibers were performed as above , and the mean myofiber CSA measured . As illustrated in Fig 4N , quantification of the myofiber CSA in SynB-/- male mice in comparison to wild-type shows a delay at day 14 , suggesting that Syncytin-B plays a role during muscle regeneration ( Fig 4N; p<0 . 05 , Mann and Whitney test ) after cardiotoxin treatment . In the case of female mice , no differences could be detected in the myofiber CSA between SynB-/- and wild-type , suggesting that Syncytin-B plays a role during muscle regeneration also in a sex-dependent manner ( Fig 4N ) . To characterize further the role of the syncytin genes in myoblast fusion , we isolated mouse primary satellite cells and quantified the expression of both syncytin-A and–B in proliferating myoblasts , as well as during the process of cell differentiation and fusion ( day 0 , day 2 and day 4 , see Fig 5A ) . Both genes were detected by quantitative RT-PCR and were found to be upregulated after 2–4 days of myogenic differentiation and myoblast fusion ( when myotubes could be clearly observed ) , whereas the level of the housekeeping gene remained stable . These data suggest that both syncytin genes could be involved in this process . To investigate further their role in myoblast fusion , we knocked-down syncytin-A and–B expression by RNA-mediated interference ( Fig 5B ) . After two days of differentiation , silencing of both syncytin genes resulted in a very significant inhibition of myoblast fusion as illustrated in Fig 5C , with a close to 50% decrease in the fusion index ( Fig 5D ) . Analysis of the nuclei distribution in myoblast cells revealed a significantly higher number of mononucleated cells in cells transfected with either syncytin-A or syncytin-B siRNAs in comparison to cells transfected with a control siRNA ( Fig 5E; * p<0 . 05 , ** p<0 . 01 , Student’s t-test ) . In addition , a significantly higher number of cells with >4 nuclei was observed in control cells in comparison to cells transfected with syncytin-A or syncytin-B siRNAs , with no myotube containing >8 nuclei in both cases . Combination of the two syncytin siRNAs had no additive effect ( Fig 5D ) suggesting that , at least ex vivo , the two syncytins are acting on similar–and most probably interactive- steps of myoblast fusion . Of note , reduction in fusion index mediated by siRNA targeted to syncytins remains smaller than that with a siRNA targeted to the myomaker gene ( ref . 17 and Fig 5D ) , consistent with the notion that syncytins are only “add-on” contributors to the basal fusogenic activity associated with the non-syncytin genes . Yet both syncytins are able to trigger myoblast cell-cell fusion . This is illustrated in Fig 6 , where transfection of C2C12 myoblasts with expression vectors for either syncytin-A or syncytin-B clearly triggered the fusion of myoblasts in their proliferative state ( Fig 6A ) , and also enhanced the fusion normally triggered upon differentiation of the cells ( Fig 6B and 6C ) . Altogether , the ex vivo data corroborate the in vivo data on syncytin-B , and further suggest that syncytin-A could also contribute to myoblast fusion . To determine if the effects observed in the mouse can be extrapolated to other placental mammals , we established primary cultures of myoblasts from the muscles of mammals where the cognate syncytins have been characterized . Accordingly , we obtained viable and fusion-prone myoblasts from human ( primate ) ( Fig 7A ) , sheep ( ruminant ) ( Fig 7B ) and dog ( carnivore ) ( Fig 7C ) . As illustrated in Fig 7 , left panels , induction of each cognate syncytin gene could be observed during the process of cell differentiation and fusion , as similarly observed for the murine myoblasts ( Fig 5 ) , whereas the level of the housekeeping genes remained stable . Experiments were then carried out as above for the mouse myoblast cells , but using siRNAs specific for each of the corresponding syncytins ( i . e . , syncytin-1 and -2 , syncytin-Rum1 and syncytin-Car1 respectively ) . Accordingly , transfection of the cells with siRNA specific for each cognate syncytin clearly resulted in a significant ( >20% ) reduction in the measured fusion index ( Fig 7 , middle panels ) , also visible in the reduction in the number of large myotubes ( Fig 7 , right panels ) .
In this study , we address for the first time the role of the two murine syncytin genes ( syncytin-A and–B , in myogenesis , both in vivo using a syncytin-B knockout mouse model , and ex vivo using primary cell cultures derived from isolated muscle satellite cells . We show that both syncytin genes are expressed ex vivo during the fusion of myoblast cells and that syncytin-B is expressed in regenerative fibers in vivo following cardiotoxin injury . Using syncytin-A and–B siRNAs , we demonstrate ex vivo that both syncytin genes contribute to myoblast cell fusion . In addition , using a knockout mouse model for syncytin-B , we show that impairment of syncytin-B leads to a reduction in muscle mass , that largely contributes to the observed reduction in body weight . This effect is male-specific , being not observed in females , that provide a clear-cut internal control . This decreased mass correlates with an observed reduction in several muscle parameters , including the muscle cross-sectional area , the individual myofiber cross-sectional area , and most importantly the number of nuclei per muscle fiber . Furthermore , we show that the total number of fibers per muscle is not altered in syncytin-B knockout mice , suggesting that syncytin-B activity is essentially involved in myofiber growth and not in the initiation of myogenesis . The reduction in the number of nuclei per myofiber would be consistent with such a role and with the fusogenic activity demonstrated in the ex vivo experiments . Finally , using a model of muscle regeneration after injury by cardiotoxin injection , we show that impairment of syncytin-B results in a delay in muscle regeneration , again only observed in males , consistent with the specific induction of syncytin-B expression observed in wild-type regenerating male myofibers . To summarize , the present data indicate that syncytin-B ( and possibly syncytin-A , see below ) , participates in myoblast fusion , and contributes to muscle growth and regeneration in the mouse , in a sex-dependent manner . This series of results raise several issues . One of the most obvious questions is related to the function of syncytin-A . Indeed , our cell fusion assays performed in C2C12 cells and in primary myoblasts strongly suggest that both syncytins have closely related activities , with similar effects of synA and synB siRNAs on myoblast fusion . This would fit with the known properties of these two genes , which have very similar activities at the placental level , each one being involved in the formation of a definite syncytiotrophoblast layer at the feto-maternal interface [8] . However a definitive characterization of the role of syncytin-A in myogenesis will require its conditional knock-out , specifically in the muscle , because homozygous null placentae are embryonic lethal . Along these lines , a mouse model obtained by crossing a lox-syncytin-A knock-in mouse with a Pax7-Cre-ERT2 mouse [23] is being developed , which should allow to generate a muscle-specific knock-out of syncytin-A , thus circumventing the detrimental effect of syncytin-A inactivation in placentation . Another important issue concerns the generality of the contribution of syncytin genes to myogenesis . We have clearly demonstrated that not only the murine syncytins but also syncytins from ruminants , carnivores and humans contribute to myoblast fusion , at least in ex vivo assays . Similar conclusions were reached for human myoblasts [15 , 24] , where either siRNAs or an antibody targeting Syncytin-1 inhibit cell fusion of cultured myoblasts . Although the physiological relevance of such assays can be questioned , they nevertheless strongly suggest that the effects observed for the murine genes might extend to all species possessing a syncytin . Indeed , in the present study , we tested all four species with identified syncytins for which we succeeded in establishing primary myoblasts prone to cell fusion ( we could not isolate myoblasts from the rabbit , in which we had previously characterized syncytin-Ory1 , [25] ) , and in all four cases the results were identical , with inhibition of fusion by the corresponding syncytin gene-specific siRNA . It is therefore tempting to speculate that syncytins , which have been captured in all placental mammals most probably primarily for their placental function , all contribute , as observed here in vivo in the mouse , to myogenesis . However , this hypothesis will remain difficult to demonstrate or invalidate unless , for instance , myoblast fusion is found to be independent of syncytin , in a species where the gene is present . At the molecular level , the myoblast fusion process is still an unresolved question , although it is clear that it involves a wide panel of genes ( for a review see ref . [9 , 12] ) . Some have been shown to be required for myogenesis in vivo ( via the use of KO mice ) , such as the recently identified myomaker gene [17 , 18] , the contributing galectin gene [26] , cell adhesion/signal transduction proteins [27] or a series of actin cytoskeletal remodeling factors ( reviewed in [9 , 28] ) . Yet , in all cases none of the identified genes are truly autonomous fusogens , but rather seem to be either co-factors , yet absolutely required for myogenesis , or partners of a still to be identified “universal” fusogen . Our data on myomaker transfection of C2C12 myoblasts ( Fig 6 ) do not provide evidence for a direct fusogenic activity in the proliferating state ( at variance with syncytin A and B ) . This result is consistent either with myomaker not being a bona fide fusogen or its fusion partner only being expressed upon myoblast differentiation . In the case of the presently identified syncytins , these are bona fide fusogens , a function inherited from the envelope gene of the ancestral retroviruses from which they were captured . Their contribution to myoblast fusion , as well as to cytotrophoblast or macrophage fusion , could in that case be directly due to their fusogenic activities . An additional important question arises from the unexpected result on the sex-dependent effect of syncytin-B in vivo , in myogenesis after birth and muscle regeneration after injury . The data clearly indicate that the syncytin-B effect is essentially an “add-on” specific to the male , not taking place in the female , and resulting in an increased number of nuclei per muscle fiber ( consequently associated with an increased muscle mass ) and an increased rate of muscle regeneration after injury . Then a question arises as to the molecular origin of this uncovered male-dependence . Among the possible mechanisms , a hormone-dependent regulation of syncytin expression would of course fit the data , and could be analyzed by searching for binding sites within the syncytin gene promoter/enhancer for hormone–dependent factors . Along these lines , it is clear that testosterone and the androgen receptor ( AR ) would be appropriate effectors , which could be tested by hormone injection to wild-type mice , with or without prior castration , or even in mice knocked-out for the AR gene [29] . It is also possible that factors known to act as transcription factors in the placenta ( among others GCM1 [30 , 31] ) could also act as syncytin activators in the muscle . Experiments are now in progress to tentatively elucidate these points . It is also noteworthy that testosterone and/or sustained physical activity ( the latter known to generate microlesion-and-repair cycles ) have been reported to result in muscle mass increase [24 , 32–34] , with , in some cases , indications for an increase in myonuclei number per fiber [35 , 36] . Moreover , one of these studies shows that long-term endurance exercise in male humans results in an increase in Syncytin-1 expression , among other genes , in muscle biopsies [24] . It will be interesting to carry out a similar study on female humans to determine whether syncytin-1 induction is male-specific , or not . Indeed , such sex-specific effects could be a hint for a generalized male-dependence of syncytin expression and role in myogenesis among placental mammals . If so , it is likely that syncytins could then be responsible for the muscle sexual dimorphism observed in mammals , where in most cases the male has a higher body and muscle mass than the female . To conclude , we propose as a working model that syncytins have been captured primarily as a placental gene , but that some expression in the muscle is taking place ( possibly as a consequence of regulatory processes and transcription factors shared by placenta and muscle ) , and that a “collateral” effect of the capture of such genes -with a clear-cut fusogenic activity- is an add-on contribution to myogenesis in males of placental mammals .
Targeted mutagenesis of syncytin-B gene , located on chromosome 14 , was described previously [8] . Briefly , the mouse syncytin-B ORF ( carried by a single 1 . 8-kb exon ) was deleted by homologous recombination using a strategy based on the Cre/LoxP recombination system for generating KO mice . Male and female mice were on a mixed 129/Sv C57BL/6 background . They were housed under controlled conditions at 24°C , and were allowed free access to food and water in full compliance with the French government animal welfare policy . All the experiments were approved by the Ethics Committee of the Gustave Roussy Institute . Before manipulations mice were anaesthetized with an intraperitoneal injection of 0 . 1 ml per 10 g body weight of a solution containing 1 mg/ml xylazine ( Bayer ) and 10 mg/ml ketamine ( Merial ) per mouse . Then , to induce muscle injury , 35 μl of Cardiotoxin ( Ctx ) ( 12 μM; Latoxan ) were injected in a single injection into Tibialis Anterior ( TA ) muscle of 8 week old mice . Muscle regeneration was followed at different times depending on the experiments . Mouse Tibialis anterior ( TA ) , Extensor Digitorum Longus ( EDL ) and Soleus ( SOL ) muscles were dissected , weighed and frozen in isopentane/liquid nitrogen baths . Cryopreserved tissues were then cut in 10 μm cryosections . Cryosections were cut in the same central region of the muscle for all analyzed animals . Muscle cryosections were fixed in 4% paraformaldehyde on ice for 8 min . Tissue sections were then saturated with 5% Goat serum 2% BSA solution for 30 min . Overnight immunolabeling for dystrophin ( Leica Biosystems ) and laminin ( Sigma ) was then performed at 4°C . Nuclei were counterstained with 4' , 6'-diamidino-2-phenylindole ( DAPI ) for 15 min at room temperature . Myofiber cross-sectional area ( CSA ) was measured from stained slides from randomly chosen fields using the ImageJ software ( at least 150 myofibers were measured per condition ) . Freshly collected muscles were fixed in 4% ( wt/vol ) paraformaldehyde and embedded in paraffin . Serial sections ( 7 μm ) were used for ISH . For the syncytin-B gene , three PCR-amplified syncytin-B fragments of 414 , 511 , and 370 bp , respectively ( primers listed in S1 Table ) were cloned into pGEM-T Easy ( Promega ) . In vitro synthesis of the antisense and sense riboprobes was performed with SP6 or T7 RNA polymerase and digoxigenin 11-UTP ( Roche Applied Science ) after cDNA template amplification . Sections were processed , hybridized at 42°C overnight with the pooled riboprobes , and incubated further overnight at 4°C with alkaline phosphatase-conjugated anti-digoxigenin antibody Fab fragments ( Roche Applied Science ) . Staining was achieved with the nitroblue tetrazolium ( NT ) and 5-bromo-4-chloro-3-indolyl phosphate ( BCIP ) phosphatase alkaline substrates as indicated by the manufacturer ( Roche Applied Science ) . Mouse primary myoblasts ( female mice ) were isolated from TA muscles of 4 week old mice by enzymatic digestion as described [37] . Myoblasts were grown on collagen-coated dishes in Ham’s F10 ( Life technologies ) growth medium supplemented with 20% fetal bovine serum ( FBS ) , bFGF ( 10 ng/ml , Invitrogen ) , penicillin ( 100 U/ml ) and streptomycin ( 100 mg/ml ) . For differentiation , myoblasts were plated at 105 cells per well in collagen-coated 24-well dishes and differentiation was induced by switching the growth medium to the differentiation medium DMEM ( Life technologies ) with 2% horse serum ( Biowest ) for different times , the day of the induction being referred to as D0 . Dog ( male ) and sheep ( female ) myoblasts were obtained from biopsies of biceps femoris . The biopsy was cut into small pieces , treated with collagenase ( Worthington; 5 ml of collagenase per g of tissue ) and incubated for 1 h at 37°C . Using a syringe and 18G needle , the mix was dissociated and filtered sequentially through 100 and 40 μm sieves . After centrifugation , the cellular pellet was suspended and in the case of dog myoblasts , cells were seeded in Myo1 medium ( Hyclone ) containing 20% FBS , gentamycin ( 25 μg/ml , Sigma ) , bFGF ( 10 ng/ml , Invitrogen ) and dexamethasone ( 1 μM , Mylan ) . Sheep myoblasts were cultivated on collagen-coated dishes in DMEM medium ( Life technologies ) with 20% FBS , bFGF ( 10 ng/ml , Invitrogen ) , penicillin ( 100 U/ml ) and streptomycin ( 100 mg/ml ) . For differentiation , growth medium was replaced with DMEM medium ( Life technologies ) supplemented with 2% horse serum ( Life technologies ) . Primary human myoblasts ( female ) were cultivated in proliferation medium ( 4 vol . of DMEM , 1 vol . of 199 medium , 20% FBS , gentamycin ( 50 mg/ml ) ) . Differentiation was induced by replacing the proliferation medium by DMEM supplemented with insulin ( 10 mg/ml ) . For desmin immunolabeling , cells were fixed for 10 min with 4% formaldehyde and rinsed with PBS . Cells were permeabilized for 10 min with PBS-0 . 1% Triton ( Sigma ) and blocked with PBS-5% BSA ( Sigma ) for 45 min before incubation with antibodies . Primary mouse anti-desmin anti-human antibodies ( clone D33 , Dako ) were incubated overnight at 4°C and revealed using secondary Alexa 488 goat anti-mouse antibodies ( life technologies ) for 1h . Nuclei were stained with DAPI ( 4’ , 6’ diamidino-2-phenylindol ) for 15 min at room temperature . The fusion index was calculated as the number of nuclei within cells with ≥2 nuclei at day 3 of differentiation ( at least 600 nuclei were counted for each condition ) . RNA interference was performed using Lipofectamine RNAi-MAX reagent ( Invitrogen ) according to the manufacturer recommendations . Cells were transfected 2 days before induction of differentiation . All siRNAs , including the siRNA control oligonucleotides , were synthesized by Dharmacon or Eurogentec . SiRNA sequences are listed in S1 Table . The C2C12 mouse myoblast cell line ( ATCC: CRL1772 ) ( female mice ) was grown in DMEM ( Dulbecco’s Modified Eagle medium , life technologies ) supplemented with 10% fetal calf serum ( FCS ) , 100 U/ml penicillin and 100 μg/ml streptomycin . Differentiation was induced by switching the growth medium to differentiation medium ( DMEM with 2% horse serum ( Biowest ) ) for four days . The GFP expression vector peGFP-C3 was purchased from BD Biosciences and the murine myomaker gene in pCMV-SPORT6 . 1 from Dharmacon . The control ( phCMV-none ) and syncytin-A and–B expression vectors were previously described [6] . Transfection of the C2C12 cells was performed using Lipofectamine LTX ( ThermoFischer ) according to the manufacturer’s protocol . Total RNAs were extracted from mouse , sheep and human myoblast cells using Trizol according to the manufacturer’s instructions ( Sigma Aldrich ) . Total RNAs were extracted from dog myoblasts using the nucleospin RNA XS kit ( Macherey Nagel ) according to the manufacturer instructions . The amount of extracted RNAs was evaluated using a NanoDrop spectrophotometer ( Thermo Scientific , Wilmington , USA ) . Syncytin mRNA expression was determined by Real-time RT-PCR ( RT-qPCR ) . Reverse transcription was performed on 500 ng of DNase-treated RNAs ( Ambion ) according to the manufacturer’s instructions . Quantitative PCR was performed on 5 μL of diluted ( 1:5 ) cDNA in a final volume of 25 μL using the SYBR-Green PCR Master Mix ( Applied Biosystems ) in an ABI PRISM 7000 sequence detection system . The parameters used were as follows: 2 min incubation at 50°C , 5 min at 95°C , followed by 40 cycles of repeated incubations at 95°C for 10 s and 60°C for 30 s . For each cDNA sample , duplicates were analyzed and data normalized to the housekeeping genes listed in S1 Table . Histological analysis of the muscle phenotype in 12 week old mice and the muscle regeneration in 8 week old mice was performed on at least 150 myofibers randomly chosen from three different tissue sections . The number of myonuclei was estimated by counting the number of myonuclei in at least 300 myofibers randomly chosen from three different tissue sections per condition . Statistical analyses were performed using the Mann-Whitney test . A p value <0 . 05 was considered as significant ( * p<0 . 05 , ** p<0 . 01 ) . When the number of samples was <4 , Student’s t-test was used and differences were considered significant when p <0 . 05 ( * p<0 . 05 , ** p<0 . 01 ) . For the ex vivo fusion assays , according to the number of samples , a Mann-Whitney test ( n = 4 , for mouse and sheep myoblast cells ) or a Student’s t-test ( n = 3 , for C2C12 , human and dog myoblast cells ) was used and differences were considered significant when p <0 . 05 ( * p<0 . 05 , ** p<0 . 01 ) .
|
Syncytins are “captured” genes of retroviral origin , corresponding to the fusogenic envelope gene of endogenized retroviruses . They are present in all placental mammals in which they have been searched for , where they play an essential role in placentation via their cell-cell fusion activity . Here we show that they also contribute to myoblast fusion and muscle formation in development and repair after injury , using both in vivo knock-out mouse models and ex vivo primary myoblast cell cultures from several mammals , including humans , carnivores and ruminants . Interestingly , the effects observed in mice are sex-dependent , thus suggesting that the added “collateral” effect of syncytins on myogenesis could be responsible for the muscle sexual dimorphism observed in placental mammals .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cell",
"physiology",
"gene",
"regulation",
"vertebrates",
"animals",
"mammals",
"cell",
"differentiation",
"muscle",
"regeneration",
"animal",
"models",
"developmental",
"biology",
"model",
"organisms",
"organism",
"development",
"stem",
"cells",
"bioassays",
"and",
"physiological",
"analysis",
"morphogenesis",
"research",
"and",
"analysis",
"methods",
"muscle",
"analysis",
"small",
"interfering",
"rnas",
"animal",
"cells",
"gene",
"expression",
"mouse",
"models",
"myoblasts",
"biochemistry",
"rna",
"cell",
"biology",
"regeneration",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"non-coding",
"rna",
"amniotes",
"organisms",
"cell",
"fusion"
] |
2016
|
Genetic Evidence That Captured Retroviral Envelope syncytins Contribute to Myoblast Fusion and Muscle Sexual Dimorphism in Mice
|
Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds , various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification . However , most of the model predictions lack direct experimental validation in the laboratory , making their practical benefits for drug discovery or repurposing applications largely unknown . Here , we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model . To evaluate its performance , we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study , and then experimentally tested 100 compound-kinase pairs . The relatively high correlation of 0 . 77 ( p < 0 . 0001 ) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps . Further , we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information . As a specific case study , we used tivozanib , an investigational VEGF receptor inhibitor with currently unknown off-target profile . Among 7 kinases with high predicted affinity , we experimentally validated 4 new off-targets of tivozanib , namely the Src-family kinases FRK and FYN A , the non-receptor tyrosine kinase ABL1 , and the serine/threonine kinase SLK . Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data , and therefore enables rigorous model validation for practical applications . These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds , and for the identification of new target selectivities for drug repurposing applications .
Deregulated kinase activity plays a role in many diseases , hence calling for therapeutic compounds that could effectively inhibit specific members of the protein kinome . Although kinase inhibitors form the largest group of new drugs approved for cancer treatment [1] , a majority of them are ATP-competitive , and therefore present a highly promiscuous mechanism of action ( MoA ) , due to the high evolutionary conservation of the kinase ATP-binding pockets [2 , 3] . The polypharmacological interactions contribute both to therapeutic and toxic responses seen in clinically-approved and investigational kinase inhibitors . Thus , improved knowledge of the complex compound-target binding interactions across the full protein kinome , including both on- and off-target effects , is of high clinical relevance for future drug discovery applications . Recent technological advances in chemoproteomic approaches , such as thermal profiling [4] , have enabled efficient determination of kinome-wide compound potency . Several commercial providers are available for preclinical kinase inhibitor testing in vitro , including DiscoverX , Millipore and Reaction Biology . Even though the experimental compound-target interaction mapping is critical to characterizing a compound’s MoA , computational methods provide a complementary and cost-effective approach with the potential to accelerate the exploration of the enormous size of the chemical universe , estimated to consist of approximately 1020 molecules exhibiting good pharmacological properties [5] . The hypothesis is that in silico models could provide fast , large-scale and systematic pre-screening of chemical probes , toward prioritization of the most potent interactions for further in vitro or ex vivo verification in the laboratory [6–10] . In particular , a lot of work has been devoted to compound-based interaction prediction methods , including quantitative structure-activity relationship ( QSAR ) models , which aim to relate structural properties of the chemical molecules to their bioactivity profiles [11 , 12] . Another class of machine learning methods , so called target-based methods , focus on evaluating similarities between amino acid sequences or three-dimensional structures of protein targets [13] . In these supervised learning approaches , models are trained using available bioactivity data , together with either compound or protein information , which allows then predicting either new target interactions for a given drug or new drugs targeting a given protein . Furthermore , such methods typically focus on a limited set of molecules of interest . As a more recent class of computational modelling approaches , systems-based frameworks take advantage of the information available on both compounds and targets . For instance , Yamanishi et al . proposed a supervised machine learning approach for categorizing drug-target pairs as interacting or non-interacting based on an integrated model of chemical and genomic molecular profiles [14] . Since this seminal work , a wide variety of systems-based prediction methods have been developed that utilize various molecular descriptors and learning techniques , including random forest , neural networks and kernel learning [15–32] . Even though such models may hold a great potential , their computational predictions are rarely being directly verified in the laboratory and , consequently , their practical benefits for the drug discovery or repurposing applications remain largely unknown . Toward testing the practical potential of systems-based machine learning models , we implemented a computational-experimental framework for prediction and verification of compound-target bioactivity profiles ( Fig 1 ) . We focused on a regression problem , where the task is to predict the actual binding affinities , instead of the standard bioactivity classification setting that treats molecular interactions as simple on/off relationships . As a prediction model , we applied a well-established kernel-based regularized least squares learning algorithm ( KronRLS [33] ) , because kernels , in addition to offering a computationally efficient means for increasing the power of linear learning algorithms , are particularly well-suited for capturing and learning complex molecular properties for prediction purposes [34 , 35] . The specific contributions of the work are the following . First , we evaluated a large number of molecular descriptors in the form of kernels , including our novel , extended target profile-based protein kernel , and a generic string kernel that has not previously been used in the context of compound-protein interaction prediction . Second , we show how these kernels guided us in filling the gaps in a large-scale compound-kinase interaction map [3] . Third , we experimentally tested a subset of 100 predicted binding affinities , achieving a high correlation of 0 . 77 between the measured and predicted bioactivities . Finally , we demonstrate the potential of the modelling approach in a more challenging task of predicting target selectivities for such a new candidate compound that has no bioactivity data available for model training . As a specific case study , we used an investigational tyrosine kinase inhibitor tivozanib whose established target profile consists only of 3 on-targets . We experimentally tested and validated 4 out of 7 kinases predicted as tivozanib’s off-targets , providing novel insights into its MoA , and thereby extending the potential therapeutic target space of tivozanib .
A key assumption in the systems-based compound-target interaction prediction algorithm is that similar drug compounds are likely to bind to similar protein targets , and therefore the first challenge lies in the representation and use of molecular similarities in the most predictive way . We encoded here similarities between drugs and similarities between proteins using different types of kernels , constructed based on chemical two- and three-dimensional structures , amino acid sequences , protein structures , and molecular interaction profiles ( see Materials and Methods for details ) . Such systematic construction of chemical and genomic molecular descriptors resulted in 12 drug kernels and 8 protein kernels . To predict compound-protein binding affinities using the regression setup , we applied a regularized least squares ( RLS ) model for each pair of drug kernel and protein kernel ( KronRLS algorithm , see Materials and Methods ) . For computational evaluation of the predictive performance of various molecular descriptors and optimization of model parameters under separate prediction scenarios , we carried out two systematic nested cross-validation ( CV ) procedures , i . e . , leave-one-out cross-validation ( LOO-CV , S12 Fig ) and leave-drug-out cross-validation ( LDO-CV , S13 Fig ) , using 16 , 265 known binding affinities ( pKi values ) between 152 kinase inhibitors and 138 protein kinases measured in a large-scale functional bioassay by Metz et al . [3] ( S9 Fig ) . We applied LOO-CV to tune model parameters and to evaluate its predictive performance when filling experimental gaps in large-scale target profiling studies ( the Bioactivity Imputation scenario , Fig 2A ) , and LDO-CV in the inference of target interactions for a new candidate drug compound ( the New Drug scenario , Fig 2B ) . LOO-CV corresponds to the design where scattered missing values are present in otherwise known compound-protein bioactivity matrix , and the aim is to predict the missing entries within the training data . LDO-CV , on the other hand , simulates more challenging inference problem , in which the aim is to predict targets of an investigational drug compound , not encountered in the training data ( Materials and Methods , Tables 1 and S1 ) . Next , we trained the KronRLS algorithm with 16 , 265 bioactivities between 152 kinase inhibitors and 138 kinases measured in the study by Metz et al . [3] , together with the best-performing under the Bioactivity Imputation scenario drug interaction profile kernel ( KD-GIP ) and kinase domain-based generic string protein kernel ( KP-GS-domain ) . We then used the optimized model to predict the remaining 4 , 711 binding affinities that were missing in this experimentally-measured compound-kinase interaction map ( S9 Fig ) . To assess the model’s practical utility , we experimentally tested a set of 100 predicted binding affinities between 5 drug compounds ( cediranib , lapatinib , gefitinib , pazopanib and vx-745 ) and 20 kinases ( ABL1 , AXL , BRK , BTK , EGFR , FAK , FYN A , HER2 , HER4 , IGF1R , InsR , ITK , JAK3 , KDR , LCK , LYN B , PYK2 , SRC , SYK , TRKA ) . Among these , new potential interactions , not present in the Metz et al . dataset , were predicted for cediranib , lapatinib and gefitinib ( S4 Fig ) . We note that pazopanib presented very high prediction accuracy , despite of having a sparse binding affinity profile available for the model training . On the other hand , vx-745 had no potent activities , either measured or predicted , against any of the kinases , and therefore it served as a negative control in the validation ( S4 Fig ) . We tested the predicted bioactivities using a cell-free ADP-Glo Kinase Assay ( see Materials and Methods for details ) . We observed a relatively high Pearson correlation of 0 . 774 ( p < 0 . 0001 ) between the model-predicted ( pKi ) and experimentally-measured ( pIC50 ) bioactivities among the 100 compound-kinase pairs ( Fig 4A ) . The IC50 readout from our assay , similarly to the inhibition constant Ki in the Metz et al . study , indicates the concentration of the compound needed to inhibit enzymatic activity of a kinase by 50% . Even though IC50 is known to depend on the concentration of the enzyme , inhibitor , and substrate , along with other experimental conditions , whereas Ki is an intrinsic , thermodynamic quantity independent of the substrate [36] , recent studies have shown a sufficiently high level of association between pIC50 and pKi readouts , permitting their reliable comparison [37 , 38] . We also observed a strong technical correlation of 0 . 769 ( p < 0 . 0001 ) between pIC50 readouts from our profiling assay and pKi values measured in the study by Metz et al . ( S5 Fig ) , which supports the feasibility of our experimental validations . Furthermore , based on the published information [39] , the ATP concentration used in our assay ( 10 μM ) is expected to be below , or in some cases equal to , the ATP Km values of the kinases tested , suggesting that the IC50 values should be very close to the respective Ki values . We also compared both the model-predicted and experimentally-measured interaction mapping to the results from another large-scale binding assay by Davis et al [40] . In this study , 72 clinically relevant kinase inhibitors were profiled against 442 kinases , providing , for each compound-kinase pair tested , dissociation constant Kd , indicating the tendency of a larger molecular complex to dissociate reversibly into the component molecules . We again observed a very good agreement ( correlation of 0 . 796 , p < 0 . 0001 ) between the computationally-predicted ( pKi ) and measured ( pKd ) binding affinities across the overlapping 95 compound-kinase pairs ( S5 Fig ) . We noted even a higher technical correlation of 0 . 916 ( p < 0 . 0001 ) between pKd values from Davis et al . study and pIC50 values from our experimental assay ( S5 Fig ) . Notably , a comparison between predicted pKi values and measured pKd readouts across a larger set of 2 , 662 compound-kinase pairs overlapping between Metz et al . [3] and Davis et al . [40] studies resulted in a lower correlation ( 0 . 642 , p < 0 . 0001 , S6 Fig ) , compared to that when considering only the pairs included in our experimental assay ( 0 . 796 , p < 0 . 0001 , S5 Fig ) . A high pKd indicates that a substrate is more likely to be bound to an enzyme , whereas pKi measures the potency of a drug . Even though both high pKi and pKd values are considered as indicators of drug activity , a drug with high pKi does not necessarily result in a high pKd ( S6 Fig ) . As expected , the correlation between the model-predicted and measured by Metz et al . pKi values in the training data ( excluding pairs blinded in the model training , marked with an orange cross points in Fig 4A ) was somewhat higher than that for the missing compound-kinase pairs ( correlation of 0 . 802 , p < 0 . 0001 , S5 Fig ) . Of note , our experimental assay confirmed computationally-predicted high binding affinities between cediranib-KDR , lapatinib-EGFR and pazopanib-KDR , the two first of which were even not measured in the study of Metz et al . Further , although some bioactivities missing in Metz et al . dataset corresponded to compound-kinase pairs already tested in other studies ( e . g . lapatinib-EGFR measured in the assay by Davis et al . ) , these were not used in the training of our model . Taken together , the observed high agreement between the predicted and experimentally-measured bioactivities demonstrates the potential of the kernel-based modeling framework with appropriately chosen kernels for filling the gaps in existing compound-target interaction maps . Finally , we tested whether the optimized model can also predict target interactions for a new chemical probe , which has no profiling data available for the model training . We used here tivozanib as an example of an investigational tyrosine kinase inhibitor , known to be potent towards all three VEGF receptors ( FLT1 , KDR , FLT4 ) [41] . Beyond VEGFRs , however , the target profile of tivozanib has otherwise remained poorly characterized , including its potential off-targets . We therefore again used 16 , 265 binding affinities between 152 compounds and 138 kinases measured in the study by Metz et al . to train the KronRLS model with shortest paths between atoms-based drug kernel ( KD-sp ) and amino acid sequence-based generic string protein kernel ( KP-GS ) , which were found to perform best under the New Drug scenario . Since the model should always be tuned separately under distinct prediction scenarios , even if the training dataset is the same , the model used here and the one described in the previous section differ in their chosen kernels and optimized value of the regularization parameter . With the optimized model , we predicted the bioactivity of tivozanib against the set of 138 kinases ( S3 Table ) . As the first positive control , the model correctly predicted high potency of tivozanib against its known on-targets FLT1 , KDR and FLT4 ( Fig 5A and S3 Table ) . To further assess the quality of the predictions , we used publicly available bioactivity data from the study of Gao et al . who profiled 158 kinase inhibitors , including tivozanib , for their inhibitory activity at 1 μM and 10 μM against 234 kinases [42] . Although the concentrations adopted in this screen were too high for pre-clinical testing of positive interactions , we used these data to evaluate the negative predictions from the model . In total , 64 out of 82 kinases with low predicted affinities ( pKi < 6 M ) were screened by Gao et al . Among these , 59 kinases ( 92% ) have at least 50% of the activity remaining at the high compound concentration of 1 μM ( S3 Table ) , thus effectively validating the model’s negative predictions ( Fig 5B ) . We next went on and tested experimentally 7 predicted off-target interactions ( ABL1 , Aurora A , FRK , FYN A , HIPK4 , RPS6KB1 , SLK ) . These 7 kinases were selected among the set of 25 kinases with the highest predicted binding affinities by focusing on off-targets unique to tivozanib . Specifically , we compared the predicted target interaction profile of tivozanib to other VEGFR inhibitors found in the ChEMBL database [43] . For instance , RET was not selected , even though it was predicted to have high potency towards tivozanib ( pIC50 = 6 . 9 M ) , since it is targeted by 76% of VEGFR inhibitors in ChEMBL ( potency of at most 100 nM ) , whereas FYN A was included in our experimental assay because it is targeted only by 26% of VEGFR inhibitors ( S3 Table ) . We tested the predicted bioactivities using a cell-free ADP-Glo Kinase Assay ( see Materials and Methods for details ) . Among the pre-selected off-target predictions , our experiments confirmed strong binding affinity between tivozanib and 4 out of the 7 tested kinases ( 57% ) , namely FRK , ABL1 , SLK and FYN A ( Fig 5A ) . Statistical significance of this success rate depends on the underlying distribution of the true target space of tivozanib , which is unknown . However , if one assumes that no more than 18 of 138 considered kinases are actual targets of tivozanib ( 13% ) , then the observed overlap is significant ( p < 0 . 05 , hypergeometric distribution ) . The observed correlation of 0 . 668 ( p = 0 . 035 , Fig 5A ) between the predicted and measured binding affinities can be considered relatively high , given the rather limited spectrum of kinases tested and the fact that instead of selecting the top predicted off-targets only , we focused on the set of kinases that were unique to tivozanib among 25 kinases with the highest predicted binding affinities against it . Our experimental results provide not only novel insights into the MoA of tivozanib , but also demonstrate how the in silico framework offers a cost-effective tool for prioritizing the most promising target interactions of an investigational compound for further experimental evaluation .
Recently , a lot of effort has been placed on the development of systems-based machine learning models that could aid drug discovery process in terms of providing cost-effective compound-target bioactivity predictions . Their main differences lie in the way how the models construct and treat molecular descriptors , and utilize various learning techniques , including those based on random forest [16 , 19 , 21] , kernel learning [15 , 22 , 23 , 32] , recommender systems [26] , matrix factorization [20 , 25 , 26] , Boltzmann machines [27] , deep neural networks [16 , 17] , logistic regression [28] , learning to rank [29] , and ensemble learning [30 , 31] . Although such models have been shown to perform well in cross-validation setups , their practical benefits still remain largely unknown due to the lack of systematic verification , using targeted experimental assays , carried out sub-sequent to the prediction phase . In model-guided mapping applications , the validation experiments are performed based on the model predictions . Such experimental validation setup effectively avoids any possible information leakage between the training and validation data , since the validation data does not exist at the time of making the predictions . The computational-experimental approach , implemented in this study , therefore makes it impossible to overfit the model to the training data . Here , we used the approach to evaluate the predictive power of a well-established kernel-based learning technique [33] . We chose this model family since kernel regression approaches have proved good performance in recent computational studies , including prediction of drug-target interactions [32] , peptide-protein binding affinities [44] , drug sensitivities in cancer cell lines [45] , as well as in metabolite identification [46] and QSAR modelling [35] . Taking into account that molecular interactions are not simple on/off relationships , we focused on a binding affinity prediction problem , using the RLS regression model with a Kronecker product kernel ( KronRLS ) . Our systematic evaluation of the predictive performance of various descriptors in the form of kernels revealed that their choice has a critical impact on the prediction accuracy . This is expected because kernel matrix is a central component of the kernel-based learning algorithm as it should capture our prior belief on the relationships between the input objects . In particular , known binding affinities , even if sparse , constitute an important information source not only for model training but also for the kernel matrix construction . Purely structure-based chemical descriptors were not able to fully capture the changes in compounds’ activity caused by minor structural differences . Furthermore , we introduced a novel protein kernel ( KP-SW+ ) , based on extended target profile , and showed how it consistently outperformed its commonly-used counterpart , in which the Smith-Waterman amino acid sequence alignment is adapted exclusively to proteins included in the dataset of interest ( KP-SW ) . This was evident particularly under the New Target setup ( Fig 2C ) , where the aim is to predict compounds targeting a new protein not encountered in the training data ( correlation of 0 . 669 for KP-SW+ vs . 0 . 506 for KP-SW; S7 Fig ) . Under the same setting , we also observed a clear advantage of using , for the first time in the context of drug-protein interaction inference , generic string kernel applied to kinase domains and ATP-binding pockets over full protein sequences ( correlations of 0 . 651 for KP-GS-domain , 0 . 628 for KP-GS-atp , 0 . 508 for KP-GS; S7 Fig ) . The majority of kinase inhibitors , including those considered here , bind to ATP-binding pockets , and short sequences of these pockets are included within the kinase domain sequences , thus capturing also the neighbouring context . However , polypharmacological activities of kinase inhibitors , originating from the conservation of kinase ATP-binding pockets , make the prediction problem highly challenging , and better accuracies are likely obtained with compounds having more distinct target profiles . The methodology introduced here could equally well incorporate other compound and protein classes , such as ion channels or G-protein-coupled receptors ( GPCRs ) , but further work will be required to investigate its practical performance under various scenarios using both computational and experimental validations . For instance , it remains an open question which kernels should be calculated to best represent such extended pharmacological spaces . Our results also demonstrate the importance of a proper evaluation procedure of the in silico models . A rigorous computational CV protocol is critical to ensure realistic performance estimates for the optimized models . In particular , the lack of the nested CV strategy in the model selection may lead to over-optimistic prediction results [32] . It is also important that CV design reflects the practical application use case of the model . Given a query drug-protein pair ( dx , px ) , four different prediction scenarios can be distinguished , depending on whether there exist other compounds with measured bioactivities against px , or proteins with measured bioactivities against dx ( Fig 2 and S1 Table ) . In turn , different types of CV designs need to be implemented in order to tune the model parameters and to evaluate its predictive performance . Here , we focused on the two most common and practical scenarios of the Bioactivity Imputation ( Fig 2A ) and New Drug ( Fig 2B ) . Additionally , we provided the CV results under the New Target setup ( Fig 2C ) in S7 Fig . We first adopted LOO-CV , a design that simulates scattered missing values in otherwise known compound-target bioactivity map , to optimize the model and asses its performance in filling the experimental gaps ( Bioactivity Imputation ) . Next , LDO-CV was applied in predicting target interactions for new candidate compounds having no measured bioactivity data for the model training ( New Drug ) . The latter is much more challenging task , which was also demonstrated in our results; the correlation between the measured and predicted binding affinities under the Bioactivity Imputation setup was much higher ( 0 . 829 ) than under the New Drug scenario ( 0 . 653 ) . However , even in the latter scenario , we still obtained a large number of statistically significant correlation values ( S3 Fig ) . We further observed a high average AUC values under the Bioactivity Imputation setup ( 0 . 945 , S2 Fig ) , but also under the New Drug prediction scenario ( 0 . 853 , S2 Fig ) , which indicates that the model is able to discriminate well the interacting from non-interacting compound-kinase pairs . As expected , the classification accuracy increased with the increasing activity threshold as the true positive set includes a growing number of most likely interactions . In practical applications , the method requires features extracted from both compounds and proteins , such as readily available chemical two-dimensional structures and amino acid sequences , respectively , based on which kernels can be then calculated . However , if one is interested in a bioactivity of an investigational drug against a protein with unknown sequence , a reasonable prediction accuracy can still be achieved if there exist other compounds with measured bioactivities against a query protein ( the New Drug setup ) . The practical solution is to replace the protein kernel matrix with an identity matrix , which implies that each protein will be considered similar to itself only and , effectively , the model will use just known bioactivity data and drug-drug similarities during both training and prediction phase . In particular , we noted only a small drop in Pearson correlation after replacing the protein kernel ( KP-GS ) with the identity matrix ( 0 . 645 vs . 0 . 653 ) . This observation is expected in multitask or transfer learning problems , such as the New Drug or New Target setups , where one of the similarities is essential for generalizing to new instances ( drug-drug similarities under the New Drug setting , protein-protein similarities under the New Target scenario ) . Ultimately , even though proper CV design is crucial to tune the model and assess its performance , subsequent experimental verification in the laboratory is the only way to really demonstrate the practical utility of the model predictions for drug discovery applications . The relatively good agreement between the computationally-predicted and experimentally-measured bioactivities validated the potential of the kernel-based algorithm , not only for filling the experimental gaps in existing drug-target interaction maps , but also in later stages of the drug development process , including prioritizing new target interactions of investigational compounds for further experimental evaluation , hence assisting in understanding of their MoA . Even though in silico inference of target interactions for new candidate drug compounds is a highly challenging task , our results with tivozanib suggest that , given enough-representative and high-quality training data , reliable off-target interaction predictions can be made . In addition to tivozanib , we initially considered also three other investigational kinase inhibitors , namely fedratinib , vx11e and ulixertinib ( a compound derived from vx11e ) . However , we finally selected tivozanib because , unlike for the other compounds , its known on-targets were placed among the strongest predicted target interactions ( S3 Table ) . This indicates that the primary on-target space of tivozanib is well-represented in our training data ( S8 Fig ) . In the future , it is therefore important to profile and build up more diverse training data sets , including more examples of compounds targeting different kinase and other target classes . Tivozanib was originally-developed as a VEGFR inhibitor meant to block angiogenesis by targeting endothelial cells in the tumor vasculature . However , its MoA has not yet been fully elucidated . Based on the model predictions , our experimental assay confirmed the two Src family kinases FRK and FYN A , as well as the non-receptor tyrosine kinase ABL1 and serine/threonine kinase SLK as tivozanib’s off-targets . Our results highlight that tivozanib has an unusual target spectrum beyond the VEGFR family of kinases , and this suggests that the best anti-cancer use of this compound may not be in diseases where other VEGFR inhibitors with different target profiles have proven effective , but rather in ones where the target spectrum of tivozanib is more unique . For example , it can be hypothesized that tivozanib may have powerful activity in Src-family kinase addicted cancers , where it would target both angiogenesis and the cancer cells directly . Tivozanib has been shown to have a better safety profile than other marketed tyrosine kinase inhibitors and , currently , it is undergoing several clinical trials for the treatment of renal cell carcinoma ( NCT03136627 ) , refractory advanced renal cell carcinoma ( NCT02627963 ) , metastatic and non-resectable soft tissue sarcomas ( NCT01782313 ) , advanced liver cancer ( NCT01835223 ) , recurrent ovarian , fallopian tube , or primary peritoneal cancer ( NCT01853644 ) , and advanced prostate cancer ( NCT01885949; July 2017 ) . It will be interesting to see which of these trials will report successful treatment outcomes , and which will be terminated due to insufficient efficacy or toxicity . Although presented here results are promising , there is much room for improvement . For instance , we formulated the predictive model using only one pair of chemical and genomic descriptors at a time . However , even better accuracies could be obtained with a multiple kernel learning framework , which integrates multiple biological and molecular data sources , along with learning their importance for the prediction task [47] . Additional improvement of the predictive performance could be achieved also by creating more sophisticated kernelized molecular descriptors , for instance , by comparing three-dimensional structures of protein binding pockets . Furthermore , we used here as the training data a single yet very comprehensive kinase inhibitor profiling assay containing large number ( 16 , 265 ) of measured compound-kinase binding affinities , spanning different kinase branches ( S11 Fig ) . However , as a future direction , we plan to work on integrating bioactivity values originating from various target profiling experiments and bioactivity end-points into a single model [3 , 19 , 40 , 48] . Recently-initiated community-driven efforts , such as Drug Target Commons ( https://drugtargetcommons . fimm . fi ) , which aim to collectively extract , manage and curate high-quality compound-target bioactivity data from public databases , literature and other resources , as well as annotate them with a common ontology , will be essential to facilitate the data standardization and computational modelling purposes . Nevertheless , we hope the current work provides a useful starting point and a practical guide on how to computationally prioritize the most promising target interactions for further experimental evaluation .
We used publicly available compound-target interaction map generated by Metz et al . using a large-scale functional bioassay , which measured the concentration of a compound needed to inhibit the reaction catalysed by a kinase enzyme of interest by 50% [3] . The readout corresponds to an inhibition constant Ki , typically expressed in the logarithmic scale as pKi = -log10Ki . Although the universal activity threshold cannot be explicitly defined for each compound-kinase pair , the higher the pKi value , the stronger the binding affinity between the compound and kinase . Among molecules included in the screen , 201 compounds are present in ChEMBL [43] , and 169 proteins belong to the group of catalytically active human protein kinases [37] . The study is not complete , and therefore we used here a subset of these data: kinases and compounds for which at least 30% of the binding affinity values were measured , resulting in 152 drug compounds and 138 kinase targets . In total , there are 16 , 265 binding affinities in this selected interaction map ( S9 and S10 Figs ) . Of note , most of the compounds constitute investigational , not yet FDA-approved , chemical probes . In supervised learning tasks , training data has the form { ( xi , yi ) }i=1N , where N denotes the number of training examples , xi ∈ X is an input object represented as a vector with the feature values ( e . g . a compound represented as a fingerprint vector ) and yi ∈ Y is its known associated label value ( e . g . a potency of a compound against a certain protein ) . The aim is to find a prediction function f that models the relationship between xi’s and yi’s , and which can then be used to predict the label values for new instances outside the training space . Classical algorithms search for linear dependencies but often the actual relations underlying the data are highly nonlinear . Kernels offer the advantage of increasing the power of the linear learning machines by providing a computationally efficient way of projecting the input data into a high-dimensional feature space . A linear model in this implicit feature space corresponds to a nonlinear model in the original space . A separation of the statistical learning technique and the data representation is another convenient attribute of kernels . Formally , a kernel is a function k that for all x , z ∈ X satisfies k ( x , z ) = ⟨ϕ ( x ) , ϕ ( z ) ⟩ , where ϕ is a mapping from the input space X to an inner product high-dimensional feature space F: ϕ: x ∈ X → ϕ ( x ) ∈ F , and it can be considered as a similarity measure between two objects x and z . It is , however , often possible to avoid the explicit computation of the mapping ϕ , and define the kernel directly in terms of the original input data items by replacing the inner product ⟨∙ , ∙⟩ with an appropriately chosen kernel function satisfying certain mathematical properties ( so-called kernel trick ) . Kernels are particularly handy for calculating similarities between structured objects , including molecules . Here , we focused on a regression problem with the objective of predicting real-valued compound-target binding affinities . We used Kronecker regularized least-squares model ( KronRLS ) [33 , 49] , a special variant of kernel ridge regression ( KRR ) which combines linear least squares with L2-norm regularization ( ridge regression ) and the kernel trick [50] . In KRR , given a set of N compound-protein pairs as training inputs xi’s , i = 1 , … , N , and associated labels yi’s indicating binding affinities between them , we aim to find the minimizer of the following objective function J: J ( f ) =∑i=1N ( f ( xi ) −yi ) 2+λ‖f‖k2 , ( 1 ) where f indicates the prediction function , f ( xi ) is the predicted binding affinity of ith compound-protein pair xi , λ denotes a regularisation parameter controlling the balance between training error and model complexity ( λ > 0 ) , and ‖f‖k is a norm of f on the space associated to kernel function k . In Eq ( 1 ) , the first term corresponds to the training error , and the second , controlled by λ , is the penalty term that is larger for complex models that are more likely to overfit to training data but not generalize well to new instances . According to the representer theorem [51] , the prediction function that minimizes J ( f ) can be expressed in terms of linear combination of the training examples: f ( x ) =∑i=1Nαik ( xi , x ) =αTk , ( 2 ) where k is a vector with kernel values k ( xi , x ) between each training point xi and test point x for which the prediction is made . The squared norm of f is therefore written as ‖f‖k2=∑i=1N∑j=1Nαiαjk ( xi , xj ) . ( 3 ) A vector α , consisting of parameters αi that define the solution to KRR , is found by solving the following system of linear equations ( K+λI ) α=y , ( 4 ) where I is the N×N identity matrix , and y is the vector consisting of labels yi . K denotes N×N pairwise kernel matrix constructed for all training examples x1 , x2 , … , xN , and thus containing similarities between all compound-protein pairs . However , the size of K makes the training of the model computationally very heavy , even for moderate number of compounds and proteins . KronRLS is a special variant of KRR , where one assumes each data point xito consist of two separate parts , such as compound and protein , each equipped with its own kernel function , which enables to speed up the model training . Indeed , pairwise kernel K is computed as the Kronecker product of compound kernel KD of size nD×nD and protein kernel KP of size nP×nP ( N = nD×nP ) : K=KD⨂KP . ( 5 ) Using Eq ( 5 ) , the solution to KronRLS can be calculated from: α=vec ( UPCUDT ) , ( 6 ) where vec ( · ) is the vectorization operator that arranges the columns of a matrix into a vector , UD and UP are orthogonal matrices with eigenvectors of drug kernel KD and protein kernel KP , respectively: KD=UDΣDUDT , ( 7 ) KP=UPΣPUPT , ( 8 ) and C is a matrix for which it holds that: vec ( C ) = ( ΣD⨂ΣP+λI ) −1vec ( UPTYTUD ) . ( 9 ) Here , ΣD and ΣP denote diagonal matrices containing eigenvalues of KD and KP . Label matrix Y stores binding affinities between nD drug compounds ( rows ) and nP protein targets ( columns ) . This way , we completely avoid the computation of the large pairwise kernel K , and therefore significantly shorten the training time . After applying the well-known property of the Kronecker product , ( A⊗B ) vec ( D ) = vec ( BDAT ) , the prediction for test point x can be calculated as f ( x ) = ( kD⨂kP ) α= ( kD⨂kP ) vec ( UPCUDT ) =kP ( UPCUDT ) kDT . ( 10 ) Of note , the above shortcuts work only if there are no missing values present in the label matrix Y . Thus , as the pre-processing step , we imputed the missing binding affinities in Y by the weighted row ( compound ) average . The contribution of each protein was weighted by its similarity ( normalized Smith Waterman score ) to the protein for which the binding affinity was missing . Such imputed values were discarded when assessing the predictive performance of the model . The implementation of KronRLS is available at https://github . com/aatapa/RLScore . We tuned the regularization parameter λ of KronRLS algorithm using nested CV ( Table 1 ) . We computed several types of drug compound and protein target molecular descriptors in the form of kernel matrices KD and KP , respectively . The summary is presented in Fig 3C . We used nested cross-validation ( CV ) procedure for model selection , and we assessed the predictive power with Pearson correlation and root mean squared error ( RMSE ) between original and predicted pKi values . In k-fold CV , the dataset is randomly divided into k subsamples of equal size , and the model is trained based on k-1 of them ( training data ) . Then , the remaining subsample ( test data ) is used to assess how well the model that has been found generalizes to new instances , i . e . to calculate the predictive performance . The procedure is repeated k times , such that each subsample is used once as the test data , and the average error over the k folds gives the final estimate . Nested CV consists of two loops , outer and inner one . In the outer CV loop , each of the k folds is kept as a test set at a time . The remaining k-1 training folds of the outer CV loop are further divided into training and test set of the inner CV loop . Here , the inner CV was performed during each round of the outer CV , with the aim of selecting the regularization parameter λ of KronRLS algorithm as well as kernel parameters ( S4 Table ) . We performed a grid search in order to select the most suitable combination of all parameters . Then , training folds of the outer CV loop were used to train the model with selected parameters , and the predictive performance was evaluated on the test set . We applied two different CV strategies , i . e . leave-one-out nested cross-validation ( LOO-CV ) and leave-drug-out nested cross-validation ( LDO-CV ) , summarized in Table 1 and S12 and S13 Figs . We note that it is critical to discard binding affinities of compound-protein pairs belonging to the test fold of both inner and outer CV prior to computing Gaussian interaction profile kernels ( KD-GIP , KP-GIP ) in order to avoid significant model overfitting . Here , we used interaction profile kernels with LOO-CV , and we removed from the compound-protein interaction matrix Y the whole column ( row ) containing the test point before computing KD-GIP ( KP-GIP ) kernel .
|
Significant efforts have been devoted in recent years to the development of machine learning models to support different stages of drug development process . Given the enormous size of the chemical universe , such models could offer a complementary and cost-effective means to experimental determination of drug-target interactions , toward prioritization of the most potent ones for further verification in the laboratory . In order to demonstrate the benefits of the prediction models in practical application cases , we carefully evaluated the predictive power of a well-established machine learning model in filling the gaps in existing profiling studies and prediction of target interactions for a new drug candidate . As a specific case study , we focused on kinase inhibitors , which form the largest class of new drugs approved for cancer treatment , but are also known to have wide multi-target activities contributing both to their therapeutic and toxic responses . The high agreement observed between the predicted and experimentally-measured drug-target bioactivities under the implemented rigorous validation setup demonstrates the potential of the machine learning approach , not only for filling the gaps in existing drug-target interaction maps , but also toward off-target interaction prediction for investigational drugs , and finding potential new uses for already approved drugs ( drug repurposing ) .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"protein",
"interactions",
"applied",
"mathematics",
"enzymology",
"simulation",
"and",
"modeling",
"algorithms",
"mathematics",
"forecasting",
"statistics",
"(mathematics)",
"artificial",
"intelligence",
"pharmacology",
"molecular",
"biology",
"techniques",
"enzyme",
"inhibitors",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"computer",
"and",
"information",
"sciences",
"sequence",
"alignment",
"bioinformatics",
"proteins",
"mathematical",
"and",
"statistical",
"techniques",
"statistical",
"methods",
"molecular",
"biology",
"biochemistry",
"kinase",
"inhibitors",
"kernel",
"methods",
"database",
"and",
"informatics",
"methods",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"drug",
"interactions",
"protein",
"sequencing",
"machine",
"learning"
] |
2017
|
Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors
|
We present a model for flicker phosphenes , the spontaneous appearance of geometric patterns in the visual field when a subject is exposed to diffuse flickering light . We suggest that the phenomenon results from interaction of cortical lateral inhibition with resonant periodic stimuli . We find that the best temporal frequency for eliciting phosphenes is a multiple of intrinsic ( damped ) oscillatory rhythms in the cortex . We show how both the quantitative and qualitative aspects of the patterns change with frequency of stimulation and provide an explanation for these differences . We use Floquet theory combined with the theory of pattern formation to derive the parameter regimes where the phosphenes occur . We use symmetric bifurcation theory to show why low frequency flicker should produce hexagonal patterns while high frequency produces pinwheels , targets , and spirals .
Ever since they were first described by Jan Purkinje in 1819 , the swirling geometric visual patterns brought on by diffuse flickering light have fascinated both scientists and artists . Helmholtz described the patterns at the turn of the twentieth century . The invention of the stroboscope enabled investigators to classify conditions in which they occurred , including , the interactions with hallucinogens . In several papers , Smythies [1] , [2] provided detailed accounts of the visual patterns reported by subjects when stimulated over a wide range of frequencies . Knoll [3] studied the interactions between stroboscopic illumination and the hallucinogens , lysergic acid diethylamide ( LSD ) , mescaline , and psilocybin . A concise history of flicker phosphenes along with their influence on the arts is provided in [4] . The recent documentary Flicker focuses on the artistic endeavors of Brion Gysin and his Dream Machine , a version of a strobe that is powered by a 78 RPM record player . The first attempts to quantify conditions which can produce flicker phosphenes are described in two papers by Remole [5] , [6] . These showed that there is a range of frequencies between 10 and 40 Hz in which geometric patterns are perceived . Remole looked at the perception as a function of the luminance and frequency and found a peak sensitivity at 15–20 Hz . He also studied how the patterns depend on the color of the light . Recently , Becker and Elliott [7] revisited this work but , in addition , included subjective descriptions of the patterns and their frequency dependence . Figure 3 in [7] of their paper depicts histograms for the occurrence of patterns as a function of the frequency . At 20–30 Hz , their subjects report spirals , waves , radials ( targets ) , and lines . At 10 Hz , zigzags , honeycombs , and rectangles are reported . In most cases , the different classes of patterns are reported over a broad range of frequencies . Billock and Tsou [8] discuss pinwheels and targets induced by flicker in human subjects by stabilizing the patterns with a small low-contrast “seed” pattern at the center of fixation . They quantified spatial aspects such as the number of spokes on the pinwheels . Allefeld et al [9] sweep through a range of frequencies from 1–50 Hz and record subjective impressions from subjects . They find that subjects have a fairly stable range of frequencies at which they report subjective patterns and that within subjects , the form of the patterns is consistent . A recent review [10] provides a comprehensive summary of the literature on geometric visual hallucinations including a large section on flicker phosphenes . Based on earlier models of hallucinations [11]–[13] , we suggest that the simplest geometric patterns during flicker have their origin in primary visual cortex . Herrmann [14] recorded visually evoked electroencephalograms of subjects exposed to flicker from 1–100 Hz and found strong resonances at 10 , 20 , 40 , and 80 Hz . Herrmann also remarks that at some of these resonance , subjects report geometric hallucinations . The work of Ermentrout and Cowan [11] was the first to suggest that patterns perceived during the early stages of drug-induced visual hallucinations were a consequence of a loss of stability of the excitatory and inhibitory network comprising the primary visual cortex . This work has been generalized to include other patterns by Bressloff and collaborators [12] , [13] . Dahlem and Chronicle [15] created computational models of spontaneous cortical patterns in the context of migraine auras while Henke et al [16] study stationary and moving patterns of activity in a cortical population model . There have been only a few attempts to explain flicker patterns . Knoll [3] describes a vague model that seems to be related to resonance . Stwertka [17] reviews the literature on flicker phosphenes and proposes that they can be viewed as “dissipative structures . ” That is , they arise as spontaneous patterns formed through bifurcations and instabilities of the cortical network . However , there was no specific model or mechanism proposed in this review . Drover and Ermentrout [18] describe a model for a periodically driven neural network which is capable of producing slowly evolving line-like contours . These patterns were presumed to reside in the retina ( rather than in the cortex ) and require , in addition to the periodic drive , an additional transient stimulus . Wilson and Cowan [19] show period-doubling ( called “frequency demultiplication” in their paper ) in the Wilson-Cowan equations when stimulated at 40 Hz , but did not mention any spatial effects . Our goal in this paper is to propose a computational and theoretical model for the spontaneous formation of geometric patterns in the presence of flickering light . We first propose a model for a spatially distributed network of excitatory and inhibitory neurons where each neuron is represented by its firing rate [19]–[21] . We simulate one- and two-dimensional ( in space ) versions of the network and demonstrate that patterns are found only at specific frequencies . We examine the global dynamics of a small network and show dynamics and bifurcations similar to those in the full spatially distributed systems . We next analyze the dynamics of the model by studying the linear stability . We use methods from Floquet theory to compute the boundaries in frequency-contrast space for which there are patterns . We use symmetric bifurcation theory to then explain why some patterns are seen at low frequencies and others at high frequencies . We then discuss some generalizations of the present model toward more realistic networks and stimuli . We close with a discussion of the relationship of these patterns to other types of pattern formation and how to experimentally test some of the ideas .
We utilize a variant of the Wilson-Cowan equations [19] , [20] to simulate the effect of flicker on a spatial neural network . The general model takes the form: ( 1 ) ( 2 ) is the activity of a population of excitatory ( ) or inhibitory ( ) neurons at a spatial location . ( Note , that this is often erroneously called the firing rate; the in the equation is the firing rate so that is the low-pass filtered firing rate or “activity” , see [22] ) is the conversion factor from input to firing rate of the population . are the time scales of the excitatory and inhibitory activity . The parameters are the maximal connection strengths from population to population The notation denotes a spatial convolution of with in order to include coupling between neighboring units in one and two spatial dimensions . The domain is either a line segment ( in one dimension ) or a square in two-dimensions . We take: For simplicity , and to avoid edge effects , in our simulations , the boundary conditions are periodic . For most of the paper , we fix parameters to be , , , , The stimulus has the formwhere is the unit step function , is the magnitude , and is the period in milliseconds . Time constants are also in milliseconds . For some of the numerical bifurcation and stability analysis , we make a smooth approximation of the step function , Parameters for the equations are chosen so that in absence of the stimulus , there is a single asymptotically stable equilibrium point for both the full spatial model and for the homogeneous equations . In the latter case , the stable equilibrium exhibits damped oscillations with a frequency of about 13 Hz . The choice of parameters is not arbitrary and the 13 Hz damped oscillations play a crucial role in the emergence of pattern formation . We remark that this frequency is in the range of the scintillation rate of migraine headaches [23] , a pathology that is often associated with spontaneous phosphenes . In the last section of the results , we couple two such two-dimensional networks to represent the left and right hemifields of the visual cortex . Coupling is achieved as follows . Let and denote the excitatory activity of the left and right networks . Then terms likeare replaced by When the two are uncoupled . Analysis of the linearized equations about the oscillatory homogeneous state ( for , the homogeneous state is not constant ) , is performed by numerically solving for the monodromy matrix and using this to determine stability ( see Results section ) . We ultimately use continuation with AUTO [24] ( implemented within [25] ) to compute stability diagrams which are compared to the simulations .
The phosphenes reported by subjects vary tremendously , but among them are the commonly seen so-called form constants ( Klüver , 1960 ) , which are simple regular geometric patterns . These include spirals , targets , light rays , honeycombs , and checkerboards . Figure 1 illustrates idealized versions of many of the reported patterns during flicker stimulation . Figures 1B , C are very typical and are the phosphenes reported by [8] when the visual system was stimulated at 15 Hz as well as by [26] over a range of frequencies between 15 and 20 Hz . Spirals ( A ) and honeycombs ( possibly figure 1E ) were also reported in this frequency range . “Rectangles” ( possibly interpreted as the checkerboard pattern , ( D ) ) were reported to occur at lower frequencies ( around 10 Hz ) . Remole [6] quantified the appearance of flicker patterns as a function of both frequency and magnitude of the stimulus . He was rather nonspecific about all the patterns but does mention “clusters of geometric shapes arranged like honeycombs . ” He states that the patterns that emerge from binocular stimulation could be “subdivided further in terms of geometric characteristics” , but does not specify them . However , he takes quantitative data from three subjects over a range of frequencies from 5–40 Hz . He plots the minimum luminance required to elicit a pattern for these frequencies . In two of his subjects , there is a single minimum value for the threshold with binocular stimulation at about 20 Hz . The third subject shows two threshold minima , one at 10–11 Hz and the other at 24 Hz . There is a well-known topographic mapping from retinal coordinates to cortical coordinates ( [27] p129 ) that is roughly the complex logarithm . That is , a point in polar coordinates on the retina is mapped to in Cartesian coordinates in the cortex . This means that , for example , the target in figure 1C perceived on the retina is mapped to a series of vertical stripes in the cortex . The other patterns in figure 1 are similarly mapped to simple doubly periodic patterns in the cortex . Ermentrout and Cowan [11] and later Bressloff et al [13] used this same argument in order to explain visual patterns during mescaline hallucinations . Thus , our goal in the remainder of the paper is to determine the types of patterns that are expected in one- and two-dimensional domains during flicker . The main consequences of this topographic mapping can be summarized as follows: ( i ) target patterns appear as vertical stripes in cortical coordinates , ( ii ) pinwheels appear as horizontal stripes , ( iii ) spirals as diagonal stripes , and ( iv ) honeycomb/hexagon/checkerboards appear as distorted versions of themselves . Thus , for example , if there are vertical stripes of activity on the cortex , the subject will perceive a target with finer structure near the fovea . We begin with simulations of a one-dimensional domain since it is much easier to visualize the spatio-temporal dynamics . Figure 2 shows example simulations when the excitatory population is stimulated by periodic pulses of fixed amplitude but varying period . At high frequency stimuli ( periods between 40 and 60 msec ) , the medium breaks up into standing oscillations in which a population at any given spatial location fires on every other cycle . Note that the pattern on one cycle is shifted half a spatial cycle on the next temporal cycle . The overall spatial frequency increases with the temporal period within the high frequency region . That is , higher frequency temporal stimuli yield lower frequency spatial responses . The patterns seen here are for a periodic spatial domain; other “boundary conditions” produce similar patterns . The right panel shows two patterns with low frequency stimuli . The patterns show similar spatial dependence in that as the period increases , within this range of long period forcing , the spatial frequency of the pattern increases . More , importantly , the simulations show an important qualitative difference between low and high frequency forcing . For high frequency ( short period ) forcing , the network responds with a period that is twice the forcing period . Furthermore , there is a clear symmetry in that after one cycle , the background is the foreground and vice versa . For long period ( low frequency ) forcing , no such symmetry exists and the network responds in a 1∶1 fashion with the stimulus . The difference in symmetries between the two responses to forcing has important consequences for the two-dimensional model as we will next see . Figure 3 shows a phase-diagram for the dynamics of the one-dimensional network . The gray scale shows the quantity If for all , there is no pattern and The time window , , is chosen to be sufficiently long so that many cycles are averaged . There is a limited region for which pattern formation takes place which takes the form of two islands: a low-frequency ( long period ) cluster and high-frequency ( short period ) cluster . Two dimensional simulations reveal some striking differences . Figure 4 shows patterns seen in a simulation on a grid . The top row contains examples with a period of 55 and 60 msec . Unlike the one-dimensional simulations , there is multi-stability . For example with a 55 msec stimulus , vertical , diagonal , and horizontal ( not shown ) stripes are all possible patterns ( corresponding to target , spiral , and pinwheel perceptual patterns ) . Similarly , at 60 msec , two types of diagonal stripes appear . Like the high frequency one-dimensional patterns , the two-dimensional simulations also have a period that is twice that of the forcing stimulus . After one cycle of the stimulus at 55 msec , the left upper pattern looks exactly the same except they are shifted by one half of a spatial cycle so that the yellow background is now the blue foreground and vice versa . The pattern is thus a standing wave in which the foreground and background are perfectly symmetric and alternate with each stimulus . The alternation between the stripes would possibly be perceived as motion and thus , we speculate that what would be seen is an expanding or pulsating target pattern ( for horizontal stripes ) or a rotating or possibly rocking pinwheel ( for vertical stripes ) . In almost all simulations , we see stripe-like patterns with high-frequency stimuli . This facet of the model is compatible with the psychophysical observations of [7] as well as [8] , [28] . We also see that the spatial frequency of the pattern with a period of 60 msec is higher than that with a period of 55 msec as was seen in the one-dimensional models . The lower row of figure 4 shows time slices of the pattern at low forcing frequencies . Unlike the high-frequency stimulation , the pattern has exactly the same period as the stimulus . That is , each spatial point fires in a 1∶1 manner with the stimulus . The patterns seen are almost always hexagonal and the foreground and background are not simple spatial shifts of each other; they are distinctive patterns . The perception would be like figure 1E ( left ) where the foreground and background pulsate on and off alternately . Finally , the larger period stimuli produce patterns with higher spatial frequency . Smythies [29] reported a result that is opposite our simulations ( lower frequencies gave him coarser patterns ) , but this result has never been replicated . In sum , the simulations show that at low forcing frequencies ( in the range of 8–12 Hz ) , the patterns are primarily hexagons . Figure 5 shows frames from a simulation at various time points over one cycle of stimulation . In 5A , the period is 55 msec ( 18 . 2 Hz ) and after one cycle of 55 msec , the pattern activity is shifted by one half of a spatial cycle . Thus , the whole cycle of firing takes 110 msec or double the forcing period . In contrast , the simulation in figure 5B shows a period identical to that of the forcing stimulus . However , there is no interchange of the background and foreground like there was in panel A . Figure 6 shows a two-parameter phase-diagram analogous to figure 3 . Each small square is a simulation of a network forced at an amplitude given by the vertical coordinate and period given by the horizontal . As with one spatial dimension , there are two islands of pattern formation . In the short period ( high frequency ) island , most of the patterns are stripe-like ( including labyrinthine patterns ) while in the long period ( low frequency ) island , the patterns are dominated by hexagons . In sum , the simulations show ( i ) high frequency stimulation tends to lead to stripes; ( ii ) low frequency tends to lead to hexagonal patterns; and within each frequency band , the higher frequencies have coarser spatial structure . We lastly remark that the two different regimes are reminiscent of Remole’s observations that one subject had two resonance regions at periods of 90 msec and 42 msec . Our goal in the remainder of this paper is to better understand the reasons for these observations . Before turning to the analysis of the spatially distributed domains , we first consider a very reduced system . Suppose that there are two E-I pairs: ( 3 ) We assume similar equations for Note that the parameters lie between 0 and 1 and determine how strong the interactions between the two pairs are . They are the analogs of the spatial coupling in the one- and two-dimensional networks . Using exactly the same parameters as in the spatial models and with , we can perform a similar numerical analysis . Figure 7B shows the phase-diagram for this system as the amplitude of the stimulus and the period vary . As with the spatial models , there are discrete regions where patterns occur . The phase diagram is created by integrating the dynamics forward in time and thus provides only the stable dynamics for a particular initial condition . To get a better picture the full dynamics , we fix the amplitude at and vary the period using AUTO to continue the periodic orbit . The red line in panel B is a fixed amplitude slice through the phase diagram in which only the stimulus period varies . At this value of , we see that the red line passes through four regions . ( The second and third region are part of a contiguous part of the phase-diagram . ) We start at the high-frequency ( low period ) 25 Hz ( 40 msec ) stimulus where equation ( 3 ) respond in a synchronous 1∶1 manner . We use AUTO to continue this solution as the frequency decreases ( period increases ) . Figure 7C shows a summary of the numerical continuation of these periodic orbits . The first bifurcation at ( marked a ) results in a period doubling bifurcation of the symmetric solution; that is , both networks fire synchronously . This period doubled solution then becomes unstable through an anti-symmetric period doubling bifurcation ( marked b ) resulting in a patterned state in which the two networks oscillate out of phase . The whole cycle is four times the period of the stimulus . Figure 7A1 shows the trajectory of the two excitatory cells at a typical point in this parameter regime . The forcing period is 36 msec , but the full cycle is 144 msec . As the stimulus period increases , this pattern disappears through another period doubling bifurcation which again joins with the period one symmetric solution . The next pair of instabilities occur at the points labeled c and d in figure 7C and arise as a period-doubling bifurcation of the symmetric period one state . Unlike the first period doubling bifurcation ( at point a ) , both of these are anti-symmetric and lead to the patterned state in which each unit has the same temporal dynamics that is twice the forcing period and shifted by a half cycle . Figures 7A2 , 3 show the temporal profiles of which are just one forcing period shifts of each other . Finally , at the longest periods there is a bifurcation ( marked e ) to a patterned state that is not symmetric and occurs at a +1 Floquet multiplier . In this pattern , as seen in Figure 7A4 , one unit is suppressed and the other active . As this system is symmetrically coupled , the bifurcation at point e is a pitchfork and the other branch of the pitchfork is a state in which is suppressed and dominates; in other words , the red and black curves are reversed in figure 7A4 . The phase diagram , figure 7B indicates a weak pattern at , but this is not evident in the bifurcation diagram in panel C . At the point labeled f in panel C , the Floquet multiplier is very close to -1 , but remains inside the unit circle . Thus , the synchronous state is stable but weakly so . The apparent pattern in panel C for is most likely an artifact of the numerical integration of the equations . In sum , even with as few as two units , the overall dynamics is qualitatively similar to the full spatially extended networks . The shape of two-network phase-diagram differs from that of the spatially extended network . This is due to the fact that the full spatial system has an infinite number of eigendirections , compared to just the two for the reduced model and that the ratio of inhibitory to excitatory coupling is slightly different . We now want to understand the mechanism for these patterns and to better quantify the dependence of the patterns on the stimulus period . To do this , we next show how to compute numerically boundaries for pattern formation as the frequency and amplitude of the flashing light change . The analysis holds in any dimension and in many types of domains as long as certain conditions are met . We describe the approach generally for populations of neurons . ( For this paper , , excitatory and inhibitory . ) We write the system of equations as ( 4 ) where is the diagonal matrix of the reciprocal time constants , is the vector of firing rate functions and is the vector of spatially uniform stimuli . is a matrix of connectivities withwhere is the spatial domain . In one-dimension , the domain is a circle ( periodic boundary conditions ) and in two-dimensions , it is a square with periodic boundary conditions . We assume several important properties of the interactions: ( a ) homogeneity and ( b ) common eigenspace . Homogeneity means that the network is such that if is independent of , then is independent of for all time . This just means that spatial homogeneity is preserved . ( Note that this does not mean that it is necessarily stable . ) The second condition means that there is a set of scalar linearly independent eigenfunctions , such that for each of the component entries , that constitute the matrix , we have For example , if the domain is the circle ( that is periodic boundary conditions in one dimension ) , then where is the circumference of the circle and if the domain is the square with periodic boundary conditions , the eigenfunctions have the form . We also assume that the eigenvalues , are real . Since we have assumed homogeneity , We define The spatially homogeneous network satisfies: ( 5 ) This is a nonlinear periodically forced system , so we are not guaranteed that there is a periodic solution . Let We make our final assumption: there is a periodic solution to equation ( 5 ) . Notice that a solution to ( 5 ) is automatically a solution to the full spatial problem , ( 4 ) by our assumptions of homogeneity . To understand pattern formation , we linearize equation ( 4 ) about the homogeneous solution : where is the infinitesimal perturbation from the homogeneous state . The linearized equations for satisfy ( 6 ) We now invoke our hypothesis about eigenfunctions . We write If we plug this into ( 6 ) , we see that ( 7 ) where is the matrix of eigenvalues and denotes the derivative of with respect to . We have reduced the stability question to the study of a system of linear differential equations with periodic coefficients . Of course , there are an infinite number of these equations , one for each . However , for reasonable functions , rapidly go to zero , so that will be close to zero and thus , solutions to ( 7 ) will decay like In practice , therefore , we need only worry about a finite number of values . The way to solve a linear equation with periodic coefficients is to compute the so-called monodromy matrix . Let be the matrix solution towhere is the identity matrix . Compute this for one period to get This matrix is called the monodromy matrix . A general result from the theory of linear periodic systems is that solutions decay to zero if and only if all of the eigenvalues of lie inside the unit circle . Since is ( there are populations ) , there will be eigenvalues for , For large , where is the time constant for the population . For our system , and is just a dimensional matrix . Eigenvalues satisfy where is the trace of and is the determinant . Thus , we need only study these coefficients to determine stability . There are three qualitatively different ways that an eigenvalue can exit the unit circle: , and where when When this occurs , we expect to see a pattern that has a spatial shape like and that has period , the same as the forcing period . The low frequency pattern with period 110 msec is such an example . When , then This leads to a period doubling bifurcation; a pattern arises that has period alternating between and ; that is , what is the foreground in one cycle is the background in the next . The pattern with period 55 is such an example . Finally , when , and quasi-periodic , complex periodic , and possibly chaotic solutions and appear . We have not seen this type of instability in our model . To compute stability boundaries , we need to find and parameterize the eigenvalues , For the models considered here , the spatial interactions are homogeneous so that the eigenfunctions will be spatially periodic and from these we can easily obtain the eigenvalues . We will illustrate this idea for a one-dimensional network on the circle of length , and in a two-dimensional square domain with periodic boundary conditions . While we sometimes simulate on domains that are not periodic , for a large enough domains , the patterns and eigenfunctions will look very similar . For a one- ( two- ) dimensional periodic domain with length , the spatial eigenfunctions have the form ( respectively , ) and the convolution operator has eigenvalues , where ( respectively , ) . Here is the Fourier transform of the spatial weight functions , We see that in both one and two spatial dimensions , the eigenvalue can be parameterized by a single variable , For large , as range through the integers , fills in nearly a continuum of numbers . Thus , we replace in equation ( 7 ) by the continuous parameterization , Now we just range through and look for stability boundaries . Suppose there is a value , such that an instability is reached . Then this value will be close to ( respectively ) for some integer ( respectively , pair of integers , ) and this will determine the spatial patterning . For our system , we have used Gaussian spatial interactions with space constants , for the excitatory and inhibitory neurons , thus , so that we need to solve equation ( 7 ) with Figure 8 shows an example of the stability calculation for two different forcing periods , 60 and 110 msec . In each of the plots A , B , three curves are plotted , ( in black ) , ( in red ) and ( in green ) . The eigenvalues of the monodromy lie in the unit circle when and Then becomes negative this means that an eigenvalue of the monodromy matrix crosses so that the homogeneous state becomes unstable . Thus , black ( respectively , red ) curves crossing zero lead to +1 ( respectively , −1 ) eigenvalues . In the 60 msec example ( panel A ) , as increases , we see that the red curve that corresponds to a eigenvalue crosses zero for between and . In panel B , when the period is 110 msec , the loss of stability occurs through a eigenvalue at between and Since , for , we compute , the wavenumber , to be between 3 and 5 which is close to the value seen in the simulations in figure 4 top . Once we have found an intersection of one of the curves , with zero , we can then follow that zero using AUTO as a function of the period , of the stimulation . Figure 8C , shows two curves in which we trace the zeros of ( For example , a vertical line at intersects the leftmost black curve , in panel C and this corresponds to the two zeros of the red curve in panel A at . ) If we change the period slightly , then the curves in panel A will look somewhat different . At some critical value of the period , , in panel A ( respectively panel B ) , the red curve , ( respectively , the black curve , ) will be tangent to the axis . This occurs at the point shown in panel C . We now follow this tangency as we vary the amplitude , of the stimulus producing the two-parameter diagram shown in figure 8C . We can understand figure 8C as follows . Suppose that we fix the magnitude of the stimulus at 0 . 6 . We start flash the strobe at 50 Hz ( a period of 20 msec ) and slow it down . When it reaches a period of about 40 msec , we enter the enclosed region in the figure labeled -1 . Inside this region , the uniform state is unstable and a pattern should appear . Since the transition occurs in the -1 region , the pattern will repeat every 80 msec with the foreground and background alternating . As the frequency continues to decrease ( and the period to increase ) , we leave the curve at about and the uniform state is stable . Continuing to increase the period ( decrease the frequency ) we run into the second region where there is a +1 instability and again we get patterns . Â However , these patterns repeat with the same frequency as the stimulus . Eventually , we run into the region where no patterns occur and the homogeneous state is stable . In figure 3 , we superimpose on the numerical simulations ( in the two colored curves ) , the stability calculations from figure 8C . There is excellent agreement . Figure 6 shows the analogous diagram for the two-dimensional simulations . The agreement is not as good . We suspect that the main reason that the simulations show a wider range of pattern formation is that the time-step we chose was too large ( the simulations are very time consuming , so we took larger than optimal time steps ) which then produces numerical artifacts . ( The numerical routine is thus solving a discrete dynamical system rather than a continuous one . ) We have made more careful ( smaller time step ) simulations at points near the edges of the colored curves and these show agreement more like is seen in the one-dimensional system . So far , the simulations and stability analyses have all been for equations ( 1–2 ) when and That is , the inhibitory population receives no external stimulation . In figure 9 , we redo stability calculations similar to those in figure 3 , but we set and Even for feed-forward inhibition as much as 60% of the excitation , it is still possible to form spatial patterns . The enclosed regions are shifted toward shorter periods ( higher frequencies ) and toward larger amplitude stimuli . They also have a smaller area indicating that feed-forward inhibition restricts the range of parameters such that patterns are possible . In order to get pattern formation we have to make several important assumptions on the local circuit dynamics and the coupling . With no coupling , the “space-clamped” system should have a damped return to a stable rest state . Furthermore , the stable equilibrium should lie on the middle branch of the excitatory nullcline ( the so-called “inhibition-stabilized” regime [30] ) . For our choice of parameters , the equilibrium is a stable spiral and the period of the damped oscillation is about 76 milliseconds . Finally , we require that the coupling implements “lateral-inhibition” , so that the effects of inhibition outreach those of excitation . This assumption is commonly made for pattern forming systems [31] . One of the most striking findings of our simulations is that low frequency stimuli mainly lead to hexagons and high frequency generally lead to stripes . There turns out to be a deep theoretical reason for this result that is based on the ideas of symmetric bifurcation theory . We do not discuss the rigorous mathematics that underlies this theory , but rather , summarize the basic ideas . Near the onset of the instability , the pattern will look like a sum of the eigenfunctions , . Suppose that the eigenfunctions are of the formand their three complex conjugates . ( This is the minimal set of eigenfunctions which could produce stripes , hexagons , or checkerboard patterns . ) We label these three functions , Thus the solution near the bifurcation has the formwhere c . c . means complex conjugates and is either a or -periodic vector function . will be for the high-frequency stimulation and for the low frequency . The linear theory tells us nothing about the coefficients . Since we look for patterns that are real , for any term like , will be accompanied by a term of the form , its complex conjugate . If , for example , is nonzero and , then the pattern will be periodic in , that is , vertical stripes . If , then the pattern will be hexagonal . If , and are nonzero , then the pattern will be rectangular . One of the key questions in symmetric bifurcation theory is how to determine what patterns are selected and which are stable . It turns out ( see [32] , page 151 ) , that the three complex amplitudes , generally satisfy ( 8 ) where the real numbers depend on the nature of the equations and is the deviation of the bifurcation parameter away from the critical value . That is , suppose that the stimulus amplitude is say , 0 . 4 and the period of the stimulus increases from 20 msec . As seen in figure 8C , at approximately 30 msec , the uniform oscillation loses stability . characterizes how far away and in which direction you are from the critical stimulus frequency . Figure 10 shows a schematic bifurcation diagram for equation ( 8 ) in the case where Figure 10A shows the case when is nonzero . An unstable branch of hexagons ( labeled Hex 1 ) emerges for and at the point turns around to become stable . This means that there are hexagons that are stable even for , that is , for parameters when the homogeneous rest state is stable . Thus , as we change the frequency of the stimulus so that becomes positive ( the uniform field loses stability ) the network will “jump” to the branch of stable hexagons . Thus , for a range of bifurcation parameters ( e . g . , intensity and frequency of illumination ) , for , stable hexagonal patterns emerge . Figure 10B shows a diagram for the case in which Here , the only stable patterns to emerge are stripes and they always occur when the uniform state is unstable . Unlike the case , there is no multi-stability . Symmetric bifurcation theory tells us one more amazing fact: if the onset of instability is through a eigenvalue ( that is , the case we saw with high frequency stimuli ) , then In contrast , if the bifurcation occurs at a eigenvalue , then , is not generally expected to vanish . Thus , what we can conclude from the nonlinear analysis is that for low frequency stimuli , the first stable patterns to emerge are hexagons . At high frequency stimuli that lead to the so-called period doubling bifurcation , either hexagons or stripes can be stable and it depends on the specific nonlinearities ( specifically , whether or not ) in the model . We have never been able to stabilize hexagons at high frequencies with the simple Wilson-Cowan model described here , so we can conclude that One way to assure that and thus have stripes rather than hexagons bifurcate at high frequency stimuli is to make sure that the resting state of the unstimulated cortex is positioned close to the inflection points of the firing rate function [33] , for , at the inflection points , the Taylor expansion of the function contains no quadratic terms . In sum , at low frequencies where there is 1∶1 firing of the neurons with the stimulus , we always expect hexagonal patterns . At high frequencies , stripes will be more likely than hexagons if we operate near the maximal sensitivity of the firing rate function ( near the inflection point ) . ( See also the discussion . ) When flicker hallucinations are perceived , they are often seen as whole-field patterns and the patterns are “pure” rather than a mixture of say pinwheels and targets . Thus , a natural question is how can the two halves of the visual cortex “synchronize” their spatial patterns . There is strong anatomical [34] and functional [35] evidence for direct corpus callosal connections between the two halves of primary visual cortex . Thus , we can simulate a pair of such networks with coupling between them . To illustrate spatial alignment , we simulate two square domains where there is reciprocal coupling from a spatial location in one domain to the same location in the other . Figure 11 shows both high- and low-frequency examples . In Figure 11A , we have chosen the initial conditions so that without coupling the left and right domains have stripes of opposite orientations . We next restart the simulation but with weak coupling between the two sides and the result is that both sides converge to the same pattern ( shown on the right ) . Figure 11B shows a similar simulation when the stimulus period is 120 msec ( low frequency ) . Without coupling the left and right sides are misaligned , but with coupling turned on , they are exactly the same ( rightmost panel ) . Thus , the coupling both aligns the patterns and forces the two sides to select the same class of pattern ( e . g . , horizontal or vertical stripes ) . We want to emphasize that the choice of coupling between hemifields was for convenience and to illustrate the general principle . Indeed , in other simulations , we couple just a thin band of neurons that would be near the “midline” of the cortex . Almost any form of coupling , if sufficiently strong , should lead to the two halves producing identical patterns . The mathematics of this “spatial synchronization” remain to be analyzed .
In this paper , we have suggested a simple mechanism for flicker-induced hallucinations . We suggest that all that is needed is a spatially extended lateral-inhibitory network of excitatory and inhibitory neurons along with some resonance properties such as a damped oscillatory return to the resting state . The lateral inhibition is necessary to produce spatial instabilities as has already been suggested by [11] and subsequently by many other authors [12] , [13] , [16] . In order to interact with flickering light , there should be an amplification of the activity at certain frequencies . The simplest way to produce this is that the resting state of the network exhibits damped oscillations in the frequency range of about 7–14 Hz ( period from 70 to 140 msec ) . Two types of resonance were evident in our model: 1∶1 resonance where low frequency flicker produces large amplitude spatio-temporal patterns in the 7–10 Hz range; and 1∶2 resonance where individual groups of neurons fire at 7–10 Hz , but out of phase with other neurons producing a pattern where some neurons fire on every cycle . The mechanism for the 1∶2 resonance is mathematically similar to that which produces Faraday waves in periodically forced fluids [36] , thus , we expect that the nonlinear analysis follows in a similar vein . Crevier and Meister [37] report period doubling in the human electroretinogram when subjects are exposed to light at 46 Hz , but 1∶1 locking at 26 Hz . Our model shows period doubling at lower frequencies , but we are modeling cortex rather than the retina; the response may be different . Our model , being based on the earlier models for hallucinations [11]–[13] , presumes that the patterns arise in primary visual cortex . Similar structure is found in higher visual cortical areas , but , in these areas , the topographical representation of visual space is much too coarse for patterns such as those in figure 1 to be perceived . ffytche [38] found that V4 was most active during flicker hallucination . This area of visual cortex contains cells that are sensitive to radial patterns such as pinwheels and targets [39] which could be activated by feed-forward connections from V1 . Thus , if V1 produces the stripe patterns that correspond to the radial phosphenes in figure 1 , these patterns would then excite V4 which could produce the large signal seen in the fMRI data of ffytche . ffytche also found no increase in the activity of V1 during flicker stimulation which would seem to contradict the present modeling efforts . However , in our model , the spatio-temporal average of the activity does not change very much during the flicker , rather , it becomes spatially structured with some areas less active than baseline and others more active . The spatial structure of our striped and hexagonal arrays is likely to be too fine to be picked up by imaging . Furthermore , in our model , stripes alternate their activity at roughly 10 Hz , so that any temporal averaging of the signals would completely wash out the pattern and the activity would remain close to baseline . In order to produce a model that is capable of creating these patterns , the cortex has to be in a particular state . Geometrically , we want the excitatory and inhibitory nullclines of the space-clamped system ( the local circuitry ) to both have positive slopes at the resting state . In a recent combination of theory and experiment , [30] ( c . f . Figure 6D ) suggested that the visual cortex lies in a so-called “inhibitory-stabilized” configuration . That is , the inhibition was necessary to overcome the strong recurrent excitation that causes a positive slope in the excitatory nullcline . There are several consequences of this configuration . Unless the inhibition is extremely fast , the return to rest will be accompanied by decaying oscillations . Furthermore , small changes in the inhibition can destabilize the resting state to produce large amplitude synchronous oscillations that could be the analog of seizure activity . Interestingly , there is a strong association with stroboscopic flicker with certain forms of seizure activity , particularly in the range of frequencies that we have studied here . Small changes in the balance of excitation and inhibition could have big effects on the ability to perceive these patterns . For example , benzodiazepines enhance the effects of the inhibitory neurotransmitter GABA , so that we would predict that the enhanced inhibition would reduce the sensitivity of flicker stimuli and result in less vivid phosphenes if perceived at all . Siegel [40] describes a patient whose LSD flashbacks were triggered by flicker , but only after heavy use of caffeine and nicotine . It should be easy to study the thresholds for phosphene generation after use of these readily available stimulants . There are many generalizations of this model which could be considered . Smythies [29] and Knoll et al [3] study the combination of flicker with hallucinogens and report that the combination of flicker with sub-clinical doses of mescaline can produce phosphenes that are as vivid as those seen with normal doses of the drugs . If we suppose that the action of hallucinogens is to shift the resting dynamics of cortex into an unstable regime [11] , say , by changing the threshold of the excitatory population , then we could easily systematically explore the combination of flicker with a shift in the stability . With very little change in the details of the equations , it should be possible to introduce the “seeding” of patterns into the model . For example , suppose that we are in the low frequency stimulation regime and now add a small bias in the form of say a low contrast target or pinwheel . ( In the equations , we would model this as a low contrast grating of the appropriate orientation . ) We could then see if the model would produce stripes instead of hexagons as stripes remain a possible pattern . Indeed , the schematic bifurcation diagram in figure 10A shows that stable stripes could be possible when ( the low frequency regime ) . The stability may be shifted toward lower values of ( the stimulus parameter ) when such a bias is applied . Many of the phosphenes reported by subjects are not the broad forms shown in figure 1; rather , they include zig-zags , filigrees and patterns that are much finer . The more general models of Bressloff et al [12] include the equations for the orientation preferences of cortical neurons and produce the fine filigree hallucinations . We expect with some adjustments ( such as using a two-population model rather than a single population ) , we should be able to obtain these more complex patterns with flicker . An exciting direction to go in this work is to explore the role of color . The phosphenes themselves are extremely colorful . In addition , the color of the light stimulus can have a strong effect on the pattern [5] . The present model does not account for any of the color effects . What is needed is a model that incorporates the color features of the visual cortex . We hope to build such a model in the future . The emergence of patterns in periodically forced spatially distributed systems has a long history , particularly , in the area of fluid mechanics [41] . Gollub and Langer [42] review pattern formation in parametrically excited granular material and Rayleigh-Benard convection . Crawford [43] was the first to derive equations like ( 8 ) for periodically driven surface waves of fluids . Rucklidge [44] analyzes more complex patterns which can arise in the Faraday experiment , including so-called quasi-patterns which are almost , but not quite , regular . ( See also [32] . ) Some of the recent work by Silber and colleagues [36] on two-frequency forcing suggests experiments that could easily be done on the visual system . While the physics of these pattern forming models is completely different from the physics that underlies spontaneous pattern formation in the nervous system , the underlying mathematics is identical . Fluids , granular material , and other physical systems have characteristic time scales which with the right temporal forcing can be excited , just like the pumping of a swing . The spatial patterns which emerge in the physical models are determined by the multiple length scales present . Near the onset of instability all spontaneous pattern formation is governed by a simple set of equations , such as ( 8 ) , whose form depends on the geometry and symmetries of the particular system . Flicker stimuli provide an excellent way to probe the intrinsic pattern forming capabilities of the visual cortex since , unlike drug-induced hallucinations , they can be readily controlled . Indeed , [8] have shown that by including a small spatially structured pattern as a “seed” during flicker stimuli , it is possible to stabilize a full-field target or pinwheel pattern . Thus , it may be possible to combine stabilized flicker and brain imaging to see actual hallucination activity in human visual areas .
|
When the human visual system is subjected to diffuse flickering light in the range of 5-25 Hz , many subjects report beautiful swirling colorful geometric patterns . In the years since Jan Purkinje first described them , there have been many qualitative and quantitative analyses of the conditions in which they occur . Here , we use a simple excitatory-inhibitory neural network to explain the dynamics of these fascinating patterns . We employ a combination of computational and mathematical methods to show why these patterns arise . We demonstrate that the geometric forms of the patterns are intimately tied to the frequency of the flickering stimulus .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"visual",
"system",
"mathematics",
"computational",
"neuroscience",
"biology",
"computational",
"biology",
"sensory",
"systems",
"neuroscience",
"nonlinear",
"dynamics"
] |
2011
|
A Model for the Origin and Properties of Flicker-Induced Geometric Phosphenes
|
KDM2A is a histone demethylase associated with transcriptional silencing , however very little is known about its in vivo role in development and disease . Here we demonstrate that loss of the orthologue kdm2aa in zebrafish causes widespread transcriptional disruption and leads to spontaneous melanomas at a high frequency . Fish homozygous for two independent premature stop codon alleles show reduced growth and survival , a strong male sex bias , and homozygous females exhibit a progressive oogenesis defect . kdm2aa mutant fish also develop melanomas from early adulthood onwards which are independent from mutations in braf and other common oncogenes and tumour suppressors as revealed by deep whole exome sequencing . In addition to effects on translation and DNA replication gene expression , high-replicate RNA-seq in morphologically normal individuals demonstrates a stable regulatory response of epigenetic modifiers and the specific de-repression of a group of zinc finger genes residing in constitutive heterochromatin . Together our data reveal a complex role for Kdm2aa in regulating normal mRNA levels and carcinogenesis . These findings establish kdm2aa mutants as the first single gene knockout model of melanoma biology .
The World Health Organisation ( WHO ) reports that 132 , 000 melanoma skin cancers occur each year across the globe , with increasing incidence rates . Melanomas are cancers of the melanocytes , which are neural crest-derived pigment-producing cells in vertebrates . Accumulation of mutations , often due to UV damage , leads to the transformation of melanocytes to become a melanoma ( reviewed in [1] ) . Zebrafish models of melanoma provide a tractable resource to study melanoma biology , however current models require lineage-specific overexpression of an activated oncogene such as BRAFV600E , often in a tp53 or mitfa mutant background , to induce melanoma [2–6] . These models have enabled the identification of additional genes implicated in melanoma development by assessing a candidate gene’s ability to accelerate or delay onset of tumour formation . For example , both the histone H3 lysine 9 methyltransferase SETDB1 [7] and the transcription factor SOX10 [8] accelerate melanoma onset when coexpressed with BRAFV600E in a tp53 mutant line , whereas overexpression of HEXIM1 in this system suppresses tumour formation [9] . Setdb1 belongs to the class of chromatin-modifying enzymes that enable the same DNA sequence in every cell to produce distinct transcriptional outputs in different tissues . Chromatin-modifying enzymes function through the chemical modification of DNA or histone proteins to promote transcriptional activation or repression , either through direct alteration of overall chromatin structure , or by altering the ability of effector molecules to bind [10] . Whereas the primary modification found on DNA is cytosine methylation , histones can have a wide variety of post-translational modifications on various amino acid residues [11] . Due to their profound involvement in transcriptional regulation it is not surprising that mutations in chromatin modifiers have been implicated in cancers and developmental defects [12–14] . The general importance of chromatin-modifying enzymes also limits the in vivo study of their function in mammalian models since mice homozygous mutant for a number of different chromatin modifiers are embryonic lethal [15–19] . In order to gain insight into the in vivo function of chromatin regulators we have studied a zebrafish knockout model of the lysine de-methylase KDM2A . KDM2A specifically removes mono- and di-methyl marks on H3K36 [20] . KDM2A has been implicated in the regulation of CpG island promoters [21] and in the silencing of heterochromatin and rDNA repeats [22 , 23] . KDM2A is recruited to H3K9me3-modified chromatin in cooperation with HP1 [24] and this interaction is blocked by DNA methylation [21 , 25] . KDM2A knockout mice are embryonic lethal at E10 . 5–12 . 5 and exhibit severe growth defects [16] pointing to a role for KDM2A during development . Furthermore , cell culture studies suggest a role for KDM2A in cancer development , but there is conflicting evidence as to whether it acts to promote or inhibit tumourigenesis [26–31] . Here we highlight the complexity of the function of KDM2A by demonstrating that the zebrafish orthologue kdm2aa is required at multiple stages throughout the life of the zebrafish . Zygotic homozygous zebrafish carrying mutations in one of the KDM2A orthologues , kdm2aa , escape early embryonic defects and thus enable the interrogation of both embryonic and adult phenotypes . We show that kdm2aa-deficient fish have reduced growth and survival , a strong male sex bias and that females exhibit a progressive oogenesis defect . Furthermore , kdm2aa-deficient fish develop braf-independent , spontaneous melanoma , providing , to our knowledge , the first single gene knockout model of melanoma . Transcriptome analysis of individual kdm2aa mutant embryos reveals widespread effects on transcript abundance as well as stable regulatory responses of epigenetic modifiers of both histones and DNA , and a specific upregulation of a group of previously uncharacterised zinc finger ( ZnF ) genes located in constitutive heterochromatin . Our results provide insights into the in vivo function of KDM2A throughout the complete life span of a vertebrate model organism and establish kdm2aa-deficient zebrafish as a new model to study the aetiology of triple wild-type melanoma .
We assessed the in vivo function of KDM2A using zebrafish mutants generated by the Zebrafish Mutation Project [32] . KDM2A has two paralogues in zebrafish , kdm2aa ( ENSDARG00000059653 ) on chromosome 1 ( chr1 ) and kdm2ab ( ENSDARG00000078133 ) on chr14 ( Fig 1A ) . Embryonic expression of kdm2ab peaks during blastula stages , whereas kdm2aa expression is highest later in embryogenesis , during gastrula and early segmentation stages ( Fig 1I ) . We raised two premature stop codon alleles affecting kdm2aa and one premature stop codon allele affecting kdm2ab ( Fig 1A ) . kdm2aasa898 and kdm2absa1479 are assumed to produce non-functional protein . kdm2aasa9360 may produce a partially functional protein lacking the F-box and LRRs . Fish homozygous for kdm2absa1479 showed no phenotype by 5 days post fertilisation ( d . p . f . ) , grew to adulthood in the expected Mendelian ratio and had healthy offspring . We therefore concluded that kdm2ab loss of function ( LOF ) does not produce an overt embryonic or adult phenotype . Equally , both kdm2aasa898 and kdm2aasa9360 homozygous embryos did not display morphological defects at 5 d . p . f . ( S1A and S1B Fig ) . We also generated double mutants between kdm2aasa898 and kdm2absa1479 to test whether there was compensation between the paralogues , but embryos homozygous mutant for both genes also showed no phenotypic difference to their siblings at 5 d . p . f . ( S1 Table ) . However , by 30 d . p . f . juvenile fish homozygous for either kdm2aa allele were thinner and shorter compared to their siblings ( Fig 1B and 1C , S2 Table and S1 File ) . The size difference persisted into adulthood at 180 d . p . f . ( Fig 1C ) . We confirmed that this phenotype was due to the loss of kdm2aa function by raising two clutches containing compound heterozygous kdm2aasa898/sa9360 fish ( Fig 1B and S1 File ) . In addition , survival of homozygotes was reduced at 30 d . p . f . from the expected 25% to below 20% and fell further by 90 d . p . f . ( Fig 1D , S2 Table and S1 File ) . Furthermore , incrosses for either kdm2aa allele produced at most two or three females out of a maximum of 20 homozygotes . We next assessed whether homozygous mutant kdm2aa adults were fertile . Initial intercrosses of kdm2aasa9360/sa9360 or compound heterozygous kdm2aasa898/sa9360 adults produced phenotypically diverse clutches in which some embryos successfully inflated their swimbladders and either developed phenotypically normally ( Fig 1E bottom panel ) , or with only mild defects ( Fig 1E middle panel ) . Later crosses of the same females produced clutches in which over half of the eggs either failed to fertilise or did not divide beyond four cells ( S1C Fig ) . The remaining eggs showed severe cleavage defects with asymmetric division , detaching cells , and slower division rate ( Fig 1F ) . By 24 hours post fertilisation ( h . p . f . ) about a third of the maternal-zygotic mutant ( MZ ) kdm2aa-/- embryos had died and those that survived displayed degrees of generalised developmental defects ( Fig 1G ) . This indicated that subsequent intercrosses from the same females displayed a progressive worsening of egg quality , with fewer eggs being fertilised and fewer embryos surviving beyond 24 h . p . f . Double labelling with DAPI and TRITC-conjugated phalloidin of 8–32 cell wild-type and MZkdm2aa-/- embryos confirmed asymmetric cells and unsynchronised division ( Fig 1H ) . To confirm that this phenotype was caused by kdm2aa LOF in the female , we outcrossed male and female kdm2aasa9360/sa9360 fish to wild-type fish of the same genetic background . Offspring from three kdm2aasa9360/sa9360 males were normal ( Fig 1F and S1C Fig ) . By contrast , the majority of embryos from initial outcrosses of two kdm2aasa9360/sa9360 females died before 5 d . p . f . , however some ( 12/64 ) embryos survived to 5 d . p . f . with 6 out of 12 showing no obvious phenotype ( S1D Fig top panel ) and the remaining 6 displaying only localised malformations ( S1D Fig ) . Subsequent homozygous female outcrosses produced clutches with low fertilisation rates and embryos with severe defects very similar to MZkdm2aa-/- embryos ( S1C and S1E Fig ) . This demonstrates that embryos from oocytes devoid of functional kdm2aa mRNA or protein can develop normally and that the maternally deposited mRNA ( Fig 1I ) [33] does not explain the lack of phenotype in zygotic homozygous mutants . Instead the increase in unfertilized eggs and severity of the phenotype in the remaining embryos point to a role for Kdm2aa in maintaining the production of healthy oocytes . From the age of 7 months , we observed that kdm2aa-deficient fish ( homozygotes for either allele and also compound heterozygous fish ) began to develop suspected cancers . We observed aberrant melanocytic pigmentation at the base of the tail extending into the tail fin ( Fig 2Ai ) , masses behind one eye causing it to protrude ( Fig 2Aii ) and masses on the body ( S2B Fig ) . 23/92 ( 25% ) of kdm2aasa898/sa898 ( Fig 2B ) and 20/204 ( 10% ) of kdm2aasa9360/sa9360 ( S2A Fig ) fish developed these suspected cancers within the first 28 months , whereas none of the heterozygous or wild-type siblings did . Of the 43 fish with potentially cancerous phenotypes , 10 fish had excessive melanocytic pigmentation on their tail , 12 fish had a tumour behind one eye causing it to protrude , 18 fish had a mass on their body , and 3 fish were found to have both excessive melanocytic pigmentation on their tail and a mass on their body . To confirm that these growths were cancerous , tissue sections from 10 affected fish and 2 control siblings were haematoxylin and eosin ( H&E ) stained and analysed by two independent clinical histopathologists . Seven of the fish had excessive melanocytic pigmentation on their tails , and all of these fish were diagnosed with spindle cell malignant melanoma on the tail , invading the surrounding skeletal muscle and bone to varying degrees ( Fig 2C ) . Furthermore pigmented melanophages were present in half of the tumours and these cells have previously been reported in zebrafish melanomas [34] . Two of the fish analysed had eye tumours , which confirmed as either spindle cell , or epithelioid and spindle cell melanoma and pigmented melanophages were present in one of the two tumours . A single fish was analysed with a suspected abdominal tumour and this was found to have a nodular lesion around the ultimobranchial body , vena cava and pancreas , composed of epithelioid and spindle cells ( S2C Fig ) . No pigmented melanophages were present . Additionally in internal sections from one of the fish with excessive melanocytic pigmentation on the tail an abnormal spindle-cell proliferation within the proximal intestinal epithelium and the pancreas was found . Given the pigmentation , spindle cell morphology and malignant proliferation these two abdominal tumours are consistent with melanoma , but further analysis would be required for a firm diagnosis . Three additional affected fish , two with excessive melanocytic pigmentation on the tail and one with an eye tumour , were analysed further using immunohistochemistry . H&E staining of both tail tumours revealed a biphasic appearance , with pseudoglandular or rosette-like structures alternating with areas of spindle cell growth ( Fig 2C ) and both the pseudoglandular and spindle cell elements stained positive for the melanoma marker Melan-A ( Fig 2D ) but negative for two alternative melanoma markers S100 and HMB-45 ( Fig 2E and 2F ) . These tumours were also diagnosed as melanoma showing divergent differentiation . Both tumours stained positive for phospho-histone H3 ( Fig 2G ) indicating that they were mitotically active . To further characterise the pseudoglandular differentiation the tail tumours were stained for the neuroendocrine marker Synaptophysin ( Fig 2H ) which was negative and for the epithelial marker Cytokeratin ( Fig 2I ) which was positive . The eye tumour shared many characteristics with the tail tumours and was diagnosed as invasive melanoma; H&E staining revealed spindle cell morphology ( Fig 2M ) , Melan-A and phospho-histone H3 were positive ( Fig 2N and 2Q ) and S100 and HMB-45 were negative ( Fig 2O and 2P ) . Interestingly , both tail tumours stained positively for phospho-ERK ( Fig 2J ) indicating activation of the MAPK signalling pathway , whereas the eye tumour was phospho-ERK negative ( Fig 2R ) . One tail tumour and the eye tumour stained positive for phospho-AKT ( Fig 3K and 3S ) indicating that PI3K signalling was activated , whereas the second tail tumour was phospho-AKT negative ( Fig 2L ) . To assess the mutational landscape in kdm2aa-deficient fish melanomas , we performed whole exome sequencing on four dissected tumours , adjacent non-tumour control tissue and sibling tissue , and called the single nucleotide variants ( SNVs ) and small insertions/deletions ( indels ) present . Across the 11 samples we obtained exome coverage of 50x ( S3 Table ) . Laboratory zebrafish are not inbred and consequently there is a high level of natural variation [35] . We therefore used exome data from 3 , 811 individual fish generated in the Zebrafish Mutation Project [32] to define a common variant catalogue of 61 , 276 , 211 SNVs and filtered the SNVs found in sibling , control and tumour tissues using this variant set . This revealed on average 951 SNVs between sibling fish and control tissues ( Table 1 ) . Tumour tissues harboured on average 517 SNVs compared to non-tumour tissue from the same fish demonstrating that the tumours had accumulated mutations and increased their SNV burden by the equivalent of 50% of the normal sibling variation . By contrast the tumours had not increased their indel frequency with each tumour only showing one additional indel when compared to control tissue ( Table 1 ) . Next , we sought to identify potential protein-disrupting mutations . Filtering the mutations for those which are predicted to disrupt the protein ( for details see Methods ) revealed between 9 and 21 disruptive mutations per tumour compared to control tissue , and there was no overlap between these acquired disruptive mutations in the different tumours ( S3 Table ) . Whole genome sequencing would be needed in order to assess whether mutations were present in non-coding regions . Although we did not detect a common mutational signature in the tumours analysed , the exome data showed that none of the oncogenes or tumour suppressors most commonly mutated in human melanomas had accumulated exonic mutations ( S3 Table ) . We looked specifically at the 13 significantly mutated genes in human cutaneous melanomas identified by The Cancer Genome Atlas , as well as HRAS and KRAS which were used to categorise the samples and also GNAQ , GNA11 , KIT , CTNNB1 and EZH2 which were found to carry mutations at low frequencies in the triple wild-type set of melanomas [36] . We used the Ensembl Compara database [37] to detect orthologues for these genes in our whole exome sequencing data , but none were found to carry potentially disruptive exonic mutations ( S3 Table ) . Analysis of four tumours is not enough to prove conclusively that kdm2aa-deficient melanomas never harbour mutations in common oncogenes . However if mutations were present at the same frequency as in human melanomas there is , for example , a 94 . 7% probability that we would have detected a braf mutation ( present in 52% of human melanomas [36] ) and a 73 . 1% probability that we would have found nras mutations which are present in 28% of human melanomas [36] . Since BRAF and NRAS hotspot mutations are almost mutually exclusive [36] there is close to a 97 . 6% probability that we would have found either a BRAF or an NRAS hotspot mutation in at least one of the tumours . Therefore the absence of potentially disruptive mutations in any of the 20 genes assessed across the four melanomas supports a role for Kdm2aa in melanoma development independent of common oncogenes and tumour suppressors . Given the role of Kdm2aa in chromatin regulation [20 , 21 , 26] we investigated whether loss of Kdm2aa resulted in an altered transcriptional profile which might explain the observed phenotypes . We performed a comparative transcriptome analysis ( polyA RNA-seq ) on four individual homozygous mutant , heterozygous and homozygous wild-type siblings each from both alleles at 5 d . p . f . and 12 d . p . f . giving us four mRNA expression profiles ( Fig 3A ) . We chose these time points and individual embryos from two different alleles to capture changes at the mRNA level in morphologically normal individuals rather than secondary transcriptional deviations due to size differences , developmental delay and genetic background . Using DESeq2 [38] we determined differential transcript abundance as significant at an adjusted p-value <0 . 05 . This revealed that kdm2aa transcripts were present at lower levels in fish homozygous for either allele and at both stages ( log2 fold change between -0 . 34 and -0 . 73 ) , indicating nonsense-mediated decay [39] had occurred . We tested for haploinsufficiency in heterozygous animals by running differential analysis between heterozygous and wild-type embryos in both alleles at 5 d . p . f . and 12 d . p . f . This yielded 0 and 1 differentially expressed ( DE ) genes for kdm2aasa9360/+ at 5 d . p . f . and 12 d . p . f . , respectively . By contrast , kdm2aasa898/+ heterozygosity led to 29 ( 5 d . p . f . ) and 80 ( 12 d . p . f . ) DE genes , suggesting a mild haploinsufficiency effect of that allele on mRNA levels ( S4 Table ) . When comparing homozygous mutants with siblings ( Fig 3B–3D and S4 Table ) between 539 and 1433 genes out of 32 , 261 detected genes were significantly differentially abundant in the four clutches . The four DE gene lists only had 19 genes in common ( S3A and S3B Fig ) with an additional 76 DE genes being significant in at least three of the four clutches ( S3A Fig ) . The discrepancy between the clutches could either be due to clutch-specific and/or stochastic effects on transcript abundance or the fact that four biological replicates per condition do not provide sufficient power to detect differential gene expression above individual embryo variability . To test this we combined the samples for each stage and ran the differential analysis of homozygous mutants against siblings while controlling for clutch in the DESeq2 model ( Fig 3A and Methods ) . The combined stage-specific analyses showed large overlap with their respective individual experiments ( 77% and 72% at 5 d . p . f . , 57% and 75% at 12 d . p . f . ) , confirming that the majority of discrepancy was due to detection power rather than clutch difference ( Fig 3B and 3C ) . The increase in power due to the larger sample size also enabled us to detect over 1100 additional DE genes for each stage ( Fig 3B and 3C ) . Combining all four experiments in the analysis while controlling for stage and clutch identified 3 , 752 DE genes ( Fig 3D ) . These results are consistent with previous findings that the number of biological replicates is the main factor in the ability to identify differentially expressed genes [40] . Gene ontology ( GO ) analysis of DE genes using topGO [41] revealed enrichment of a large number of terms relating to translation , DNA replication , energy metabolism , and chromosome organisation in the biological process ( BP ) domain in the four separate and the combined 5 d . p . f . RNA-seq analyses ( S5 Table ) . The translation enrichment was driven mostly by upregulation of genes encoding ribosomal proteins ( 19/72 contributing genes in the 5 d . p . f . analysis ) , translation elongation or initiation factors ( 14/72 ) and mitochondrial ribosomal proteins ( 9/72 ) , which is consistent with KDM2A’s described function in repressing ribosomal RNA genes [23] . This upregulation of ribosomal genes and energy generation processes together with differential expression of DNA replication genes suggests cellular stress . We also found stage-specific differences . While different terms relating to translation , chromosome organisation and metabolism appeared in all individual analyses , the GO enrichment at 12 d . p . f . also included a large number of terms related to development of different tissues . This is very likely to reflect the emerging growth retardation observed morphologically from 30 d . p . f . onwards . To visualise this stage difference we filtered the lists for terms that are present in the stage analysis as well as their individual experiments ( Fig 3G and 3H and S3C and S3D Fig ) . This showed a dominance of translation , DNA replication and chromosome segregation at 5 d . p . f . , whereas the list at 12 d . p . f . contains mostly translation- and development-related terms . In accordance with the role of Kdm2aa in chromatin regulation the theme of DNA replication and chromatin remodelling represents the core gene expression profile even in the comparatively small set of 95 DE genes that overlapped between at least three individual clutches across both stages and alleles ( S3E Fig ) . Included in this core set are chromatin modifiers such as nsd1b , a methyltransferase for the KDM2A target H3 lysine 36 , and the de novo DNA methyltransferase dnmt3bb . 2 which is recruited to DNA by H3K36me3 [42] , both of which were downregulated ( Fig 3E and 3F ) . By contrast , the gene encoding the Snf2-related CREBBP activator protein Srcap , the catalytic subunit of a protein complex that incorporates the histone variant H2A . Z at promoters and eu- and heterochromatin boundaries was upregulated ( S3F Fig ) ( S2 File for all count plots ) . This gene expression signature suggests a compensation for loss of H3K36-demethylase activity and a wider concerted response to chromatin disruption . When plotting up- and downregulated genes separately onto their chromosomes , we noticed enrichment of upregulated genes on the long arm of chr4 ( Fig 4A ) . This region is repeat rich ( Fig 4A ) , and contains extensive constitutive heterochromatin [43 , 44] . We therefore speculated that kdm2aa LOF causes generalised de-repression of genes located within this heterochromatin stretch . However , of the 208 genes that were upregulated on the long arm of chr4 in the combined analysis , 183 genes were annotated as containing a zinc finger ( ZnF ) domain . ZnF domain-containing genes represented 49 . 2% of detected genes on the long arm of chr4 , but rose to 85 . 5% in the DE gene set and thus demonstrated specific enrichment ( Fig 4B ) . Furthermore , none of the 183 DE ZnF genes on the long arm were downregulated whereas this was the case for 6 of the other 31 DE genes ( Fig 4C ) . Kdm2aa therefore seems to have a function in repressing heterochromatic ZnF genes on the long arm of chr4 in a gene-specific manner . We have shown previously that these genes are normally expressed in a sharp peak at zygotic genome activation [33] , pointing to a role for these genes in regulating zygotic transcription .
In this study we have used two non-complementing point mutations to identify a complex set of phenotypes caused by kdm2aa LOF , which affect different stages of development and adulthood: oogenesis is impaired , juveniles display reduced survival and grow to smaller adults with a strong male sex bias . We also demonstrate that Kdm2aa is not required for early embryonic development as a proportion of embryos from early clutches devoid of maternal wild-type transcript or protein develop normally . Furthermore , while oogenesis is abnormal , Kdm2aa is not required for meiosis per se , since embryos from homozygous male outcrosses are phenotypically wild type . Importantly , a significant proportion of mutants develop cancerous growths . All of the tumours analysed were diagnosed as melanomas , however they are atypical given their unusual histologic and immunologic characteristics and the absence of a mutational signature common to human melanomas . Cell culture studies have pointed to a role for KDM2A and other histone demethylases [45] in the development of human cancers , but it is unclear whether KDM2A acts to promote or suppress carcinogenesis [26–31] . Here we demonstrate that in vivo kdm2aa acts as a tumour suppressor . This is consistent with previous studies identifying chromatin modifiers as key players in cancer development [14 , 46–49] and makes the kdm2aa mutant the first single gene knockout animal model of melanoma . It has been shown previously that fish homozygous mutant for genes known to be involved in DNA damage repair , such as brca2 , develop as all males [50] . The female-to-male sex reversal is caused by oocyte death , presumably due to an inability to repair the damage caused by recombination during meiosis [50 , 51] . The strong male sex bias that we observe in homozygous mutant Kdm2aa adults raises the possibility that the DNA damage response might also be impaired in Kdm2aa-deficient fish . A defect in DNA damage repair would also fit with the incidence of melanoma , since patients with Xeroderma Pigmentosum ( XP ) have a vastly increased risk of skin cancer [52] . XP is caused by mutations in genes involved in the nucleotide excision repair pathway which functions to repair bulky DNA helix distorting lesions such as those produced as a result of UV irradiation or endogenous reactive oxygen species [53 , 54] . The effects of kdm2aa loss of function on the DNA damage repair pathway thus warrants further investigation . Our RNA-seq analysis was carried out at 5 d . p . f . and 12 d . p . f . time points where the mutants do not display any discernible morphological phenotype . Nevertheless , we discovered significant effects on mRNA levels , indicating that we were able to identify the transcriptional profile underlying the later observed morphological phenotypes . We were able to confirm the core DNA replication and chromatin remodelling gene signature by examining the DE genes common to either all 4 or at least 3 of the 4 experiments . Out of the 19 DE genes significant in all four sets six genes are known to be involved in chromatin structure and function ( rbbp5 , smg9 , chd3 , rad23aa , kdm2aa and nsd1b ) . This reproducible gene signature suggests that kdm2aa LOF generally affects chromatin structure and function which is a main factor in transcriptional control . The de-repression of ZnF genes in heterochromatin on chromosome 4 , which are normally expressed in a sharp peak at zygotic genome activation [33] and therefore likely to be involved in regulation of transcription at that stage , could also contribute to impaired control of gene expression . Consistent with our observations , disruption of transcriptional control is emerging as a key feature of cancer development and is proposed to favour malignancy [49 , 55–58] . Disruption to chromatin has been shown to play a role in melanoma development . For example reduced acetylation and H3K4me2/3 marks at specific regions have been observed in a tumourigenic melanocyte cell model system [59] . Furthermore altered expression of chromatin modifiers has been associated with melanoma development . The histone demethylase KDM5B is highly expressed in many cancers [60] including melanoma cell lines and patient tumours and causes a slowing of the cell cycle which promotes resistance to chemotherapeutic drugs [61] . In a zebrafish melanoma model , overexpression of the histone methylase SETDB1 accelerates the onset of melanoma development [7] . Our Kdm2aa-deficient zebrafish model identifies kdm2aasa898 and kdm2aasa9360 as driver mutations in melanoma and therefore fits with current models demonstrating an important involvement of chromatin modifiers in melanoma . In further support of this , a recent study analysing whole genome sequences from cutaneous , acral and mucosal melanomas identified a number of chromatin modifiers as candidate driver genes harbouring protein-disrupting aberrations [62] . KDM2A is not among the commonly mutated chromatin modifiers in melanoma , but code-disrupting mutations have been identified in melanomas and other cancers [63 , 64] . We also cannot exclude the possibility that kdm2aa-deficient fish additionally develop other types of cancers which were not assessed in this study . Immunohistochemistry of Kdm2aa-deficient tumours with antibodies routinely used for clinical melanoma diagnoses revealed that they stained positive for Melan-A , but negative for two other melanoma markers S100 and HMB-45 . Whilst this is unusual , a number of human melanomas do not stain positively for all three markers [65–67] . Additionally H&E staining revealed pseudoglandular or rosette-like features alternating with areas of spindle cell growth , and both tail tumours stained focally positive for the epithelial marker Cytokeratin , suggesting divergent epithelial differentiation within a melanoma . Divergent differentiation towards a range of cell types is a well-recognised although rare phenomenon in human melanoma [68] but the significance of this finding in several of our tumours is uncertain . At this time , with the diagnosis of two independent pathologists , these tumours are best classified as melanoma with divergent differentiation , although the atypical nature of the tumours , and the lack of similarity with human and other zebrafish melanomas suggest that additional evidence is needed to confirm the cell of origin . All three tumours assessed were mitotically active , shown by phospho-histone H3 antibody staining . The rate of mitoses within a tumour has been identified as the second most powerful predictor of patient survival; a mitotic rate of 1 or more per square millimetre is associated with reduced survival [69 , 70] . Furthermore MAPK signalling is activated in over 90% of human melanomas [71] and our immunohistochemical analysis showed that despite an absence of exonic mutations in braf or nras , both tail tumours but not the eye tumour had activated MAPK signalling . The eye tumour and one tail tumour however showed activated PI3K signalling . This suggests that there is not a uniform pathway to melanoma development in kdm2aa-deficient fish , but instead activation of either of the two major pathways known to be involved in human melanomas [62] leads to melanoma development in these fish . This mutant provides an alternative genetic system to study melanoma development to previous zebrafish and mouse models which require overexpression of an activated oncogene or use xenografts [2 , 4 , 5] ( reviewed in [72] ) . Our RNA-seq data show that key genes in melanocyte development , including mitfa and sox10 , are expressed at normal levels . This is in contrast to fish that overexpress activated BRAF in a tp53-deficient background which already show altered expression of neural crest genes by 80 h . p . f . [57] . We also do not find a significant overlap between our core set of 95 genes DE in at least 3 of the 4 clutches and the gene signatures of either MITF high expressing or AXL high expressing human melanoma cells determined by single cell RNA-seq [73] . Taken together this suggests that the emergence of melanoma at later stages is not due to a direct effect on genes involved in melanocyte development . The melanoma predisposition due to a single gene knockout is comparable to deleterious germline variants in a number of genes such as CDKN2A and POT1 that have been shown to underlie familial melanoma cases in human patients [74 , 75] . Due to the disparity between common human melanomas and Kdm2aa-deficient tumours this melanoma model is different from classic BRAF mutation model systems . It does not mimic all hallmarks of common melanomas , but it provides a unique opportunity to interrogate the relationship between chromatin regulation and cancer development . Indeed , transcriptional fluctuations rather than acquired mutations have recently been identified to underlie drug resistance in melanoma cells [76] and chromatin regulators have been demonstrated to function not only in melanoma development but also specifically in the emergence of resistance to BRAF inhibitors ( [77] and reviewed in [48] ) . Taken together , our work interrogates for the first time in vivo and across the vertebrate life span the role of Kdm2aa in development and disease . We uncover a function for Kdm2aa in oogenesis as opposed to embryogenesis and identify its role as a tumour suppressor . This loss of function model will be invaluable to further dissect the interplay of chromatin structure and transcription , and its impact on cancer .
Zebrafish were maintained in accordance with UK Home Office regulations , UK Animals ( Scientific Procedures ) Act 1986 , under project licence 70/7606 , which was reviewed by the Wellcome Trust Sanger Institute Ethical Review Committee . Embryos were obtained either through natural matings or in vitro fertilisation and maintained in an incubator at 28 . 5°C up to 5 days post fertilisation ( d . p . f . ) . The mutant alleles kdm2aasa898 , kdm2aasa9360 and kdm2absa1479 were obtained from the Zebrafish Mutation Project [32] . Standard length ( SL ) and height at the anterior margin of the anal fin ( HAA ) of anaesthetised offspring from heterozygous intercrosses were measured at 30 , 90 and 180 d . p . f . Measurements were taken as previously described [78] . Tissue samples were taken from each measured fish for genotyping either by sacrificing whole individuals at 30 d . p . f . or by caudal fin biopsies at 90 and 180 d . p . f . To test whether there is a difference in SL or HAA as a function of genotype , we performed ANOVA on each clutch to check for significant differences between the three genotype groups of homozygous mutant , heterozygous and homozygous wild-type fish . Post-hoc testing ( Tukey HSD ) was used to assess which groups differed significantly . DNA from embryos or fin biopsies was extracted and DNA samples were genotyped for kdm2aasa898 , kdm2aasa9360 or kdm2absa1479 using KASP genotyping as previously described [79] . Fish samples were either collected in formalin and sent to Advance Histopathology Laboratory Ltd , 75 Harley Street , London , UK , for H&E staining and analysis , or fixed , processed and stained as described in [80] . Briefly , fish tissue was fixed in 4% PFA at 4°C for 3 days , decalcified in 0 . 5M EDTA ( pH 8 ) at 4°C for 5 days and transferred to 70% ethanol . It was then processed in 95% ethanol , absolute alcohol , xylene and paraffin wax , embedded in wax blocks , cut into 5 μm thick sections and placed onto glass slides . Hematoxylin and eosin staining and immunohistochemistry were performed as described in [80] . The slides were de-waxed by xylene and ethanol washes , stained , dehydrated and mounted with DPX . Antigen retrieval for IHC was performed in 0 . 01 M citrate buffer ( 1 . 8 mM citric acid , 8 . 2 mM sodium citrate , distilled water—pH 6 ) in a microwave pressure cooker . The samples were stained with the primary antibody ( monoclonal mouse anti-human Melan-A clone A103 , DAKO , Cat . No . M7196 concentration 1:75 , anti-phospho-Histone 3 , Cell Signalling Technology , rabbit , 1:200 , anti-phospho-p44/42 MAPK ( Erk1/2 ) , Cell Signalling Technology , rabbit , 1:400 and anti-phospho-Akt , Cell Signalling Technology , rabbit , 1:50 ) overnight at 4°C and secondary antibody ( HRP rabbit/mouse , DAKO ) for 30 min at room temperature . DAKO Real EnVision Detection System ( Peroxidase/DAB+ , Rabbit/Mouse , Cat . No . K5007 ) was used to visualise the IHC staining . S100 , HMB-45 , Cytokeratin AE1/3 and Synaptophysin antibody stainings were performed under standard laboratory conditions at the Immunohistochemistry Laboratory in the Department of Pathology , Royal Infirmary of Edinburgh . The stained slides were imaged using Pathology Nanozoomer SlideScanner and the images were processed using NDP . 2 software . For DAPI and TRITC-Phalloidin staining , embryos at the 8–32 cell stage were fixed in 4%PFA/PBS overnight at 4°C , washed in PBST ( 0 . 1% Tween-20 in PBS ) and dechorionated . After 4 x 30 minute washes in 2% Triton/PBS they were incubated with 4' , 6-diamidino-2-phenylindole ( DAPI ) ( 1:300 ) in PBST and TRITC-Phalloidin ( 1:200 ) in PBST in the dark at 4°C overnight . Embryos were washed 3-4x in PBST , mounted in Vectashield Antifade Mounting Medium and imaged using a Leica SP5 confocal microscope . Using Sera Mag beads , total nucleic acid was isolated from 96 larvae from heterozygous sibling intercrosses for both kdm2aa alleles at 5 d . p . f . and 12 d . p . f . resulting in four experiments . KASP genotyping was performed on all samples to identify 4 individual homozygous mutant , heterozygous and wild-type sibling samples for each of the four experiments . From these 48 samples 300 ng total RNA were used to prepare sequencing libraries with Ambion ERCC spike-in mix 1 ( Cat . No . 4456740 ) according to the manufacturer’s instructions using the Illumina TruSeq Stranded mRNA Sample Prep Kit Set A and B ( RS-122-2101 and RS-122-2102 ) . Paired end sequencing with a read length of 75 bp was performed on four lanes of Illumina HiSeq 2500 machines . Quality control of sequenced samples was performed using QoRTs [81] and 7 libraries showing characteristics of RNA degradation were excluded from further analysis . Sequence was aligned to the GRCz10 reference genome with TopHat 2 . 0 . 13 , using a known transcripts file from Ensembl v87 ( ftp://ftp . ensembl . org/pub/release-87/gtf/danio_rerio/Danio_rerio . Zv9 . 87 . gtf . gz ) and the "fr-firststrand" library type option . Read counts were obtained with htseq-count and used as input for differential expression analysis with DESeq2 . For the analyses of individual clutch experiments , the DESeq2 model was “~ condition” where the condition is either “hom” or “het_wt” . For the stage-specific analyses , the model was “~ group + condition” with the same conditions as previously and where the group is either “sa898” or “sa9360” , corresponding to the different alleles . For the combined analysis , the model was also “~ group + condition” with the same conditions as previously and where the groups are “sa898_day5” , “sa898_day12” , “sa9360_day5” or “sa9360_day12” , corresponding to the different alleles and stages . Enrichment analysis for Gene Ontology terms from Ensembl v87 annotation was performed with topGO [41] using the Kolmogorov-Smirnov test and the "elim" algorithm with a nodeSize of 10 . RNA-seq data were submitted to ENA under Study Accession Number: ERP007082 and to ArrayExpress under Accession Number: E-ERAD-326 . Biopsies were taken from tumours and adjacent non-tumour control tissue of homozygous mutants and from corresponding tissues of wild-type or heterozygous siblings . Dissected tissues were placed in 400 μl of 100 μg/ml proteinase K overnight at 55°C , followed by 30 min at 80°C to heat inactivate the proteinase K . DNA was precipitated by adding 400 μl of isopropanol and centrifuging for 40 min at 4100 rpm at room temperature . DNA pellets were washed twice with 400 μl of 70% ethanol followed by centrifugation at 4100 rpm for 25 min and 10 min , and resuspended in ddH20 . The isolated DNA was whole exome enriched using Agilent SureSelect and used to generate standard Illumina sequencing libraries , which were paired end sequenced with a read length of 75 bp using two lanes of Illumina HiSeq 2500 machines . SNVs were called using MuTect [82] and indels were called using Strelka [83] . Known SNPs , obtained from the Zebrafish Mutation Project [32] , were removed from the MuTect output . Potential protein-disrupting SNVs were identified using the Ensembl Variant Effect Predictor ( VEP ) [84] and filtering the output for stop_gained , missense_variant , transcript_ablation , splice_acceptor_variant , splice_donor_variant and frameshift_variant consequences . Whole exome sequencing data were submitted to ENA under Study Accession Number: ERP016095 .
|
Epigenetic modifications of DNA and histones , the major components of chromatin , play a central role in transcriptional regulation . KDM2A is a histone demethylase that integrates DNA and histone modification signatures and is involved in transcriptional silencing through heterochromatin maintenance . Here we show that adult zebrafish homozygous for the orthologue kdm2aa develop melanomas , a malignant form of skin cancer , independently from oncogenes known to drive melanoma formation . We observe that transcript abundance is widely affected in kdm2aa mutants and find that gene expression of several DNA- and histone- modifying enzymes is stably altered . We furthermore demonstrate a specific de-repression of a group of genes encoding zinc finger-containing proteins that has the potential to be involved in transcriptional regulation . We suggest that these molecular disruptions underlie the melanoma formation , as well as the other observed phenotypes such as reduced growth and survival , a male sex bias and an oogenesis defect . This work demonstrates in vivo a role for Kdm2aa as a tumour suppressor and establishes , to our knowledge , kdm2aa-deficient fish as the first single gene knockout vertebrate model of melanoma .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"cancers",
"and",
"neoplasms",
"vertebrates",
"animals",
"oncology",
"animal",
"models",
"osteichthyes",
"developmental",
"biology",
"negative",
"staining",
"model",
"organisms",
"experimental",
"organism",
"systems",
"epigenetics",
"embryos",
"eyes",
"chromatin",
"research",
"and",
"analysis",
"methods",
"specimen",
"preparation",
"and",
"treatment",
"embryology",
"staining",
"fishes",
"chromosome",
"biology",
"gene",
"expression",
"melanomas",
"head",
"zebrafish",
"cell",
"staining",
"cell",
"biology",
"anatomy",
"genetics",
"biology",
"and",
"life",
"sciences",
"ocular",
"system",
"organisms"
] |
2017
|
Loss of the chromatin modifier Kdm2aa causes BrafV600E-independent spontaneous melanoma in zebrafish
|
RAS-induced MAPK signaling is a central driver of the cell proliferation apparatus . Disruption of this pathway is widely observed in cancer and other pathologies . Consequently , considerable effort has been devoted to understanding the mechanistic aspects of RAS-MAPK signal transmission and regulation . While much information has been garnered on the steps leading up to the activation and inactivation of core pathway components , comparatively little is known on the mechanisms controlling their expression and turnover . We recently identified several factors that dictate Drosophila MAPK levels . Here , we describe the function of one of these , the deubiquitinase ( DUB ) USP47 . We found that USP47 acts post-translationally to counteract a proteasome-mediated event that reduces MAPK half-life and thereby dampens signaling output . Using an RNAi-based genetic interaction screening strategy , we identified UBC6 , POE/UBR4 , and UFD4 , respectively , as E2 and E3 enzymes that oppose USP47 activity . Further characterization of POE-associated factors uncovered KCMF1 as another key component modulating MAPK levels . Together , these results identify a novel protein degradation module that governs MAPK levels . Given the role of UBR4 as an N-recognin ubiquitin ligase , our findings suggest that RAS-MAPK signaling in Drosophila is controlled by the N-end rule pathway and that USP47 counteracts its activity .
The RAS-MAPK pathway is one of the principal signaling conduits controlling proliferation and differentiation in metazoans . Perturbations in pathway activity are closely associated to oncogenesis and developmental disorders [1 , 2] . Signaling through the small GTPase RAS is typically initiated by signal-receiving transmembrane receptors . Active RAS triggers the successive activation of the three pathway kinases: RAF , MEK , and MAPK/ERK . A complex network of factors is now known to regulate the steps leading up to MAPK activation [3 , 4] . To date , much effort has been devoted to identifying and characterizing the post-translational mechanisms that govern pathway signaling dynamics [3–5] . In the case of MAPK , MEK is the principal activator that operates by phosphorylating MAPK’s activation loop , and MAPK-specific phosphatases dephosphorylate these residues to inactivate the kinase . Other sources of regulation include scaffold proteins and factors that control subcellular localization [6] . As is the case for most other RAS-MAPK pathway components , very little is known about the processes that act to regulate MAPK abundance . Recently , we reported the identification of a series of factors in a genome-wide RNAi screen that impacted RAS-MAPK signaling at a pre-translational level [7 , 8] . Unexpectedly , these factors were in large part associated with either the transcription or splicing of mapk pre-mRNA transcripts , indicating that this might be an important source of regulatory input into this pathway . Here , we focus on USP47 ( also commonly referred to as UBP64E; FBgn0016756 ) , a factor that was also identified in the aforementioned RNAi screen . USP47 is a deubiquitinase ( DUB ) of the ubiquitin specific protease ( USP ) family ( Fig 1A ) [9] . It has previously been associated with the regulation of the transcription factors TTK ( tramtrack; FBgn0003870 ) and SLBO ( FBgn0005638 ) in Drosophila [10 , 11] . Its human orthologue , USP47 interacts with the beta-TRCP E3 ligase complex [12] and has been found to regulate base-excision repair ( BER ) by controlling the levels of Polymerase β [13 , 14] . USP47 has also been linked to axonal growth by working antagonistically to the E3 ligase CHIP in regulating the microtubule severing protein katanin-p60 [15] . Additionally , it has been associated with cell—cell adhesion through its ability to prevent E-cadherin degradation , which stabilizes adherens junction formation [16] . Finally , USP47 has recently been shown to control Wnt signaling by controlling β-catenin stability both in human and Drosophila cells [17] . In the present study , we show that USP47 acts post-translationally to stabilize Drosophila MAPK ( rl; FBgn0003256 ) levels both in cell culture and in vivo , and that this mechanism is independent of MAPK activity . Moreover , this event appears to be specific to MAPK , as no other core components of the pathway nor other Drosophila MAPK-like proteins , JNK ( bsk; FBgn0000229 ) and p38B ( FBgn0015765 ) , are modulated by USP47 function . To identify putative MAPK destabilizing components that might act in opposition to USP47 , we conducted a targeted RNAi screen for genetic interactors of Usp47 depletion . Interestingly , this approach led to the identification of three factors , namely , the E2 ubiquitin conjugating enzyme UBC6 ( FBgn0004436 ) and the putative E3 ligases POE ( FBgn0011230; the fly UBR4 ortholog ) and UFD4 ( FBgn0032208 ) , which restored MAPK levels when individually co-depleted with USP47 . Moreover , in a search for additional factors that act similarly to the newly identified E2/E3 enzymes , we found that KCMF1 ( FBgn0037655 ) , a zinc finger-containing protein that physically interacts with POE , also antagonized USP47 activity . Remarkably , UBC6 , UBR4 , and UFD4 are all linked to a mechanism called the N-end rule pathway , which has mostly been studied in yeast and whose relevance to metazoan cells remains largely unknown . The N-end rule pathway is a ubiquitin/proteasome-dependent protein degradation process that hinges on the recognition of a degron located at the N-terminal extremity ( N-degron ) of peptides and whose principal determinant is the identity of the N-terminal amino acid . This N-terminal amino acid is recognized by the ubiquitin protein ligase E3 component n-recognin ( UBR ) box domain of a class of E3 ligases called N-recognins , which includes POE/UBR4 [18–21] . UBC6 is the fly ortholog of RAD6 , which is the main E2 enzyme that functions with UBR box N-recognins [18 , 22] , whereas UFD4 is a poorly characterized HECT domain E3 ligase whose yeast homolog , Ufd4 , has also been linked to N-end rule function [23] . These new findings suggest that the N-end rule pathway plays a role in establishing MAPK levels and , together with previously characterized pre-translational mechanisms , fine-tunes MAPK signaling output during development .
We initially identified Usp47 in a genome-wide RNAi screen for factors modulating RASV12-induced ( Ras85D; FBgn0003205 ) pMAPK activity in Drosophila S2 cells [8] . Like most of the factors identified in this screen , Usp47 was found to act downstream of MEK ( Dsor1; FBgn0010269 ) . Indeed , signaling by constitutively active RAS , RAF , or MEK were all equally suppressed by a dsRNA targeting Usp47 to a degree that was similar to that induced by mago ( FBgn0002736 ) , a factor known to act downstream of MEK ( Fig 1B ) [7] . In contrast , cnk ( FBgn0021818 ) , a well characterized regulator of RAF activation [24] , reduced pMAPK signaling in the active RAS assay , but had no impact in active RAF ( FBgn0003079 ) or MEK assays ( Fig 1B ) . Usp47 also demonstrated a broad capacity to modulate MAPK signaling in different contexts; signal induced by the Insulin-like Receptor ( InR; FBgn0283499 ) , Sevenless ( sev; FBgn0003366 ) , or Epidermal Growth Factor Receptor ( Egfr; FBgn0003731 ) was suppressed to the same extent by Usp47 RNAi ( Fig 1C ) . JNK activity induced by RAC1V12 ( Rac1; FBgn0010333 ) was , however , not modified by Usp47 depletion ( Fig 1C ) . We next conducted genetic interaction experiments to validate Usp47’s newfound function in vivo . In flies , RAS-MAPK signaling is important in many aspects of development , including the induction of photoreceptor and cone cell differentiation in the eye [27] as well as in promoting the formation of veins during wing development [28 , 29] . Overexpression of a mapk gain-of-function allele ( mapkSem ) [30] in Drosophila wings using a salEPv-Gal4 driver led to the production of extra wing vein material ( Fig 1D ) [31] . The severity of this ectopic wing vein phenotype was significantly reduced by co-expressing Usp47 RNAi ( Fig 1D ) . In the eye , genetic lesions that lower pathway activity cause a readily observable rough eye phenotype in adults that is due to missing photoreceptor cells ( S1A Fig ) [32 , 33] . Two alleles of Usp47 ( Usp47Δ1 and Usp47Δ2 ) that reduce USP47 protein expression [10] were found to increase the severity of the rough eye phenotype of rl1 homozygous flies carrying a hypomorphic allele of mapk ( S1A Fig ) . Lastly , similar observations were also made in hemizygotes for cswlf ( S1B Fig ) , a dominant negative form of the csw/shp-2 ( FBgn0000382 ) phosphatase that regulates RAS activity [34] . In sum , these experiments support our cell culture-based observations pointing towards a positive regulatory role for USP47 in MAPK signaling . Like many of the candidates identified in our initial genome-wide screen , Usp47 RNAi was found to cause a decrease in MAPK protein levels ( Fig 2A ) [8] , whereas it did not visibly alter the expression levels of JNK ( FBgn0000229 ) and p38b ( FBgn0024846 ) , two other members of the MAPK family , nor of other RAS-MAPK pathway proteins ( Fig 2B ) . This effect was also observed in vivo , where knocking down Usp47 in both fly larval wing and eye imaginal discs caused a reduction in MAPK protein levels ( Fig 2C and 2D ) . Moreover , lysates prepared from Usp47Δ1 homozygotes were found to contain less MAPK protein than WT controls ( Fig 2E ) . A clear distinction between Usp47 and other candidates from our initial screen arose when examining their impact on mapk mRNA . Indeed , while the other factors caused changes in mapk mRNA levels or splicing , Usp47 had no impact at these levels ( Fig 2F and 2G ) . Moreover , it did not cause a change in the rate of mapk translation as measured by polysomal loading of mapk mRNA transcripts ( Fig 2H ) . Thus , regulation of MAPK levels by USP47 does not appear to be pre-translational . In order to determine if USP47 is acting at a post-translational level , we resorted to pulse-chase metabolic labeling with [35S]-methionine . We found labeled MAPK to be degraded more rapidly in cells treated with Usp47 RNAi ( Fig 3A and 3B ) . Indeed , in comparison to a half-life of 13 . 68 h in control conditions , the half-life of MAPK went down to 10 . 34 h upon USP47 depletion , which is consistent with the ~50% reduction of steady-state levels of MAPK typically seen by western blot following 4 d of dsRNA treatment ( Fig 2A ) . Importantly , an exogenous HA-MAPK cDNA , but not HA-JNK or HA-p38B , stably expressed in S2 cells also displayed sensitivity to Usp47 dsRNA ( S2A Fig ) . Taken together , these experiments strongly suggest that USP47 functions post-translationally to specifically regulate MAPK protein levels . One likely mechanism for post-translational control of MAPK levels by a deubiquitinase would be the removal of ubiquitin moieties from specific lysine residues on MAPK . As no ubiquitin ligases are associated to the regulation of MAPK expression in Drosophila , and as we had not identified any such factors in our genome-wide RNAi screen , we next sought to determine if the ubiquitin proteasome system ( UPS ) was required for the destabilization of MAPK following Usp47 knockdown . We found that co-depletion of Uba1—the sole ubiquitin-activating enzyme ( E1 ) in Drosophila—and Usp47 by RNAi rescued MAPK levels to near baseline ( Fig 3D ) . Proteasome inhibition using epoxomicin had a similar effect ( Fig 3E ) . Thus , these results indicate that the ubiquitin-proteasome system acts in opposition to USP47 to control MAPK protein levels . Despite these indicators of UPS involvement , we did not , however , detect any polyubiquitination of MAPK when co-expressing HA-tagged ubiquitin in S2 cells ( S3 Fig ) , which contrasted with the readily observable ubiquitination of controls such as RAF and KSR ( FBgn0015402 ) ( S3 Fig ) . Since ubiquitin might likely be attached to an exposed lysine at the surface of MAPK , we also mutated all predicted surface-exposed lysine residues . Unexpectedly , MAPK modified in this manner still responded to Usp47 RNAi to a degree comparable to WT ( Fig 3C ) . In contrast , mutation of all lysine residues did abrogate the effect of Usp47 ( Fig 3C ) . However , this latter mutant was not phosphorylatable by MEK ( unlike the WT or surface-exposed lysine mutant; S2B Fig ) , thus suggesting that its structure is significantly altered . Accordingly , mutations predicted to disrupt the N- or C-lobe of the kinase domain of MAPK also displayed a reduced sensitivity to Usp47 RNAi ( S2C Fig ) . Moreover , deletion of MAPK’s C-terminal αL16 helix , which interacts with the N-lobe and stabilizes it [6 , 35 , 36] , also led to a loss of sensitivity to Usp47 knockdown ( S2D Fig ) . Thus , while we could not identify specific MAPK lysine residues as potential ubiquitination sites to explain the requirement in USP47 activity , our data strongly suggest that a properly folded MAPK protein is required . This conclusion implies that turnover of misfolded or unstructured MAPK protein products is unlikely to account for the observed impact of USP47 . Still , it is possible that MAPK is ubiquitinated on one of its buried lysines . However , USP47’s post-translational access to buried lysines would have to occur in a manner that does not involve a prior significant disruption of MAPK structure . One alternative way in which buried lysines might be readily targeted for ubiquitination is during co-translational quality control that occurs when the nascent peptide is not yet fully folded [37] . To investigate this possibility , we again used [35S]-methionine metabolic labeling , but , unlike the previously described pulse-chase experiment ( Fig 3A ) , labeling was performed for a shorter period ( 4 h ) , after which MAPK levels were immediately assayed . The newly synthesized MAPK proteins marked in this manner did not show any changes when we compared Usp47 RNAi treated samples to negative controls ( S4 Fig ) . Therefore , this indicates that USP47 is not acting co-translationally on newly synthesized MAPK peptides during a hypothetical ribosomal quality control step . We next sought to examine the importance of two key regions of MAPK that are linked to its function and regulation . First , we mutated the tyrosine and threonine residues of the activation segment of MAPK to see if phosphorylation by MEK , which is required for MAPK activation , could have an impact on regulation by USP47 . However , we found that such a TEY→AEF MAPK mutant was still sensitive to Usp47 depletion ( S2E Fig ) . We then mutated MAPK’s D-site recruitment site ( also known as “DRS” ) that binds to MAPK D-site docking domains , which are present on some MAPK substrates and regulators [6] . The DRS mutant still responded to Usp47 depletion ( S2F Fig ) . This is also in line with our in vivo data showing that mapkSem , a MAPK gain-of-function point mutation that disrupts the DRS [30] , is also sensitive to Usp47 knockdown ( Fig 1D ) . In addition to the DRS , MAPK comprises an F-site recruitment site ( FRS ) that is involved in binding to substrates such as Elk1 and c-Fos , and which is exposed only following MAPK activation [6] . Since the non-activatable AEF mutant still responds to USP47 depletion , this implies that the FRS also does not play a role in determining sensitivity to USP47 . Finally , in parallel to this , we tested the impact of Usp47 RNAi on mammalian ERK1 and ERK2 stably expressed in S2 cells . Interestingly , we found them to respond to the same extent as Drosophila MAPK ( S2G Fig ) . Together , these results indicate that post-translational regulation of MAPK activity by dynamic phosphorylation is likely uncoupled from regulation by USP47 . Also , because the evolutionary distant mammalian ERK1/2 also respond to Usp47 RNAi , sensitivity to USP47 is most likely determined by an evolutionarily conserved structural feature of MAPK that is distinct from the activation segment , DRS , or FRS . Given that proteasome inhibition and Uba1 knockdown counteracted Usp47 depletion by restoring MAPK levels , we reasoned that at least one E2 ubiquitin conjugating enzyme and E3 ligase might also have similar properties . Previous studies have shown that RNAi co-depletion can be used as an effective means to identify related factors and establish epistasis relationships [38–40] . Thus , in order to identify such specific regulators involved in counteracting USP47 activity , we sought to isolate candidates that , like Uba1 , interacted genetically with Usp47 . Genetic interaction is defined here as a dual depletion effect that differs significantly from the sum of the individual depletion effects ( S5 Fig ) [39 , 41 , 42] . We used an immunofluorescence assay to quantify MAPK level variations that allowed us to obtain both a robust reduction following Usp47 RNAi as well as a rescue effect induced by co-depletion of Uba1 ( Fig 4A ) . We employed this assay to conduct a targeted RNAi screen using a focused dsRNA library encompassing factors linked to ubiquitin-proteasome function ( S1 Table; S1 Text ) that might interact genetically with Usp47 . RNAi reagents were tested in combination with a GFP control dsRNA or in co-depletion with Usp47 dsRNA ( Fig 4B and 4C , S6A Fig and S1 Table ) . In order to distinguish specific modifiers of Usp47 from other candidates that might simply alter MAPK levels in an unrelated process ( such as transcription or splicing regulation ) , we derived a genetic interaction score ( Δm ) that was used for primary hit selection ( S5A Fig and S1 Text ) . The Δm indicates the difference between the expected and observed outcomes of the combined depletion of Usp47 and gene x . Absence of genetic interaction occurs when the expected and observed outcomes are equivalent ( Δm = 0 ) , such as in the case of a purely additive effect when combining two dsRNAs . An aggravating genetic interaction is said to occur when the observed effect of combined Usp47 and x depletion is greater than the expected sum of the individual depletion effects ( Δm > 0; x is an enhancer of Usp47 ) . Conversely , an alleviating interaction ( Δm < 0; suppressor ) occurs when the observed depletion effect of Usp47 and x is lesser than the expected sum ( S5B and S5C Fig ) . Using Uba1 as a reference , we selected a Δm cutoff to guide our choice of primary hits for follow-up validation ( Fig 4D , S6B and S6E Fig and S1 Text ) . A number of components from the basal UPS displayed genetic interaction with Usp47 , and , as might be expected , many of these factors also caused a reduction in cell count indicative of an impact on cell viability ( S1 Table ) . We thus used a more stringent cutoff for factors that caused an appreciable reduction in cell number ( S6E Fig and S1 Text ) , thus removing some UPS factors from our primary hit selection in order to focus on non-cell lethal candidates . A total of 55 primary hits were recovered from the screen and further selected for validation using independent dsRNA reagents ( S2 Table ) . In our validation experiments , only candidates alleviating the impact of Usp47 dsRNA on MAPK levels were confirmed ( Δm < 0 ) ( S2 Table ) . The absence of aggravating genetic interactions ( Δm > 0 ) could signify that this type of regulation is not operating on USP47/MAPK . However , as Usp47 RNAi only partially depletes MAPK levels compared to the effect of mapk dsRNA itself ( Fig 2A ) , it might be expected that another DUB is acting redundantly to protect MAPK from complete degradation , in which case this factor should display an aggravating genetic interaction with Usp47 RNAi ( S5 Fig ) . In order to further explore this hypothesis—and the possibility that a redundant DUB might have been missed in our primary screen—we re-screened the other 41 predicted DUBs in the fly genome using a set of distinct dsRNAs . This second experiment confirmed our primary screen data , yielding no other DUBs that significantly impacted MAPK levels , either alone or when co-depleted with Usp47 ( S7 Fig and S3 Table ) . Taken together , these results suggest that Usp47 is not acting redundantly with another DUB and that the partial impact on MAPK levels must have another cause . For instance , dsRNA may not allow for knockdown below a certain threshold , and the degradation machinery may not be able to access the entire pool of MAPK or de novo synthesis of MAPK compensates for Usp47 RNAi-induced destabilization . We next further narrowed down our list to 13 candidates that displayed a significant rescue effect that was consistent with what we had observed in our primary screen ( S4 Table and S1 Text ) . Of particular interest , Ubc6 , an E2 ubiquitin-conjugating enzyme , and two E3 ligases , poe ( pushover or purity of essence ) and CG5604 ( hereafter referred to as Ufd4 or Ubiquitin fusion-degradation 4-like , after the name of its closest yeast counterpart ) were identified as the sole E2/E3 enzymes in this set ( Fig 5A and 5B ) . All three factors displayed a capacity to restore normal MAPK levels following Usp47 RNAi treatment , although Ufd4 was the weakest ( Fig 5A ) . We were also able to confirm the rescue effects of poe and Ubc6 on endogenous MAPK levels by western blot from S2 cell lysates ( Fig 5C ) as well as by immunofluorescence in Drosophila wing imaginal discs ( Fig 5D and S8 Fig ) . Additionally , poe RNAi was able to counteract Usp47 and restore the ectopic wing vein phenotype induced by mapkSem expression in wings ( Fig 5E ) . The ability of POE depletion to restore MAPK levels appeared specific to Usp47 co-depletion , as demonstrated by poe RNAi’s inability to rescue MAPK levels following the depletion of eIF4AIII , a regulator of mapk splicing ( Fig 5F ) . Moreover , unlike Uba1 , both Ubc6 and poe did not otherwise seem to have a generalized impact on protein ubiquitination in S2 cells ( S9A and S9B Fig ) , indicating that their activity is restricted to a subset of substrates . Finally , depletion of poe and Ubc6 RNAi also displayed an ability to rescue exogenously expressed HA-tagged MAPK ( Fig 5G ) , which is consistent with a post-translational rescue mechanism . Among the two putative E3 ligases recovered in the screen , poe RNAi was the strongest in terms of its capacity to rescue MAPK levels ( Fig 5A ) . In comparison , Ufd4 depletion produced only a partial rescue effect on MAPK levels ( Fig 5A ) , and this was the case for both dsRNAs tested ( S2 Table ) . However , POE does not contain a readily identifiable E3 ligase domain [21] and is only putatively designated as an E3 because it belongs to the UBR family of E3 ligases , with which it shares a UBR box domain . Because of this , we reasoned that another yet unidentified factor might be acting in conjunction with POE and UFD4 . Interestingly , recent work found that UBR4 , the human orthologue of POE , physically associates with the zinc-finger protein Potassium Channel Modulatory Factor 1 ( KCMF1 ) [43] . This factor was shown to contain an atypical C6H2-type RING finger domain that may function as a E3 ligase domain [44] . We tested four predicted Drosophila orthologues of Kcmf1 , which were absent from our ubiquitome dsRNA library and thus had not been previously tested for their capacity to rescue the impact of Usp47 dsRNA on MAPK levels . Strikingly , one of these factors , CG11984 ( hereafter referred to as Kcmf1 as it is also the closest predicted orthologue to its human counterpart ) , was found to rescue MAPK levels in a manner that was comparable with poe ( Fig 6A and 6B ) . Like poe and Ubc6 , Kcmf1 knockdown did not have a major impact on global protein ubiquitination in S2 cells ( S9A Fig ) , indicating a similar degree of specificity to MAPK . Moreover , exogenous KCMF1 was found to bind to at least two distinct portions of POE ( Fig 6C ) . Interestingly , knockdown of both poe and Kcmf1 also caused an increase in UFD4 levels ( Fig 6A ) , which provides another indication that these three factors are functionally interrelated . Furthermore , the human orthologues of KCMF1 , POE , and UBC6 have recently been shown to co-localize , and the KCMF1 N-terminus was found to bind to UBR4 , while the C-terminus associated with human RAD6 ( UBC6 counterpart ) [43] , strengthening the notion that they are functioning as part of an E2-E3 complex . When we stably expressed N- and C-terminal truncations of KCMF1 in S2 cells , we observed a dominant negative suppression of Usp47 RNAi ( Fig 6D ) , presumably due to the truncated protein segregating its binding partners . In sum , these results show that KCMF1 , likely in conjunction with UBC6 , POE , and UFD4 , plays an important part in destabilizing MAPK and acts in opposition to USP47 . One striking commonality shared by the three factors identified in the Usp47 genetic interaction screen is that they are all linked to the N-end rule ubiquitin-dependent protein degradation process . The identification of these three factors together thus suggests that MAPK might be the target of N-end rule regulation . This prompted us to test additional N-end rule related factors . Experiments in which we knocked down a series of known and predicted Drosophila N-end rule factors by RNAi and assayed their ability to counteract Usp47 knockdown did not bear fruit , as none of these displayed a capacity to restore MAPK levels ( S6 Table ) . However , upon testing the different Drosophila UBR family orthologues by RNAi , we found that the UBR1/2 orthologue also showed a capacity to restore MAPK levels ( S10A Fig and S6 Table ) . Unlike POE/UBR4 , the UBR1/2 orthologue caused a significant reduction in cell count ( S6 Table ) , explaining why it was not retained in our Usp47 genetic interaction screen . Thus , this additional result lends further support to the notion that MAPK might be the target of an N-end rule mechanism . Since a majority of proteins undergo co-translational cleavage of their initiator methionine by methionine aminopeptidases [46] , it is possible that the glutamate following the methionine of MAPK might be exposed , which could constitute an N-degron . Most of our experiments in which MAPK is expressed exogenously make use of an N-terminally tagged HA-MAPK . As HA-MAPK still responds to Usp47 RNAi , this suggests that the glutamate residue that follows the N-terminal methionine on WT MAPK is not involved in regulation by USP47 . To further investigate this possibility , we employed the ubiquitin-fusion technique , which allows for selective exposure of a residue at the N-terminus of a peptide [47] , and used this method to expose the penultimate glutamate residue on MAPK ( S10B Fig ) . However , both the N-terminally exposed glutamate ( WT ) and glycine ( stabilizing mutant ) forms were sensitive to Usp47 depletion , indicating that Usp47 does not intervene on the N-terminal penultimate residue of MAPK ( S10C and S10D Fig ) . This also precludes the possibility that the initiator methionine itself might constitute an N-degron ( recent work has shown that when the initiator methionine is not cleaved , N-acetylation of the methionine can also constitute an N-degron [19] ) as MAPK , without its N-terminal methionine , still responded to Usp47 RNAi ( S10D Fig ) . Thus , our results suggest that MAPK turnover is regulated by an N-end rule-mediated process and that USP47 counteracts the destabilizing impact of these factors on MAPK . However , it remains unclear whether or not MAPK is a direct target of this process , and , in the event that it is a direct target , it appears that neither the N-terminal methionine nor the glutamate that follows it is involved in recognition by the degradation machinery .
In this study , we show that USP47 , which was previously identified in a genome-wide screen for factors acting downstream of RASV12 [8] , acts to stabilize MAPK post-translationally through a mechanism involving the UPS . We also present results from a new candidate-based RNAi screen for genetic interactors of Usp47 that led us to identify three new factors that act to destabilize MAPK and thus counteract USP47 activity . Moreover , we present evidence for in vivo regulation of MAPK levels and signaling by USP47 , POE , and UBC6 that corroborates our cell culture data . Lastly , we identify a fourth destabilizing factor , the putative E3 ligase KCMF1 , and demonstrate that it acts in a similar manner to POE . Together , these results present strong evidence for a new means of post-translational control of MAPK levels ( Fig 7 ) , which unravel a completely novel aspect of MAPK regulation . The screening approach we employed in this study differs from classical techniques in that it was based on a genetic interaction approach by RNAi co-depletion . This approach is reminiscent of synthetic genetic interaction screens commonly conducted in yeast , but has only recently been introduced to the RNAi screening field [39 , 40] . Our results further support this approach as a viable technique in identifying functionally related factors , i . e . factors counteracting Usp47 , in this case . The advantage of such co-depletion-based approaches is highlighted by the fact that the ubiquitin-proteasome components and ligases identified here were not identified in the initial RAS-MAPK pathway screen that uncovered Usp47 . This can be readily explained by our observation that these factors only have a sizeable impact on MAPK levels in a context in which Usp47 is depleted . Thus , while the regulatory landscape of the RAS-MAPK pathway has now been extensively explored through single-depletion RNAi screening [8 , 48] , it is likely that other regulatory components would be uncovered by further co-depletion/genetic interaction RNAi screens . The most striking result from our Usp47 co-depletion RNAi screen was the identification of the three N-end rule related factors , poe , Ubc6 , and Ufd4 . In contrast to the canonical N-recognins UBR1 and UBR2 , non-canonical N-recognins such as UBR4 remain relatively poorly studied . UBR4 in particular is characterized by the absence of a readily identifiable ubiquitin ligase domain , which may indicate that they function by associating with catalytically factors such as UBR1/2 , UFD4 or possibly KCMF1 . CG5604 is referenced as Ufd4 in this study because it is the fly gene with highest sequence homology to Saccharomyces cerevisiae UFD4 , a HECT domain ubiquitin ligase that is a member of the ubiquitin fusion degradation ( UFD ) family ( Fig 5B ) . The UFD pathway is specifically linked to the recognition and ubiquitination of fusion proteins that incorporate a N-terminal ubiquitin moiety [49] . UFD4 in particular has been shown to recognize the N-terminal ubiquitin moiety of UFD substrates via its armadillo repeats [50] . Interestingly , UFD4 ( and other UFD pathway factors ) has also been shown to work synergistically with the N-end rule pathway [23] and to interact physically and functionally with UBR1 and contribute to the ubiquitination and degradation of N-end rule substrates [51] . Intriguingly , UBC4 ( the main E2 associated to UFD4 ) was not found to act on MAPK levels , indicating that if there is such a UBR4-UFD4 complex , it most likely would operate differently from the UBR1/RAD6-UFD4/UBC4 complex described in yeast . One alternative to the N-end rule pathway hypothesis is that these factors instead control lysosomal proteolysis of MAPK . Indeed , recent studies have linked RAD6 , UBR4 , and KCMF1 to lysosomal degradation and autophagy . UBR4 has both been shown to be degraded through autophagy and to have an impact on autophagic flux in mice [52] . RAD6 has been shown to promote mitochondrial turnover ( mitophagy ) in both flies and mammals [53] and also to promote the autophagic degradation of HP1 , a heterochromatin factor involved in DNA repair [54] . More recently , RAD6 , UBR4 , and KCMF1 have been found to interact physically and to bind lysosomal/mitophagy factors as well [43] . However , based on our data , a hypothetical autophagy-based mechanism for MAPK degradation would also have to incorporate proteasomal regulation , invoking a model involving both systems . To test this possibility , we assayed some of the principal autophagy-related factors by RNAi , but observed no impact of MAPK levels ( S6 Table ) , and this was also the case for predicted RAD6-associated factors from Hong et al . ( S6 Table ) [43] . Thus , despite previously published links , our data does not support an autophagy/lysosome-based degradation mechanism for MAPK . USP47 does not uniquely work on MAPK , as it has been found to stabilize other proteins . For instance , both the human and Drosophila forms have recently been found to stabilize β-catenin [17] . Human Usp47 has also been shown to be transported to adherens junctions by the KIFC3 kinesin , where it then acts to stabilize E-cadherin [16] . In flies , Usp47 has also been previously described as a negative regulator acting downstream of MAPK in the context of eye development , in which it stabilizes the transcriptional repressor TTK [10]; our description of Usp47 as a positive regulator seemingly opposes this previously described role and implies that Usp47 may play a dual role in the RTK/MAPK pathway with respect to eye development . Importantly , another E3 ligase , SINA , was implicated in the context of TTK , indicating a difference in the way these two components are regulated . This would explain the different outcomes we obtained in our genetic interaction experiments ( Fig 1D and S1 Fig and [10] ) ; specific genetic lesions in the MAPK pathway may be more sensitive to the positive or negative impact of regulation by USP47 . One plausible model is that USP47 inactivation leads to a rapid degradation of TTK followed by a slower degradation of MAPK . Thus , inactivating USP47 could initially provide a positive input into the pathway as TTK levels drop rapidly , which would then transition into a negative input as MAPK levels gradually decrease . Our findings indicate that USP47 , together with UBC6 , POE , UFD4 , KCMF1 , and the UPS , is part of a regulatory process that acts to control MAPK levels post-translationally . This discovery was unexpected given that MAPK was not generally known to be regulated by the UPS . Two notable exceptions to this deserve mention . Firstly , human ERK1/2 has been observed to undergo proteasomal degradation in a hyperosmotic condition induced by a prolonged treatment with high levels of sorbitol [55] . Secondly , ERK1c , an alternatively spliced isoform of ERK1 , has been shown to accumulate in the Golgi apparatus and undergo monoubiquitination in conditions of elevated cell density [56] . However , to our knowledge , the UPS was not previously known to regulate the stability of the main isoforms of MAPK ( ERK1/2 ) in physiological conditions . The impact of the UPS on MAPK may have eluded previous investigation attempts due to the presence of USP47 , which stabilizes MAPK in basal conditions . Indeed , it is only in USP47-depleted conditions that we were able to observe the effect of ubiquitin/proteasome components on MAPK . Furthermore , our inability to observe direct ubiquitination of MAPK may constitute another reason why this phenomenon had eluded prior investigation . It is possible that MAPK is subjected to a non-canonical form of ubiquitination [57] that is more difficult to detect or that it is altogether not the direct target for ubiquitination and is instead degraded through a mechanism that relies on unstructured regions or a ubiquitinated chaperoning protein for targeting to the proteasome ( Fig 7B ) [57 , 58] . Future work examining the regulation of USP47 and the associated ubiquitin ligases will be needed to determine in what physiological context this system is used to control MAPK levels . In terms of our understanding of pathway function , these findings add another layer of regulation to the set of transcriptional and splicing regulators we have described previously [7 , 8] , thereby positioning MAPK at the center of an intricate web of regulators acting on its expression . Further exploration of the environmental or developmental cues that control these factors will no doubt have important implications for our understanding of RAS-MAPK signaling dynamics .
Following the recommended Drosophila nomenclature , gene names are italicized . Protein product names are not italicized and , with the exception of p38 , are capitalized to distinguish from gene names . CG5604 is referred to here as Ufd4 based on the name of its predicted yeast orthologue and the naming scheme used for other UFD family members in Drosophila such as Ufd1-like ( FBgn0036136 ) . CG11984 ( FBgn0037655 ) is referred to as Kcmf1 based on the name of its human counterpart . Upon first mention of a specific Drosophila gene or protein , the FlyBase gene ID number is provided . The FlyBase gene symbol is also listed when it differs from the symbol used in this text . All dsRNAs were generated by in vitro transcription using T7 RNA polymerase . Following NaOAc ethanol precipitation , dsRNA concentration was assessed by gel dosage . Individual dsRNA probes were added to cells to a final concentration of 10 μg/ml . RNAi in S2 cells was conducted using dsRNA following previously described procedures [8] . For the Usp47 co-depletion screen and other quantitative microscopy-based experiments , cells were pre-incubated with Usp47 or GFP control dsRNA for 3 d and then distributed in clear-bottom 384 well plates ( Greiner ) containing the dsRNA sets for a second 3 d depletion . This was done as Usp47’s impact on MAPK levels was strongest at incubation times greater than 3 d , whereas other dsRNAs can cause lethality at longer depletion times . The full list of dsRNA reagents and primer sequences used in this study is presented in S7 Table . Subsequent to the Usp47 candidate screen , we selected one of the two dsRNAs from the validation step to use for follow-up experimentation . See S1 Text for further information on the targeted USP RNAi library . RT-qPCR was conducted using TaqMan Gene-specific assays ( primer sets and TaqMan hydrolysis probes ) , which were designed using the Universal Probe Library assay design center ( Roche Applied Science ) . Primer sequences and the Universal Probe Library probe number are listed in S7 Table . Assays were designed such that the amplified regions did not overlap with sequences targeted by dsRNA . Usp47 , Uba1 , Kcmf1 , and hit validation qPCR experiments were performed on S2 cell lysates obtained using the Cells-to-cDNA ( Ambion ) lysis buffer following treatment with the indicated dsRNAs . Samples were biological triplicates ( validation and Usp47 ) , quadruplicates ( Uba1 and Kcmf1 ) , or six biological replicates ( polysome RNA extraction ) . The in vivo experiments were biological triplicate samples prepared from the brain and eye/antennal disc tissue of four L3 larvae per sample . Samples from the polysome fractionation experiment were prepared as previously described [7] , after which RNA extraction was performed using TRIzol reagent ( Invitrogen ) . With the exception of the polysome fractionation experiment in which Ct values are presented , all other qPCR values were log normalized to negative control samples ( GFP dsRNA for the cell culture dsRNA experiments , or mCherry RNAi controls in the case of the in vivo experiments ) . Act5C and RpL32 ( FBgn0002626 ) were used as reference genes for standardization . Student’s t tests ( homoscedastic , two-tailed ) were performed by comparing the experimental values to those of the negative control samples . For RT-PCR , 5 μg of total RNA extracted from S2 cells ( RNeasy , Qiagen ) was primed with random primers followed by reverse transcription ( RT ) with SuperScript II Reverse Transcriptase ( Invitrogen ) . Primers are also listed in S7 Table . RNAi-treated S2 cells in 384 well plates were fixed in 4% paraformaldehyde and stained overnight with an α-MAPK antibody ( 1/1000 , Cell Signaling #4695 ) , phalloïdin , and DAPI . Plates were imaged on an Operetta ( PerkinElmer ) imaging platform . Acquired images were segmented and analyzed using Harmony ( PerkinElmer ) to derive the average MAPK and actin levels per cell as well as the average cell count per well . S2 cell culture , transfection , lysates , and western blotting were performed as previously described [59] . Pulse-chase radioactive labeling was performed by incubating S2 cells in ESF medium containing [35S]-methionine for 6 h . Cells were then harvested at different time points ( 0–36 h ) following this incubation period , after which an α-HA immunoprecipitation was performed to enrich for HA-MAPK . Fluorographic reagent ( NAMP100 , GE Healthcare ) was used to amplify [35S] signal . To visualize newly translated MAPK , we used a shorter labelling period of 4 h , after which cells were immediately lysed and submitted to immunoprecipitation . In both metabolic labeling experiments , RNAi treatment was performed for 5 d at a concentration of 15 μg/ml . Epoxomicin treatment was performed at a concentration of 1 μM ( DMSO used for controls ) 18 h prior to lysis of S2 cells . Whole fly lysates were prepared from 20 adults homogenized in 500 μl of RIPA buffer . Fly husbandry was conducted according to standard procedures . All crosses were performed at 25°C . The Usp47Δ1 , Usp47Δ2 , and Usp47rev lines were initially described in [10] . The rl1 ( FBst0000386 ) and UAS-rlSem ( FBst0059006 ) lines were obtained from Bloomington . The salEPv-Gal4 line was a kind gift from J . F . de Celis , and the cswlf line was originally obtained from L . Perkins . RNAi clones were generated using a line carrying a heat shock inducible flip-out actin promoter driving the expression of GAL4 and GFP in clonal tissues ( hs-flp;; Act5C . CD2 . Gal4 , UAS-GFP ) . L1 larvae were heat shocked for 15 min at 37°C and later collected for dissection upon reaching late L3 ( wandering ) stage . RNAi expression in wing discs was carried out using an engrailed-Gal4 driver line also carrying UAS-dcr2 to enhance RNAi depletion effectiveness . Staining was performed as previously described [8] , and immunofluorescence confocal microscopy was conducted using a Zeiss LSM 700 . For the in vivo qPCR experiments , RNAi expression was induced by a prolonged 30' heat shock using hs-flp;; Act5C . CD2 . Gal4 , UAS-GFP flies to express the indicated RNAi constructs in large clones that encompassed most of the larval tissue . Ubc6 RNAi expression was more problematic as the RNAi caused larval lethality , and clonal expression was not as prominent in tissue recovered from escapers . Accordingly , qPCR results for Ubc6 showed a weaker and more variable depletion .
|
The RAS-MAPK pathway plays a central role in the development of multicellular organisms , predominantly by regulating cell proliferation and differentiation . At its core , the pathway includes the RAS GTPase and three kinases ( RAF , MEK , and MAPK ) that transmit RAS signals through a phosphorylation cascade . Several factors have been discovered over the years that modulate signal transmission by altering the kinetics of phosphorylation/dephosphorylation of the core pathway components . In contrast , scant information is available on the mechanisms governing their expression and steady-state levels . Here , we report the characterization of a deubiquitinase ( DUB ) known as USP47 , which stabilizes MAPK protein levels by opposing the activity of the proteasome , which is a major protein degradation machinery in eukaryotic cells . A search for enzymes that opposed USP47 activity identified several components of the N-end rule pathway centered on the poorly characterized POE/UBR4 E3 ligase . These components appear to work together to target MAPK for proteasome-mediated protein degradation and thereby reduce MAPK protein half-life . Together , this work identifies a new means by which RAS-MAPK signaling output is controlled in Drosophila . As the role of the N-end rule pathway remains largely uncharacterized in metazoans , this work opens new opportunities for studying its function in higher organisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"invertebrates",
"rna",
"interference",
"chemical",
"compounds",
"enzymes",
"enzymology",
"animals",
"organic",
"compounds",
"animal",
"models",
"drosophila",
"melanogaster",
"model",
"organisms",
"basic",
"amino",
"acids",
"amino",
"acids",
"epigenetics",
"genetic",
"interactions",
"ligases",
"mapk",
"signaling",
"cascades",
"drosophila",
"research",
"and",
"analysis",
"methods",
"genetic",
"interference",
"proteins",
"gene",
"expression",
"ras",
"signaling",
"ubiquitination",
"chemistry",
"insects",
"arthropoda",
"biochemistry",
"signal",
"transduction",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"post-translational",
"modification",
"organic",
"chemistry",
"gene",
"identification",
"and",
"analysis",
"genetics",
"biology",
"and",
"life",
"sciences",
"lysine",
"physical",
"sciences",
"cell",
"signaling",
"organisms",
"signaling",
"cascades"
] |
2016
|
The Deubiquitinase USP47 Stabilizes MAPK by Counteracting the Function of the N-end Rule ligase POE/UBR4 in Drosophila
|
The communities in fishing villages in the Great Lakes Region of Africa and particularly in Uganda experience recurrent cholera outbreaks that lead to considerable mortality and morbidity . We evaluated cholera epidemiology and population characteristics in the fishing villages of Uganda to better target prevention and control interventions of cholera and contribute to its elimination from those communities . We conducted a prospective study between 2011–15 in fishing villages in Uganda . We collected , reviewed and documented epidemiological and socioeconomic data for 10 cholera outbreaks that occurred in fishing communities located along the African Great Lakes and River Nile in Uganda . These outbreaks caused 1 , 827 suspected cholera cases and 43 deaths , with a Case-Fatality Ratio ( CFR ) of 2 . 4% . Though the communities in the fishing villages make up only 5–10% of the Ugandan population , they bear the biggest burden of cholera contributing 58% and 55% of all reported cases and deaths in Uganda during the study period . The CFR was significantly higher among males than females ( 3 . 2% vs . 1 . 3% , p = 0 . 02 ) . The outbreaks were seasonal with most cases occurring during the months of April-May . Male children under age of 5 years , and 5–9 years had increased risk . Cholera was endemic in some villages with well-defined “hotspots” . Practices predisposing communities to cholera outbreaks included: the use of contaminated lake water , poor sanitation and hygiene . Additional factors were: ignorance , illiteracy , and poverty . Cholera outbreaks were a major cause of morbidity and mortality among the fishing communities in Uganda . In addition to improvements in water , sanitation , and hygiene , oral cholera vaccines could play an important role in the prevention and control of these outbreaks , particularly when targeted to high-risk areas and populations . Promotion and facilitation of access to social services including education and reduction in poverty should contribute to cholera prevention , control and elimination in these communities .
Cholera remains a major public health problem in many countries in sub-Saharan Africa . In 2014 , 190 , 549 cases and 2 , 231 deaths were reported to the World Health Organization ( WHO ) . Among 2 , 231 deaths , 1 , 882 ( 84 . 3% ) were from Africa [1] . Some authors estimate that the real number of deaths might be as high as 95 , 000 per year [2] . In the last twenty years , sub-Saharan Africa , and especially the Great Lakes Region , has suffered the highest disease burden [3–6] . Uganda is located in the Great Lakes Region and reports cholera cases and deaths annually since 1998 [7–11] . Communities most vulnerable to cholera in Uganda are situated along the lakes [8 , 9] , where the major economic activity is fishing or fish selling . Fish export rank third as a contributor to the Ugandan Gross Domestic Product ( GDP ) [12] . Fishermen in Uganda like those in other parts of Africa are poor with high rates of infectious diseases , such as human immunodeficiency virus ( HIV ) infection [13–15] . While the proximity to water bodies is a known risk factor [4 , 6 , 16] , little is known about people inhabiting these at-risk areas and so far no interventions targeting fishing villages have been proposed . Generally , WHO recommends integrated cholera prevention and control interventions comprising Water , Sanitation and Hygiene ( WASH ) measures and Oral Cholera Vaccine ( OCV ) use in endemic settings . For this article we collected and reviewed epidemiological data on cholera with a focus on fishing villages for the period 2011 to 2015 . Our aim was to generate information on the burden and characteristics of cholera epidemics so as to make recommendations on how to target cholera control interventions as recommended by WHO . Given the importance of artisanal fishing throughout sub-Saharan Africa , we expect that our findings will be relevant to other fishing villages in Uganda , the Great Lakes Region and similar settings across Africa .
The data used in this study were collected as part of routine MOH disease surveillance work . Permission to conduct the study was obtained from the Makerere University School of Public Health Institutional Review Board ( IRB 00011353 ) . Surveillance forms were anonymized to ensure confidentiality . We collected , reviewed and documented epidemiological and socioeconomic information on cholera outbreaks in the fishing villages known for frequent cholera outbreaks in Uganda for the period 2011–15 focusing on the outbreaks along African Great Lakes in Uganda and river Nile . Additionally , we performed household surveys in 2015 to get better understanding of the socioeconomic characteristics and practices of cholera affected households in urban and rural fishing communities . We worked through Ugandan Ministry of Health ( MOH ) which routinely conducts disease surveillance and response to outbreaks of cholera and others diseases/events nationwide [17] . Based on this surveillance , the health workers at community , health facility , sub-district , district and central MOH levels investigate potential public health threats or outbreaks and take appropriate preventive and control actions . The investigation teams and district level units report to central level in Kampala on actions taken and gaps that need additional support . Delay in reporting and response are determined by the public-health relevance of the reported event . Since 2000 , the MOH requires districts to regularly compile weekly reports from their health facilities and aggregate the data into a district summary report that is submitted to the central level in Kampala and used to produce a national level weekly surveillance report [18] . In 2015 , Uganda had 112 districts which regularly compiled weekly reports: http://health . go . ug/content/weekly-epidemiological-bulletins . In regards to cholera , more detailed individual case information ( line list ) is collected from the patient registers and sent to the district and central ( national ) level MOH head office in Kampala . Information on the line lists includes: patient name , age , sex , place of origin ( district , sub-county ( SC ) , parish , village–from larger to smaller geographic unit ) , symptoms and signs , date of illness onset , date of admission , treatment administered , date of discharge and outcome . For this study , we reviewed weekly nationwide cholera surveillance reports , laboratory and outbreak investigation reports , and available line lists from the MOH Uganda for 2011–2015 . We also assessed for seasonal pattern of the cholera outbreaks using satellite rainfall data ( precipitation ) for Uganda that was obtained from Weatherbase at http://www . weatherbase . com/weather [19] . We used the long-term average rainfall ( precipitation ) collected for 101 years from 80 cities/districts in Uganda . We focused on cholera outbreaks that occurred in the fishing villages along the Great Lakes ( lakes Victoria , Albert , Edward , George and Kyoga ) and along the River Nile ( Fig 1 ) . In addition to the routine MOH disease surveillance system , Uganda with other eight ( 8 ) sub-Saharan African countries participated in the African Cholera Surveillance Network ( AFRICHOL ) project which carried out cholera case-based surveillance in selected surveillance areas [20] . In Uganda , these areas included Kasese district in Western Uganda plus Mbale , Busia , Manafwa , Butaleja and Tororo districts in Eastern Uganda . The patients in Hoima and Kasese districts were followed up within two weeks of discharge from the Cholera Treatment Centre ( CTC ) by a joint MOH and Makerere University School of Public Health team to collect detailed information on the household characteristics , home environment and implementation of cholera preventive measures . One urban fishing community ( Katwe Kabatoro Town Council ( KKTC ) in Kasese district ) and one rural fishing community ( in Albertine rural Hoima , Hoima district ) were followed up . The information was collected using a structured questionnaire administered to the heads of the households by trained health workers mainly the health assistants from the nearest health facilities . During home visits , health education and referral of new identified cholera cases was carried out . In addition , information on the characteristics of cholera affected household to understand sanitation and hygiene practices , availability of safe water , socioeconomic status , education level , knowledge and behaviors was collected . We included only confirmed cholera outbreaks reported to MOH and occurring in the fishing villages along the Great lakes ( lakes Victoria , Albert , Edward , Kyoga , George and Katwe ) or River Nile with available line lists during the study period 2011–15 . Additional inclusion criteria were: Availability of weekly district surveillance reports , outbreak investigation reports during the study period and laboratory reports confirming the outbreak ( at least one confirmed case ) through the isolation of Vibrio cholerae from stool samples . We collected and reviewed information on the total aggregated number of reported suspected cholera cases and deaths at national level during the same time period from all districts in Uganda by a analysis of the weekly epidemiological reports available at MOH , Kampala . Stool collection and testing were conducted as recommended by the MOH laboratory and cholera prevention guidelines [21 , 22] . Stool samples for laboratory testing were collected from patients meeting the standard case definition for cholera before antibiotic administration . Only 10–20 stool samples were collected in accordance with national ( Uganda ) laboratory guidelines to confirm the cholera outbreak . A few additional random stool samples were later collected to monitor antimicrobial sensitivity . Finally , the collection of additional stool samples was done to determine the end of the outbreaks . To transport cholera stool samples , fresh stool samples or cotton tipped rectal swabs soaked in fresh liquid stools were individually placed in Cary-Blair transport media , and then sealed in sterile zip lock bags . The bags were packed into cool boxes , and transported to the Uganda National Health Laboratory Services ( Central Public Health Laboratories , http://www . health . go . ug/content/central-public-health-laboratorycphl ) in Kampala within seven days for cholera culture identification using standard laboratory cholera identification procedures [23] . Data were collected by district health workers after training on study protocol and tools . Data with personal identifiers were coded or made anonymous . The data were entered , cleaned , stored and analyzed using Stata 12 software ( StataCorp , Texas , USA ) and Microsoft Excel spreadsheets . The differences between groups were tested using chi-square test for proportions ( or Fisher exact test for small size ) . Comparison of age between outbreaks was tested for significance with Kruskall-Wallis test . The analysis of factors associated with cholera deaths was performed using logistic regression models . Variables with p values<0 . 20 in the univariate analysis were entered into a multivariate model . Variables were kept in the final model if p < 0 . 05 . For each outbreak , attack rates ( number of cases/population ) were calculated and expressed per thousand , using sub-county population data as the denominator . Sub-county data used was obtained from the Uganda Bureau of Statistics [UBOS] , http://www . ubos . org . Population data from the most recent census ( 2014 ) [24] was used for outbreaks occurring in 2014 or 2015 ( adding district growth rates for 2015 data , based on 2014 census: 2 . 45% , 4 . 27% and 6 . 61% for Kasese , Hoima and Wakiso districts , respectively ) . For outbreaks that occurred in 2011–2013 , population projections derived from UBOS census data of 2002 were used [25] . Information on residence was self-reported by the patients to the health workers at the health facilities . Based on this information , we used Quantum Geographic Information System ( QGIS ) [26] to create maps . The administrative boundary GIS layer was obtained from UBOS on the Humanitarian Data Exchange website [27] in ESRI shapefile format . The unit of geographic analysis was the sub-county ( administrative level 5 ) , which was recorded for each suspected cholera case . This was the minimum mapping unit that corresponded to the population census data conducted by UBOS [24] . Individual sub-counties ( SC ) on the map were shaded according to the number of cases occurring within them . We applied several amendments to the SC names . In Namayingo district , Buhemba was replaced by Buyinja because , in the GIS layer we used , Buhemba was a parish of the Buyinja SC . Similarly in Wakiso district , Bussi was replaced by Kasanje . For the Hoima 2012 cholera outbreak , one case from Masindi SC ( which belonged to Masindi district ) did not appear on the Hoima map . There were two cholera cases during the first cholera outbreak in Moyo district in 2014 from Ciforo SC ( Adjumani district ) who did not appear on the Moyo map .
During the study period 2011–15 , a total of 5 , 059 suspected cholera cases and 113 deaths ( CFR: 2 . 2% ) were recorded . There were several outbreaks recorded in 12 districts of Uganda with fishing villages as in Fig 2 . In six out of 12 districts that reported cholera outbreaks there were ten ( 10 ) outbreaks for which detailed information was collected for the period 2011–2015 . All reported cholera outbreaks in fishing villages during the study period were located along the African Great Lakes ( Victoria , Albert , Edward and Katwe ) or the River Nile . There were no cholera outbreaks in the fishing villages on Lakes Kyoga and George . Of the ten ( 10 ) cholera outbreaks with detailed information , three ( 3 ) were reported in fishing villages on Lake Edward: one in Rukungiri district in 2011 ( Bwambara SC , Rwenshama parish ) and two in Kasese district ( Nyakiyumbu SC , Kayanzi parish in 2011 and Katwe-Kabatoro Town Council [KKTC] SC , Kyarukara parish in 2015 ) ( Fig 3 ) . Three ( 3 ) cholera outbreaks were also reported on Lake Albert in the district of Hoima in 2012 , 2013 and 2015 ( Fig 4 ) . Along Lake Victoria , two ( 2 ) cholera outbreaks were reported in the districts of Namayingo in 2014 ( mainly Mutumba SC , Lubango parish ) and Wakiso in 2015 ( on the islands of Bussi / Kasanje SC ) ( Fig 5 ) . In addition to the three affected lakes , cholera outbreaks were reported along the River Nile ( Albert/White Nile ) in 2014 in the fishing communities in Moyo district ( Fig 6 ) . Over the study period , the total number of reported cholera cases and deaths from these outbreaks on the line lists were 1 , 827 and 43 respectively ( Case-Fatality Ratio [CFR] , 2 . 4% ) . There were a limited number of affected sub-counties in each outbreak ( between 1–5 sub-counties ) , representing 0 . 8% to 47 . 4% of the total district population . Half of the outbreaks occurred in only one SC within the affected district . Some sub-counties in Hoima district along Lake Albert had recurrent outbreaks , in particular Buseruka , which was affected by cholera in 2012 , 2013 and 2015 . Within Buseruka SC , Tonya was the most affected parish contributing 96 . 3% of all cholera cases in the SC . Overall , cholera outbreaks lasted an average of 35 days ( 5 weeks ) with a range of 8–90 days . Fewer cholera cases occurred during January-March , while April-May had the highest proportion of total cases ( 22 . 5% ) . The majority of the outbreaks occurred during the rainy season ( 82 . 4% ) with a big peak during the months of April-June and a smaller peak during September–November of each year ( Fig 7 ) . Attack rates in affected sub-counties ranged from 0 . 2 to 9 . 1 per 1000 ( Table 1 ) . The highest attack rate was in Katwe-Kabatoro Town Council ( KKTC ) , where no deaths were reported . Male to female sex-ratio was 1 . 3 but varied greatly between outbreaks ( from 0 . 7 in Moyo to 2 . 0 in Kasese/Kayanzi village ) . Overall , males were more affected than females in all age groups ( Fig 8 ) . The mean age was 21 . 2 years ( range 0 . 1–81 ) and median age 20 years ( Interquartile range [ ( IQR] 7–30 ) , with significant differences between outbreaks ( p<0 . 001 ) from a mean age of 18 years in Namayingo district in 2014 , to mean age of 40 . 2 years in Moyo district during the second outbreak in 2014 . A total of 537/1 , 827 cases ( 29 . 4% ) were children below age 10 years , with 311 children under 5 years and 226 children age 5–9 years . Outbreaks with the highest proportions of under the age of 10 years were in Hoima district during 2015 ( 37 . 2% ) , 2013 ( 34 . 6% ) and 2012 ( 34 . 1% ) and the 2015 Kasese KKTC outbreak ( 36 . 7% ) . Nine ( 9 ) of ten outbreaks had a CFR>1% ( Table 1 ) . The CFR was significantly higher among males than females ( 3 . 2% vs . 1 . 3% , p = 0 . 02 ) and in six out of ten outbreaks , no deaths among females were notified . The CFR also varied between outbreaks within a district , e . g . in Kasese from 0% in the urban area of KKTC in 2015 to 5 . 8% in Kayanzi fishing village during 2011 . There was a higher average CFR ( = 3 . 4% ) among adults aged >25 years based on available line lists . Forty-two percent ( 40% ) of deaths occurred within the first week of the outbreaks . Factors independently associated with death in the multivariate analysis were gender ( male vs . female OR = 2 . 6 ( CI 95% , 1 . 2–5 . 6 ) ) and month of onset ( reference April , OR July = 19 . 2 ( CI 95% , 4 . 9–75 . 6 ) , OR September = 12 . 3 ( CI 95% , 2 . 2–69 . 6 ) ) . Laboratory tests showed that Vibrio cholerae O1 was responsible for all outbreaks . The two serotypes Inaba ( 8 out of 10 outbreaks ) and Ogawa ( responsible for 2 out of 10 outbreaks , in Namayingo and Wakiso districts ) were isolated from stool samples . Regardless of serotype , the strains were sensitive to cefuroxime , tetracycline , erythromycin and ciprofloxacin and resistant to nalidixic acid , cotrimoxazole and chloramphenicol . Outbreak investigation reports indicated that epidemics in different locations within fishing villages and periods presented similarities regarding environmental , personal and household characteristics . Common findings in the outbreak investigation reports were the low latrine coverage of less than 50% , open defecation and bathing in lake waters by the communities in the fishing villages . A total of 137 households ( 51 in KKTC-Kasese district , and 86 in Hoima district ) were visited within two weeks of discharge of cholera cases by the health assistants to collect socio-economic and environmental information on the households . Majority of household heads in Hoima district were illiterate ( 61% , 50/82 ) with the average monthly household income of USD 37 , and 60% had less than USD 30 per month . The most common source of drink water was lake Albert ( 72% , 62/86 ) . The majority ( 64% ) of households stored water in open containers and 18% practiced open defecation . Less than half ( 42 . 3% ) knew how to treat cholera using Oral Rehydration Salt ( ORS ) . In contrast , KKTC ( urban area ) , the majority of households got their drinking water from the protected springs and stored it in covered containers ( 100% ) , had community latrines for the homesteads ( 98% ) and knowledge on cholera treatment with ORS was 84% . Nationwide reported cholera data during the study period showed that outbreaks in fishing villages were responsible for an average of 58% of cholera cases and 55% of deaths in Uganda ( Table 2 ) . Majority of the ten outbreaks in Fig 7 above occurred in the rainy season . This seasonal pattern is much clearer for the aggregated overall national annual cholera cases that occurred during the study period 2011–15 in fishing villages in Uganda . There were two peaks that corresponded to bimodal rainfall seasonal pattern . The highest cholera peak stretches from April-August and smaller one in October–December . Cholera peaks lagged behind the rainfall peak with an average of 5–6 weeks . The average monthly rainfall and the number of reported cholera cases during the study period in fishing villages are shown in figure ( Fig 9 ) . The MOH used a multi-sectoral approach to implement all or some of the following interventions: disease surveillance; patient care; infection control; health education; promotion of latrine construction and use; promotion of hand washing; enforcement of sanitation and hygiene using strategies such as sanitation improvement campaigns; decongesting the lake by reducing the number of illegal boats; and imposing a ban on the sale of cold foods . The most common challenges were: lack of alternative water source ( other than the lake ) ; inadequate hand washing facilities; ignorance regarding the importance of hand washing; difficulty in implementing sanitation measures since many of the community members had a livelihood based on the mobile profession of fishing; and preference for open defecation due to the perception that it increased fish catch . Some homes were located close to the lakeshore making it difficult to construct latrines near to residences . The high level of poverty impeded effective cholera control interventions . For example , in Kasese district repair/maintenance of safe water systems could not be done due to failure to mobilize adequate money from the community . Also , many patients reported late for medical care leading to poor outcome . Finally , language barriers between actors and cholera affected populations since the languages spoken by the affected communities were not the languages used on the local radio messages to mobilize the communities .
These findings reflect available data from the MOH regarding cholera outbreaks occurring in often remote lakeshore communities . They may not be exhaustive , so the proportion of those cases among country-wide cases occurring in the same period is possibly underestimated . Few variables were available in the line lists , which did not allow us to perform detailed analysis of risk factors at individual level . Because we could not include control conditions , we are unable to comment on the degree to which specific factors contributed to the occurrence of cholera outbreaks in the fishing villages under the study . Therefore a follow up a case-control or cohort study should be carried out to better inform the global community on this issue . Finally , few laboratory reports were available making it difficult to draw firm conclusions of cholera serotypes or antibiotic resistance . Furthermore , due to inadequate laboratory facilities and techniques in most of the affected communities , isolation and storage of the cholera organism for advanced testing such as molecular characterization could not be done . In order to conduct such tests in future , Uganda government and local partners should institute a feasible mechanism to safely transport stool samples and or store them securely .
Cholera is a big public health problem in the fishing villages along the Great Lakes and River Nile in Uganda . In the short term , a comprehensive cholera prevention and control approach as recommended by WHO [41 , 42] that includes complementary use of OCV to Water , Sanitation and Hygiene ( WASH ) interventions ( latrine construction and use , water chlorination , hand washing campaigns with soap , compact water filtering pumps ) and others should be instituted . This approach could provide economic benefits by boosting tourism and food exports which shall reduce the poverty levels and the cholera disease burden in the fishing villages and Uganda . Because cholera is seasonal , the periods without outbreaks provide an opportunity to intervene and prevent future outbreaks . This preventive approach is particularly appealing since the short duration of most outbreaks makes a reactive approach very challenging . Targeting smaller geographic areas , and high-risk groups within these areas , may provide an efficient means of reducing overall cholera burden . Further studies namely; a case control study , studies to assess community knowledge and practices regarding cholera prevention and molecular typing should be conducted . In the long term , improving income , education , and living conditions in the fishing villages will provide the best means of reducing cholera and other diseases of poverty .
|
Cholera , though a preventable and treatable disease , remains a major cause of morbidity and mortality in the Great Lakes Region of Africa , including Uganda . The communities in the fishing villages constitute 5–10% of the total Ugandan population . Most fishing villages are located along Lakes Victoria , Albert and Edward and the River Nile . During the study period , 2011–2015 these villages were responsible for over 50% of the reported annual cholera cases and deaths in Uganda . The CFR was significantly higher among males than females ( 3 . 2% vs . 1 . 3% , p = 0 . 02 ) . Our study is the first to systematically describe the epidemiology of these outbreaks and socioeconomic characteristics of communities in the fishing villages in Uganda . Our study found that persons in the fishing villages were at increased risk of cholera outbreaks due to poor access to safe water , sanitation , and hygiene . Furthermore , the villages had similar population characteristics such as illiteracy , ignorance regarding cholera transmission , poverty and constant population migration . In addition to improvements in water , sanitation , and hygiene , complementary use of oral cholera vaccines could play an important role , particularly when targeted to high-risk areas and populations . As a long term strategy , improvements in education and reduction in poverty should contribute to cholera prevention , control and elimination in the fishing villages and Uganda as whole .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion",
"Conclusions"
] |
[
"medicine",
"and",
"health",
"sciences",
"ecology",
"and",
"environmental",
"sciences",
"surface",
"water",
"tropical",
"diseases",
"geographical",
"locations",
"uganda",
"health",
"care",
"bacterial",
"diseases",
"aquatic",
"environments",
"bodies",
"of",
"water",
"neglected",
"tropical",
"diseases",
"sanitation",
"africa",
"hydrology",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"cholera",
"lakes",
"epidemiology",
"marine",
"and",
"aquatic",
"sciences",
"people",
"and",
"places",
"environmental",
"health",
"freshwater",
"environments",
"earth",
"sciences",
"disease",
"surveillance"
] |
2017
|
Epidemiology of cholera outbreaks and socio-economic characteristics of the communities in the fishing villages of Uganda: 2011-2015
|
Rift Valley fever ( RVF ) , a mosquito-borne zoonosis , is a major public health and veterinary problem in sub-Saharan Africa . Surveillance to monitor mosquito populations during the inter-epidemic period ( IEP ) and viral activity in these vectors is critical to informing public health decisions for early warning and control of the disease . Using a combination of field bioassays , electrophysiological and chemical analyses we demonstrated that skin-derived aldehydes ( heptanal , octanal , nonanal , decanal ) common to RVF virus ( RVFV ) hosts including sheep , cow , donkey , goat and human serve as potent attractants for RVFV mosquito vectors . Furthermore , a blend formulated from the four aldehydes and combined with CO2-baited CDC trap without a light bulb doubled to tripled trap captures compared to control traps baited with CO2 alone . Our results reveal that ( a ) because of the commonality of the host chemical signature required for attraction , the host-vector interaction appears to favor the mosquito vector allowing it to find and opportunistically feed on a wide range of mammalian hosts of the disease , and ( b ) the sensitivity , specificity and superiority of this trapping system offers the potential for its wider use in surveillance programs for RVFV mosquito vectors especially during the IEP .
Rift Valley fever ( RVF ) is a mosquito-borne zoonosis which is of major public health and veterinary concern in sub-Saharan Africa ( SSA ) . In the last 20 years , epidemics of the disease have occurred at irregular intervals with hundreds of thousands of infections in humans and livestock . The 1997–1998 RVF outbreak in East Africa including Kenya , Somalia , and Tanzania represents the largest outbreak of RVF infection ever recorded in SSA that affected over 100 , 000 humans with over 450 deaths in Kenya alone [1] . The emergence and re-emergence of the disease especially in East Africa , poses not only a huge threat to livestock , and human health , but it also represents a looming health threat likely to spread beyond Africa due to global environmental , demographic and societal changes and trade [2] . Additionally , the economic losses due to zoonotic disease outbreaks can be staggering including trade sanctions , travel warnings or restrictions , animal disease control efforts such as animal culling ( intentional slaughter ) , and declining public confidence in animal products [3] . For example , once RVF is known to be circulating in an animal herd , the World Organization for Animal Health ( OIE ) places a three-year export embargo on those animals . Mosquito bites are the most important transmission mechanisms of the disease to mammalian hosts including humans [4] , [5] . Although several mosquito species in diverse genera have been implicated as vectors following isolation of the RVF virus ( RVFV ) [5]–[8] , there is strong evidence that in Kenya , Aedes mcintoshi and Ae . ochraceus play key roles in the transmission of the disease [8]–[10] . During the 2007/2008 RVF outbreak in Kenya , these two species were identified as primary RVFV vectors , accounting for over 77% of positive pools of mosquitoes sampled in the field [8] which occur predominantly in North-Eastern Kenya . In addition , Mansonia and other Culex mosquitoes are important RVFV vectors in Marigat District of Rift-Valley area , a highly endemic area of the disease [8] . In spite of the apparent emergence and re-emergence of arboviral diseases and especially RVF in East Africa , sensitive surveillance programs to actively monitor vector populations to provide an early warning system are lacking . Entomologic arbovirus surveillance is advantageous because it ( i ) provides the earliest evidence of transmission in an area , ( ii ) identifies the potential risk to humans , and ( iii ) allows emergency control operations to be set in motion in advance of epidemics . Vectors once infected , remain infected with the virus for the duration of their life , unlike in humans and other vertebrates which are only transiently infected [11] . The option of serologic surveillance in animals is complicated by problems of cross reactivity among arbovirus groups [12]–[14] . Moreover , other challenges that may compromise the efficacy of animal hosts as a surveillance tool include ethical issues associated with using animals; challenges of bleeding larger animals which represents an occupational health and safety issue [12]; and reduced sensitivity due to the development of herd immunity [13] , [14] which may dampen seroconversion . This makes vector surveillance the best option to target for arbovirus activity especially as RVF epidemics in these susceptible animals , initiated by bites of infected mosquitoes , are also involved in sustaining the disease . Until now , RVFV vectors have been monitored using CO2-baited CDC light traps , which are generally non-specific and trap a wide range of non-target insect species such as beetles and moths , in addition to mosquitoes . Additionally , because of low sensitivity , this trapping system is inadequate for use during the low intensity inter-epidemic period ( IEP ) of enzootic virus transmission where viral activity may remain undetected among mosquito species [4] , [12] , [15] . Thus , there is a critical need to develop more sensitive and effective monitoring tools to increase trap captures of mosquito vectors so as to maximize detection of virus activity . Like most hematophagous insects , RVFV vectors use olfactory cues to locate their hosts for a blood meal [16] , [17] which may involve more than mammalian breath odors such as CO2 , a non-specific semiochemical , commonly used in the CDC light trap . We therefore refined the sensitivity of the existing trapping system for RVFV vectors by combining it with known mammalian host skin-derived semiochemicals in order to target only mosquitoes . Here we report the identification of key kairomones responsible for attraction of RVFV vectors which demonstrate the commonality of mammalian host skin-derived attractants for mosquito vectors of the disease , and development of a highly efficient monitoring tool for RVFV vectors which exploits a semiochemical lure , developed from skin odors of these mammals that can potentially impact RVFV mosquito surveillance during the IEP .
All experiments were carried out at two ecologically distinct sites , i . e . Ijara and Marigat districts of Kenya ( Figure 1 ) , both highly endemic areas for epidemic RVF [8] and which are currently under active surveillance for arbovirus activities . Ijara District is located in the North Eastern Province of Kenya , where traps were set out in two major locations: Sangailu ( 01 . 31°S , 40 . 71°E ) and Kotile ( 1 . 97°S , 40 . 19°E ) . The entire district is semi-arid and normally experiences two rainy seasons a year which frequently fail: the so-called short rains between October and December and the long rains in March and April . The area is located at an altitude of about 60 m above sea level ( asl ) and typical annual rainfall averages between 300 to 500 mm . The people in North Eastern Province are predominantly ethnic Somali and practice pastoralism , keeping livestock including cattle , goats , sheep , camels , and donkeys . Vegetation predominantly consists of shrubs and acacia bushes . In Marigat District located in the Rift Valley Province of Kenya , traps were set in surrounding villages/communities namely N'gambo ( 0 . 50°N , 36 . 06°E ) , Salabani ( 0 . 55°N , 36 . 06°E ) , Lerocho ( 0 . 56°N , 36 . 01°E ) , Bogoria ( 0 . 37°N , 36 . 05°E ) and Sirata ( 0 . 46°N , 36 . 10°E ) . The vegetation in the low lying arid part of Marigat district consists of northern Acacia-Commiphora bushlands and thickets and has experienced severe land degradation caused by uncontrolled grazing . The local inhabitants who are mainly agro-pastoralists subsist on limited crop production and livestock rearing . This district located around 1000 m asl receives annual rainfall ranging from 300 to 700 mm . Skin odors from cow , donkey , goat and sheep were collected by rubbing stockinette cotton material ( Clinitex , FL Orthopedics , USA , latex free with antimicrobial protection ) on the belly and back areas avoiding the head and anal regions for 12–15 minutes using 4 pieces of the material per animal each measuring 10 cm×26 . 5 cm . The stockinette material was handled with latex gloved hands in order to minimize contamination from human skin . Human odor used in the experiment consisted of four pieces of worn stockinette ( of same material and sizes as used for the animals ) containing trapped foot odors from a 30-year old African male volunteer by wearing them for 20 hours ( 21:00–17:00 the following day ) prior to the start of the experiments . Stockinette materials with animal odors were wrapped in at least four layers of aluminium foil , kept in a cold box ( 10°C ) and immediately transported to the trapping site . Once at the trapping site , the stockinette human and animal odors were placed in separate canisters ( cylindrical in shape with diameter 9 . 5 cm and height 22 . 5 cm ) designed from Brass mesh wire ( mesh size , 0 . 006 Inch , McNichols , Tampa , FL ) and hung close to the air flow of CO2 released at the bottom of an Igloo thermos container both mounted close to the fan of the CDC trap without a light bulb ( Figure 2 ) . All canisters made from the same material and of similar size , were boiled in l0% bleach solution after each night's trapping to eliminate any residual odor . The stockinette material containing collected odors were replaced each day for a repeat of the experiment . In total , six treatments were tested consisting of animal skin odor of each animal type+CO2 ( five treatments ) and CO2 only . Carbon dioxide was added nightly and delivered by placing 1 kg dry ice in Igloo thermos containers ( 2 L ) ( John W Hock , Gainesville , FL ) with a 13-mm hole in the bottom center . With an inter-trap distance of 40 m , the treatments and control were randomly assigned to a predetermined similar area following a Latin square design with days as replicates . Traps were activated within 30 min of sunset ( 1800–1830 hr ) and trap contents collected within 30 min after sunrise ( 0600–0630 hr ) . Traps were rotated on every trapping day to minimize variability due to trap placement . The field experiments were conducted at Ijara and Marigat districts which are two highly endemic areas for RVFV activities in Kenya . The ‘attraction’ of animal/human odor was estimated by the number of mosquitoes collected from the CO2-baited CDC trap ( model 512 , John W Hock , Gainesville , FL ) without a light bulb containing the bait odor from that animal compared to the control ( CO2 alone without a light bulb only ) in several replicate exposures . Mosquitoes were morphologically identified to species using taxonomic keys [18]–[20] . Mosquitoes were categorized as engorged when blood fed or gravid based on observation of their abdominal condition as illustrated in the WHO Manual [21] . Daily counts of number of mosquitoes per treatment were analyzed using a generalized linear model ( GLM ) with negative binomial error structure and log link in R 2 . 11 . 0 software [22] . Using the CO2 baited CDC trap ( control ) as the reference category , the incidence rate ratios ( IRR ) , a likelihood measure , that mosquito species chose other treatments instead of the control were estimated including Confidence Interval ( CI ) and corresponding P-values . The IRR for the control is 1 ( unity ) and values above this indicates better performance and values below under performance of the treatments relative to the control . Chi-square goodness-of-fit was used to analyze the effects of odors on the proportion of total engorged mosquito ( i . e . , total counts of blood fed+gravid mosquitoes in the total captures ) recorded for each trap treatment . Also , a pair-wise test of significant differences in the proportions of engorged mosquitoes between each of the combined CO2+animal odor treatments relative to the control ( CO2 only ) was performed using chi-square goodness-of-fit . Another measure was derived based on the ratio of these proportions for each of the animal odors relative to the control . This measure is termed the catch index; odors which , say , double or halve the catch from a trap would have catch indices of 2 and 0 . 5 , respectively . We collected headspace odors ( 24 hrs ) from stockinette samples brought from the field under cold storage on Super Q adsorbent ( 30 mg , Alltech , Nicholasville , KY ) and eluted filters with 200 µl dichloromethane ( DCM ) /hexane mixture ( 50∶50 ) . Headspace trapping was performed using the Volatile entrainment system using the trapped odors on 4 pieces of stockinette material of equal size taken per animal . All the eluents were concentrated under nitrogen to 100 µl and followed by GC-EAD and GC-MS analyses . Mosquito head excised with a scalpel from adult female mosquitoes ( aged 5–8 days , for laboratory-reared mosquitoes ) were used for electrophysiological recordings . Similar preparations were made for wild caught adult mosquitoes ( age unknown for field collected insects ) trapped with CO2-baited CDC miniature light trap and maintained on 6% glucose solution . The excised head was mounted between two glass capillary ( 1 . 1 mm I . D . ) electrodes filled with Ringer solution ( prepared by dissolving 6 . 5 g NaCl , 0 . 42 g KCl , 0 . 25 g CaCl2 and 0 . 1 g NaHCO3 in one liter of distilled water ) . Silver–silver chloride junctions were used to maintain electrical contact between the electrodes and input of preamplifier ( 10A; Syntech , Hilversum , The Netherlands ) . The grounded reference ( indifferent electrode ) was connected to the base of the antenna , and the tip connected to the recording electrode . The analog signal was detected through a probe ( INR-II , Syntech , Hilversum , The Netherlands ) , captured and processed with a data acquisition controller ( IDAC-4 , Syntech , The Netherlands ) and into a personal computer . Recordings were later analyzed with software ( EAG 2000 , Syntech , Hilversum , The Netherlands ) . In GC-EAD analysis , 2 µl of the odor extract was injected into a GC linked to the antenna recording setup . Injections of the extracts were conducted on a HP 5890 gas chromatograph fitted with a splitless injector ( 220°C ) and flame ionization detector ( FID ) ( 280°C ) . Compounds were separated on a nonpolar capillary column HP-1 ( 30 m×0 . 25 mm×0 . 25 µm film thickness ) with nitrogen as the carrier gas . The oven temperature was held at 35°C for 5 min and then increased at 10°C/min to a final temperature of 280°C , which was held for 10 min . The GC was fitted with a split at the end of the column , delivering half the column effluent to the flame ionization detector ( FID ) and the other half to a humidified airstream ( 1 ml/min ) flushing over the antenna via a heated transfer line ( 260°C ) ( Syntech ) . Commercial authentic standards were used to confirm EAG activity of tentatively identified components following similar procedures . For GC-MS analysis , 1 µl of volatile extract from the different animals were analyzed on an Agilent system consisting of a model HP 6890A gas chromatograph , a model 5973 mass selective detector ( EIMS , electron energy , 70 eV ) , and an Agilent ChemStation data system . The GC column was an HP-5 ms fused silica capillary with a ( 5% phenyl ) -methylpolysiloxane stationary phase ( 30 m×0 . 25 mm×0 . 25 µm film thickness ) . The carrier gas was helium with a column head pressure of 7 . 07 psi and flow rate of 1 . 0 ml/min . Inlet temperature was 200°C and MSD detector temperature was 280°C . The oven temperature was held at 35°C for 2 min and then increased at 10°C min−1 to a final temperature of 280°C , which was held for 10 min . The identity of EAG-active compounds was determined by comparison with references from mass spectral libraries ( NIST05 , Agilent Technologies [NIST database , G1036A , revision D . 01 . 00 , ChemStation data system ( G1701CA , version C . 00 . 01 . 08 ) . Final confirmation of identity was achieved by coinjection with synthetic reference compounds . Additionally , trapped odors from unused cotton material were included as control in all GC/MS analysis . Solvent blanks ( hexane or DCM ) were concentrated and analyzed to identify contaminants . The blanks were analyzed as described for the samples . Compounds present in the blank analyses were excluded from the composition percentages of compounds in the samples . To estimate ratio of abundance of aldehyde components , in each GC-MS run , the percent composition of each of the aldehyde components in the overall chemical profile of headspace odor from each animal was recorded . The percent abundances based on peak areas from an integrated chromatogram were then used to establish mean ratio of one component in relation to the other for each animal after 3 replicate runs ( Table S1 ) . The ratios of synthetic blends were also compared to the ratios in the naturally occurring blends . Similar chromatographic data were used to estimate release rates of the constituent aldehydes from each of the animals ( Table S2 ) by recording the peak area of each constituent obtained from an integrated chromatogram . External quantification was done using authentic sample of nonanal . Peak areas were recorded for different known concentrations of nonanal covering the expected analyte concentration range and a calibration curve and subsequent linear equation obtained which was then used to estimate the amount or quantity of each component produced following GC-MS analysis of crude animal volatiles . Estimated release rate was calculated taken into consideration the trapping duration , total volume of sample eluted in solvent ( dichloromethane ) and quantity analyzed by GC-MS ( 1 µl ) . Heptanal , octanal , nonanal , decanal ( >98% pure , Sigma-Aldrich ) were formulated in hexane at different concentrations with antioxidant , 2 , 6-di-tert-butyl-4-methylphenol ( butylated hydroxytoluene , BHT , Aldrich ) added for field evaluation . Initial field assessments of lures at icipe's Duduville Campus , followed by GC-MS analysis showed that the aldehydes oxidized to their corresponding fatty acids after at least 18 hr exposure . Therefore for long term assay , we formulated our lures using this anti-oxidant . Two milligrams ( 10% of individual component ) of antioxidant was added to 20 mg of each component in 1 ml of hexane to obtain a stock concentration and this was serially diluted to obtain various concentrations . Blends were constituted by mixing equal amounts of the respective components . All lures either singly or blends were released by diffusion from 0 . 5 ml polyethylene tubes with a pin hole in the center of the cap . Preliminary trials to determine possible range of attractive doses of compounds were conducted at icipe's Nairobi campus ( Table S3 ) . No mosquitoes were trapped in the control CDC trap with the light bulb removed . Informed by similar response profile in all the RVFV mosquitoes , it was clear that likely attractive doses would be at most 5 mg/ml for the individual compounds . Consequently , concentrations of individual compounds , including 0 . 1 , 0 . 5 , 1 , 2 and 5 mg/ml were evaluated in preliminary field assessments to determine optimal attractive doses of each compound in three replicate trials ( Figure S1 ) . We also conducted an analysis of the amount of aldehyde released from the different animals per hour ( Tables S2 and S3 ) . Representative blends simulating ratio of occurrence of the compounds in each of the animal odors were also evaluated . Details of these blends are shown in Table S4 . Nominal release rates were measured in the laboratory at 20°C and 0 . 5 m/sec airflow ( Table S5 ) by loading 0 . 5 ml of each compound tested in replicated dispensers and then measuring weight loss every 12 hours . Final weight loss measurements were recalculated as micrograms of compound lost per hour . Lures were attached underneath in the airflow of CO2 ( dry ice ) released from an Igloo cooler . These were mounted close to the fan of the CDC trap without a light bulb suspended 1 . 5 m off the ground on a tree . Effect of individual components and blends were evaluated for mosquito trap captures in field experiments conducted in January 2012 at Ijara district following a randomized experiment in the same predetermined similar area with days as replicates . Traps were rotated on every trapping day to minimize variability due to trap placement . Mosquito captures in CDC traps without a light bulb baited with a combination of CO2 and different doses of individual components/blends and were compared with captures to control trap with CO2 alone . Traps were activated within 30 min of sunset ( 1800–1830 hr ) and trap contents collected within 30 min after sunrise ( 0600–0630 hr ) . Daily counts of number of mosquitoes in the different trap treatments were recorded and analyzed using negative binomial regression following GLM procedures in R as described previously . Also proportion of engorged mosquitoes and catch indices for each treatment were analyzed as described previously . Data were analyzed for total mosquito captures of primary RVFV vectors ( Ae . mcintoshi and Ae . ochraceus ) and for total mosquito collections including other important RVFV Culex vectors such as Cx . pipiens quinquefasciatus , Cx . univittatus , Cx . poicilipes . The study was conducted with the approval of the national ethics review committee based at the Kenya Medical Research Institute ( KEMRI ) and is renewed on an annual basis after a scientific audit . The Animal use component was also given approval by the KEMRI Animal Use and Care committee ( KEMRI-AUCC ) . KEMRI-AUCC complies with the national guidelines for care and use of laboratory animals in Kenya developed by the Kenya Veterinary Association and the Kenya lab animal technicians association 1989 . The KEMRI-AUCC which approved the study protocol has an assurance identification number A5879-01 from the Office of Laboratory Animal Welfare ( OLAW ) under the Kenyan department of health and human services . For purposes of livestock use , funds from the project were used to purchase animals to monitor RVFV seroprevalence and used for all experimental activities described in this study . The animal owners consented to the use of their animals . These animals were owned and maintained for the study by the project . The project bought 492 animals comprising 5 sentinel herds; two in Marigat , three in Ijara district ( one in Kotile and 2 in Sangailu ) . The animals were left with the owners as part of their flocks but they were not allowed to sell or slaughter them because the project was monitoring the animals . The animals were reverted back to the owner at the end of the project activity . Any new borns born out of the tagged animals belonged to the farmers . We worked in collaboration with the department of veterinary services and veterinary doctors mandated by the government to do livestock sampling and research . The above terms were stipulated well in an agreement between the farmers and the international Centre of Insect Physiology and Ecology ( icipe ) , the hosting institution for the AVID Project Consortium . Human odor was collected from one of the authors , DPT , on worn stockinette and the mosquitoes are the subject of the experiment which responded to the stimuli on the stockinette . Entomological surveys were conducted away from homesteads and on community land as authorized by Community Elders after explaining the purpose of the study to them .
Compared to the control CO2-baited trap , the addition of mammalian skin odors in all the treatment traps not only selectively targeted mosquitoes , but also significantly increased trap captures of primary RVFV vectors ( Ae . mcintoshi/Ae . ochraceus ) ( p = 0 . 043 ) ; cow [IRR = 2 . 01 , CI ( 1 . 14–3 . 57 ) ] , donkey [IRR = 1 . 95 , CI ( 1 . 10–3 . 45 ) ] , goat [IRR = 2 . 12 , CI ( 1 . 20–3 . 75 ) ] and sheep [IRR = 1 . 66 , CI ( 1 . 03–2 . 95 ) ] ( Figure 3 ) . Notably , addition of human skin odors did not significantly increase mosquito captures over the control [IRR = 1 . 33 CI ( 0 . 74–2 . 38 ) ] . We observed a similar pattern of mosquito captures for secondary vectors of RVFV , mainly Culex and Mansonia species in the mammalian skin-baited traps with a combination of CO2 and skin odors of these hosts although no significant differences were found compared to CO2 only ( p = 0 . 872 for Culex spp . and p = 0 . 964 for Mansonia spp ) . Performance on captures of total Culex spp . ( Culex . pipiens , Cx . univittatus , Cx . poicilipes and Cx . bitaenorryhnchus ) were: cow [IRR = 1 . 13 , CI ( 0 . 70–1 . 82 ) ] , donkey [IRR = 1 . 18 CI ( 0 . 74–1 . 91 ) ] , goat [IRR = 1 . 09 , CI ( 0 . 68–1 . 76 ) ] , human [IRR = 1 . 34 , CI ( 0 . 83–2 . 15 ) ] , sheep [IRR = 1 . 26 , CI ( 0 . 78–2 . 02 ) ] and for total Mansonia spp . ( Mansonia . uniformis and Mn . africana ) : cow [IRR = 1 . 30 , CI ( 0 . 62–2 . 75 ) ] , donkey [IRR = 1 . 37 , CI ( 0 . 65–2 . 90 ) ] , goat [IRR = 1 . 09 , CI ( 0 . 52–2 . 30 ) ] , human [IRR = 1 . 26 , CI ( 0 . 60–2 . 66 ) ] , sheep [IRR = 1 . 18 CI ( 0 . 56–2 . 48 ) ] . We also observed a significant difference in the proportion of engorged mosquito ( i . e . , blood fed+gravid ) recorded in the different treatments for both primary vectors ( χ2 = 28 . 838 , df = 5 , p<0 . 001 ) and secondary vectors ( χ2 = 122 . 897 , df = 5 , p<0 . 001 ) . We found a higher proportion of engorged mosquito in baited traps containing CO2 plus animal odor relative to the control CO2 trap alone ( Table 1 ) . By comparing GC-EAD patterns , we observed four identical peaks in volatiles from each host that consistently elicited antennal responses from the different mosquito species ( Figure 4 ) . Using GC-MS , we identified the components representing these peaks as heptanal , octanal , nonanal and decanal and confirmed their identities by comparing retention times and fragmentation patterns with authentic standards . The total amount of these aldehydes in the volatiles varied with the host viz: cow , 29–43%; goat , 45–56%; donkey , 35–63%; sheep , 26–44%; and human , 18–40% . To maximize sensitivity of the trapping system to target selectively RVFV mosquito vectors , we first compared mosquito trap captures in preliminary field dose-response assays using CO2-baited CDC trap without a light bulb combined with individual synthetic EAG-active compounds to the control trap baited with CO2 alone . Since our chemical analysis showed that the total amount of aldehydes varied within and between replicates of individual host odors , we therefore formulated a blend ( Blend F ) from the four aldehydes based on the doses of individual components that elicited optimal attraction in the preliminary field assays ( Figure S1 ) . These were heptanal , 2 mg/ml; octanal , 0 . 5 mg/ml; nonanal , 0 . 1 mg/ml; and decanal , 0 . 1 mg/ml ( Figure S1 ) . We also constituted representative blends reflecting the mean ratio of occurrence of these aldehydes in each of the animals: Blend A ( cow ) ; Blend B ( human ) ; Blend C ( goat ) ; Blend D ( sheep ) ; Blend E ( donkey ) ( Table S4 ) . In subsequent dose-response field assays , we then compared the attractiveness of these blends ( A–F ) to these individual components at their respective optimal doses . Overall for individual components , heptanal recorded the highest captures ( 61% increase at 2 mg/ml ) , followed by nonanal ( 44% increase at 0 . 1 mg/ml , decanal ( 36% increase at 0 . 1 mg/ml and octanal ( 34% increase at 0 . 5 mg/ml ) ( Table 2 ) . However , these increases were not significantly different from the control captures ( Table 2 ) . We found that there was significant treatment effect on mosquito captures of primary RVFV vectors ( Ae . mcintoshi and Ae . ochraceus ) , ( χ2 = 104 . 81 , df = 10 , p = 0 . 003 ) and for total mosquito captures ( χ2 = 107 . 28 , df = 10 , p = 0 . 01 ) . Expectedly , we observed increased captures of total primary RVFV vectors in traps baited with CO2 combined with the optimal doses of each component and blends representing the mammalian odors ( Blends A–E ) although these captures were not significantly different from the control ( Table 2 ) . Interestingly , Blend F formulated from the optimal attractive doses of individual components performed far better than any of the representative mammalian blends ( A–E ) and the individual components ( Table 2 ) , trapping significantly three-fold more of the primary RVFV vectors than the control trap [ ( IRR = 3 . 23 , CI ( 1 . 76–5 . 91 ) ] ( Table 2 ) . Equally interesting , we found a significant increase in total mosquito captures including Culex RVFV secondary vectors relative to the control [IRR = 2 . 35 , CI ( 1 . 33–4 . 18 ) ] . An equally interesting finding we observed , was a significant difference in the proportion of engorged mosquito in the total mosquito captures for the optimal compounds and blends tested ( χ2 = 56 . 174 , df = 10 , p<0 . 001 ) relative to the control ( Table 3 ) . Similarly as found for animal crude odors , we observed a clear pattern of a higher proportion of engorged mosquitoes in all traps containing CO2 plus single compounds/blends relative to the control ( Table 3 ) .
RVF represents a looming health threat to various parts of the world [2] . The virus continues to circulate among animals and humans in many areas , both during intermittent epidemics/epizootics and IEPs [4] , [23] . Thus , the presence and intimate association of capable vectors and susceptible hosts such as livestock , humans in the same ecosystem as in Marigat district and Ijara district of North-Eastern Kenya , may help sustain this virus . As such , low numbers of vectors may be required for virus maintenance in a population of large susceptible vertebrate hosts in the environment as in the IEP . Spread and introduction of infection to new areas over long distances via movement of infected vectors remains plausible . In this study , we have demonstrated overwhelmingly through robust field-guided and chemical analyses that mammalian skin odors attract RVFV vectors . Our findings concur with published literature highlighting the importance of animal skin odors in mosquito attraction [17] , [24]–[26] . Among the animal hosts examined , primary RVFV vectors ( comprising Ae . mcintoshi and Ae . ochraceus ) showed a bias towards skin odors of animal hosts compared to humans in agreement with previous observations showing that during the 2007/2008 RVF outbreak in North Eastern Kenya these two mosquito species accounted for approximately 80% of positive pools of mosquitoes sampled in the field [8] . Furthermore , this observation supports the epidemiology of RVF as a zoonosis with circulation mainly among vertebrate animals which serve as efficient amplifiers of the virus and only incidental transmission to humans [10] , [27] . For the secondary Culex and Mansonia vector species , although there was a marked effect of odors on mosquito captures , there was no clear pattern of preference in their attraction among the animal host odors examined . This may suggest a more widespread feeding pattern of these vectors and cement their role as bridge vectors in the extension of the disease to humans . Our results show that the amounts of host skin-derived aldehydes varied within and between hosts , and further that given the commonality of the host skin-derived volatiles; these profiles may extend to related mammals attractive to RVFV mosquito vectors including wildlife . Host skin-derived aldehydes seem to play an important role in the attractiveness of RVFV mosquitoes , but better as a blend rather than as individual components . This pattern has been observed for other mosquito species and tsetse flies in combination with other chemical compounds [28]–[30] . Our data show that blends of synthetic aldehydes representing different host animals worked in combination with CO2 to attract RVFV mosquito vectors differentially . Because these captures were comparable to those we obtained with the crude skin volatiles , our data lends strong support for the role of aldehydes in mosquito attraction to host animals . However , a striking feature of our evaluation of aldehyde blends is the higher attractiveness for the altered blend formulated from doses of the individual aldehydes that elicited optimal attraction of vectors , clearly demonstrating its potential for practical use in monitoring RVFV mosquito vector populations . From this observation we believe that the ratios and release rates of aldehydes from an individual animal , irrespective of the host , determine its relative attractiveness in a herd . As such , some individuals in a herd would be relatively more attractive and serve as a sponge for RVFV mosquito vectors than others as demonstrated in this study when addition of individual compounds , at certain doses , reduced mosquito trap capture while other blends of components significantly increased trap capture . Nonetheless , because of the commonality of the host chemical signature required for attraction , the host-vector interaction would appear to favor the mosquito vector allowing it to find and opportunistically feed on a wide range of mammalian RVFV hosts . Hence , the natural survival of this arboviral pathogen is tightly bound to the success of this olfactory-based activity which these mosquito vectors use in seeking and feeding on multiple hosts . It is possible that microbial endogenous breakdown products of surface lipids may be the likely origin of these compounds on animal skin or from exogenous deposition via contact with foreign substances [31] , which would require additional research . Furthermore , the influence of skin bacteria on mosquito attraction has recently been highlighted [32] . A number of studies have reported the importance of aldehydes in the sensory ecology of mosquitoes [26] , [33] , [34] and various blood feeding arthropods , including ticks [35] , triatomine bugs [36] , and tsetse flies [37] , [38] . For mosquitoes in particular , their roles in the balance of attraction and inhibition have been suggested [39] , [40] mainly in laboratory assays and with limited efforts in field settings . Nonetheless , in a recent study CO2 was reported to synergize nonanal to increase trap captures of Culex mosquito vectors of West Nile Virus [26] . Clearly , our data stresses a fascinating dose-dependent behavioral blend effect of four aldehydes as kairomones which in combination with CO2 significantly increases trap captures for RVFV mosquito vectors . Furthermore , our data also suggests that individually , heptanal , octanal and decanal can also be exploited in a similar manner to increase field captures of RVFV mosquito vectors . Surveillance to monitor virus movement among vectors and hosts is crucial in informing public health decision makers for early warning and rapid response . Although trapping of adult female mosquito vectors remains a cornerstone of this strategy , efficient trapping tools for most of these RVFV vector species remain wanting especially during the IEP due to low sensitivity and non-specificity of currently available CO2-baited light trap . Moreover , enzootic transmission of arboviral diseases continues to occur at a low intensity among mosquito vectors in Kenya [23] and remain undetected . The development of the attractant blend described here circumvents the challenges by increasing captures not only for key RVFV vectors but also diverse mosquito species . This constitutes an important landmark as a practical effective population monitoring tool especially during the IEP so as to maximize trap captures for viral isolation in order to reveal the true burden of arbovirus circulating in affected communities . Furthermore , once mosquito vectors have been trapped and identified , their populations can be tracked to reveal important epidemiological parameters such as , population age structure , infection status , blood feeding patterns all culminating in assessing disease transmission risk . Improved arboviral vector surveillance equally requires knowledge of the mosquito population being sampled . Blood fed and gravid mosquito cohort because of their previous host encounter can be advantageous during surveillance as testing this cohort increases the likelihood of viral detections . Our results clearly indicate that traps baited with CO2 and animal skin odors both crude and synthetic compounds captured a higher proportion of engorged mosquitoes ( blood-fed+gravid ) than control traps . Our findings therefore , suggest the inclusion of attractive skin odors to CO2-baited traps for improved entomological surveillance . The blend we have developed requires combination with CO2 supplied in the form of dry ice . This commercial source of CO2 is not only expensive but may readily be unavailable in remote areas , which may hamper mosquito collection . However , alternative forms of generating CO2 as an attractant using yeast have been evaluated [41] , [42] . However , the efficacy of CO2 supplied as dry ice has been shown to increase mosquito captures significantly over that generated using yeast [43] . As the search for a suitable substitute for CO2 continues , a recent study identified 2-butanone as a mimic of CO2 activity following similar activation patterns in the odorant receptor neurons of mosquitoes [44] . The behavioral significance in terms of attraction in the natural habitat of mosquito vectors remains to be evaluated towards developing economical lures for use in trap-based mosquito surveillance especially in remote settings . In summary , this work is the first comprehensive report of translational research utilizing chemical ecology to generate better tools for surveillance of RVFV vectors . We have employed field bioassay guided experiments in combination with conventional chemical ecology approaches to identify four compounds , heptanal , octanal , nonanal and decanal , which when tested in the field , singly and in blends , increased capture of a number of RVFV mosquito vectors . The most effective blend ( Blend F ) , significantly improves attraction when used in conjunction with CO2 over that of the CO2-baited CDC traps alone , the latter currently used for surveillance and provides a clear improvement in our ability to monitor mosquito vectors especially during the IEP .
|
Enzootic transmission of arboviral diseases such as Rift Valley Fever ( RVF ) continues to occur at a low intensity among mosquito vectors in Kenya , which may remain undetected by most monitoring programs unless very sensitive tools are employed to detect virus activity before an outbreak occurs . Here , we report a more sensitive and mosquito-specific surveillance trapping system for RVF virus ( RVFV ) mosquito vectors based on mammalian-skin derived semiochemicals . We show that RVFV mosquito vectors detect similar components ( heptanal , octanal , nonanal , decanal ) in the skin of RVFV mammalian hosts . In field trials , each of these compounds when combined with CO2 increased captures of these mosquito vectors in a dose-dependent manner . Additionally , a blend formulated from optimal attractive dose of each of these compounds combined with CO2 significantly increased trap captures compared to control traps baited with CO2 alone . The four-component blend attracted multiple mosquito vectors of the disease under field conditions suggesting that a trapping system based on this formulation offers opportunity for its use as a tool for RVFV vector surveillance .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"chemistry",
"biology"
] |
2013
|
Common Host-Derived Chemicals Increase Catches of Disease-Transmitting Mosquitoes and Can Improve Early Warning Systems for Rift Valley Fever Virus
|
It has been suggested that Schistosoma infection may be associated with Plasmodium falciparum infection or related reduction in haemoglobin level , but the nature of this interaction remains unclear . This systematic review synthesized evidence on the relationship of S . haematobium or S . mansoni infection with the occurrence of P . falciparum malaria , Plasmodium density and related reduction in haemoglobin level among children in sub-Saharan Africa ( SSA ) . A systematic review in according with PRISMA guidelines was conducted . All published articles available in PubMed , Embase , Cochrane library and CINAHL databases before May 20 , 2015 were searched without any limits . Two reviewers independently screened , reviewed and assessed all the studies . Cochrane Q and Moran’s I2 were used to assess heterogeneity and the Egger test was used to examine publication bias . The summary odds ratio ( OR ) , summary regression co-efficient ( β ) and 95% confidence intervals ( CI ) were estimated using a random-effects model . Out of 2 , 920 citations screened , 12 articles ( five cross-sectional , seven prospective cohort ) were eligible to be included in the systematic review and 11 in the meta-analysis . The 12 studies involved 9 , 337 children in eight SSA countries . Eight studies compared the odds of asymptomatic/uncomplicated P . falciparum infection , two studies compared the incidence of uncomplicated P . falciparum infection , six studies compared P . falciparum density and four studies compared mean haemoglobin level between children infected and uninfected with S . haematobium or S . mansoni . Summary estimates of the eight studies based on 6 , 018 children showed a higher odds of asymptomatic/uncomplicated P . falciparum infection in children infected with S . mansoni or S . haematobium compared to those uninfected with Schistosoma ( summary OR: 1 . 82; 95%CI: 1 . 41 , 2 . 35; I2: 52 . 3% ) . The increase in odds of asymptomatic/uncomplicated P . falciparum infection among children infected with Schistosoma remained significant when subgroup analysis was conducted for S . haematobium ( summary OR: 1 . 68; 95%CI: 1 . 18 , 2 . 41; I2: 53 . 2% ) and S . mansoni ( summary OR: 2 . 15; 95%CI: 1 . 89 , 2 . 46: I2: 0 . 0% ) infection . However , the density of P . falciparum infection was lower in children co-infected with S . haematobium compared to those uninfected with Schistosoma ( summary-β: -0 . 14; 95% CI: -0 . 24 , -0 . 01; I2: 39 . 7% ) . The mean haemoglobin level was higher among children co-infected with S . haematobium and P . falciparum than those infected with only P . falciparum ( summary-mean haemoglobin difference: 0 . 49; 95% CI: 0 . 04 , 0 . 95; I2: 66 . 4% ) The current review suggests S . mansoni or S . haematobium co-infection may be associated with increased prevalence of asymptomatic/uncomplicated P . falciparum infection in children , but may protect against high density P . falciparum infection and related reduction in haemoglobin level .
Malaria and schistosomiasis are common in tropical and sub-tropical areas , causing high burden of morbidity and mortality , particularly in children [1 , 2] . In 2015 , about 214 million people were infected and 438 , 000 estimated to have died globally due to malaria [1] . Additionally , more than 261 million people required preventive treatment for schistosomiasis and close to 200 , 000 are estimated to die due to this disease annually [2] . About 90% of the malaria deaths and 90% of those who require treatment for schistosomiasis live in sub-Saharan Africa ( SSA ) , with children being the most affected group [1 , 2] . Plasmodium falciparum ( P . falciparum ) is responsible for most malaria cases and deaths due to the disease in SSA [3–5] . Likewise , two schistosome species , Schistosoma mansoni ( S . mansoni ) and S . haematobium are responsible for almost all of schistosomiasis cases in SSA [6] . S . mansoni causes intestinal and hepatic schistosomiasis and S . haematobium causes urogenital schistosomiasis [6] . Both Schistosoma spp . cause inflammation that leads to anaemia , growth stunting or cognitive impairment [6] . Humans infected with Plasmodium species that cause malaria can manifest a wide range of symptoms that vary from asymptomatic infection to severe complications resulting in death [6–8] . Severe malaria complications such as cerebral malaria , respiratory failure , acute renal failure or severe anaemia usually occur when unimmune individuals get infected with P . falciparum [7] . On the other hand , people living in regions where there is stable malaria transmission will usually show common symptoms such as fever , chills , fatigue , malaise when infected with Plasmodium spp . [8 , 9] . Still some immune individuals infected with Plasmodium may not develop fever , chills or other acute clinical symptoms of malaria , or may show symptoms intermittently but not severe enough to require attention from a health care provider [8] . Schistosoma co-infection can affect the development of Plasmodium infection related symptoms by altering the immune function [10] . Distributions of Plasmodium and Schistosoma species overlap in most of SSA , resulting in high rates of co-infection [11] . Based on the immunological findings in murine models and human subjects , it is hypothesized that there is a down-regulating effect of Schistosoma on the immune system of individuals , which in turn , may affect the course of other intracellular infections like Plasmodium [10] . However , research on the course of P . falciparum infection and related outcomes during Schistosoma co-infection has generated contradictory findings . While some studies report increased odds of P . falciparum infections and/or malaria-related complications associated with S . haematobium or S . mansoni co-infections [12–14] , others reported lower incidence and density of P . falciparum infection in children with S . haematobium infection [15 , 16] . Some studies reported lack of statistically significant association between S . mansoni or S . haematobium and the risk of P . falciparum infection [17 , 18] . The differences in study design , age groups , and outcomes may have blurred attempts to reach clear conclusions . In order to reduce adverse impact of schistosomiasis on child health , it is recommended that children living in endemic regions be treated with praziquantel [19] . However , if co-infection with schistosomiasis may reduce children’s susceptibility to the more severe form of P . falciparum malaria , then such treatment might have unwanted consequences . Thus , a clear understanding of the epidemiology of malaria during Schistosoma co-infection is essential to inform decisions on appropriate control strategies for schistosomiasis and malaria in SSA . Two previous reviews examining helminth and malaria co-infection , partially addressed the issue of Schistosoma and malaria co-infection in the general population based on studies conducted in various regions of the world [20 , 21] . However , the current systematic review and meta-analysis was undertaken to quantify the odds of asymptomatic/uncomplicated P . falciparum infection , parasite density and P . falciparum malaria-related reduction in haemoglobin level in Schistosoma co-infected children in SSA .
All epidemiological studies except case studies which reported prevalence or incidence of P . falciparum infection and/or Plasmodium density stratified by the presence or absence of S . haematobium or S . mansoni infection among children living in SSA were included . Studies that reported immunology of P . falciparum malaria and S . haematobium or S . mansoni co-infection in the general population were also included if they reported the prevalence or incidence of P . falciparum infection in children separately . Unpublished studies , conference abstracts , protocol , gray literature , review protocols , studies that involved only adults or pregnant women , animal or in vitro studies , and studies conducted outside of SSA were excluded after screening the titles and abstracts . Studies were additionally excluded after full text review when children infected with Schistosoma were co-infected with soil transmitted helminths or if they lacked epidemiologic data on P . falciparum and Schistosoma co-infection . The primary outcomes were prevalence/incidence of P . falciparum infection . Asymptomatic/uncomplicated P . falciparum malaria was defined as microscopic confirmation of the Plasmodium parasite in blood without clinical evidence ( signs or symptoms ) of severe malaria [25] . Seven studies included in this review did not clearly differentiate cases based on the clinical stages of malaria as asymptomatic and/or symptomatic . Although three studies indicated malaria cases as asymptomatic and symptomatic , results on P . falciparum and Schistosoma co-infection was not done on basis of the clinical stages of malaria . Hence , this review did not make any distinction between asymptomatic and uncomplicated P . falciparum malaria while estimating the nature of association of Schistosoma co-infection with P . falciparum malaria . Secondary outcomes included asexual stage ( ring forms ) Plasmodium density per microliter of blood , haemoglobin levels per deciliter of blood and anaemia . Anaemia was defined as haemoglobin level below the cut-off values defined by WHO: 11 . 0 g/dl for children 6–59 months; 11 . 5 g/dl for children 5–11 years; 12 . 0 g/dl for children 12–14 years [26] . S . haematobium and S . mansoni infections were confirmed in all studies by urine filtration and Kato Katz techniques , respectively . Two authors ( AD and DD ) independently conducted a search in Pubmed , Embase , Cochrane Library , CINAHL databases using keywords: malaria OR Plasmodium OR “Plasmodium falciparum” OR “Plasmodium vivax” in combination with helminth OR Schistosoma OR “Schistosoma mansoni” OR “Schistosoma haematobium” ( S2 Table ) for articles published before May 20 , 2015 . The search was limited to humans , but no limits were made on language . References of some related reviews [10 , 11 , 20 , 21] and African Online Journals database were also searched for relevant studies . Following exclusion of duplicates; abstracts and titles of 2 , 149 papers were screened for eligibility criteria and 45 were chosen for full text evaluation . Any discrepancies in the choice of articles being included in the review were resolved by third reviewer adjudication . However , there was a very low degree of discrepancy between the two authors in the choice of articles for the review . Information about the author , study area , study design , sample size , age range , Schistosoma species investigated , prevalence of P . falciparum and Schistosoma co-infection , diagnosis techniques and the main findings on prevalence/incidence and density of P . falciparum infection and mean haemoglobin level/ prevalence of anaemia were abstracted and entered into an excel sheet by two authors independently . Any discrepancies were resolved by consensus between the two authors . There was a very low degree of discrepancy between the two authors data that were extracted from the articles . Quality and risk of bias of the studies was evaluated using the effective public health practice project [27] . The quality of the studies was assessed on the basis of selection of the study participants , study design , confounder , blinding , data collection methods and withdrawals and drop-outs comparability . Heterogeneity was assessed using Cochrane Q ( Chi-square ) and Moran’s I2 ( Inconsistency ) using STATA software ( Version 11 , Texas , USA ) [28] . Publication bias was evaluated using a funnel plot and statistical significance was assessed by the Egger test ( bias if p<0 . 1 ) [29] . Sub-group analyses were conducted for S . mansoni and S . haematobium . Odds ratio , relative risk , regression coefficients , mean differences along with the 95% confidence intervals were used as effect measures . When studies did not report 95% CI for mean differences in Plasmodium density or haemoglobin level , we estimated it using p-values as suggested by Altman and Bland [30] . The 95% CI for mean difference in haemoglobin level between children co-infected with S . haematobium and P . falciparum and those infected with only P . falciparum for the studies by Deribew et al . [31] and Ateba-Ngoa et al . [32] was estimated using the mean and standard deviations values . The mean and standard deviation of haemoglobin levels for Ateba-Ngoa et al . [32] was estimated from the median and interquartile range based on the formula suggested by Wan et al . [33] . A random effects model was used to estimate the summary Mantel-Haenszel odds ratio of P . falciparum infection , among children infected with Schistosoma and those uninfected with Schistosoma . A random effects model was also used to estimate the summary regression coefficients and mean differences of P . falciparum density among children infected with S . haematobium and those uninfected with Schistosoma .
A total of 2 , 920 citations were identified from PubMed ( n = 999 ) , Embase ( n = 1 , 847 ) , Cochrane library ( n = 50 ) and CINAHL ( n = 24 ) , of which 771 articles were found to be duplicates . All relevant articles identified from reviews on malaria and helminth co-infection [10 , 11 , 20 , 21] and African Journal database were similar with the articles found from the four databases . Of the 2 , 149 articles screened; 2104 articles were excluded after reading the titles and abstracts . Of the 45 full-text articles reviewed , 33 were excluded . A total of 12 articles were considered for the systematic review , of which 11 were included in the meta-analysis ( Fig 1 ) . The characteristics of the 12 studies with 9 , 337 subjects included in this review are summarized in Table 1 . Five studies were cross-sectional and seven were prospective cohorts . Nine studied S . haematobium and P . falciparum co-infection , and three studied S . mansoni and P . falciparum co-infection . Eight studies compared the odds of asymptomatic/uncomplicated P . falciparum infection and six studies compared P . falciparum density between children infected and uninfected with Schistosoma . Four studies compared mean haemoglobin level/prevalence of anaemia between children co-infected with P . falciparum and Schistosoma and those infected with only P . falciparum . Two studies reported uncomplicated P . falciparum infection , three studies reported both asymptomatic and uncomplicated P . falciparum infection , but seven studies did not clearly differentiate cases based on the clinical stages of malaria as asymptomatic and/or uncomplicated . Longitudinal studies by Sangweme et al . [34] , Courtin et al . [35] and Doumbo et al . [36] did not report incidence , hence the prevalence data reported in these studies during the baseline surveys were used when estimating the summary-odds of P . falciparum infection in children infected and uninfected with S . haematobium . However , Sokhna et al . [37] reported incidence of P . falciparum infection in S . mansoni infected children , the study was thus excluded from the meta-analysis . Six studies examined the nature of the relationship of S . haematobium infection with the odds of asymptomatic/uncomplicated P . falciparum infection ( Fig 2 ) . A cross-sectional study in Ethiopia [31] and a prospective cohort study in Mali [36] showed increased odds of asymptomatic/uncomplicated P . falciparum infection among children infected with S . haematobium compared to children uninfected with S . haematobium . A cross-sectional study in Kenya also showed higher odds of asymptomatic/uncomplicated P . falciparum infection in children infected with S . haematobium compared to those uninfected with S . haematobium after adjusting for the effects of water contact , night activity , bednet use and distance from water ( adjusted OR: 1 . 79; 95% CI: 1 . 32 , 2 . 44 ) [38] . Although the difference was not statistically significant , prospective cohort studies in Zimbabwe [34] and Senegal [35] showed higher odds of asymptomatic/uncomplicated P . falciparum infection in children infected with S . haematobium compared to those who were not infected with S . haematobium . However , a cross-sectional study in Gabon showed similar odds of asymptomatic/uncomplicated P . falciparum infection in children infected with S . haematobium and those uninfected with S . haematobium [32] . The overall estimates based on six studies showed higher odds of asymptomatic/uncomplicated P . falciparum infection in children infected with S . haematobium than those uninfected with S . haematobium ( summary OR: 1 . 68; 95%CI: 1 . 18 , 2 . 41; I2: 53 . 2% ) [31 , 32 , 34–36 , 38] . However , longitudinal studies in Mali showed similar risk of uncomplicated P . falciparum infection in children infected and uninfected with S . haematobium [16 , 36] . Age-stratified analysis of the data in the study by Lyke et al . [16] , showed association of S . haematobium infection with fewer episodes of P . falciparum infection in children of ages 4 to 8 years , but the association was no longer present in children aged 9 to 14 years . Additionally , the study by Doumbo et al . [36] showed association of baseline co-infection with S . haematobium and P . falciparum with reduced risk of febrile P . falciparum infection . A cross-sectional study of 5 , 000 children in Uganda showed higher odds of uncomplicated P . falciparum infection among children infected than those uninfected with S . mansoni [39] . Another cross-sectional study in Tanzania documented increased odds of asymptomatic/uncomplicated P . falciparum malaria among children infected with S . mansoni than children without S . mansoni infection [40] . A summary analysis based on the two studies [39 , 40] showed higher odds of asymptomatic/uncomplicated P . falciparum infection among children infected than those not infected with S . mansoni ( summary OR: 2 . 15; 95%CI: 1 . 89 , 2 . 46; I2: 0 . 0% ) . Similarly , a prospective cohort study in Senegal showed increased risk of uncomplicated P . falciparum infection in children with S . mansoni egg load >1000 eggs/gram as compared to those uninfected with Schistosoma ( RR: 2 . 24 , 95% CI: 1 . 20 , 4 . 20 ) [37] . However , this association was not seen in children with moderate intensity S . mansoni egg load ( 100 to 399 eggs/gram ) . The overall estimates based on eight studies [31 , 32 , 34–36 , 38–40] showed higher odds of asymptomatic/uncomplicated P . falciparum infection in children infected with S . mansoni or S . haematobium as compared to those uninfected with Schistosoma ( summary OR: 1 . 82; 95%CI: 1 . 41 , 2 . 35; I2: 52 . 3% ) . There was no publication bias detected in the meta-analysis ( Egger test = -1 . 89; p = 0 . 11 ) , which included eight studies ( S1 Fig ) . Subgroup analysis showed that the increase in odds of asymptomatic/uncomplicated P . falciparum infection among children infected with Schistosoma was significant for studies which used microscopy for the diagnosis of Plasmodium infection ( summary OR: 1 . 91; 95%CI: 1 . 52 , 2 . 40; I2: 31 . 8% ) and those conducted in East African region ( summary OR: 1 . 91; 95%CI: 1 . 51 , 2 . 42; I2: 31 . 7% ) . However , significant association was not seen between asymptomatic/uncomplicated P . falciparum infection and Schistosoma among studies which used polymerase chain reaction ( PCR ) for the diagnosis of Plasmodium infection and those conducted in the West African region ( S2 Fig ) . Out of the 12 studies included in this review , six prospective cohort studies examined the effect of S . haematobium infection on the density of P . falciparum infection . Among these six studies , two reported lower P . falciparum density among children with low intensity of S . haematobium infection ( <10 eggs/10ml urine ) as compared to those uninfected with Schistosoma [15 , 41] . A study in Mali showed lower P . falciparum density in children with heavy intensity S . haematobium infection compared to those uninfected with Schistosoma [36] . A summary analysis based on the three studies showed significantly lower P . falciparum density in children infected with S . haematobium as compared to those uninfected with Schistosoma ( summary β = -0 . 14; 95% CI = -0 . 24 , -0 . 01; I2 = 39 . 7% ) [15 , 36 , 41]; the difference was greater in children with low intensity S . haematobium infection ( summary β = -0 . 30; 95% CI = -0 . 47 , -0 . 12; I2 = 4 . 4% ) ( Fig 3 ) . A study in Mali also showed significantly lower P . falciparum density among children aged 4 to 8 years with low-intensity S . haematobium infection compared to those uninfected with S . haematobium , however this difference in P . falciparum density was not significant ( p = 0 . 19 ) when data were analyzed without stratifying by age and intensity of S . haematobium infection ( 5521 vs . 6761 ) [16] . Although the difference was not statistically significant , the density of P . falciparum infection tended to be lower in children infected with S . haematobium ( mean = 1764 ) compared to those uninfected with S . haematobium ( mean = 2509 ) , irrespective of the intensity of S . haematobium infection ( p = 0 . 4 ) [34] . In contrast , a study in Senegal showed lack of significant difference in mean P . falciparum densities in infected children ( mean = 626 ) compared to those uninfected ( mean = 444 ) with S . haematobium but data were not stratified based on intensity of S . haematobium infection ( p = 0 . 56 ) [35] . Based on a summary analysis of the data in the three studies [16 , 34 , 35] , P . falciparum density tended to be lower in children infected with S . haematobium than those uninfected with the parasite ( mean difference = -924 . 2 , 95% CI = -2151 . 8 , 303 . 4; I2 = 0 . 0 ) . The differences in some of the aforementioned studies in mean P . falciparum densities among children with moderate or heavy ( >10 eggs/10ml urine ) intensity of S . haematobium infection compared to those not infected with the parasite were not significant [15 , 16 , 41] . The significant protective effects were largely seen in children with light intensity of S . haematobium infection . Out of 12 studies included in this review , four studies reported findings on mean haemoglobin level among children co-infected with P . falciparum and S . haematobium and those infected with only P . falciparum . A study in Gabon reported higher haemoglobin level among children co-infected with S . haematobium and P . falciparum than those infected with only P . falciparum ( mean haemoglobin difference = 0 . 7; 95% CI = 0 . 21 , 1 . 19 ) [32] . However , three studies in Mali , Ethiopia and Zimbabwe showed similar mean haemoglobin levels in children co-infected with S . haematobium and P . falciparum and those infected with only P . falciparum [16 , 31 , 34] . A summary estimate based on three studies [31 , 32 , 34] showed higher mean haemoglobin level in children co-infected with S . haematobium and P . falciparum than those infected with only P . falciparum ( summary mean haemgolobin level difference = 0 . 49; 95% CI: 0 . 04 , 0 . 95; I2: 66 . 4% ) ( Fig 4 ) . Among the four studies , two studies reported the odds of anaemia among children co-infected with P . falciparum and S . haematobium and those infected with only P . falciparum . The study in Ethiopia reported increased odds of anaemia [31] , but the study in Zimbabwe [34] reported similar odds of anaemia in children co-infected with S . haematobium and P . falciparum as compared to those infected with only P . falciparum . All the four studies reported similar mean haemoglobin level in children infected with S . haematobium , and those uninfected with S . haematobium and P . falciparum [16 , 31 , 32 , 34] . Table 2 summarizes the quality of the studies included in this review in terms of selection bias , study design , confounders , blinding , data collection methods , withdrawals and dropouts . The majority of the studies showed strong quality in data collection methods . The quality of most studies included in this review was moderate in terms of design ( prospective cohort ) , control of selection bias and blinding . Overall rating based on the six criteria showed that two studies were strong quality , six studies were of moderate quality and four studies were of weak quality . None of the studies were excluded from the review because of quality issues .
The findings of this review suggest that treatment of children in SSA for schistosomiasis may reduce the risk of asymptomatic/uncomplicated P . falciparum infection . Hartgers and Yazdanbakhsh , [10] suggested that praziquantel treatment may boost antimalarial immune response by reducing the down-regulating effect of Schistosoma [10] . However , the reasons why studies reported lower Plasmodium density among children with light [15 , 16 , 41] , heavy [36] or any [34] intensity S . haematobium infection remains unclear . In addition , the impact of Schistosoma infection on risk of acquiring P . falciparum infection and severity of the disease is poorly understood . Thus , the question of whether the present mass treatment of children living in SSA against schistosomiasis could have some detrimental impact through more severe P . falciparum infection cannot be definitely answered based on this review . Mass treatment programs generally focus only on the control of schistosomiasis among school-age children in SSA . Since malaria and schistosomiasis frequently co-exist in school-age children and schistosomiasis appears to be associated with increased odds of P . falciparum infection , collaboration among control programs for both infections and other services for young children might be advantageous . This is the first meta-analysis to study association of P . falciparum and Schistosoma co-infection conducted and reported according to the PRISMA guidelines [22] . Studies included in the meta-analysis did not show publication bias . The summary estimates of P . falciparum density in children infected with S . haematobium compared with those uninfected with Schistosoma were adjusted estimates [15 , 36 , 41] . However , the present summary results in the meta-analyses of odds of asymptomatic/uncomplicated P . falciparum infection in children infected with Schistosoma were based on crude estimates of individual studies . Only one study provided adjusted estimates of the effect size [38] . Hence , the current summary estimates might have been affected by confounders that can influence the nature of relationship of Schistosoma and P . falciparum in the original studies . In addition , there was a moderate level of bias in selecting the study participants within the studies included in this review . This might have resulted in overestimation of the relationship between Schistosoma and asymptomatic/uncomplicated P . falciparum malaria in the current review . Intervention measures taken against Schistosoma or malaria in different regions before the studies could have also altered the nature of relationship between P . falciparum and Schistosoma . This could also have introduced bias into the review . Furthermore , there was a moderate level of heterogeneity among the studies examining association of P . falciparum and Schistosoma co-infection ( Moran’s I2: 52 . 3 . 1% , Cochran’s Q: 14 . 66 , p = 0 . 041 ) . However , after removing one study [32] , the heterogeneity decreased ( Moran’s I2: 31 . 1% , Cochran’s Q: 8 . 66 p = 0 . 191 ) ] and subgroup analysis further minimized the risk of heterogeneity among studies evaluating the effect of S . mansoni infection on the odds of P . falciparum infection ( I2: 0 . 0% ) . Limitations of the original studies related to the diagnosis of Plasmodium and Schistosoma infection could also affect the present results [44 , 45] . In addition , some studies which examined S . haematobium and P . falciparum co-infection did not examine the study participants for infection with soil-transmitted helminths . Therefore , children without S . haematobium infection may have had other helminth infections that could have further confounded the results [46 , 47] . Moreover , some studies included in this review did not follow WHO criteria to determine classes of intensity of Schistosoma infection . This made it difficult to clearly evaluate the potential effect of classes of intensity of Schistosoma infection on the current result . Finally , the lack of sufficient data precluded performing a meta-analysis to estimate the effect of Schistosoma co-infection on the risk of acquiring P . falciparum infection and related anaemia . We did not find any studies which examined the relationship between Schistosoma and P . vivax infection or severe malaria that were eligible in the present review .
The present review suggests that S . mansoni or S . haematobium co-infection may increase susceptibility of children for asymptomatic/uncomplicated P . falciparum infection . However , S . haematobium co-infection may protect against high P . falciparum density and related reduction in haemoglobin level . Findings on the effect of Schistosoma infection against the risk of P . falciparum infection are heterogeneous .
|
A clear understanding of the epidemiology of malaria during Schistosoma co-infection is essential to inform decisions on appropriate control strategies for schistosomiasis and malaria in SSA . In this systematic review and meta-analysis , we synthesized evidence on the nature of relationship of S . haematobium and S . mansoni infection with the prevalence/incidence of P . falciparum infection , density of the parasite and related reduction in haemoglobin level among children in SSA . We searched all published articles available in PubMed , Embase , Cochrane library and CINAHL databases before May 20 , 2015 without any language restriction . We found five cross-sectional and seven prospective cohort studies eligible to be included in the systematic review , and 11 of these studies were included in the meta-analysis . A summarized analysis of the study findings showed that S . haematobium and S . mansoni infection is associated with an increased odds of asymptomatic/uncomplicated P . falciparum infection . However , density of P . falciparum infection decreased and haemoglobin level increased during S . haematobium co-infection .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Conclusions"
] |
[
"schistosoma",
"invertebrates",
"schistosoma",
"mansoni",
"parasite",
"groups",
"medicine",
"and",
"health",
"sciences",
"plasmodium",
"helminths",
"tropical",
"diseases",
"microbiology",
"plasmodium",
"falciparum",
"parasitic",
"diseases",
"animals",
"parasitic",
"protozoans",
"parasitology",
"pediatrics",
"apicomplexa",
"protozoans",
"pediatric",
"infections",
"malarial",
"parasites",
"schistosoma",
"haematobium",
"virology",
"co-infections",
"biology",
"and",
"life",
"sciences",
"malaria",
"organisms"
] |
2016
|
Plasmodium falciparum Infection Status among Children with Schistosoma in Sub-Saharan Africa: A Systematic Review and Meta-analysis
|
In this study , we investigate if phase-locking of fast oscillatory activity relies on the anatomical skeleton and if simple computational models informed by structural connectivity can help further to explain missing links in the structure-function relationship . We use diffusion tensor imaging data and alpha band-limited EEG signal recorded in a group of healthy individuals . Our results show that about 23 . 4% of the variance in empirical networks of resting-state functional connectivity is explained by the underlying white matter architecture . Simulating functional connectivity using a simple computational model based on the structural connectivity can increase the match to 45 . 4% . In a second step , we use our modeling framework to explore several technical alternatives along the modeling path . First , we find that an augmentation of homotopic connections in the structural connectivity matrix improves the link to functional connectivity while a correction for fiber distance slightly decreases the performance of the model . Second , a more complex computational model based on Kuramoto oscillators leads to a slight improvement of the model fit . Third , we show that the comparison of modeled and empirical functional connectivity at source level is much more specific for the underlying structural connectivity . However , different source reconstruction algorithms gave comparable results . Of note , as the fourth finding , the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome , indicating a considerable amount of functionally relevant synchrony taking place with near zero or zero-phase lag . The combination of the best performing alternatives at each stage in the pipeline results in a model that explains 54 . 4% of the variance in the empirical EEG functional connectivity . Our study shows that large-scale brain circuits of fast neural network synchrony strongly rely upon the structural connectome and simple computational models of neural activity can explain missing links in the structure-function relationship .
In this study we probed this assumption of a strong structure-function relationship by simulating local node dynamics based on SC and comparing the phase relationships emerging from the simulated neural activity with empirically measured phase relationships . To this end , we combined SC from DTI data using probabilistic fiber tracking and FC from EEG data recorded during wakeful rest in 17 healthy individuals . We then used computational modeling approaches to link SC and empirical FC at the alpha frequency range . We demonstrate that empirical networks of resting-state fast oscillations are strongly determined by the underlying SC and that additional variance between structure and function can be explained by modeling dynamic activity based on white matter architecture . Specifically , the simulated FC explained 28 . 5% of the variance in the empirical FC that was left unexplained by SC alone . To further understand the explanatory power of our model we investigated its performance at the local level by assessing specific properties of ROIs ( nodes ) or connections ( edges ) . We found that the model error was highest for large highly interacting ROIs . However , modeling large-scale brain dynamics based on structural priors brings up several methodological alternatives , not only regarding the modeling itself , but also regarding the comparison of simulated and empirical data . Especially with resting-state MEG/EEG activity , the specificity of analytic routines requires methodological decisions which potentially lead to tremendous differences in modeling outcomes . We systematically assessed the effect of technical variations on results and their influence on the interpretation of structure-function relations . Specifically , we used our modeling framework to explore several technical alternatives along the modeling path and evaluate the alternative processing steps based on their effect on the performance of the model in simulating empirical FC . Specifically , we addressed the effects of five critical aspects in the modeling pipeline: We used DTI and probabilistic tracking algorithms to compile a whole-brain structural connectome [37] . However , several studies suggested that current fiber tracking algorithms fail at capturing particularly transcallosal motor connections that are observed in non-human primate tracer studies [38 , 39] . In addition , structural connection strength modeled by probabilistic tractography algorithms is influenced by fiber length due to the progressive dispersion of uncertainty along the fiber tract [15 , 40] . Therefore , we evaluateed the effect of normalizations for fiber length of the SC and examined the effect of weighting homotopic connections in our model . Our results show that the correction for fiber distance leads to a small decrease in the performance of our model . The additional weighting of homotopic transcallosal connections , however , increased the model fit [24 , 25] . Several alternative computational models of neural dynamics are available . In the choice of a more abstract version to a more realistic description of cortical interactions , these models vary in the complexity of their formulation and therefore might explain more or less variance in the observed FC . The downside of complex models , however , is the increased number of free parameters . These have to be approximated , need to be known a priori , or explored systematically . All these aproaches are problematic . For an assessment of the factor of model complexity , we compared a simple spatial autoregressive ( SAR ) model to the Kuramoto model of coupled oscillators . We find that the SAR model explains already a large portion of the variance and that the Kuramoto model only gives a slight improvement . The comparatively few existing studies on large-scale modeling of MEG/EEG data differ systematically with respect to the comparison with empirical data . Some approaches project the observed time series onto the cortex using an inverse solution , whereas others project the simulated cortical signals into sensor space using the forward model [21 , 41 , 42] . We used our analytic framework to compare empirical and simulated FC at different spatial levels . We found that the importance of structural information is dramatically reduced if the higher spatial resolution obtained by source reconstruction is bypassed . Estimating the spatiotemporal dynamics of neuronal currents in source space generating the EEG and MEG signals is an ill-posed problem , due to the vastly larger number of active sources compared to the number of sensors . Therefore , we assess the impact of specific source reconstruction algorithms on the match of simulated and empirical FC . We compared three routinely used algorithms that differ regarding the assumptions made about the source signal , such as smoothness , sparsity , norms , correlation between source signals . However , we found no compelling superiority of one algorithm over another . Functional connectivity describes statistical dependencies between two signals often based on undirected temporal averages such as correlation . In the last decades , various additional FC metrics have been introduced . These differ with regard to the relative weighting of phase and amplitude or concerning the removal of zero-phase lag components prior to correlation . The theoretical superiority of one approach over another is debated [43] . However , no consensus appears achieved and currently no single metric is dominantly used over the others . Therefore , we compared several widely used metrics to compare empirical and simulated FC . We found that the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome . In the following sections , we first present a reference procedure for modeling FC based on DTI and the comparison with empirical FC as measured by EEG . After an initial short overview of the modeling approach ( see the Workflow section ) , we guide the reader step by step through the model details with the resulting outputs of each processing stage ( see the Reference Procedure section ) . From there , the impact of technical alternatives on the performance of the model is presented ( see the Alternative Modeling Approaches section ) .
We compared the simulated FC based on SC with the empirical FC derived from EEG data ( Fig 1 ) . Our model includes the processing steps as shown in Fig 1 with the DTI measurements on the left and the EEG measurements on the right . We address preprocessing of DTI data in the form of homotopic reweighting . Then , the 66 ROIs of the cerebral cortex according to the ‘Desikan-Killiany’ cortical atlas made available in the Freesurfer toolbox , were individually registered for 17 healthy subjects using Freesurfer ( surfer . nmr . mgh . harvard . edu ) [44] . The SAR model used in the reference procedure was selected based on simplicity ( low number of parameters ) and performance ( computationally very efficient ) . Furthermore , the SAR model allows to systematically evaluate the full parameter space with a high resolution grid-search , which is necessary for an unbiased comparison of all alternatives along the modeling path . We reconstructed source activity at the geometric center of each ROI based on the EEG time series by a linear constraint minimum variance spatial beam former ( LCMV ) . Then we assessed FC between source time series band pass filtered at 8 Hz where the averaged coherence showed a peak ( see supporting material S1 Fig ) . Finally , we evaluated the match of simulated and empirical FC based on the correlation between all pairs of ROIs [17] . Following this modeling approach , several alternative ways at each processing stage arise . Choices exist , for example , for the level of abstraction of the model type [45] , metrics to compare functional connectivity and the approach to the inverse problem in interpreting EEG data . The modeling of large-scale brain dynamics based on structural priors brings up several methodological alternatives . As a principal choice , the model may be evaluated either in source or in sensor space . In the baseline model that was presented above , we made specific choices at each processing stage based on simplicity and good explanatory performance . Especially with resting-state EEG activity , a lack of analytic routines requires methodological decisions to be made heuristically , which could potentially lead to substantial differences in the conclusions drawn . In the following section we systematically compare different alternatives of the procedural stages delineated above and compare the outcome regarding global correlation between simulated and empirical FC . First , we assessed the influence of distance normalization and weighting of homotopic connections in the structural connectome on simulated FC . Second , we tested if a more complex simulation model of coupled oscillators is able to capture a larger part of the variance of the empirical data that is not explained by the simple SAR model . Third , we evaluated an alternative comparison in the sensor space using a forward projection of the source time series in contrast to source reconstruction . Then , we compared different source reconstruction methods . Finally , we tested the impact of removing zero-phase lags in functional interactions .
It has been assumed that for the resting-state networks based on fast dynamics the underlying anatomical skeleton is less important compared to the slow resting-state networks , but this issue has not yet been systematically investigated [10] . We calculated the performance of the reference model as the correlation between all modeled pairwise interactions and all empirical pairwise interactions in an empirical functional phase relation connectome of the alpha rhythm and found a good match of 45 . 4% ( Fig 2C ) . This finding is in contrast to the prior assumptions and shows that the anatomical skeleton is equally crucial for fast timescale functional interactions [29 , 36] . To better understand the reference model performance we investigated the model error in relation to node and edge characteristics ( Fig 3 ) . In general , the model error decreased with longer fiber distance and Euclidean distance . Specifically , for short fiber distances , the model overestimated FC ( negative residuals blue in Fig 3 ) . Why are short connections in general more difficult to model based on white matter tracts ? The empirical connectome was extracted from resting-state alpha topographies in which propagating waves play an important role for adjacent and remote brain areas to communicate with each other . Cortico-cortical axons in the white matter tracts are considered as the major route for traveling waves . However , a recent study presented compelling evidence for intracortical axons accounting for spatial propagation of alpha oscillations [79] . Such a mechanism would enable high local synchrony in the relative absence of structural connectivity measured by DTI . We used a stepwise linear model to extract node characteristics explaining most of the model error and found that ROI size and betweenness centrality play an important role . Regarding ROI size , the smaller model error for larger ROIs could be attributed to the measurements of structural as well as functional connectivity being more reliable for larger ROIs: In the case of the SC measurements using DTI , a larger parcellated cortical region allows to track more streamlines with different initial conditions ( i . e . for more voxels ) and thereby allows a more reliable estimation of the connection probabilities between regions . In EEG as well as DTI , the localization and inter-subject registration of large ROIs can be assumed to be less effected by small deviations because a small spatial shift of a large ROI still allows a large overlap with the correct ROI volume whereas a small spatial shift of a small ROI could displace it completely outside of the original volume . For betweenness centrality , the opposite scenario was the case: the smaller the betweenness centrality the smaller was the model error . Central hubs in a structural network offer anatomical bridges which enable functional links between regions that are structurally not directly related [63] . Hard-wired connections do not necessarily contribute at all times to FC in the network and , vice-versa , functionally relevant connections do not necessarily have to be strongly hard-wired [13] . Possibly , the simple SAR model , which captures only stationary dynamics , has weaknesses at these central hub nodes . In order to capture the empirical FC at these nodes , a more complex dynamical model able to capture non-stationary dynamics with context switches at slower time scales is needed . Nodes with a high betweenness centrality can be expected to communicate with certain cortical modules only at certain times in specific dynamical regimes . We hypothesize that a more complex dynamical model of neural activity could capture this behavior more accurately . Therefore we suggest that further research could especially improve the model in these cases of dynamical context switches in central hub nodes , which cannot be captured by simple models such as the SAR model . Using our modeling framework to compare different alternatives of reconstructing the structural connectome , we found that the best match between simulated and empirical FC was obtained when an additional weighting of connections between homotopic transcallosal regions was applied . Additional weighting for fiber distances did not improve the simulation performance significantly . Overall , the differences were very small proving the modeling approach to be rather robust regarding the evaluated choices of reconstruction as long as the total input strength per region is normalization prior to the simulation . Currently , there is no common approach to correct for the influence of fiber distance on the probabilistic tracking algorithm [16 , 40 , 80] . Although we found that the model error was largest for small fiber distances ( modeled FC higher than empirical FC ) , a correction for fiber lengths did not improve the result of the simulation . This suggests that the high local connection strength of SC obtained by DTI reflects actual structural connectivity . Methodically , this finding is supported by the fact that accuracy of probabilistic fiber reconstrunction decreases with distance , whereas short-distance connections are reconstructed with high reliability [38] . However , it remains a challenge to correct probabilistic tracking results for the impact of fiber distance and further work is needed to address this methodological limitation . Our model improved with an additional added weight of homotopic connections , which is supporting the data by Messé et al . [24] . This finding points to a related limitation of the probabilistic tracking algorithms to correctly assess long distance and lateral transcallosal fibers . In agreement with previous studies , we show that this limitation can be addressed by adding a preprocessing step to the structural connectome reconstruction . Lastly , we want to point out that the parcellation scheme , especially the spatial resolution , has a strong effect on the SC and FC , as shown in previous studies [81–83] . We did not include other parcellation schemes as alternatives in this work because a different parcellation effects all steps in the processing pipeline at the same time . Most importantly , a different parcellation also changes the predefined space in which the model prediction is evaluated , so that the resulting correlation values are not directly comparable to the results of our presented reference procedure . We chose a parcellation scheme which has been used in several previous studies [16 , 22 , 40 , 44 , 84] and implemented in Freesurfer . The effect of parcellation schemes on structure-function relationships is a very important topic that is currently under investigation . We show that our SAR model already explains much of the variance in the empirical EEG data . Our results indicate that the Kuramoto model moderately improved results compared to the reference model . The SAR model has a small number of parameters allowing a fast exploration of the parameter space [49] and the SAR model served several studies in which complexity and information-theoretical measures characterizing FC were explored [49 , 85 , 86] . As a downside , the SAR model has a smaller number of parameters and therefore lacks the modeling capacity to further optimize the dynamics to better fit to the empirical data . Furthermore , the SAR model cannot model individual frequencies and their interactions , making the Kuramoto model a viable alternative . It has been shown that the Kuramoto model features complex synchronization dynamics which can be related to the explanation of oscillatory phenomena in the human cortex , such as fluctuating beta oscillations [48] or metastable synchronization states [21] . A more detailed analysis of the synchronization properties of the Kuramoto model in the human connectome was done by Villegas et al . [87] , where frustration and the transition between synchronous and asynchronous phases were analyzed [88] . The Kuramoto model was also used to study the effects of lesions on cortical dynamics and binding by synchrony [69 , 89] . However , it has been shown that more complex models with more parameters are usually not better in explaining fMRI functional connectivity from structural data [24–26] . Highly parameterized models which require the numerical integration of differential equations take several orders of magnitude more computational time to obtain a reliable estimate of FC than the simple model used here . For certain neurophysiological questions however , the wider parameter space of complex models can be used to explore neural processing properties . The relative benefit of a dynamical model has to counterbalance the higher computational demand . Therefore , the choice of model depends on the investigated scientific question [26 , 45] . In this study we used the simpler SAR model as a reference because the focus was to investigate alternatives also in many other stages of the processing pipeline and a more complex simulation model would impede identifying the best alternative in the other stages of the processing pipeline , due to the high dimensional parameter space . The inverse problem is ill-posed since the higher number of possible active neuronal sources is higher than the number of recording channels . Thus , the ground truth of brain activity patterns generating the measured signal is impossible to infer . A variety of alternative methodological approaches have been developed regarding source imaging . Particular caution should be exercised concerning the influence of different inverse solutions on the resulting data [90] . Here , we presented a comparison of the performance of three commonly used inverse methods regarding the global correlation between empirical and simulated FC in our technical framework ( Fig 7 ) . All source reconstruction algorithms perform in a similar range with resembling r-values between 0 . 674 and 0 . 728 . Although the algorithms differ regarding the assumptions made about the source signal , the high correspondence in performance of the three source reconstruction techniques mutually validates their respective inverse solutions . Next , we aimed to investigate the best approach for comparing empirical and simulated FC particularly in sensor and source space , see Fig 1 . In the sensor space scenario , the simulated signal , as the mean field source activity generated by the SAR model , was projected into sensor space to generate a simulated EEG signal by applying the leadfield ( i . e . forward model ) . For this approach we found slightly higher correlations between simulated and empirical data ( Fig 6A ) . However , we also found that the high correspondence between empirical and simulated EEG sensor space FC was independent of the underlying SC: Shuffling SC before the simulation did not abolish the correlation between the empirical and simulated FC as was the case when the comparison was done at the source space level . This lack of specificity of the simulated FC regarding the anatomical skeleton strongly suggests that the sensor level connectivity matrix is shaped mainly by the leadfield ( Fig 6 ) . In fact , the leadfield can already explain most of the variance ( 81 . 9% ) in the empirical FC of the sensor space . In contrast , the inverse solution in the source reconstruction procedure removes much of these volume conduction correlations so that the comparison of coherence in source space appears reasonable . We conclude that the volume conduction model of the head is mixing the source time series such that the coherence in sensor space reflects to a high degree the structure within this mixing matrix and the sensor space is a suboptimal stage for investigating structure-function relationships by large-scale modeling approaches . Thus , one should refrain from such a comparison in sensor space with metrics that do not exclude zero-lag interactions . In order to assess the accuracy of simulated global network characteristics , the comparative spatial level should be at source space in order to avoid signal mixing by the leadfield matrix and allow to include zero-lag interactions . The results offer an important ground for modeling studies using source connectivity analyses for MEG/EEG data . One of the main differences between fMRI/PET and MEG/EEG connectivity studies is that for MEG/EEG a multitude of different metrics to quantify FC are currently available and no single metric is predominantly employed or has emerged as being superior [91 , 92] . This issue hampers comparability between studies and physiologic interpretation . It was our aim to use our theoretic framework for a systematic comparison of different functional connectivity metrics . We compared six commonly used metrics that differ regarding their sensitivity towards zero-phase lag coupling and amplitude variations . The definition of PLV , PLI , WPLI and LPC theoretically renders those metrics insensitive to amplitude variations . We found no major difference in performance between COH and PLV and no major difference between ICOH , PLI , WPLI , and LPC . This result is easily understood on the basis that the SAR model presents the steady state solution including a small noise component only . An important finding is the high correspondence in model performance between coherence and PLV . Coherence is the cross-spectrum between two sensors normalized with the auto-spectra whereas PLV quantifies the consistency of a phase difference between two signals across time . Both measures are high if there is a consistent phase difference regardless of whether the latter is near zero , 180° or inbetween . Similar results between coherence and PLV have been found in previous studies [93–95] . The similarity of both measures in our study suggests that amplitude variations between areas are of less weight than phase variations . Another main finding is the drop in model performance with the metrics ICOH , PLI , WPLI and LPC which are by design less sensitive to zero-phase coupling . Regarding the latter , a major concern exists whether such coupling in scalp recordings would be contaminated by volume conduction artifacts . Obviously , synchrony at sensor level could result from two channels picking up activity from a common source since the activity of the source signal passes through the layers of cerebrospinal fluid , dura , scalp and skull acting as a spatial filter . This effect leads to the detection of spurious synchrony , even if the underlying sources are independent [96] . Based on the assumption that the quasi-static approximation holds true for EEG , volume conduction would occur with zero-phase lag [97] . Thus , the most commonly used approach to deal with the problem of volume conduction is to neglect interactions that have no phase delay . This is , however , a potentially overly conservative approach . To address the question of how these biased measures of interactions are suited for comparing modeled and empirical connectomes , we compared global model performance based on connectivity metrics that are sensitive and robust to zero-phase lags in this study . ICOH , PLI , WPLI and LPC all showed a significantly lower match between simulated and empirical FC ( around r = 0 . 18 ) compared to coherence and PLV ( Fig 8 ) . For all six metrics , the global correlation was essentially abolished if the underlying SC was shuffled prior to simulation ( yellow bars in ( Fig 8 ) ) . Also , the overall model performance for ICOH , PLI , WPLI and LPC was considerably smaller than the mere correlation between SC and empirical FC ( middle row in Fig 5A ) . What are the possible reasons for this performance drop with ICOH , PLI , WPLI and LPC ? One reason could lie in the fact that the reference model SAR does not include delays , thus the simulated FC mainly consists of instantaneous interactions and a comparison with an empirical FC in which those interactions have largely been removed would be futile . However , the results were very similar using the Kuramoto model . The large-scale connectomes derived from all of the four biased metrics did not much reflect the coupling that emerged from our model of fast dynamics based on structural connectivity . Presumably , a considerable amount of functionally relevant synchrony takes place with near zero or zero-phase lag which is not detected using the biased scores . In fact , zero-phase lag synchronization has been detected between cortical regions in a visuomotor integration task in cats [98] . More recently , a study of spike train recordings showed how paths among somatosensory areas were dominated by instantaneous interactions [99] . But synchrony across areas incorporating delays can also lead to high coherence [100] . A recent modeling study investigated the detection rates of synchrony by different EEG phase synchronization measures ( PLV , ICOH , WPLI ) in a network of neural mass models . They found that no single phase synchronization measure performed substantially better than all the others , and PLV was the only metric able to detect phase interactions near ±0° or ±180° [91] . This study challenged the supposed superiority of biased metrics in practical applications , because they are biased against zero-phase interactions that do truly occur in the brain . Taken together we argue that by using biased metrics to detect neural synchrony a major portion of relevant coupling is neglected . However , as the relevant stage for comparisons is the source space , the undesired influence of volume conduction effects on the estimated connectivity is partly reduced [101] . Since effects of field spread can never be completely abolished also in the source space , we cannot rule out that volume conduction artifacts have influenced the high correlation in our model . The empirical functional connectome was constructed based on band-pass filtered EEG in the alpha frequency range . Since different FC maps have been detected for different frequency bands [9] , it is conceivable that biased vs . unbiased FC metrics might vary in their performance depending on the frequency . In summary , our framework demonstrates how technical alternatives and choices along the modeling path impact on the performance of a structurally informed computational model of global functional connectivity . We show that determining the resting-state alpha rhythm functional connectome , the anatomical skeleton has a major influence and that simulations of global network characteristics can further close the gap between brain network structure and function .
|
Brain imaging techniques are broadly divided into the two categories of structural and functional imaging . Structural imaging provides information about the static physical connectivity within the brain , while functional imaging provides data about the dynamic ongoing activation of brain areas . Computational models allow to bridge the gap between these two modalities and allow to gain new insights . Specifically , in this study , we use structural data from diffusion tractography recordings to model functional brain connectivity obtained from fast EEG dynamics occurring at the alpha frequency . First , we present a simple reference procedure which consists of several steps to link the structural to the functional empirical data . Second , we systematically compare several alternative methods along the modeling path in order to assess their impact on the overall fit between simulations and empirical data . We explore preprocessing steps of the structural connectivity and different levels of complexity of the computational model . We highlight the importance of source reconstruction and compare commonly used source reconstruction algorithms and metrics to assess functional connectivity . Our results serve as an important orienting frame for the emerging field of brain network modeling .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"neural",
"networks",
"engineering",
"and",
"technology",
"nervous",
"system",
"applied",
"mathematics",
"brain",
"electrophysiology",
"electrophysiology",
"neuroscience",
"simulation",
"and",
"modeling",
"algorithms",
"mathematics",
"brain",
"mapping",
"network",
"analysis",
"bioassays",
"and",
"physiological",
"analysis",
"neuroimaging",
"electroencephalography",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"imaging",
"techniques",
"clinical",
"neurophysiology",
"electrophysiological",
"techniques",
"centrality",
"connectomics",
"diagnostic",
"medicine",
"neuroanatomy",
"anatomy",
"physiology",
"software",
"engineering",
"biology",
"and",
"life",
"sciences",
"preprocessing",
"physical",
"sciences",
"cognitive",
"science",
"neurophysiology"
] |
2016
|
Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path
|
The mammalian neocortex has a repetitious , laminar structure and performs functions integral to higher cognitive processes , including sensory perception , memory , and coordinated motor output . What computations does this circuitry subserve that link these unique structural elements to their function ? Potjans and Diesmann ( 2014 ) parameterized a four-layer , two cell type ( i . e . excitatory and inhibitory ) model of a cortical column with homogeneous populations and cell type dependent connection probabilities . We implement a version of their model using a displacement integro-partial differential equation ( DiPDE ) population density model . This approach , exact in the limit of large homogeneous populations , provides a fast numerical method to solve equations describing the full probability density distribution of neuronal membrane potentials . It lends itself to quickly analyzing the mean response properties of population-scale firing rate dynamics . We use this strategy to examine the input-output relationship of the Potjans and Diesmann cortical column model to understand its computational properties . When inputs are constrained to jointly and equally target excitatory and inhibitory neurons , we find a large linear regime where the effect of a multi-layer input signal can be reduced to a linear combination of component signals . One of these , a simple subtractive operation , can act as an error signal passed between hierarchical processing stages .
For more than a century , neuroscientists have worked to refine descriptions of cortical anatomy , either differentiating or consolidating models of cortical circuits [1] . The notion that a fundamental neuronal circuit performs a canonical computation in neocortex , that can be generalized across species and areas , is of fundamental value to both experimental and theoretical neuroscientists . Douglas and Martin provided evidence for such a canonical microcircuit in the cat striate cortex , as well as a descriptive model of its structure [2 , 3] . The fundamental building block of circuits on this scale is the cell type specific population . For example , the Douglas and Martin microcircuit model implicated distinct cell types and cortical laminae in its function . Each individual population in the circuit might perform linear or nonlinear transformations on its inputs , depending on the parameterization of the model [4] . The individual cells that make up the population might be spatially segregated ( i . e . distinguished by layer ) or might be intermingled , and distinguished by genetically defined cell type or projection pattern . The microcircuit can then be conceptualized as a modular collection of populations , with scale and composition dependent on function . Whole brain regions are assembled from ensembles of microcircuits that together perform its overall function , the clearest example being orientation columns in V1 . Over time , the microcircuit model can be refined , constrained by including cell type specific parameterizations , synaptic properties , detailed microcircuit anatomy , and other relevant experimentally measured data of a particular cortical area . When taken together , the cumulative result of multiple recurrently connected canonical circuits might perform the complex nonlinear computations necessary to implement models of higher-order cognitive function . Furthermore , many theoretical models of cortical processing involve hierarchical arrangements of processing stages , and the evidence for such a hierarchical organization , particularly in the visual system , is generally accepted ( for example , [5] ) . Informed by the seminal work of Hubel and Wiesel [6] in the perception of orientation , the catalogue of algorithms for which there exists models relying on a staged , hierarchical implementation has grown significantly . Beyond perception , hierarchical theories include invariant object recognition ( for example [7]; see [8] for a review ) , selective visual attention ( see [9] for a review ) and models of Bayesian inference via predictive coding ( for example [10] ) . In order to perform any of these hierarchical computations , individual elements within the hierarchy must perform an intermediate stage of processing . It is hypothesized that these intermediate stages implement a local canonical computation , and their hierarchical arrangement subserves ( or even defines ) a global information processing stream . In this study , we examine the computational properties of the Potjans and Diesmann [11] cortical column . The main focus of that study was the construction of a realistic computational model of cortex . Here we ask what type of computation this model might subserve as a candidate canonical model of cortical processing . Based on its properties , we then speculate about the role of such a processing unit in an abstract hierarchical computational scheme . We find that simultaneous excitation to L2/3 and L4 offset in their effects on L5 , in essence performing a subtractive computation between two step inputs . Additionally , we find that the model possesses a linear computational regime under the condition that incoming inputs do not preferentially target inhibitory or excitatory populations within a layer . We then examine the response of the model to sinusoidal inputs , again finding evidence of linear computation . In the discussion , we relate these findings to the role of such a processing element in light of theories of hierarchical computation .
The Potjans and Diesmann [11] cortical column model is composed of 8 recurrently connected homogeneous populations of neurons , totaling approximately 80 , 000 neurons and . 3 billion synapses . Each neuron receives background Poisson input , and is recurrently connected to neurons in other populations via a population-specific connection probability matrix derived by combining data and methods from several studies . In our study both network and single neuron parameters from [11] are used . The population statistic approach used in Iyer et al . [12] assumes synapses that instantaneously perturb the voltage distribution of the postsynaptic population . Therefore , we assume that the fast kinetics of synapses in the Potjans and Diesmann cortical column ( τs = . 5 ms ) can be well-approximated by the DiPDE formalism ( For a discussion regarding the effect of non-instantaneous synapses , see [12] , “Methods: Non-instantaneous synapses” ) . As a consequence , the coupling of these shot-noise synapses is instantaneous , perturbing the voltage distribution directly by a constant . 175 mV for excitatory synapses , and - . 7 mV for inhibitory synapses . These values are computed from the total charge resulting from a single synapse of weight w in [11] using their notation ( See [12] for additional details ) : Δ v = Q C m = 1 C m ∫ 0 ∞ I ( t ) d t = w C m ∫ 0 ∞ exp ( - t / τ s ) d t . ( 1 ) Connection probabilities , synaptic weight distributions , and delay distributions are taken directly from [11] ( with the exception of the L4e → L2/3e connection probability , which was doubled to . 088 , following [13] ) . This was done to define equal synaptic strength for all excitatory connections ( The original strength for this one connection was doubled relative to other projections ) , while maintaining roughly the same overall projection strength . The connection probability was multiplied by the size of the presynaptic population to parameterize an effective multiplier ( in-degree ) on the incoming firing rate from a presynaptic population . Because they minimally impact the firing rate dynamics of the leaky integrate-and-fire model , refractory periods were simplified from 2 ms to zero . The only significant deviation from the Potjans and Diesmann model was a decrease in the mean background firing rate across all populations by a factor of 8 . 54 , and subsequent increase in the synapse strength of these connections by an equal amount . This modification leaves the mean synaptic input from background unchanged from the original model , but increases the variance of this stochastic input . After this change , the intrinsic oscillations of the original NEST model are significantly damped ( but not completely eliminated; see Fig 1 ) . The matched DiPDE model does not exhibit intrinsic oscillations , although in general population density models are capable of exhibiting this phenomenon [14] . Each population is initialized to a normal distribution of membrane voltages , with a mean at the reset potential and standard deviation of 5 mV . Before application of any additional input ( i . e . , step or sinusoidal drive ) , background excitation is applied to each population as specified in [11] , and simulated for 100 ms to reach a pre-stimulus steady state . When driving the model , additional layer-specific excitatory stimulus is input into the target layers ( s ) , and simulated for an additional 100 ms . For step inputs , the difference of the final steady-state less the pre-stimulus steady-state ( i . e . after discarding the initial start-up transient dynamics ) define the layer-specific firing rate output perturbation . In this study , all simulations of the model were performed using a numerical simulation of the displacement partial integro-differential equation ( DiPDE ) modeling scheme proposed in [12] with a time-step of . 1 ms . At this temporal resolution , a 200 ms DiPDE simulation requires 31 seconds running on a 2 . 80 GHz Intel Xeon CPU . The corresponding NEST simulations [15 , 16] included in Fig 1 ( c ) and 1 ( d ) require 402 seconds each ( single processor ) , and results from 100 of these simulations are averaged to obtain the mean firing rate pictured . In each of these 100 averaged NEST simulations , connectivity matrices and initial values for voltages were randomized . The population density approach in computational neuroscience seeks to understand the statistical evolution of a large population of homogeneous neurons . Beginning with the work of Knight and Sirovich [17] ( See also [18 , 19] ) , the approach typically formulates a partial integro-differential equation for the evolution of the voltage probability distribution receiving synaptic activity , and under the influence of neural dynamics . Neuronal dynamics typically follow from the assumption of a leaky integrate-and-fire model . We implement a numerical scheme for computing the time evolution of the master equation for populations of leaky integrate-and-fire neurons with shot-noise current-based synapses ( For a similar approach , see [20] ) . τ m d v d t = - v + Δ v ∑ i δ ( t - t i ) v > v t h ⇒ v → v r ( 2 ) Here τm is the membrane time constant , v is the membrane voltage , Δv is the synaptic weight , vth is the threshold potential , and vr is the reset potential ( here taken to be zero for simplicity ) . Extending [12] , each population receives input from both background Poisson input and recurrent connections from each cortical subpopulation . We emphasize that this is not a stochastic simulation; for example , the background Poisson drive is not a realization of a Poisson process , but rather the effect of a Poisson-like jump process on the evolution master equation . At each time step , a density distribution representing the probability distribution of membrane voltages for each population is updated according the differential form of the continuity equation for probability mass flux J ( t , v ) ( Here p ( t , v ) is the probability distribution across v at time t on ( −∞ , vth ) ; see [21] for more information ) : ∂ p ∂ t = - ∂ J ∂ v ( 3 ) The voltage distribution is modeled as a discrete set of finite domains ( See Fig 2 ) . Synaptic activation of input connections drive the flux of probability mass between nodes , while obeying the principle of conservation of probability mass . As a result , a numerical finite volume method is an ideal candidate for computing the time evolution of the voltage density distribution , and we numerically solve Eq 3 with a finite volume method . The spatial ( voltage ) domain D = [ v m i n , v θ ] ⊂ R ( 4 ) is subdivided into a set of non-overlapping subdomains V = { v i ⊂ D } . ( 5 ) Each subdomain contains a control node pi that tracks the inflow and outflow of probability mass due to synaptic activation and passive leak . At each time step , pi is updated by considering probability mass flow resulting from synaptic activation from all presynaptic inputs as well as leak; for simplicity we will describe the update rule assuming a single presynaptic input . Additionally , we will assume a single synaptic weight , although in general this approach works equally well for a distribution of synaptic weights . Under these assumptions , the discretized version of Eq 3 can be formulated as: d p i d t = - Δ J i Δ v i ( 6 ) Δ J i = f i + 1 2 - f i - 1 2 ( 7 ) = ( j ( s , i ) - - j ( l , i ) + ) - ( j ( s , i ) + - j ( l , i ) - ) . ( 8 ) Here f i ± 1 2 denotes flux across the right or left subdomain boundary , js denotes flux resulting from the input population ( via synaptic activation ) , and jl denotes flux from the leak; the superscript is a convenience that denotes the overall sign ( i . e . inflow or outflow ) of the contribution of the term to pi . Synaptic activation contributes j ( s ) to the overall flux by displacing probability mass ( pΔv ) with a transition rate λin , the presynaptic firing rate . By directly computing the probability mass flux as Δt → 0 over the subdomain boundary ( while enforcing probability mass conservation ) , the contribution of passive leak j ( l ) to the overall flux can be formulated as a transition rate that increases exponentially with time constant τm as the voltage of the subdomain boundary being crossed increases . To summarize , the flux contributions to the ith subdomain are: j ( s , i ) + = p k Δ v k λ i n ( 9 ) j ( s , i ) - = p i Δ v i λ i n ( 10 ) j ( l , i ) + = p i + 1 v i + 1 2 τ m ( 11 ) j ( l , i ) - = p i v i - 1 2 τ m ( 12 ) Here the synaptic influx j ( s , i ) + depends on pk , the probability mass in subdomain vk located a distance w = vi − vk ( the synaptic weight ) from vi . In the special case of i = 0 and w > 0 , the node that acts as the reset value for probability mass that exceeds the spiking threshold vθ ( i . e . the boundary condition ) receives probability mass from all nodes less than w from vθ . Because these updates result from a linear update from probabilities , the entire time evolution can be formally represented as: d p d t = ( L + S ) p ( 13 ) where leak and synaptic input contributions have been separated into two separate discrete flux operator matrices . At this step , it is trivial to include additional synaptic inputs S0 , S1 , …Sm , yielding a formal solution over a single time step Δt: p ( t + Δ t ) = exp Δ t L + ∑ s = 0 m S s p ( t ) ( 14 ) for some initial probability distribution p ( t ) . At each time step , the synaptic input matrices Sk are updated to reflect the changes in firing rate of the presynaptic populations ( if necessary ) . Probability mass that is absorbed at threshold and inserted at the reset potential defines the fraction of the population that spiked; after normalization by the discrete time step Δt , this defines the output firing rate . The output firing rate provides the rate of a Poisson process that drives any recurrently-connected postsynaptic populations . Probability mass flux through the boundary vθ into the subdomain at i = 0 defines the instantaneous firing rate of the population , computed as: λ o u t ( t ) = ∑ s = 0 m j ( s , 0 ) + Δ t ( 15 ) Recurrent coupling between simulated populations is accomplished by assigning λout of the presynaptic population to λin of the postsynaptic population . The source code for DiPDE is released as an open source python package under the GNU General Public License , Version 3 ( GPLv3 ) , and is available for download at http://alleninstitute . github . io/dipde/ . The package includes an example implementation of the cortical column model analyzed in the main text , absent any inputs in excess of background excitation .
In this section we describe the repertoire of computations caused by step inputs over and beyond background excitation ( See Fig 1 ( c ) and 1 ( d ) for an example simulation , compared to 100 averaged leaky integrate-and-fire ( LIF ) simulations ) into a coarse-grained population-statistical version of the Potjans and Diesmann cortical column model ( See Fig 1 ( a ) for a visual summary of projections in the column model; for a complete model description see [11] , Tables 4 and 5 ) . The targeting of cell types ( i . e . target specificity ) has important consequences for the responses caused by incoming inputs . We examine the consequences of three types of target specificity , summarized in Fig 1 ( b ) for incoming excitatory projections into a given layer within the column . The excitatory and inhibitory target specificity regimes excite their respective cell types , while the balanced regime does not preferentially target either subpopulation . Unless otherwise specified , the step input has a firing rate of 20 Hz , and models a convergent connection with 100 independent presynaptic sources per target neuron . Fig 3 provides an overview of output perturbations evoked by step input into a given layer , under each target specificity condition . In effect , this provides an at-a-glance summary of the catalogue of computations that the cortical column can perform , given a 20 Hz step pulse excitatory input into a single layer . We find that the effect of driving L2/3 under any specificity condition has a depressing effect on the activity in L5 . In contrast , when driving L4 or L5 , activity across almost every population in the network increases or decreases when driving the excitatory or inhibitory subpopulation , respectively . Fig 3 also demonstrates that under balanced target specificity ( yellow ) , the effects of inputs into L2/3 and L4 are nearly equal-and-opposite with respect to the output of L5 . We summarize this comparison across all output layers in Fig 4 which additionally plots the combined effect of inputs simultaneously into L4 and L2/3 . This plot demonstrates that these two inputs approximately offset; we explore this observation further in the next section . We note that the input layers involved in this subtraction are implicated in bottom-up vs . top-down comparisons in the theory of hierarchical predictive coding [22] . Also conspicuous is the output population reporting this subtraction; L5 pyramidal neurons provide the dominant cortical output , including the pons , striatum , superior colliculus , and to value encoding dopaminergic neurons in the VTA or SNc [23] where subtraction errors might skew reward expectations ( see Discussion for further details ) . Linear computations are characterized by simultaneously exhibiting homogeneity ( i . e . multiplicative scaling in the sense of a linear map ) and additivity with respect to inputs . The previous section examined output perturbations across layers and target specificity profiles of a single strength ( 20 Hz firing rate ) . In this section , we first examine the effect of linearly increasing the strength of the input , testing the homogeneity of the system . Fig 5 extends Fig 3 by providing a summary across an increasing range of input strengths . Under balanced target specificity ( middle column of panels ) , the magnitude of each population response exhibits a scaling behavior , linear in the input magnitude . In contrast , when neurons are targeted with a cell type specific bias , the response of certain subpopulations can be nonlinear . The clearest example of the nonlinear influence of the inhibitory subpopulation occurs when driving layer 5 . Through both an increase in direct self-inhibition , and indirect reduction of self-excitation via the L5e subpopulation , excitatory drive into L5i can paradoxically decrease activity , an effect described previously in inhibition-stabilized recurrent networks [24] . Eventually this effect reverses when the L5e activity is completely inhibited . Fig 6 demonstrates that , likewise , additivity is violated ( somewhat , as the points deviate from the identity line ) when preferentially targeting inhibitory neurons . Each point in the figure depicts the result of driving two separate layers with a 20 Hz firing rate input , and considering the perturbation in firing rate of each subpopulation ( specified in the legend ) . For a given target specificity condition , two independent simulations are run , for each of the two input layers; the sum of the perturbation they evoke is plotted on the vertical axis . The output resulting from a single simulation with two equal inputs into each input layer , is plotted on the horizontal axis . When a point lies along the identity line , this implies additivity . This figure implies a conclusion similar to the homogeneity study above: as the target specificity moves from excitatory to inhibitory , the firing rate computation performed on laminar inputs by the cortical column changes from linear to weakly nonlinear . In the previous section , we demonstrated that balanced 20 Hz firing rate inputs to L2/3 and L4 approximately offset each other in the output evoked in L5 . The homogeneity and additivity demonstrated above indicate that L5 will actually reflect a subtraction operation on these two inputs . We return to this point in the discussion . Given the amount of recurrent connectivity in the model , its linear response to step inputs under balanced target specificity might seem surprising . However , it is known that balanced networks can exhibit linear responses to external inputs ( See , for example , [25] ) . Although the model parameterization is taken from the literature , we also investigated the sensitivity of this linear response to perturbations in model parameters . By perturbing the connection probability matrix ( Table 5 “Connectivity” in [11] ) , we defined 1000 alternative models . Specifically , each entry in the matrix was multiplied by a normally distributed random number with unit mean , and standard deviation taken as 5% of the entry ( negative values were thresholded to zero ) . The homogeneity of response to each new model was assessed by linearly extrapolating the perturbation resulting from a 10 Hz firing rate step input from the results obtained from a 5 Hz step input . The absolute value of the prediction error: Δ F = ( F 10 - F 0 ) - 2 · ( F 5 - F 0 ) ( 16 ) quantifies the difference between the extrapolated value , and the true value obtained by direct simulation of a 10 Hz firing rate input . Here F indicates the firing rate after reaching steady-state , and the subscript indicates the strength of the step input . Intuitively , this quantity will be zero when a linear extrapolation can predict the data ( i . e . a linear relationship between inputs and outputs ) . Nonzero values indicate the failure of a linear extrapolation , and thus a nonlinear dependence of the output firing rate on the input over the regime of 0–10 Hz perturbations . S1 Fig shows a stacked histogram of this prediction error for the 1000 perturbed models , across all combinations of target specificity , laminar drive , and output population . Under balanced target specificity ( middle column ) , the prediction error is reliably smaller , particularly when layers 4 and 5 are targeted ( middle two rows ) . This implies that the linear relationship between inputs and outputs under balanced input of the original cortical column model is insensitive to small perturbations in the connection probability matrix . A similar result holds for additivity predictions , shown in S2 Fig . For the same perturbed models , the additive prediction error is defined as the sum of output responses in two layers from two different simulations , minus the output resulting from driving the two layers in the same simulation . Again , the model under balanced target specificity is less sensitive to perturbations than when cell types are selectively driven . Therefore , we conclude that the observation of linear responses in model output in the previous section is not a result of fine tuned parameters . In the previous section , we demonstrated that target specificity can determine the linearity of the model response under step inputs . To further investigate the linearity of the transformation that the column applies on its inputs , we next consider sinusoidal drive above and beyond background drive ( See Fig 1 ( d ) for an example simulation , compared to 100 averaged LIF simulations ) . S3 Fig summarizes the nonlinear distortion in each populations response under a 5 Hz peak amplitude sinusoidal drive . Only responses with a peak amplitude greater than . 05 Hz are plotted . Total harmonic distortion ( THD ) compares the power present in the harmonics of the driving frequency in the sinusoidal input signal that perturbs a subpopulation above and beyond the background firing rate: T H D ( f ) = ∑ i = 2 ∞ V i 2 V 1 ( 17 ) Here Vi is the power spectral density ( PSD , [26] ) of the ith harmonic of the principal ( driving ) frequency . This figure reinforces the conclusion from the previous section , that the target specificity of the sinusoidal drive can affect the nonlinearity of transformations resulting from population-level processing . In particular , balanced drive minimizes the harmonic distortion imposed by the dynamics within the model . In contrast , inhibitory drive into layer 5 produces nonlinear responses throughout the column , in agreement with observations about the homogeneity of responses to step inputs ( cf . Fig 7 ) The low THD of the output signals from balanced drive indicate that the firing rate y ( t ) of a population in the column model can be approximately modeled as a linear filter on the input signal x ( t ) plus a baseline x0: y ( t ) = x 0 + ∫ 0 ∞ x ( t - τ ) h ( τ ) d τ ( 18 ) Fig 8 provides a numerically computed description of three examples of this linear filtering , resulting from balanced drive from L2/3→L5e , L4→L5e , and L4→L23e . Of all possible input/output pairs , these examples show the least signal attenuation from the amplitude Ain of the input signal x ( t ) to the amplitude Aout of the output signal y ( t ) ( i . e . the largest impact on changes to subpopulation firing rate ) . Clearly evident in the first two figures are first-order lowpass filters , similar to feedforward systems found in [4] with significantly higher synaptic weights ( relative to threshold ) . These filters both have a cutoff frequency near 15 Hz , implying a corresponding RC time constant near the membrane time constant ( 10 ms ) of neurons in the system . Interestingly , this observation is in agreement with the very general prediction of predictive coding theories , that high frequencies should be attenuated when passing from superficial to deep layers [22] ( See Discussion ) . Transmission from L4 to L2/3 is band-passed near the 10–30 Hz range .
In this study , we examine what input/output transformations a popular model of a cortical column performs on layer-specific excitatory inputs . Transformations are defined as perturbations to the steady-state mean firing rate activity of subpopulations of cells in response to step and sinusoidal inputs in excess of background drive . Because the mean firing rate is a population-level quantity , we use a population statistic modeling approach , by numerically computing the population voltage density using DiPDE ( http://alleninstitute . github . io/dipde/ ) , a coupled population density equation simulator ( See Numerical Methods ) . This approach enables a fast , deterministic exploration of the stimulus space and model parameterization . Our approach begins with a data-driven model as a starting point , and then examines the computations its dynamics subserve , as opposed to fitting a model to a preselected set of dynamical interactions resulting from assumptions about cortical computation . Our goal is to discover robust evidence for theories of cortical function using knowledge about structure ( synthesized by Potjans and Diesmann [11] into their cortical column model ) , while limiting biases and a priori functional assumptions . We consider three discrete regimes of input specificity: excitatory preference , no preference ( i . e . balanced , in which both excitatory and inhibitory cells in any one layer receive the same external input ) , or inhibitory preference . We find that balanced target specificity results in output perturbations that scale linearly with input strength and combine linearly across input layers . In contrast , selective targeting of a particular cell type ( especially the inhibitory subpopulation ) leads to nonlinear interactions . Additionally , we find that equal , simultaneous , and balanced inputs into L2/3 and L4 are offset in their effect on the L5 firing rate; combining this with the observation of linearity implies that perturbations in L5 activity represent a subtraction from L4 activity of L2/3 activity . The inhibitory effect of L2/3 input on L5e output appears to be largely mediated by L2/3 interneurons inhibiting L5 pyramids ( c . f . [27] , their Fig 4 ) while the excitatory effect of L4 on L5 is a network effect resulting from multiple projection pathways . We conclude that the cortical column model implements a subtractive mechanism that compares two input streams and expresses any differences in the mean activity of L5 . While this computation can be implemented via other inputs , this combination is interesting because no target cell type specificity is required . How does this observation of a mechanism for subtraction relate to existing theories of cortical processing ? Predictive coding [10 , 28] postulates a computation that compares an internal model of the external environment to incoming sensory signals , in order to infer their probable causes [29 , 30] . The subtractive dynamics supported by the cortical column model could accomplish this . However , this would imply that sensory signals are represented dynamically in one layer , an environmental model in the supragranular layer , and that their functionally relevant difference is relayed by the infragranular layer ( layer 5 ) . The internal granular layer ( layer 4 ) is the obvious candidate for incoming environmental evidence , given its specialized role in receiving input from the primary sensory thalamus . Similarly , the role of the infragranular layer in driving subcortical structures involved in action ( basal ganglia , colliculus , ventral spinal cord ) seem compatible with the proposition of layer 5 representing the output of a comparison operation . Although more speculative , this leaves the supragranular layer responsible for generating the internal environmental model , which seems reasonable given its abundance of intracortical projections and increased development in higher mammals . These speculative roles of the various cortical layers conform to abstract models of canonical microcircuits ( See , for example , [31] ) . This is especially true when placed in a hierarchy of processing stages , for example in hierarchical predictive coding ( hPC ) [22] . In this framework , sequential processing stages generate top-down predictions , and pass bottom-up prediction errors , at each level in the hierarchy . In primates , the laminar segregation of these streams is easily aligned with the anatomical characterization from Felleman and Van Essen [5] , with feedforward connections targeting L4 , and feedback connections avoiding L4 . In rodents , the relation between lamination and hierarchy is less clear [32] . Although the central theme of distinct populations of forward-projecting neurons targeting L4 vs . backward projecting neurons avoiding L4 in the visual system seems conserved [33] , these distinct populations are not segregated by layer , but instead intermingled [34] . Therefore , future experimental attempts to establish connections between hierarchically defined visual processing regions and theoretical models may require projection-target-segregated ( or perhaps genetically-segregated , if projection markers can be established ) , as opposed to laminae-segregated , cellular subpopulations . An additional connection between the hPC model and the results of the simulations in this study is presented in Fig 8 . Here the response of the deep layer of the model to stimulation in either of the two superficial layers is well characterized by a linear low-pass filter . Interestingly , this filtering is a prediction of hPC , where high frequencies should be attenuated when passing from superficial to deep pyramidal cells [22] . The low-pass filtering prediction arises from the hypothesis that cortex is performing a form of Bayesian filtering , by attempting to update an estimated quantity using noisy measurements . These noisy estimates by their nature have higher-frequency content than the uncorrupted “true” quantity being estimated , and so the appearance of a smoothing transform is not surprising . However , it is surprising that our model , formulated without assuming any underlying computation ( especially not Bayesian filtering ) , performs this smoothing at a dynamical stage precisely where the anatomically-informed hPC model requires it . Taken together , the convergence of experimental , anatomical , theoretical , and simulation evidence is striking . As mentioned above , neurons in the infragranular layer project both cortically and subcortically . Based on the hPC model , and because of the model’s ability to compute a subtraction between inputs to the granular and supragranular layers , we have speculated this computation could represent an error signal between reality and expectation . What would this conclusion imply for the subcortical projections ? Watabe-Uchida et al . [23] found that dopaminergic neurons in the ventral tegmental area and substantia nigra pars compacta receive sparse input from the deep layers of cortex ( for example their Fig 5 ) . Because of the well established role of these midbrain structures in valuation , motivation , and reinforcement learning , these authors suggest that these dopaminergic neurons might “calculate the difference between the expected and actual reward ( i . e . , reward prediction errors ) . ” While speculative , it is possible that this prediction is calculated cortically and relayed either directly or indirectly [35] . There are a number of concrete steps that can be taken to strengthen the relationships between model , theory , and experiment . A more comprehensive model parameterized by experimental data with additional layers and cell types could be combined with matched optogenetic in vivo and in silico perturbation experiments . These manipulations could validate model predictions , suggest refinements , and test specific conclusions related to theories of population-based cortical processing , for example the functional role of different classes of genetically defined interneuron populations . Such models might also suggest a reinterpretation of how different cell populations contribute to the computation of error signals , or suggest new canonical computations carried out by population-level activities . Either way , in our view , the population density modeling approach will continue to provide a valuable tool for quickly exploring the dynamical consequences of population level computational models .
|
What computations do existing biophysically-plausible models of cortex perform on their inputs , and how do these computations relate to theories of cortical processing ? We begin with a computational model of cortical tissue and seek to understand its input/output transformations . Our approach limits confirmation bias , and differs from a more constructionist approach of starting with a computational theory and then creating a model that can implement its necessary features . We here choose a population-level modeling technique that does not sacrifice accuracy , as it well-approximates the mean firing-rate of a population of leaky integrate-and-fire neurons . We extend this approach to simulate recurrently coupled neural populations , and characterize the computational properties of the Potjans and Diesmann cortical column model . We find that this model is capable of computing linear operations and naturally generates a subtraction operation implicated in theories of predictive coding . Although our quantitative findings are restricted to this particular model , we demonstrate that these conclusions are not highly sensitive to the model parameterization .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"engineering",
"and",
"technology",
"nervous",
"system",
"signal",
"processing",
"electrical",
"circuits",
"membrane",
"potential",
"electrophysiology",
"neuroscience",
"signal",
"filtering",
"simulation",
"and",
"modeling",
"probability",
"distribution",
"mathematics",
"population",
"biology",
"microcircuits",
"research",
"and",
"analysis",
"methods",
"animal",
"cells",
"probability",
"theory",
"population",
"metrics",
"cellular",
"neuroscience",
"cell",
"biology",
"anatomy",
"synapses",
"physiology",
"neurons",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"population",
"density",
"electrical",
"engineering",
"neurophysiology"
] |
2016
|
The Computational Properties of a Simplified Cortical Column Model
|
Mycobacterium ulcerans disease ( Buruli ulcer ) is a neglected tropical disease common amongst children in rural West Africa . Animal experiments have shown that tissue destruction is caused by a toxin called mycolactone . A molecule was identified among acetone-soluble lipid extracts from M . ulcerans ( Mu ) -infected human lesions with chemical and biological properties of mycolactone A/B . On thin layer chromatography this molecule had a retention factor value of 0 . 23 , MS analyses showed it had an m/z of 765 . 6 [M+Na+] and on MS:MS fragmented to produce the core lactone ring with m/z of 429 . 4 and the polyketide side chain of mycolactone A/B with m/z of 359 . 2 . Acetone-soluble lipids from lesions demonstrated significant cytotoxic , pro-apoptotic and anti-inflammatory activities on cultured fibroblast and macrophage cell lines . Mycolactone A/B was detected in all of 10 tissue samples from patients with ulcerative and pre-ulcerative Mu disease . Mycolactone can be detected in human tissue infected with Mu . This could have important implications for successful management of Mu infection by antibiotic treatment but further studies are needed to measure its concentration .
Mycobacterium ulcerans ( Mu ) disease ( Buruli ulcer ) is common in humid rural tropical areas mainly in West Africa and predominantly affects children between 5 and 15 years of age [1] . The classic lesion is a painless nodule which breaks down centrally to form an ulcer with undermined edges . Histology shows clumps of acid fast bacilli in areas of subcutaneous fatty necrosis with acute and chronic inflammation remote from the necrotic areas . Granulomas are found in later lesions [2] . This histopathology led to the suggestion that Mu causes disease by secretion of a toxin which can destroy human tissue and inhibit the development of local inflammation [3] . Subsequently it was found that M . ulcerans culture filtrate could produce similar lesions after injection into guinea pig skin [4] . Initial attempts to isolate this substance were frustrated by low yields from cultures until the late 1990s when a toxin called mycolactone was partly purified and its chemical structure defined [5] . Subsequently mycolactone has been characterised as a 743 Da molecule consisting of a 12-membered ring macrolide with two polyketide derived side chains ( figure 1 ) synthesised by giant polyketide synthases and polyketide modifying enzymes whose genes are carried on two identical copies of a 174 kb plasmid known as pMUM001 [6] . Mycolactone causes a cytopathic effect on mouse fibroblast L929 cells characterised by cytoskeletal rearrangement with rounding up and subsequent detachment from tissue culture plates within 48 hours . The toxin causes cell cycle arrest in the G0/G1 phase within 48 hours , proceeding to cell death by apoptosis after 72 hours [7] . Various elegant in-vitro and in-vivo studies in mice and guinea pigs have demonstrated that this polyketide toxin is central to the pathogenesis of M . ulcerans disease . George et al demonstrated that injection of 100 µg of mycolactone was sufficient to cause characteristic ulcers in guinea pig skin [7] and that histopathological changes could be detected with 10 µg . Although direct inoculation of mycolactone intradermally into guinea pigs caused necrotic lesions similar to those produced by the injection of live organisms , an isogenic toxin-negative mutant M . ulcerans was phagocytosed by macrophages and stimulated a typical mycobacterial inflammatory response , including granuloma formation . Chemical complementation of this mutant with mycolactone restored virulence [8] . Histologically these lesions showed significant apoptotic cell death [9] , a feature which has been observed in human lesions [10] . Mycolactone has also been associated with vacuolar nerve tissue damage in mice and this observation may account for the painlessness of Buruli ulcer lesions [11] . The classic histological feature of human Buruli ulcer lesions is subcutaneous fatty necrosis with clumps of AFB in the absence of inflammatory cells . The necrosis is explained by cytotoxic properties of mycolactone but the paucity of inflammatory cells despite extensive skin damage may be due to its immunosuppressive properties . Mycolactone has been shown to inhibit the responses of macrophages and activated T-cells in-vitro [12] , to inhibit phagocytosis by murine macrophages [13] , to induce lysis of cultured macrophages after a transient intracellular growth [14] as well as to impair the production of TNF-alpha by these cells [15] and to inhibit induction of chemokine secretion by dendritic cells [16] . Therefore high concentrations of mycolactone in necrotic foci may kill cells by apoptosis or necrosis but its diffusion away from the centre of lesions where infiltrates of neutrophils and macrophages are found may serve to modulate the release of chemokines and cytokines by these inflammatory cells without killing them . Despite the obvious role of mycolactone in virulence , the fact that the molecule is a lipid rather than a protein has made it difficult to study in vivo . The molecule has not been detected in vivo in human Buruli ulcer lesions making it impossible to answer questions regarding its distribution and stability . Although it has been assumed that mycolactone is present in all stages of infection , there has been no evidence to support this speculation . The aim of the present studies was therefore to identify mycolactone among lipids extracted from human skin infected with Mu .
Lipids were extracted from Buruli ulcer lesions using chloroform:methanol 2:1 ( vol/vol ) followed by a Folch extraction with 0 . 2 volumes water . Briefly , the punch biopsy was weighed and placed in a 1 . 5 ml green top matrix tube containing 500 µl of diatoms ( Q-bio ) and homogenised in extractant solution for 45 seconds at a power of 6 . 5 in a Fast Prep Ribolyser . The homogenate was transferred into a microfuge tube and allowed to stand for 30 minutes . After centrifugation at 10 , 000 g for 2 minutesthe organic phase was harvested . The organic phase was dried in a roto-evaporator and re-suspended in ice-cold acetone for 1 hour to precipitate phospholipids . Acetone soluble lipids ( ASL ) were harvested for mycolactone detection by thin layer chromatography , mass spectrometry and cytopathicity assays . For cytopathicity assays , half of the total volume of lipid extracts were dried under nitrogen gas and shipped to the University of Tennessee , U . S . where analyses were performed . The other half of tissue lipid extracts were analysed for cytotoxicity using an MTT assay , fluorescent microscopy for characterisation of cell death and TNF-alpha induction assays at St . George's , University of London , U . K . To optimise the extraction technique and determine the limit of detection , 100 µg , 10 µg , 1 µg and 0 . 1 µg quantities of mycolactones were used to spike approximately 100 mg of healthy skin tissue . Healthy human skin was obtained during excision of a lipoma with patient permission . Mycolactone was extracted from spiked skin samples as described above . Mycolactone was prepared from M . ulcerans extracts as previously described [5] . Briefly , M . ulcerans cultures were grown in Middlebrook M7H9 broth with OADC supplement until early stationary phase . Bacteria were harvested by centrifugation and mycolactone was extracted from dried bacterial pellets using chloroform:methanol 2:1 followed by Folch extraction with 0 . 2 volumes water . Acetone-soluble lipids ( ASL ) highly enriched for mycolactone were obtained by drying lipids under nitrogen and enriched for mycolactone by precipitating phospholipids with ice cold acetone . Purified mycolactone was obtained from ASL by centripetal chromatography using a Harrison chromototron ( Palo Alto , CA ) . Samples were assessed by mass spectroscopic analysis to validate purity . Purified mycolactone A/B was applied to a silica thin layer chromatography ( TLC ) plate and analysed using chloroform-methanol-water ( 90:10:1 vol/vol/vol ) as a solvent system . Lipid bands were visualised under UV light and by oxidative charring with ceric sulphate-ammonium molybdate in 2M sulphuric acid . Serial dilutions of purified mycolactone from 125 µg/ml to 1 . 4 µg/ml were spotted onto TLC plates to determine the detection limit for the system . 20 µl of acetone soluble lipids from spiked skin samples , negative controls and infected human tissues were analysed by TLC to detect the presence of mycolactone bands . Acetone soluble lipids were dissolved in ethanol and directly perfused into an electrospray ionization source on a Bruker Esquire 2000 mass spectrometer using a Cole Palmer 74900 series syringe pump . The electrospray MS conditions were initially optimized to a mycolactone standard before applying them to the lipid extracts . The electrospray MS conditions were: infusion rate 1 , 000 µl/h; nebulizer pressure 30 lb/in2; dry gas flow 10 l/min; dry temperature 320°C; capillary voltage −4 , 000 V; end plate offset −500 V . Detection of mycolactone was determined by the presence or absence of ions characteristic of mycolactone: the more abundant sodium adduct [M+Na]+ ( m/z 765 . 5 ) ; the protonated molecular ion [M+H]+ ( m/z 743 . 5 ) , and the dehydrated protonated molecular ion [M+H - H2O]+ ( m/z 725 . 5 ) . ESI-MS/MS analyses were performed on the m/z 765 . 5 component for the characteristic fragmentation pattern comprising ions corresponding to the core lactone and the polyketide side chain respectively . L929 murine fibroblasts were maintained in Dulbecco's modified Eagle's medium with 5% foetal calf serum in tissue culture flasks and incubated in 5% carbon dioxide at 37°C . ASL samples which had been dried under nitrogen were dissolved in 50 µl absolute ethanol . 2 . 5 µl ( 5% ) or 5 µl ( 10% ) of the resultant solution was added to cells in a 96-well tissue culture plate to determine cytopathic effect ( CPE ) . CPE was defined as the minimal concentration of ASL per millilitre necessary to produce 90% cell rounding in 24 h and loss of the monolayer by 48 h . Cytotoxicity of ASL from infected human tissues was further assessed by the ability of mycolactones to inhibit mitochondrial succinate dehydrogenases of human embryonic lung fibroblasts ( HELF ) from reducing dimethylthiazolyl diphenyl tetrazolium bromide ( MTT ) dye to form purple formazan crystals . Induction of cell death was assessed by staining these cells with a dye mix of ethidium bromide and acridine orange after treatment of HELF cells with mycolactones positive controls , acetone soluble lipids from infected patient samples and negative controls comprising ASL a lipoma lesion , ethanol and untreated wells . Briefly HELF cells ( kindly donated by Dr . Kay Capaldi ) were maintained in Dulbecco's modified Eagle's medium supplemented with 10% foetal calf serum and 2 mM L-glutamine in the presence of penicillin and streptomycin 100 mU/ml and 100 mg/ml respectively and incubated in 5% carbon dioxide at 37°C . For cytotoxicity assays proliferating HELF cells were seeded at a density of 105/well in microtitration plates overnight . ASL from lesions , positive controls and negative controls were dissolved in 100 µl of absolute ethanol of which 5 µl was used to treat HELF cells in quadruplicates . After 48 h incubation , 20 µl of 5 mg/ml of MTT ( Sigma ) was added to each well and incubated for a further 4 h for purple coloured formazan crystals to develop following which 100 µl of detergent solution of isopropanol: HCl ( 2N ) in a ratio of 49∶1 was used to dissolve formazan crystals for spectrophotometric quantification in multiplate well reader at 570–690 nm . To characterise the pattern of cell death caused by mycolactones in patient samples HELF cells were stained with 8 µl of a combination of 100 µg/ml of ethidium bromide ( Sigma ) and 100 µg/ml of acridine orange ( Sigma ) ( EB/AO ) dye mix in microtitration plates as previously described [18] . Cells were observed with a DM1 6000B Leica fluorescent microscope and images taken at x10 magnification . To confirm the immunosuppressive properties of mycolactones extracted from human lesion we measured release of TNF-α by J774 macrophages pre-treated with ASL from patient lesions after stimulation with lipopolysaccharide ( LPS ) . Briefly , J774 macrophages ( kindly donated by Dr . Rajko Reljic ) were maintained in Dulbecco's modified Eagle's medium supplemented with 10% foetal calf serum and 2 mM L-glutamine in tissue flasks and incubated in 5% carbon dioxide at 37°C . Proliferating macrophages were seeded at a density of 105 in 96-well microtitration plates and allowed to adhere to culture plates over 4 h . Cells were exposed to 5 µl of ASL from lesions or to purified mycolactones as a positive control for 6 h and stimulated with 0 . 5 µg/ml of LPS ( Sigma ) with a 16 h incubation in 5% CO2 at 37°C . Samples were analysed in quadruplicates and experiments were repeated twice . Supernates were harvested and TNF-α quantities assayed in duplicate with a Quantikine ELISA kit ( R & D Systems ) according to the manufacturer's protocol . The limit of detection of TNF-α was 23 . 4 pg/ml . Viability of macrophages after cytokine induction assays was assessed with the MTT assay previously described . Student's t-test was used to test for significance by comparing cytotoxicity and TNF-alpha release by cultured cell lines treated with lipid extracts from patient samples and negative controls with a p<0 . 01 considered significant . The Mann-Whitney U test was used to compare TNF-alpha release by macrophages treated with lipid extracts from untreated and antibiotic treated lesions with p<0 . 05 considered statistically significant . The study protocol was approved by the ethics review committees at the School of Medical Sciences , Kwame Nkrumah University of Science and Technology , Kumasi , Ghana , and St . George's Hospital in London , United Kingdom . Informed consent was obtained orally since the patients were illiterate and consent was given by thumb print as approved by the ethics committees .
Table 1 shows the characteristics of the 10 patients with PCR-confirmed M . ulcerans infection whose mean age was 17 years . There were 5 nodules , 4 oedematous lesions , 3 of which had ulcerated , and 1 ulcer without oedema . Five patients had received antibiotic therapy consisting of intramuscular streptomycin 15 mg/kg daily and oral rifampicin 10 mg/kg daily , 4 for 4 weeks and one for 6 weeks . Four out of 5 untreated lesions were culture positive and 3 out of 5 lesions were also culture positive after 4 or 6 weeks of treatment as shown in Table 1 . Using TLC , purified mycolactone A/B yielded a major UV active band at a retention factor ( Rf ) value of 0 . 23 and two minor bands . The detection limit of mycolactone on TLC was 2–8 µg/ml as shown in figure 2A . Figure 2B shows that acetone soluble lipids from 5 lesions had detectable bands at Rf 0 . 23 which were UV active and corresponded to the purified mycolactone A/B ladder M . These bands were from 1 untreated oedematous BU lesion with ulceration , 2 untreated nodules and 2 antibiotic treated nodules . All those with detectable mycolactone signals on TLC were also culture positive . However 5 other lesions did not show perceptible bands on TLC plates . Three of these patients were on antibiotics while the other 2 patients had untreated oedematous lesions . Mass spectral analysis of acetone soluble lipids from infected human lesions under microspray conditions revealed the presence of an ion with mass to charge ratio ( m/z ) of 765 . 5 [M+Na+] which corresponded to that of mycolactone A/B ( Figure 3A ) . Fragmentation of this ion yielded ions with m/z 429 . 3 corresponding to the conserved core lactone ring present in all mycolactone congeners and with m/z of 359 . 2 corresponding to the polyketide side chain of mycolactone A/B . In addition ions were also observed at m/z 341 . 2 , 565 . 3 , 579 . 4 , 659 . 4 , 677 . 4 , 707 . 4 , 721 . 3 , 747 . 4 and 766 . 4 ( Figure 3B ) . Mycolactone A/B was detected in acetone soluble lipids from all 10 patients by mass spectrometry . Using mycolactone 100 µg , 10 µg , 1 µg and 0 . 1 µg to spike approximately 100 mg of healthy skin tissue , mycolactone signals could be detected on MS from those spiked with 100 µg and 10 µg but not at lower concentrations . Mycolactone exhibits a characteristic cytotoxic phenotype which includes cell rounding at 10 hours , cell cycle arrest at 36 hours and apoptotic cell death at 72 hours . We studied the effects of ASL on L929 murine fibroblasts without purifying mycolactone . This was justified since previous work has shown that the cytotoxicity of lipids obtained by chloroform∶methanol 2∶1 extraction is entirely due to the presence of mycolactone [7] . The classic cytotoxic phenotype of mycolactones was observed within 48 h upon treatment of murine fibroblasts with 5% ASL from infected Mu lesions . However , using 10% ASL , a high mycolactone concentration phenotype characterised by osmotic swelling of cells with eccentric nuclei was observed at 24 hours as previously described by Adusumilli et al [8] in all 10 patient samples analysed ( data not shown ) . In MTT based cytotoxicity assays , ASL from Mu infected lesions significantly inhibited mitochondrial succinate dehydrogenases of human embryonic lung fibroblasts ( HELF ) compared with ASL from uninfected human skin as shown in Figure 4 . ASL from patient extracts also caused significant apoptotic cell death comparable to 5 µg/ml of purified mycolactone after treatment of HELF for 48 h ( Figure 5 ) . No qualitative differences were observed in the physico-chemical properties , cytopathicity or pro-apoptotic activities of lipid extracts from the different forms of Mu infected lesions and there was no significant decline in percentage cytotoxicity between 5 untreated and 5 antibiotic treated lesions ( Figure 4 ) . Mycolactone is known to potently inhibit the release of cytokines at nanomolar , non-cytotoxic concentrations [16] . Figure 6 shows that ASL from infected human lesions significantly inhibited TNF-α release by murine macrophages compared with negative controls . There was no significant macrophage cytotoxicity despite the profound inhibition of cytokine release . Although there were no apparent differences in the degree of inhibition of TNF-α by lipid extracts from the different Buruli lesion types , significant recovery was observed in the release of TNF-α by macrophages treated with lipid extracts from antibiotic treated human lesions compared to untreated lesions ( Figure 7 ) which may indicate a decline in tissue levels of mycolactone during antibiotic treatment .
Our primary objective in this study was to isolate mycolactone from infected human lesions . We have extracted lipids from infected human tissue using organic solvents and identified mycolactone A/B by its recognised physical properties on chromatography and mass spectrometry and by its biological activities such as cytotoxicity and immunosuppression . Our findings are the first definitive proof of the molecule's presence in infected human tissue and provide a foundation for further studies on the kinetics of mycolactone-mediated virulence . Using ESI-MS , mycolactone A/B was detected in all 10 patient samples as an ion with an m/z of 765 . 5 [M+Na]+ and confirmed by its MSMS spectrum , the conserved core lactone ring and fatty acid side chain producing characteristic ions at m/z 429 . 3 and 359 . 2 respectively following fission of the ester bond with additional ions observed identical to those previously reported and identified in the MSMS spectrum of mycolactone A/B [19] , [20] . Acetone soluble lipids from Mu infected lesions but not those from negative control human skin showed the typical phenotypic cytopathicity in cultured fibroblasts that is associated with mycolactone . Cytotoxicity was assessed by the ability of ASL from infected tissue to cause cytoskeletal re-arrangement , rounding up of cells and detachment from culture plates as well as inhibition of mitochondrial dehydrogenases . Furthermore TNF-α induction by LPS was significantly impaired by lipid extracts from infected human lesions but not by negative controls ( Figure 6 ) . These findings confirm that mycolactone in these clinical samples was biologically active both in its ability to cause cell death and to modulate immune responses . The data from these experiments provide the first direct evidence for the presence of mycolactone in Mu infected human skin tissue . Our observation that lipid extracts from human lesions induced cell death by both necrosis and apoptosis implicates mycolactone as the cause of the extensive tissue damage observed in humans with Mu disease . The detection of mycolactone A/B in three common clinical forms of Mu lesions encountered in West Africa , namely nodular , oedematous and ulcerative , in this study re-emphasises its pivotal role in the pathogenesis of Mu disease . It is noteworthy that the molecule was detected throughout the course of disease from early nodular lesions to late ulcers . We also detected the molecule in lesions during the course of curative antibiotic therapy . Obiang et al in their landmark study reported that there was no correlation between mycolactone profiles of bacterial isolates from different patients and lesion type or severity [21] . These observations were not addressed in the present study but our findings open up the possibility that mycolactone concentration and type can be related to clinical presentation in future studies . Mycolactone was not detected by TLC in two specimens that were AFB positive , one of which was also culture positive . This may be because the organisms were producing only small amounts of mycolactone , below the detection limit for TLC , or because the biopsies were taken from adjacent but different sites . Figure 2B shows bands at rf 0 . 78 in most of the human samples but not in 9 and 10 , and only weakly in 8 . There was some variation in the weight and lipid content of specimens which might account for the different strength of non-mycolactone bands in human samples . Sample 8 showed a clear mycolactone band at rf 0 . 23 but only a very weak band at rf 0 . 78 suggesting that this sample contained a high concentration of mycolactone relative to the total lipid content . The purified mycolactone showed a band at rf 0 . 85 ( Figure 2B ) suggesting that it still contained some impurities but this does not affect the validity of the results . The observation that LPS stimulated TNF-α release from murine macrophages was inhibited significantly more by lipid extracts from untreated lesions than by ASL from antibiotic treated lesions suggests that tissue mycolactone concentrations declined during antibiotic therapy . However the sample size was small and further prospective studies using quantitative mycolactone assays are needed to investigate the pharmacokinetics of tissue mycolactone and to relate them to clinical responses to antibiotic therapy and the recovery of systemic gamma interferon secretory responses we have observed [22] . The recent demonstration that mycolactone can be detected in circulating murine mononuclear cells [23] may prove to be pivotal in answering questions about the dynamics of the local and systemic immune response to Mu during antibiotic therapy . In conclusion we have demonstrated for the first time the presence of mycolactone A/B in Mu infected human tissue . Given the central role played by mycolactone in the immunopathogenesis of Mu disease , it may be a useful marker for diagnosis and for assessment of the response to antibiotic therapy . Ultimately it may be possible to design molecules which can inhibit its synthesis or block its actions for the purpose of treatment .
|
Skin infection with bacteria called Mycobacterium ulcerans causes Buruli ulcer , a disease common in West Africa and mainly affecting children . M . ulcerans is the only mycobacterium to cause disease by production of a toxin . This lipid molecule called mycolactone diffuses from the site of infection , killing surrounding cells and , at low concentration , suppressing the immune response . The aim of this study was to show that mycolactone can be detected among lipids extracted from human M . ulcerans lesions in order to study its role in the pathogenesis of M . ulcerans disease . Lipids were extracted from skin biopsies and tested for the presence of mycolactone using thin layer chromatography and mass spectrometry . The extracts were shown to kill cultured cells in a cytotoxicity assay . Mycolactone was detected in both pre-ulcerative and ulcerative forms of the disease and also in lesions during antibiotic treatment but with reduced bioactivity , suggesting a lower concentration compared to untreated lesions . These findings indicate that there is mycolactone in affected skin at all stages of M . ulcerans disease and it could be used as a biomarker for monitoring the clinical response to antibiotic treatment .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/bacterial",
"infections"
] |
2010
|
Detection of Mycolactone A/B in Mycobacterium ulcerans–Infected Human Tissue
|
Parasite egress from infected erythrocytes and invasion of new red blood cells are essential processes for the exponential asexual replication of the malaria parasite . These two tightly coordinated events take place in less than a minute and are in part regulated and mediated by proteases . Dipeptidyl aminopeptidases ( DPAPs ) are papain-fold cysteine proteases that cleave dipeptides from the N-terminus of protein substrates . DPAP3 was previously suggested to play an essential role in parasite egress . However , little is known about its enzymatic activity , intracellular localization , or biological function . In this study , we recombinantly expressed DPAP3 and demonstrate that it has indeed dipeptidyl aminopeptidase activity , but contrary to previously studied DPAPs , removal of its internal prodomain is not required for activation . By combining super resolution microscopy , time-lapse fluorescence microscopy , and immunoelectron microscopy , we show that Plasmodium falciparum DPAP3 localizes to apical organelles that are closely associated with the neck of the rhoptries , and from which DPAP3 is secreted immediately before parasite egress . Using a conditional knockout approach coupled to complementation studies with wild type or mutant DPAP3 , we show that DPAP3 activity is important for parasite proliferation and critical for efficient red blood cell invasion . We also demonstrate that DPAP3 does not play a role in parasite egress , and that the block in egress phenotype previously reported for DPAP3 inhibitors is due to off target or toxicity effects . Finally , using a flow cytometry assay to differentiate intracellular parasites from extracellular parasites attached to the erythrocyte surface , we show that DPAP3 is involved in the initial attachment of parasites to the red blood cell surface . Overall , this study establishes the presence of a DPAP3-dependent invasion pathway in malaria parasites .
Malaria is a devastating infectious disease caused by Apicomplexan parasites of the Plasmodium genus and is transmitted by Anopheles mosquitoes during a blood meal . After an initial asymptomatic liver infection , parasites are released into the blood stream where they replicate within red blood cells ( RBCs ) . This asexual exponential growth is responsible for all the pathology associated with malaria , causing close to half a million deaths every year[1] . Over the last 15 years , the world has seen a significant decrease in malaria incidence mainly due to the distribution of insecticide-impregnated bed nets and the introduction of ACT ( artemisinin-based combination therapy ) as the standard of care for uncomplicated malaria[2] . However , the recent emergence of artemisinin resistance[3] has made the identification of viable therapeutic targets extremely important[4 , 5] . P . falciparum is the most virulent Plasmodium species accounting for most of malaria mortality . Its 48 h asexual erythrocytic cycle consists of RBC invasion , intraerythrocytic parasite growth and division into 16–32 daughter merozoites , followed by parasite egress for further RBC invasion . Parasite egress and RBC invasion are key for parasite replication and blocking either one of these processes would lead to a quick drop in parasitemia and malaria pathology . Proteases have been shown to play essential roles in both processes and might therefore be viable therapeutic targets[6] . RBC invasion is a multistep process involving initial recognition of RBC receptors by adhesin proteins on the surface of the merozoite ( invasive extracellular parasite form ) , tight attachment to the RBC membrane ( RBCM ) , reorientation of the merozoite apical end towards the RBCM , active invasion driven by an actin-myosin motor with invagination of the RBCM and formation of the parasitophorous vacuole ( PV ) , and finally , sealing of the RBCM and PV membrane ( PVM ) [7 , 8] . The PV is a membrane-bound vacuole within which the parasite grows and replicates isolated from the RBC cytosol . Rupture of the PV and RBC membranes is required for parasite egress and is mediated by proteases . In particular , subtilisin-like protease 1 ( SUB1 ) , an essential serine protease residing in apical secretory organelles known as exonemes , is released into the PV right before egress where it processes several proteins important for egress and invasion[9–13] . These include cleavage and likely activation of serine repeat antigen 6 ( SERA6 ) [14] , an essential papain-fold cysteine protease[15–17] . In a forward chemical genetic approach , P . falciparum dipeptidyl aminopeptidase 3 ( DPAP3 ) was identified as a potential regulator of parasite egress acting upstream of SUB1[18] . DPAPs are papain-like cysteine proteases that cleave dipeptides off the N-terminus of protein substrates[19] . In that study [18] , the vinyl sulfone inhibitor SAK1 was shown to preferentially inhibit DPAP3 over other cysteine proteases . This compound arrests parasite egress at mid-micromolar concentrations , blocks processing of SUB1 substrates , and prevents proper expression and maturation of SUB1 and apical membrane antigen 1 ( AMA1 ) , a micronemal protein secreted onto the merozoite surface that is essential for RBC invasion[9 , 20 , 21] . These results led to the hypothesis that DPAP3 might act as a general maturase of secretory proteins involved in egress and invasion . However , while the function ( or essentiality ) of P . falciparum DPAP3 has not been validated genetically , in the rodent parasite P . berghei , DPAP3 knock out ( KO ) parasites are viable but replicate significantly slower[22–24] . Here , we combine chemical , biochemical and conditional genetic approaches to show that DPAP3 is an active protease that resides in apical secretory organelles , and that its activity is critical for efficient RBC invasion . We also provide very strong evidence showing that DPAP3 does not play a significant function in parasite egress .
Using single homologous recombination we were able to replace the endogenous catalytic domain of dpap3 with a C-terminally tagged ( GFP , mCherry or HA ) version . However , our multiple attempts to replace the DPAP3 catalytic Cys with a Ser failed despite using the same homology region upstream of the catalytic domain ( Fig 1A and 1B and S1A and S1B Fig ) . Our attempts to KO dpap3 by double homologous recombination also failed . These results strongly suggest that DPAP3 activity is important for parasite development . Parasites containing differently tagged dpap3 were cloned by limited dilution , and the clones selected for this study will be referred to as DPAP3-GFP , DPAP3-mCh , and DPAP3-HA . Analysis of parasite extracts by western blot ( WB ) using a polyclonal antibody that targets the C-terminal half of DPAP3 showed a shift in migration pattern in accordance with the tag molecular weight ( Fig 1C ) . In all our tagged lines , DPAP3 consistently localizes to the apical pole of merozoites ( Fig 1D ) . To determine when it is expressed , tightly synchronized DPAP3-HA and DPAP3-mCh parasites were collected every 3 h throughout the erythrocytic cycle and were either lysed and analyzed by WB ( Fig 1E ) , or fixed for immunofluorescence analysis ( IFA , Fig 1F and S1C Fig ) . Consistent with its transcription profile[25] , DPAP3 is most abundant in late schizonts and merozoites , but could also be detected in rings and trophozoites by WB . To confirm that the small amount of DPAP3 observed by WB at ring stage is not due to schizonts contamination in our cultures , we analyzed 50 fields of five Giemsa-stained thin blood smears at 1 hour post invasion ( h . p . i . ) , i . e . around 75 , 000 RBCs . We did not observe any schizonts in these slides , only ring stage parasites at around 5% parasitemia . Also , WB analysis using an MSP1 ( merozoite surface protein 1 ) antibody as a marker of schizogony showed no significant staining at ring or trophozoite stages ( S1D Fig ) . Although we could detect multiple processed forms of DPAP3 by WB , these processed forms are rarely observed in live parasites ( Fig 1C ) and are likely an artefact of parasite lysis ( see below ) . By IFA , DPAP3 was first detected in young schizonts ( 6–8 nuclei ) and seems to be expressed at the same time as rhoptry ( rhoptry neck protein 4 , RON4 , and high molecular weight rhoptry protein 2 , RopH2 ) and inner membrane complex ( glideosome-associated protein 45 , GAP45 ) proteins ( Fig 1F and S1 and S2 Figs ) . During schizont maturation , DPAP3 and rhoptry proteins localization changes from a diffuse and granular cytosolic staining to a clear punctuated apical staining in daughter merozoites ( Fig 1F and 1G and S1E and S2 Figs ) , probably reflecting protein trafficking and organelle biogenesis . By contrast , exonemal ( SUB1 ) and micronemal proteins ( AMA1 ) are expressed and localize to their apical organelles at a later stage ( Fig 1F and S2 Fig ) . Note that the diffuse staining observed at 39 h . p . i . for DPAP3 , RON4 and RopH2 is not background fluorescence signal given that no staining was observed in these same slides for the few parasites that were lagging behind in development , i . e . infected RBCs ( iRBCs ) with a single nucleus ( S2A Fig ) . Using standard confocal microscopy , we could not observe consistent colocalization of DPAP3 with any apical organelle marker tested: AMA1 and EBA175 ( erythrocyte binding antigen 175 ) for micronemes , SUB1 for exonemes , RON4 and RopH2 for rhoptries , and Exp2 ( exported protein 2 ) for dense granules ( Fig 1F and S2B Fig ) . We therefore decided to increase the resolution of our images by using structured illumination microscopy ( SIM ) . By SIM we could clearly observe DPAP3 staining in small but well-defined dot-like structures at the apical pole of each merozoite in the DPAP3-HA ( Fig 1H and S3A Fig ) and DPAP3-GFP ( S3B Fig ) lines . Although we did not observe colocalization with exoneme ( SUB1 ) , microneme ( EBA175 ) or rhoptry ( RON4 , RopH2 ) protein markers ( Fig 1H and S3A and S3B Fig ) , DPAP3 seems to be closely associated with RON4 , suggesting that it resides in apical organelles that surround the neck of the rhoptries ( Fig 1H ) . Timely discharge of proteins from the different apical organelles is crucial to regulate parasite egress and RBC invasion[26] . Secretion of exonemal and micronemal proteins is mediated through activation of cGMP-dependent protein kinase G ( PKG ) , which takes places 15–20 min before egress[9] . Release of SUB1 into the PV leads to parasite egress . Secretion of micronemal proteins , such as AMA1 or EBA175 , onto the parasite surface is essential for merozoite attachment to the RBCM and invasion . To determine whether DPAP3 is secreted , we used the DPAP3-HA line to compare the level of DPAP3 present within parasites , in the PV and RBC cytosol , and in the culture supernatant at three different stages of egress: schizonts arrested before exoneme/microneme secretion with the PKG reversible inhibitor compound 2 ( C2 ) , schizonts arrested between PVM and RBCM breakdown using the general cysteine protease inhibitor E64 , and free merozoites collected after egress . In E64-arrested schizonts , the erythrocyte is still intact but the RBCM is highly porated allowing leakage of RBC and PV proteins into the culture supernatant[17 , 27] . Parasite pellets , PV and RBC cytosol proteins , and proteins secreted in the culture supernatant under these three conditions were collected after treating the cultures with the fluorescent activity-based probe FY01[28] . FY01 is a cell-permeable probe that covalently modifies the catalytic Cys of cysteine proteases including DPAP3[18] . Fluorescently labelled DPAP3-HA can then be visualized by in-gel fluorescence in a SDS-PAGE gel as a band running around 130 kDa that matches the WB band observed with a HA antibody ( Fig 2A and S4A Fig ) . DPAP3 was mainly detected in the culture supernatant and saponin soluble fraction of E64-arrested or rupturing schizonts , but not in C2-arrested schizonts , indicating that it is secreted downstream of PKG activation but before merozoites become extracellular . The presence of DPAP3 in E64-arrested schizont pellets and free merozoites suggests only partial secretion before egress . To confirm proper fractionation of our samples , we used SERA5 , BiP ( binding immunoglobin protein ) and Hsp70 ( heat shock protein 70 ) as PV , ER , and cytosol protein markers , respectively , in WB analysis ( Fig 2A ) . Processing of SERA5 was also used to confirm that C2 or E64 treatments arrested parasite development at the expected stage . Upon exoneme secretion , SUB1 sequentially cleaves SERA5 into a 73 and 56 kDa forms . This processing is blocked by C2 since exoneme secretion is regulated by PKG . SERA5 is further processed into a 50 kDa form by an unknown cysteine protease that is inhibited by E64 . Two additional biological replicates showing secretion of DPAP3 are shown in S4B and S4C Fig . To determine the timing of DPAP3 secretion , DPAP3-mCh schizonts were arrested with C2 and parasite egress monitored by live microscopy after C2 wash out . Using this assay , PVM breakdown is clearly observable on the DIC channel when merozoites become more spread out within the RBC and their shape is better defined[12 , 27] . This is followed by RBCM rupture and merozoites dispersal ( Fig 2B and S1–S3 Videos ) . Quantification of fluorescence signal in these egress videos shows a 40% decrease in mCherry signal right after PVM breakdown but before RBCM rupture ( Fig 2C ) . Interestingly , conditional KO ( cKO ) of SUB1 has recently been shown to prevent breakdown of the PVM but not AMA1 secretion , suggesting that microneme secretion takes place before PVM breakdown [17] . This implies that DPAP3 secretion probably takes place downstream of microneme secretion and coincides with PVM breakdown . To confirm that DPAP3 is secreted into the RBC cytosol after PVM breakdown , we performed immunoelectron microscopy ( IEM ) on DPAP3-GFP schizonts collected at the time of egress ( Fig 2D and S3D and S3E Fig ) . In schizonts containing an intact PVM , DPAP3-GFP staining was observed at the apical end of merozoites , and in some sections , in close proximity to the rhoptries or enclosed within membrane bound vesicles , thus supporting our IFA results . In IEM images collected on schizonts after PVM breakdown , immunostaining was mainly observed in the RBC cytosol . Almost no DPAP3 staining was detected in the PV of schizonts with an intact PVM . DPAPs are generally processed from a zymogen form ( full-length protein after removal of the signal peptide ) into an active form through removal of an internal prodomain and cleavage of the catalytic domain into two polypeptides[29 , 30] ( S5A Fig ) . Three different isoforms of DPAP3 ( p120 , p95 , and p42 ) consistent with the canonical processing of DPAPs were previously shown to be labelled by FY01 in merozoite lysates[18] . However , we have now shown that this processing is an artefact of parasite lysis ( S5B–S5D Fig and S1 Text ) . In addition , our WB and FY01-labelling experiments show that full length DPAP3 ( p120 ) is the predominant form found in live parasites ( Figs 1 and 2 and S5 Fig ) . To determine whether this full-length p120 form is active , we recombinantly expressed wild type ( WT ) and mutant ( MUT , replacement of the catalytic Cys504 to Ser ) DPAP3 in insect cells using the baculovirus system[31] . Expression and purification of recombinant DPAP3 ( rDPAP3 ) from insect cells culture supernatant yielded predominantly the p120 and p95 forms ( Fig 3A and S5B Fig ) . While WT DPAP3 is able to efficiently cleave the VR-ACC fluorogenic DPAP substrate[32] , no activity was observed with MUT DPAP3 ( Fig 3B ) . We also show that rDPAP3 is active under mild acidic conditions with an optimal pH of 6 ( Fig 3C ) . Importantly , in one of our purifications we were able to separate the p120 form from a fraction containing a mixture of the p95 and p120 forms . We used these fractions to show that the p120 form is fully active ( S5E Fig ) . This result strongly suggests that the predominant DPAP3 form present in live parasites ( p120 ) is the one performing a biological function . Since we were unable to directly KO DPAP3 , we generated DPAP3 cKO lines on the 1G5 parasite line background that endogenously expressed DiCre[33] . In the DiCre system , Cre recombinase is split into two domains fused to rapamycin ( RAP ) binding domains . Addition of RAP triggers dimerization and activation of DiCre , leading to rapid recombination of specific DNA sequences known as loxP sites[34 , 35] . We used this system to conditionally truncate the catalytic domain of DPAP3 rather than excising the full gene to prevent potential episomal expression of DPAP3 after excision . Two independent strategies , which differ in how the first loxP site was introduced within the dpap3 open reading frame ( ORF ) , were used ( Fig 4A and S6A Fig ) . In both cases , one loxP site was introduced downstream of the 3’-UTR . The other was inserted either within an Asn-rich region of DPAP3 predicted not to interfere with folding or catalysis , or within an artificial intron ( loxPint ) , which does not alter the ORF of the targeted gene and has been shown to be well tolerated in several P . falciparum genes[12 , 17 , 36 , 37] . In both instances , the recodonized catalytic domain was tagged with mCherry such that RAP-induced truncation would result in the loss of mCherry signal . A control line containing only the 3’-UTR loxP site was also generated . After transfection of 1G5 parasites , drug selection , and cloning , three DPAP3cKO clones ( F3cKO and F8cKO with loxPint , and A1cKO with loxP in Asn-stretch ) , and the E7ctr line ( only one loxP ) were selected for further studies . Evidence of integration by PCR is shown in S6B Fig . Analysis of genomic DNA of DMSO- or RAP-treated cKO lines by PCR showed highly efficient excision ( Fig 4B ) resulting in the loss of DPAP3-mCh expression in mature schizonts ( Fig 4C and 4D ) . Although we consistently achieved more than 95–99% excision efficiency ( Fig 5A ) , a fraction of non-excised parasites was always present after RAP treatment , which explains the presence of a non-excision DNA band after RAP treatment ( Fig 4B ) . To confirm that any phenotypic effect observed upon conditional truncation of DPAP3 is due to the loss of DPAP3 activity , A1cKO and F8cKO parasites were transfected with plasmid expressing WT or MUT DPAP3-HA under the control of the dpap3 or ama1 promoters ( S6A Fig ) , resulting in the following complementation lines: F8cKO+WTdpap3 , F8cKO+MUTdpap3 , F8cKO+WTama1 , F8cKO+MUTama1 , A1cKO+WTdpap3 , A1cKO+WTama1 , and A1cKO+MUTama1 . All complementation lines grew normally before RAP treatment and showed no apparent delay in parasite development . IFA analysis confirmed colocalization between chromosomal DPAP3-mCh and episomal DPAP3-HA ( Fig 5B and S6C Fig ) . Episomal expression was only high enough to be detected by IFA in 60–80% of schizonts , but was independent of RAP treatment ( Fig 5C ) . This is probably due to different levels of episomal expression and plasmid segregation in schizonts . Efficient , but not complete , truncation of DPAP3-mCh was observed in all our complementation lines ( Fig 5A ) . Labelling of DPAP3 with FY01 in parasite lysates from these lines show clear labelling of chromosomal DPAP3-mCh and episomal WT DPAP3-HA but not MUT DPAP3-HA ( Fig 5D ) . As expected , RAP treatment results in a decrease of labelling of chromosomal DPAP3-mCh but not of episomal WT DPAP3-HA . To measure the effect of DPAP3 truncation on parasite proliferation , we used the recently published plaque assay[16] where the wells of a 96-well flat bottom plate containing a thin layer of blood were seeded with ~10 iRBCs/well . After 10–14 days , microscopic plaques resulting from RBC lysis can be detected with an inverted microscope . RAP treatment of our cKO lines resulted in 90% less plaques , an effect that could be partially rescued through episomal complementation with WT but not MUT DPAP3 ( Fig 6A , S1 Table ) . After RAP treatment of the F3cKO and F8cKO lines , some wells contained a single plaque , suggesting that only one clonal parasite population grew in these wells . Parasites present in 12 of these wells were propagated and dpap3 excision checked by PCR . All contained non-excised parasites but dpap3 excision was detected in three cultures ( Fig 6B ) . The presence of excised parasites in some of the wells suggests that DPAP3KO parasites replicate less efficiently and might not have had enough time to form a visible plaque within the 14 days of the assay . That said , the presence of non-excised parasites in all samples indicates that WT parasites quickly outcompeted DPAP3KO ones . To test this hypothesis , we perform standard parasite multiplication assays after RAP or DMSO treatment . To prevent parasite overgrowth , cultures were diluted 10-fold in fresh blood and media whenever parasitemia reached 5% . RAP treatment of DPAP3cKO parasites results in a 10- to 15-fold decrease in parasitemia after 3–4 cycles , corresponding to an overall 50% decrease in multiplication rate per cycle compared to DMSO treatment ( Fig 6C ) . However , these values underestimated the importance of DPAP3 on parasite replication since 5 cycles after RAP treatment , 60% of iRBC are non-excised parasites expressing DPAP3-mCh ( S7A Fig ) . This result proves that the small fraction of non-excised parasites quickly outcompetes the excised ones . After RAP treatment , parasites complemented with WT DPAP3 grew significantly faster than those complemented with MUT DPAP3 ( Fig 6D ) . Importantly , our multiple attempts to clone DPAP3KO parasites after RAP treatment failed , indicating that DPAP3 activity is required for parasite proliferation under our culturing conditions . To determine which point of the erythrocytic cycle is disrupted by the loss of DPAP3 , a tightly synchronized culture of A1cKO parasites at ring stage was treated with DMSO or RAP for 3 h , and the culture monitored for the following 80 h . Samples were collected every 2–4 h , fixed , stained with Hoechst , and analyzed by FACS . No significant difference in DNA staining was observed between WT and KO parasites , suggesting that DPAP3 is not required for intracellular development ( Fig 6E , left graph ) . Quantification of iRBCs belonging to the first or second cycle after treatment shows that DPAP3KO parasites egress at the same time as WT but produce ~50% less rings ( Fig 6E , right graph ) . This suggests that DPAP3 is only important for RBC invasion , which is in direct contradiction with its previously suggested role in egress[18] . Despite being expressed early during schizogony ( Fig 1F ) , we did not observe any delay in parasite development between 36–48 h . p . i . ( Fig 6E ) . This was confirmed by IFA by counting the number of mature schizonts at the end of the cycle after RAP treatment . Apical localization of SUB1 to the exonemes was used as a marker for schizont maturity . No significant difference was observed between DMSO and RAP treatment of cKO or complementation lines ( S7B Fig ) . Importantly , proper localization of MSP1 , SUB1 , AMA1 , EBA175 , RON4 , and RopH2 was observed in all RAP-treated cKO lines ( Fig 4D and S7C and S7D Fig ) . Previously published work using the SAK1 inhibitor showed arrest of egress upstream of SUB1 activation[18] . This result suggested that DPAP3 might be important for parasite egress . Although we have been able to reproduce these results , we show that SAK1 treatment of schizonts 6 h before egress arrests schizogony upstream of SUB1 and AMA1 expression rather than parasite egress ( S8 Fig ) . This explains why no SUB1 or AMA1 could be detected by WB in the previous study[18] . In addition to SAK1 , we also synthesized a more selective DPAP3 inhibitor by replacing the nitro-tyrosine N-terminal residue of SAK1 with L-Trp ( L-WSAK ) and its diastereomer negative control containing D-Trp ( D-WSAK ) . To test the specificity of these inhibitors , merozoite or schizont lysates were pre-incubated with a dose response of compound followed by 1 h labelling with FY01 ( Fig 7A ) . Although SAK1 blocks labelling of DPAP3 at lower concentration than L-WSAK , it also inhibits all the falcipains ( FP1 , FP2 , and FP3 ) above 5 μM . L-WSAK only inhibits other targets above 200 μM . As expected , the D-WSAK control compound is unable to inhibit any of the labelled cysteine proteases and is at least 100-fold less potent than L-WSAK at inhibiting DPAP3 ( Fig 7A ) . We then compared the effect of these inhibitors in parasite egress on the A1cKO line . Surprisingly , all compounds blocked egress independently of RAP treatment ( Fig 7B ) , and no difference in potency between L- and D-WSAK was observed . These results prove that these vinyl sulfone compounds do not act through inhibition of DPAP3 but rather through off-target or toxicity effects . As a final proof to show that DPAP3 is not involved in parasite egress , we arrested DMSO or RAP treated DPAP3cKO schizonts with C2 and monitored egress by live microscopy after removal of the PKG inhibitor . Analysis of these videos showed no significant difference in the number of schizonts that ruptured , nor on how fast merozoites egressed after C2 wash out ( Fig 7C and S4 Video ) . Also , we could not detect differences in the levels and/or processing of AMA1[38] or SUB1 substrates ( SERA5[10] and MSP1[13] ) between DMSO and RAP treated parasites ( Fig 7D ) . These findings together with the lack of colocalization between DPAP3 and SUB1 ( Fig 1H and S3 Fig ) clearly demonstrate that DPAP3 is not responsible for proper processing and activation of SUB1 , and that DPAP3 does not play a role in egress . However , its localization in an apical secretory organelle ( Figs 1 and 2 ) and the time-course analysis of the A1cKO line ( Fig 6E ) strongly suggest a function in RBC invasion . Mature schizonts obtained after DMSO or RAP treatment of our different parasite lines were incubated with fresh RBCs for 8–14 h , fixed , and the population of schizonts and rings quantified by FACS ( Fig 8A ) . On average , we observed a 50% reduction in the number of iRBCs after RAP treatment of our cKO lines . This invasion defect could be rescued by episomal expression of WT but not MUT DPAP3 independently of the promoter used ( ama1 or dpap3 ) , thus indicating that DPAP3 activity is important for RBC invasion ( Fig 8B ) . To determine which invasion step is impaired by the loss of DPAP3 , DMSO- or RAP-treated cKO parasites were arrested at schizont stage with C2 , incubated with fresh RBCs after C2 washout , and samples collected at different time points for FACS analysis . Fluorescent wheat germ agglutinin ( WGA-Alexa647 ) binds to lectins on the RBC surface and when combined with Hoechst staining allows us to differentiate free merozoites from iRBCs ( Fig 8C ) . Quantification of the different parasite stage populations over time clearly shows a decrease in the number of rings upon RAP treatment , with an inversely proportional increase in the number of free merozoites ( Fig 8C and S9A Fig ) . The RBC population showing similar levels of DNA signal as free merozoites is mainly composed of ring stage parasites but likely contains a small proportion of extracellular merozoites tightly attached to the surface of the erythrocyte . These two populations were differentiated by staining the samples with a monoclonal MSP1 antibody ( m89 . 1 ) , whose epitope is within the portion of MSP1 that is shed during invasion ( Fig 8D ) . Using this assay , we did not observe a significant difference in the number of attached merozoites relative to the number of rings between DMSO and RAP treatment of our cKO lines . This suggests that after DPAP3KO parasites tightly attach to the RBC surface they invade as efficiently as WT parasites . Therefore , DPAP3 is likely important for the initial recognition of and attachment to RBCs .
This study provides the first characterization of the biological function of DPAP3 in parasite development . We have shown that DPAP3 is important for efficient RBC invasion and parasite proliferation . DPAP3 is expressed early during schizogony , it localizes to small apical organelles that are closely associated with the neck of the rhoptries , and it is secreted at the time of PVM breakdown but before parasite egress . We have also demonstrated that DPAP3 has dipeptidyl aminopeptidase activity but contrary to other DPAPs , removal of the prodomain is not required for activation . Our cKO and complementation studies provide strong evidence that DPAP3 activity is only required for efficient RBC invasion , but not for intracellular parasite development or parasite egress . Importantly , we have proven that the block in egress phenotype previously reported using vinyl sulfone inhibitors is not due to DPAP3 inhibition but rather to off-target or toxicity defects . Two cysteine proteases have been shown to play an important role in egress: human calpain-1 [39] and SERA6 [17] . It is therefore possible that the block in egress phenotype might be due to inhibition of either one of these two proteases . This study illustrates the importance of using negative control compounds when trying to associate a specific phenotype to the inhibition of a particular target , as well as the need to genetically validate functional information obtained through chemical methods [6] . Interestingly , in a recently published study , the Koning-Ward lab used the glmS riboswitch system to conditionally knockdown DPAP3 in P . falciparum [40] . However , they did not observe any significant effect in parasite proliferation , nor in egress or RBC invasion . We think that this is likely due to the fact that they only achieved partial knockdown of DPAP3 , and that the residual level of DPAP3 activity was sufficient to perform its function . That said , their localization studies are consistent with ours and confirm that DPAP3 resides in novel apical secretory organelles , and that it does not colocalize with rhoptry , microneme , or dense granule markers . Our initial characterization of the invasion defect associated with the loss of DPAP3 suggests that this protease might play a role in the attachment of merozoites to the RBC surface . Indeed , in our invasion assays we observed a significant increase in the number of free merozoites upon cKO of DPAP3 . Moreover , while DPAP3 KO results in a significant decrease in the number of rings , the ratio between merozoites attached to the RBC surface and those that have successfully invaded RBCs is the same between WT and KO parasites , suggesting that the decrease in invasion efficiency is likely upstream of merozoite attachment and tight junction formation , and that DPAP3 is likely important for the initial recognition of RBCs . We think it is unlikely that DPAP3KO parasites might be less efficient at forming a tight junction since we predict that such a defect would result in an increase of attached parasites relative to the total number of invaded RBCs . However , this is a possibility that we cannot completely rule out at this moment . Interestingly , we observed a wide variation in the decrease of invasion efficiency upon cKO of DPAP3 , ranging from 25 to 75% inhibition . This variation is much larger than the experimental variation observed with our E7 control line , and it is likely due to heterogeneity among the different batches of blood used to perform invasion assays . This observation suggests that DPAP3 activity might be important to recognize host cell receptors that are differentially expressed in the human population , and is consistent with our propose role of DPAP3 in RBC attachment . The facts that DPAP3 is expressed early during schizogony , that it localizes in secretory apical organelles , and that it is active under mild acidic conditions ( pH 5–7 , maximum activity at pH 6 ) , suggest that DPAP3 might process its substrates within the apical organelle where it resides . However , at this stage we cannot discard the possibility that DPAP3 might process its substrates at neutral pH , either during trafficking ( in the ER or Golgi ) , extracellularly after secretion , or even during secretion of proteins from apical organelles . Micronemes are trafficked to the merozoite apex underneath the inner membrane complex before releasing their protein cargo , thus coming in very close proximity to the neck of the rhoptries [41 , 42] . It is therefore possible that DPAP3 might interact with proteins of other secretory organelles as they are secreted , similarly to how micronemal CyRPA ( cysteine rich protective antigen ) and Ripr ( Rh5 interacting protein ) come together with rhoptry Rh5 ( reticulocyte binding protein homologue 5 ) at the merozoite apex after egress [43] . Finally , we think it is unlikely that DPAP3 acts extracellularly on RBC surface proteins because co-culturing equal amounts of WT and DPAP3KO parasites did not rescued the DPAP3KO invasion defect ( S9B Fig ) . It is difficult to speculate about the nature of DPAP3 substrates since we could not colocalize this protease with any of the tested apical markers . However , its substrates are likely to be proteins in the secretory pathway that are directly or indirectly important for invasion . Also , given that DPAPs cleave N-terminal dipeptides from protein substrates , DPAP3 likely recognizes the N-terminus of its substrates after they have been cleaved by another protease . Most proteins secreted into the PV or onto the merozoite surface are processed during traffic or after secretion , thus exposing one or multiple N-termini that might be potential DPAP3 substrates . For example , DPAP3 might trim the N-terminus of its substrates after signal peptide removal , thus potentially affecting their localization or stability . Indeed , the N-terminal sequence downstream of the signal peptide has been shown to be important for proper localization of rhoptry and micronemal proteins both in P . falciparum [44 , 45] and T . gondii [46 , 47] . Interestingly , two recent studies have shown that the aspartyl protease plasmepsin IX ( PMIX ) is essential for RBC invasion and that it acts as a maturase of rhoptry proteins[48 , 49] . In both studies , PMIX was shown to localize at the apical end of merozoites either within or , similarly to DPAP3 , in close proximity to the rhoptries . This similar localization raises the possibility that DPAP3 might trim the N-terminus of rhoptry proteins after being processed by PMIX . Finally , most surface proteins that are involved in RBC invasion ( EBAs , Rhs , MSPs , AMA1 , RON2 , etc ) expose one or more extracellular N-termini[7 , 50] , and the N-terminal domains of some of these proteins have been shown to be important in mediating protein-protein interactions and biological function . Interestingly , in T . gondii extracellular trimming of the N-terminus of surface proteins has been well documented [51 , 52] . T . gondii also expresses DPAPs in secretory organelles[53] , and a recent proteomic study has shown evidence that the N-terminus of certain secreted proteins , such as TgSUB1 and TgMIC11 , is trimmed through the removal of dipeptides[54] . It is therefore possible that DPAP3-mediated trimming of the N-terminus of certain parasite surface proteins might modulate their affinity towards host cell receptors . Our biochemical studies have shown that DPAP3 is an unusual papain-fold protease since removal of its prodomain is not required for activation . Most proteases prodomains act as endogenous inhibitors and internal chaperones . Although the prodomain of DPAP3 might be required for proper folding of this large protein ( 941aa ) , we cannot discard the possibility that it might have other biochemical functions , such as recognizing substrates or binding to cofactors that modulate DPAP3 activity . Interestingly , the pro-form of P . falciparum DPAP1 has been shown to localize to the PV in mature schizonts[30] , and processing of recombinant DPAP1 from its ‘zymogen’ form to is fully processed form only increases its activity 2–3 fold [32] , suggesting that similarly to DPAP3 , the ‘zymogen’ form of DPAP1 is active and might be able to process the N-terminus of PV or merozoite surface proteins . Therefore , both DPAP1 and DPAP3 are secreted before egress , raising the possibility that they might play redundant or complementary functions during RBC invasion .
Three synthetic genes codon-optimized for insect cells were synthesized by Genewiz and cloned into the puc57 vector backbone: puc57-rDPAP3-Nt , puc57-rDPAP3-Ct-wt , and puc-rDPAP3-Ct-mut . The first one codes for the N-terminal portion of DPAP3 ( Met1-Asp469 ) and the other two for the C-terminal portion ( Lys455-Stop941 ) containing the catalytic domain of DPAP3 and harboring either the catalytic cysteine Cys504 or the C504S inactivating mutation . All synthetic sequences contained a C-terminal His6-tag and were flanked with the BamHI and HindIII restriction sites at the 5’ and 3’ end , respectively . A ClaI restriction site is present in the 45 bp overlapping sequence ( Lys455-Asp469 ) between puc57-rDPAP3-Nt and puc57-rDPAP3-Ct-wt/mut . Digestion of these plasmids with BamHI , HindIII , and ClaI , followed by ligation of the N- and C-terminal products into the puc57 backbone yielded puc57-rDPAP3-WT and puc57-rDPAP3-MUT . The BamHI and HindIII restriction sites were used to clone full length dpap3 into the pFastBacHT vector ( Thermo Fisher Scientific ) for expression of WT or MUT DPAP3 ( pFB-rDPAP3wt and pFB-rDPAP3mut ) in insect cells . rDPAP3 was expressed in Sf9 insect cells using the baculovirus system . E . coli DH10Bac cells ( Invitrogen ) were transformed with pFB-rDPAP3wt and pFB-rDPAP3mut following the manufacturer recommendation . Baculovirus DNA was extracted using the BACMAX DNA purification kit ( Epicentre ) and transfected into a 5 mL culture of Sf9 cells ( 2x106 cells/mL ) using Cellfectin ( Thermofisher ) . After 3 days , the culture supernatant containing baculovirus particles was collected ( P1 stock ) . To increase the viral load of our stocks , 1 mL of culture supernatant was serially passage twice into 25 mL of Sf9 cultures at 2x106 cell/mL for 3 days to obtain P2 and P3 viral stocks , which were stored at 4°C or frozen in liquid N2 in the presence of 10% glycerol . Insect cells were grown in SF-900-II serum free medium ( Gibco ) at 27°C under shaking conditions . For rDPAP3 expression , Sf9 cells at 2x106 cells/mL were infected with 0 . 4 mL of P3 viral stock per liter of culture . The supernatant containing rDPAP3 was collected 72 h after infection , supplemented with protease inhibitors ( 1 mM PMSF , 0 . 5 mM EDTA , 1 μM pepstatin , 1 μM bestatin , and 10 μM E64 ) , and its pH adjusted by adding 50 mM TrisHCl from a 1 M solution at pH 8 . 2; 10% glycerol was added before storage at -80°C . Note that despite being a general Cys protease inhibitor , E64 does not inhibit DPAP3 . A three steps purification consisting of ion exchange , affinity , and size exclusion chromatography was used to purify rDPAP3 . First , culture supernatant was passed 3 times through 0 . 05 volumes of Q-sepharose ( GE Healthcare ) pre-equilibrated with Buffer A ( 50 mM Tris pH 8 . 2 containing the above-mentioned protease inhibitors ) . The resin was washed with 5 volumes of Buffer A and 2 . 5 volumes of 50 mM NaCl in Buffer A , and rDPAP3 eluted with 400 mM NaCl in Buffer A . Fractions containing rDPAP3 were pooled , diluted 1:1 into Buffer B ( 100 mM sodium acetate , 100 mM NaCl , pH6 , and the protease inhibitors mentioned above ) , and passed through 0 . 05 volumes of Ni-NTA resin ( Qiagen ) . The resin was washed with 10 volumes of Buffer B , and rDPAP3 eluted by lowering the pH of Buffer B to 5 . Fractions containing rDPAP3 were pooled , concentrated using a Centricon Plus-70 Centrifugal Filter Unit ( Millipore ) , loaded on a Superdex 200 10/300 GL size exclusion column , and run on an AKTA FPLC with Buffer A . Fractions containing rDPAP3 were pooled , concentrated , and stored at -80°C in the presence of 10% glycerol . DPAP3 activity was measured either using the DPAP fluorogenic substrates VR-ACC[32] or FR-βNA ( Sigma ) , or with the FY01 activity-based probe . When using FY01 , samples ( intact parasites , parasite lysates , insect cell supernatant , or rDPAP3 purification fractions ) were labelled with 1 μM FY01 for 1 h , boiled in loading buffer , run on a SDS-PAGE gel , and the fluorescence signal measured on a PharosFX ( Biorad ) flatbed fluorescence scanner [18] . To determine the potency and specificity of inhibitors against DPAPs and the falcipains , parasite lysates diluted in acetate buffer ( 50 mM sodium acetate , 5 mM MgCl2 , 5 mM DTT , pH 5 . 5 ) were pretreated for 30 min with a dose response of inhibitor followed by FY01 labelling . When using VR-ACC ( 10 μM ) or FR-βNA ( 100 μM ) , substrate turnover was measured on a M5e Spectramax plate reader ( λBex = 355 nm/λem = 460 nm or λex = 315 nm/λem = 430 nm , respectively ) in 50 mM sodium acetate , 20 mM NaCl , 5 mM DTT , and 5 mM MgCl2 , pH5 . 5 . The pH dependence of rDPAP3 was determined at 10 μM VR-ACC using a 20 mM sodium acetate , 20 mM MES and 40 mM TRIS triple buffer system containing 5 mM DTT , 0 . 1% CHAPS , 20 mM NaCl , and 5 mM MgCl2 . All constructs designed to integrate into the dpap3 locus by single-crossover recombination ( tagged or cKO lines ) contained either a 1065bp C-terminal homology region fused to GFP , or a 1210bp homology region upstream of the catalytic Cys504 ( Asp39-Glu392 ) fused to a recodonized C-terminal region ( Lys393-Stop941 ) tagged with mCherry or triple HA tag ( HA3 ) . The recodonized region contained either WT Cys504 , or the C504S mutation . The construct designed to generate the DPAP3-HA , DPAP3-mCh , and DPAP3-GFP tagged lines we obtained as shown in S10A and S10B Fig . Briefly , the dpap3 C-terminal homology region was inserted into the pPM2GT plasmid to generate pPM2GT-DPAP3Ct-GFP . The N-terminal region of dpap3 in the pFB-rDPAP3-wt/mut vector was replaced with the homology region to generate pFB-chDPAP3-wt/mut . The dpap3 ORF was then introduced into the pHH1-SERA5ΔCt-HA—obtained after removal of the C-terminal part of SERA5 in the pHH1-SERA5-loxP-DS_PbDT3’[33]—resulting in plasmids pHH1-chDPAP3-wt/mut-HA , which harbor a C-terminal HA3 tag and a loxP site downstream of the Pb3’ UTR . The HA3 sequence of pHH1-SERA5ΔCt-HA was replaced with mCherry—amplified from the pREST-B plasmid[55]—to generate the pHH1-SERA5ΔCt-mCh , and the ORF of WT or MUT DPAP3 introduced into this plasmid to generate the pHH1-chDPAP3-wt/mut-mCh constructs . To conditionally truncate dpap3 we used two different approaches . The first approach introduced a loxP site within the ORF of dpap3 , in an Asn-rich region ( Asn414-Asn444 ) upstream of the catalytic domain , resulting in replacement of Asn430-Asp434 with a loxP coding peptide ( ITSYSIHYTKLFTG ) . To make the pHH1-chDPAP3_loxP-mCh construct ( S10C Fig ) , the N- and C-terminal portions of DPAP3 were amplified from the pHH1-chDPAP3-wt-mCh , which contains a 3’UTR loxP site , and ligated into the plasmid backbone . A loxP site was introduced in the backward primer used to amplified the N-terminal region . The second approach inserted a loxPint[36] between the homology and recodonized regions . A synthetic 1600bp sequence ( GeneWiz ) containing the loxPint fragment flanked by targeting sequences was introduced into construct pHH1-chDPAP3-wt-mCh to generate the pHH1-chDPAP3_loxPint-mCh plasmid . Finally , for episomal complementation of the cKO lines , plasmids pHH1-chDPAP3-wt/mut-HA ( S10A Fig ) were modified in order to express full-length WT or MUT DPAP3 under the control of the dpap3 or ama1 promoters . Firstly , to select for parasites containing the complementation plasmids after transfection , the puromycin N-acetyltransferase ( pac ) gene , which confers resistance to puromycin , was amplified from mPAC-TK ( a kind gift of Alex Maier ) and subsequently ligated into pHH1-chDPAP3-wt/mut-HA plasmids . The homology region of this plasmid was replaced with a recodonized N-terminal dpap3 amplified from puc57-rDPAP3-Nt , resulting in pHH1-rDPAP3-wt/mut-HA plasmids ( S10D Fig ) . The dpap3 and ama1 promoters ( 970 and 1456bp upstream of the start codon , respectively ) were amplified from genomic DNA and ligated into these plasmids to generate the pHH1-dpap3-rDPAP3-wt/mut-HA , and pHH1-ama1-rDPAP3-wt/mut-HA complementation constructs . All final construct sequences were verified by nucleotide sequencing on both strands . Primers for PCR amplification and restriction sites used to generate these plasmids are listed in S2 Table and indicated in S10 Fig , respectively . All constructs were transfected at schizont stage using a 4D-Nucleofector electroporator ( Lonza ) as previously described[12] . DPAP3-tagged and DPAP3-cKO lines were obtained through multiple on and off drug selection cycles with WR99210 ( Jacobus Pharmaceuticals ) and cloned by limited dilution as previously described[56] . To generate the DPAP3-GFP , DPAP3-HA and DPAP3-mCh lines , pPM2GT-DPAP3Ct-GFP , pHH1-chDPAP3-wt-HA and pHH1-chDPAP3-wt-mCh were transfected into P . falciparum 3D7 parasites . Plasmid pHH1-chDPAP3-mut-mCh was transfected multiple times into 3D7 in an attempt to swap the endogenous catalytic domain of DPAP3 with one containing the inactivating C504S mutation , but no integration was observed even after five drug cycles . The A1cKO and F3cKO & F8cKO lines were obtained after transfection of pHH1-chDPAP3_loxP-mCh and pHH1-chDPAP3_loxPint-mCh , respectively , into P . falciparum 1G5 parasites that endogenously expressed DiCre . Finally , the E7ctr line containing only the 3’UTR loxP site was generated by transfecting 1G5 parasites with pHH1-chDPAP3-wt-mCh . Complementation lines were obtained after transfection of A1cKO or F8cKO with pHH1-dpap3-rDPAP3-wt-HA , pHH1-dpap3-rDPAP3-mut-HA , pHH1-ama1-rDPAP3-wt-HA , or pHH1-ama1-rDPAP3-wt-HA and selection with WR99210 and puromycin . All parasite lines were maintained in RPMI 1640 medium with Albumax ( Invitrogen ) containing WR99210 ( plus puromycin for the complementation lines ) and synchronized using standard procedures[57] . To conditionally truncate the catalytic domain of DPAP3 , tightly synchronized ring-stage parasites were treated for 3–4 h with 100 nM RAP ( Sigma ) or DMSO at 37°C , washed with RPMI , and returned to culture . Schizonts purified at the end of the cycle were used to determine the excision efficiency at the DNA ( PCR ) or protein ( IFA , WB ) level . To determine exactly when DPAP3 is secreted in relation to PVM and RBCM breakdown , 20 μL of purified schizonts in 4 mL of RPMI were arrested with C2 or E64 , or were allowed to egress for 1 h in the presence of 1 μM FY01 . These cultures were centrifuged at 3000 rpm to separate intact schizonts from free merozoites and the culture supernatant . Merozoites were isolated from the culture supernatant by centrifugation ( 10 min at 13000 rpm ) , and proteins in the culture supernatant precipitated with 10 volumes of ice cold methanol and overnight incubation at -80°C . The schizont fractions were treated with 30 μL of 0 . 15% saponin in PBS to lyse the PVM and RBCM , and thus separate PV and host cytosolic components ( saponin soluble fraction ) from the parasite pellets ( saponin insoluble fraction ) , which were washed once with PBS . Each fraction was then dissolved into PBS to a final volume of 50 μL , boiled for 10 min after adding 17 μL of 4X loading buffers . Equal volumes of each fraction ( 20 μL ) were run in a SDS-PAGE gel under reducing conditions for WB and FY01 labelling analysis . Thin films of P . falciparum cultures were air-dried , fixed in 4% ( w/v ) formaldehyde ( PFA ) for 20 min ( Agar Scientific Ltd . ) , permeabilized for 10 min in 0 . 1% ( w/v ) Triton X100 and blocked overnight in 3% ( w/v ) bovine serum albumin ( BSA ) or 10% ( w/v ) goat serum ( Invitrogen ) in PBS . Slides were probed with monoclonal antibodies or polyclonal sera as described previously[58] ( See S3 Table for antibodies used in this study ) , subsequently stained with Alexa488- , Alexa594- , Alexa647-labelled secondary antibodies ( Molecular Probes ) and DAPI ( 4 , 6-diamidino-2-phenylindole ) , and mounted in ProLong Gold Antifade ( Molecular Probes ) . Images were collected using AxioVision 3 . 1 software on an Axioplan 2 Imaging system ( Zeiss ) using a Plan-APOCHROMAT 100x/1 . 4 oil immersion objective or LAS AF software on an SP5 confocal laser scanning microscope ( Leica ) using a HCX PL APO lamda blue 63x/1 . 4 oil immersion objective . Super-resolution microscopy was performed using a DeltaVision OMX 3D structured illumination ( 3D-SIM ) microscope ( Applied Precision ) . Images were analyzed with ImageJ ( NIH ) , Adobe Photoshop CS4 ( Adobe Systems ) and Imaris x64 9 . 0 . 0 ( Bitplane ) software . For IEM , mature schizonts from the DPAP3-GFP and 3D7 control lines were concentrated using a magnetic activated cell sorting ( MACS ) LD separation column ( Miltenyi Biotec ) . Briefly , iRBCs were loaded onto an LD column attached to Midi MACS pre-equilibrated with media . The column was washed twice with media and schizonts eluted with media after detaching the column from the magnet . Parasites were then fixed in 4% paraformaldehyde/0 . 1% glutaraldehyde ( Polysciences ) in 100 mM PIPES and 0 . 5 mM MgCl2 , pH 7 . 2 , for 1 h at 4°C . Samples were embedded in 10% gelatine and infiltrated overnight with 2 . 3 M sucrose/20% polyvinyl pyrrolidone in PIPES/MgCl2 at 4°C . Samples were trimmed , frozen in liquid nitrogen , and sectioned with a Leica Ultracut UCT cryo-ultramicrotome ( Leica Microsystems ) . Sections of 70 nm were blocked with 5% FBS and 5% NGS for 30 min and subsequently incubated with rabbit anti-GFP antibody 6556 ( Abcam ) at 1:750 overnight at 4°C . Colloidal gold conjugated anti rabbit ( 12 nm ) IgG ( Jack Imm Res Lab ) was used as secondary antibody . Plaque assays were performed as previously described[16] . Briefly , the 60 internal wells of a flat-bottom 96 well plate were filled with 200μL of DMSO- or RAP-treated parasite culture at 10 iRBCs/well and 0 . 75% hematocrit , incubated for 12–14 days at 37°C , and the number of microscopic plaques counted using an inverted microscope . For all FACS-based assays , samples were fixed for 1 h at RT with 4% PFA and 0 . 02% glutaraldehyde , washed with PBS , and stored at 4°C . Samples were stained with SYBR Green ( 1:5000 ) or Hoechst ( 2 μg/mL ) , run on a FACScalibur or FortessaX20 flow cytometers ( Becton-Dickinson Bioscience ) , and the data analyzed with CellQuest Pro or FlowJo . For replication assays , cultures at 0 . 1% parasitemia ( ring stage ) and 2% hematocrit were grown for 4 cycles after DMSO or RAP treatment . Aliquots were fixed every 48 h at trophozoite stage , stained with SYBR Green , and parasitemia quantified by flow cytometry . To avoid parasite overgrowth , cultures were diluted 10-times whenever they reached 5% parasitemia . The cumulative percentage parasitemia ( CP ) over 4 cycles was fitted to an exponential growth model: CP = Pt0 . MRN , where Pt0 is the initial parasitemia , MR the multiplication rate per cycle , and N the number of cycles after treatment . To look at the effect of DPAP3 truncation on the full erythrocytic cycle , A1cKO parasites were synchronized within a 2 h window , treated with DMSO or RAP for 3 h , and put back in culture for 76 h . Sample were collected every 2–4 h , fixed , stained with Hoechst , and analyzed by flow cytometry . DNA levels were quantified as the median fluorescence signal of iRBC divided by the background signal for uninfected RBCs ( uRBCs ) . For standard invasion assays , schizonts purified from DMSO or RAP treated cultures were incubated with fresh RBCs for 8–14 h , fixed , and stained with Hoechst . The population of uRBC , rings and schizonts was quantified based on DNA content . The invasion rate was determined as the ratio between the final population of rings and the initial population of schizonts . Invasion time courses were performed by arresting purified schizonts with 1 μM C2 for 4 h , washing twice with warm media , and culturing with fresh RBCs under shaking conditions . Samples were collected at different time points , fixed , and split into two aliquots: One was stained with Hoechst and WGA-Alexa647 and run on a flow cytometer at a high forward scattering voltage in order to detect free merozoites . The populations of uRBCs , free merozoites , rings , and schizonts were quantified with FlowJo . The other aliquot was blocked with 3% BSA in PBS overnight at 4°C , and stained without permeabilization with the MSP1 monoclonal antibody 89 . 1 ( 1:100 ) , and subsequently with anti-mouse Alexa488 ( 1:3000 ) . The population of uRBCs , schizonts , rings , and merozoites attached to the RBC surface were quantified using FlowJo . Time lapse video microscopy of egress was performed as previously described[9] . Briefly , tightly synchronized schizonts were percoll-enriched and arrested with 1 μM C2 for 4 h . After C2 wash out , DIC and mCherry images were collected every 5 and 25 s , respectively , for 30 min using a Nikon Eclipse Ni-E wide field microscope fitted with a Hamamatsu C11440 digital camera and a Nikon N Plan Apo λ 100x/1 . 45NA oil immersion objective . For each experiment , videos of the RAP- and DMSO-treated parasites were taken alternately to ensure that possible differences in the rate of egress were not a result of variation in the maturity of the parasite populations . The images were then annotated using Axiovision 3 . 1 software and exported as AVI movie or TIFF files . Individual egress events were annotated by detailed visual analysis of the movies , and the delay to the time of egress was recorded for each schizont for subsequent statistical analysis . Mean fluorescence intensity values of individual mCherry-expressing schizonts right before and after PVM breakdown were determined from exported raw image files ( TIFF format ) as described previously[9] and using the elliptical selection tool and ‘Histogram’ options of ImageJ/Fiji V1 . 0 . DPAP3KO parasites were analyzed to determine the residual background fluorescence derived from the hemozoin . Saponin pellets of parasites from different erythrocytic stages as well as samples from culture supernatant harvested during egress and invasion were syringe filtered ( Minisart , 0 , 2 μm , Sartorius ) , boiled in SDS-PAGE loading buffer under reducing conditions , run on a SDS-PAGE gel , and transferred to Hybond-C extra nitrocellulose membranes ( GE Healthcare ) . The membranes were blocked with 5% ( w/v ) nonfat milk in PBS , probed with monoclonal or polyclonal antibodies ( see S3 Table for antibodies used in this study ) , and followed by application of horseradish peroxidase-conjugated secondary antibodies ( Pierce ) . The signal was detected using SuperSignal West Pico chemiluminescent substrate ( Thermo Scientific ) and a ChemiDoc MP imager ( BioRad ) . PCR was performed using GoTag ( Promega ) , Advantage 2 ( Clontech ) , or Q5 High-Fidelity ( NEB ) polymerases . Diagnostic PCR to detect integration of targeting constructs was performed using extracted genomic DNA as template . Primer pairs specific for detection of integration , namely P21 and P22 for integration of pHH1-chDPAP3-mCh and pHH1-chDPAP3-HA , and II-inte_F and II-wt_R for integration of pPM2GT-DPAP3Ct-GFP , were designed such that the forward primer hybridized in a genomic region upstream of the plasmid homology region , and the second in a region unique to the introduced plasmid . Primer pairs P21 and P23 were designed to detect presence of the unmodified dpap3 locus . To assess whether dpap3-mCh had been excised after RAP treatment , diagnostic PCR was performed using extracted genomic DNA as template . Primers P24 and M13 were used to detect non-excision dpap3 at the genomic locus and hybridize upstream and downstream of the second loxP site , respectively . Primers P25 and SP6 were used to detect presence of excision and hybridize upstream and downstream of the first and second loxP sites , respectively .
|
Malaria remains one of the most devastating infectious diseases and its clinical manifestation is caused by the exponential multiplication of parasites in patients . This asexual replication cycle consists of red blood cell invasion , intracellular parasite multiplication , and release ( also known as egress ) of daughter parasites for further red blood cell invasion . Host cell invasion is therefore essential for parasite replication and the only moment in this cycle when parasites are exposed to the immune system . Understanding the molecular mechanisms that control red blood cell invasion might not only lead to the identification of novel antimalarial targets but also to the development of better invasion blocking vaccines . DPAP3 is a druggable cysteine protease that was previously believed to be essential for parasite egress . In this study , we show that parasites lacking DPAP3 activity are unable to efficiently invade red blood cells but escape the confines of the host cell normally . Overall , this study increases our understanding of the proteolytic pathways that govern host cell invasion by the malaria parasite .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"parasite",
"groups",
"nuclear",
"staining",
"parasite",
"replication",
"plasmodium",
"parasitic",
"protozoans",
"parasitology",
"plasmid",
"construction",
"apicomplexa",
"protozoans",
"dna",
"construction",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"specimen",
"preparation",
"and",
"treatment",
"staining",
"malarial",
"parasites",
"artificial",
"gene",
"amplification",
"and",
"extension",
"merozoites",
"molecular",
"biology",
"genetic",
"loci",
"dapi",
"staining",
"eukaryota",
"polymerase",
"chain",
"reaction",
"genetics",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2018
|
Plasmodium falciparum dipeptidyl aminopeptidase 3 activity is important for efficient erythrocyte invasion by the malaria parasite
|
Among animal species , cell types vary greatly in terms of number and kind . The number of cell types found within an organism differs considerably between species , and cell type diversity is a significant contributor to differences in organismal structure and function . These observations suggest that cell type origination is a significant source of evolutionary novelty . The molecular mechanisms that result in the evolution of novel cell types , however , are poorly understood . Here , we show that a novel cell type of eutherians mammals , the decidual stromal cell ( DSC ) , evolved by rewiring an ancestral cellular stress response . We isolated the precursor cell type of DSCs , endometrial stromal fibroblasts ( ESFs ) , from the opossum Monodelphis domestica . We show that , in opossum ESFs , the majority of decidual core regulatory genes respond to decidualizing signals but do not regulate decidual effector genes . Rather , in opossum ESFs , decidual transcription factors function in apoptotic and oxidative stress response . We propose that rewiring of cellular stress responses was an important mechanism for the evolution of the eutherian decidual cell type .
Multicellular organisms consist of numerous specialized cells , or cell types , that play an important role in the structural and functional diversity of organisms . Evolutionary diversification of cell types in metazoans has been a significant source of novelty and was essential to the elaboration of increasingly complex body plans . One model that can explain the evolution of novel cell types is the “sister cell type” model , which suggests that cell types originate by differentiation from an ancestral cell type [1 , 2] . According to this model , novel cell types have arisen from ancestral cell types through modification of developmental programs leading to two derived cell types , termed “sister cell types . ” The origination of novel cell types may allow for organisms to manage an imposed physiological or environmental challenge that may have induced stress or morbidity in the ancestral condition . However , while it is clear that cell types have diversified prodigiously in evolution , the molecular mechanisms leading to the origination of a novel cell type are not well understood [3] . The evolution of mammalian pregnancy offers an opportunity to investigate cell type origination . Intensive selective pressures during the evolution of mammalian pregnancy led to the evolution of many functional specializations of the uterus that accommodate the implantation of the embryo and development of the placenta , including proper control of an ancestral implantation-induced inflammatory response [4 , 5] . These novelties include the origin of specialized cell types such as the decidual stromal cell ( DSC ) , the uterine natural killer cell , and a specialized form of resident macrophages [6] . During the menstrual cycle and pregnancy , human decidual stromal cells ( HsDSCs ) differentiate from endometrial stromal fibroblasts ( HsESFs ) on exposure to progesterone and signals from the embryo [7] . Responding to these signals , genes critical to human DSC differentiation drive and install a complex decidualization gene regulatory network ( GRN ) . Numerous transcription factors have been shown to transcriptionally and post-translationally interact to regulate effector gene sets conferring DSC cell type identity . Phylogenetic cell type studies make clear that eutherian endometrial stromal fibroblast ( ESF ) and DSC are sister cell types [8] . While ESFs are found in the oviduct of numerous amniotes , DSCs are exclusive to eutherians [9] . Moreover , it is clear that DSCs evolved from an ancestral ESF cell type , hereafter referred to as paleo-ESF , i . e . , ESF that cannot give rise to DSC and are not derived from cells that can . This cell type existed prior to the stem lineage of eutherian mammals , having diverged 65 to 80 million years ago [10] , i . e . , DSC evolved after the most recent common ancestor of marsupials and eutherians and prior to the most recent common ancestor of eutherian mammals [11] . Hence , the evolutionary origin of DSC is an outstanding model to investigate the molecular mechanisms that led to the origin of a novel cell type . To characterize the molecular changes that gave rise to the origin of the decidual stromal cell type , we isolated ESFs of the marsupial grey short-tailed opossum Monodelphis domestica , hereafter called MdESFs , which we use as a proxy for paleo-ESF . In humans and other eutherians , neo-ESF differentiates into DSC in utero when exposed to progesterone and estrogen , as well as ligands upstream of cyclic AMP ( cAMP ) /protein kinase A ( PKA ) signaling such as prostaglandin E2 ( PGE2 ) [12–14] and relaxin ( RLN ) [15] . We assayed the response of MdESF to the stimuli that differentiate HsESF to HsDSC in vitro in order to identify the ancestral gene regulatory program from which the core network of DSC evolved . We found , surprisingly , that core components of the decidual GRN are responsive to progesterone and cAMP in opossum ESF , but rather than undergoing DSC differentiation , these genes regulate a cellular stress response .
We utilized an established protocol to isolate MdESF by Percoll column gradient [16] . We validated by immunostaining and western blotting that cells isolated by this procedure are positive for the mesenchymal marker vimentin and negative for the epithelial marker cytokeratin ( S1 Fig ) . Relative to other layers in the column , these cells expressed higher levels of the ESF markers HOXA11 , HOXA10 , and PGR . We also show that these cell preparations have low levels of CD45 , a marker of white blood cells , compared to RNA isolated from opossum spleen ( S1 Table ) . We assayed the response of MdESF to treatment with eutherian ESF differentiation media containing the cAMP analogue 8-br-cAMP and the progesterone analogue medroxyprogesterone acetate ( MPA ) ( Fig 1A ) , hereafter referred to as decidualizing stimuli or 8-br-cAMP/MPA . RNA sequencing ( RNAseq ) of both stimulated and unstimulated MdESF revealed endogenous expression of numerous core regulatory genes critical to eutherian decidualization ( Fig 1B ) . From a curated list of 28 transcription factor ( TF ) genes with documented roles in decidualization ( Table 1 ) , 22 are expressed in stimulated MdESF and 13 are significantly up-regulated ( p < 0 . 05 ) ( Fig 1B and S2A Fig ) . Seven decidualization TF genes are down-regulated , though still expressed , and two TFs are unchanged in expression . Most notably , the up-regulated gene set contains numerous TFs with well-characterized roles in decidualization: FOXO1 [17 , 18] , PGR [19] , CEBPB [17 , 20] , HOXA10 [21 , 22] , HOXA11 [23 , 24] , GATA2 [25] , ZBTB16 [26 , 27] , KLF9 [28–30] , HAND2 [31] , STAT3 [32 , 33] , and MEIS1 [34] ( Fig 1B and S2A Fig ) . In contrast to this conserved transcriptional regulatory response , classical markers of decidualization , e . g . , PRL , IGFBP1 , CGA , and SST , are neither expressed in unstimulated MdESF nor induced in response to decidualizing stimuli ( Fig 1C ) . We conclude that a substantial part of the DSC core GRN is also in place in opossum ESF and is responsive to progesterone and cAMP but does not control a decidual phenotype . We conducted experiments to determine if the observed up-regulation of regulatory genes critical to eutherian decidualization is specific to both the progesterone receptor as well as specific to the M . domestica ESF cell type . As MPA can also stimulate the glucocorticoid receptor ( GR ) , we knocked down GR with small interfering RNA ( siRNA ) and subsequently assayed the transcriptional response of MPA-responsive regulatory genes in MPA-stimulated MdESF . We observed no significant change in RNA abundance for five decidualization regulatory genes ( S2B Fig ) , suggesting that the observed up-regulation of these factors in response to MPA is not associated with stimulation of GR . It could also be argued that the observed response is a more general feature of M . domestica fibroblasts rather than specific to MdESF . Thus , we sought to determine if up-regulation of decidualization regulatory genes is specific to the M . domestica ESF cell type or whether a similar response also occurs in skin fibroblasts from M . domestica . We isolated skin fibroblasts from M . domestica , stimulated them with either 8-br-cAMP/MPA or PGE2/MPA , and assayed six decidualization TFs by qPCR . Our results showed a strong up-regulation of ZBTB16 in response to 3-day treatment of either 8-br-cAMP/MPA or PGE2/MPA ( S2C Fig ) . Conversely , five other TFs that were up-regulated in MdESF in response to either treatment were unchanged in RNA abundance or were down-regulated . This result suggests that the induction of decidual regulatory genes is specific to endometrial fibroblasts in the opossum and , interestingly , that ZBTB16 may be a more general inducible factor in fibroblasts with elevated intracellular levels of cyclic AMP . Gene ontology ( GO ) enrichment analysis of differentially expressed genes after 8-br-cAMP/MPA treatment revealed up-regulation of genes associated with oxidative stress , mitochondrial stress , and apoptosis , as well as down-regulation of genes associated with mitosis , DNA replication , and cytoskeletal organization ( Fig 1E , Fig 2A ) . Outwardly , stimulated MdESF exhibited a rapid morphological response suggestive of cytoplasmic architectural remodeling ( Fig 1D , Fig 2B , S1 Movie ) . The extent of this morphological response was dependent on both 8-br-cAMP concentration and duration of treatment ( Fig 2B , S3A Fig ) . Remarkably , this morphological effect was reversible insofar as the cells reverted back to their normal morphology within 19 hours after withdrawal of decidualizing stimuli ( Fig 2C , S3B Fig ) . GO treemaps , which represent the function of genes and degree of their differential expression in response to 8-br-cAMP/MPA , supported the hypothesis that stimulated MdESF undergo a cellular stress response , as GO terms associated with endoplasmic reticulum ( ER ) stress , apoptosis , reactive oxygen species ( ROS ) metabolism , and protein folding response were significantly up-regulated ( S2D Fig ) . In line with this observation , stimulated MdESF exhibited elevated levels of intracellular ROS relative to unstimulated cells or cells stimulated with MPA alone ( Fig 1D , S3C Fig ) . These data indicate that treating MdESF with decidualizing stimuli results in a rapid morphological response that is associated with increased intracellular ROS and the induction of genes counteracting oxidative stress , suggesting that , rather than leading to decidual differentiation , MdESF exposed to decidualizing stimuli undergo a classical cellular stress response . Next , we considered whether stress induced by treatment with decidualizing stimuli could be an artifact of treating cells with extracellular 8-br-cAMP , rather than a natural ligand activating intracellular cAMP signaling . To address this , we sought a physiologically relevant signal that increases intracellular cAMP in these cells . PGE2 signaling is of particular interest given that ( 1 ) PGE2 is able to induce decidualization via cAMP signaling in human and rodent ESFs [12 , 14] , ( 2 ) the PGE2 receptor PTGER4 is widely expressed in ESFs in mammals [16] , and ( 3 ) the recent finding that prostaglandin synthase ( PTGS , also known as “COX2” ) and prostaglandin E synthase ( PTGES ) are both expressed in the opossum uterus after embryo attachment [5] . Furthermore , PGE2 is likely a key component of the inflammatory signaling from which the eutherian implantation reaction is derived [5 , 6 , 47] . In our 8-br-cAMP/MPA stimulated cells , we see a particularly striking effect on lipid metabolism , a critical pathway in the production of phospholipid-derived prostaglandins ( S2D Fig ) . Indeed , prostaglandin Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathway genes were enriched in lipid metabolism , e . g . , 33% of genes listed in fatty-acid derivative metabolic process are involved in prostaglandin metabolism ( S2D Fig ) . Furthermore , transcriptomic analyses of stimulated MdESF suggested that 8-br-cAMP/MPA treatment negatively regulates the predominant PGE2 receptor , PTGER4 , as well as genes for synthesis of prostaglandins , e . g . , PTGS2 and PTGES ( Fig 3A ) , and positively regulates catabolic enzymes that function to degrade prostaglandins , e . g . , HPGD and PTGR1 ( Fig 3A ) . These data suggest stimulated MdESFs compensate for the effect of 8-br-cAMP by modulating the prostaglandin synthesis and signaling pathways , further suggesting that PGE2 is likely the natural ligand of MdESF activating the cAMP/PKA pathway . A survey of RNAs present in unstimulated MdESF showed that only two prostaglandin signaling receptors , PTGER4 and TBXA2R , are expressed in these cells ( Fig 3B ) . In order to test whether PGE2 could be the natural ligand inducing intracellular cAMP signaling in these cells , we assayed by RNAseq the response of MdESF to PGE2 with and without MPA . KEGG pathway genes involved in prostaglandin synthesis and inactivation exhibited similar differential regulation in response to PGE2/MPA as do decidualizing stimuli , suggesting similar regulatory responses by MdESF ( Fig 3C ) . Interestingly , a survey of prostaglandin signaling components revealed a strong up-regulation of the prostacyclin receptor ( PTGIR ) across all treatment groups ( Fig 3B ) . Uterine tissue from pregnant and nonpregnant M . domestica showed substantially higher amounts of PGE2 in pregnant females versus nonpregnant females , suggesting that PGE2 increases in utero during gestation ( Fig 3D ) . Contrary to treatment with decidualizing stimuli , MdESF treated with PGE2 and with or without MPA did not exhibit a readily apparent dendritic phenotype ( Fig 3E , S4A Fig ) . Nevertheless , PGE2/MPA-treated MdESF do show elevated levels of intracellular ROS ( S4B Fig ) . We next investigated the effect of PGE2/MPA on the expression of core decidualization regulatory genes . Remarkably , all 22 decidual TF regulatory genes expressed in 8-br-cAMP/MPA–stimulated MdESF cells are also expressed in PGE2/MPA-stimulated cells ( Fig 3F , S2A Fig ) , showing a marked nonparametric correlation in expression levels ( Spearman’s rho = 0 . 863 , p = 3 . 46 × 10−9 ) , and 12 of the 13 up-regulated genes under 8-br-cAMP/MPA are also up-regulated with PGE2/MPA . However , this up-regulation was not seen when MdESFs were treated with PGE2 alone ( S4C Fig ) , indicating a synergistic effect of elevated intracellular cAMP and components responding to MPA . Similar to the response to MPA alone and 8-br-cAMP/MPA , previously characterized marker genes of human decidualization did not respond to 2-day treatment with either PGE2 or PGE2/MPA ( Table 2 ) . We conclude that responding to PGE2 signaling is a physiological part of MdESF biology and that PGE2/MPA also regulates the expression of the same TF network as 8-br-cAMP/MPA treatment . Moreover , the results suggest that this PGE2/MPA-induced TF network is homologous to that activated during the differentiation of human DSCs . We next asked if , as seen in cells treated with decidualizing stimuli , PGE2/MPA treatment also induced a stress response in MdESF . We surveyed differential expression of genes involved in oxidative stress response , apoptosis , and ROS-associated ER stress ( unfolded protein response [UPR] ) ( Fig 4 ) . MdESF treated with either 8-br-cAMP/MPA or PGE2/MPA significantly up-regulated genes associated with counteracting oxidative stress , including GCLM , GPX3 , GPX4 , SOD1 , SOD3 , and CAT ( Fig 4A ) . Both treatments up-regulated the apoptotic genes BCL2L11 ( BIM ) and GADD45A ( Fig 4B ) . In contrast to treatment with PGE2/MPA , cAMP/MPA induced a distinct stress response in genes associated with UPR and TNF-related apoptosis-inducing ligand ( TRAIL ) -related apoptosis , up-regulating ERN1 ( IRE1 ) , HSPA5 , HSP90B1 , HSP90AA1 , CALR , and TNFSF10 ( Fig 4B and 4C ) . Lastly , to determine if clusters of genes associated with oxidative stress and apoptosis were differentially expressed in both cAMP/MPA and PGE/MPA , we analyzed GO term clusters shared between the treatments . This analysis also suggested a shared stress response in MdESF treated with decidualizing stimuli or PGE2/MPA , in which GO terms associated with stress and inflammation , e . g . , “regulation of reactive oxygen species metabolism , ” “protein folding , ” and “leukocyte degranulation , ” were shared between these treatments ( S2D Fig , S5 Fig ) . Similarly , PGE2 alone and PGE2/MPA shared GO terms specifically related to “hypoxia , ” “autophagy , ” and “regulation of cell death” ( S5 Fig , S6 Fig ) . These results suggest that PGE2/MPA , signals that are present in the pregnant opossum uterus , induce a stress response similar to treatment with 8-br-cAMP/MPA , including elevated levels of intracellular ROS ( one-tailed t test on log transformed data , p = 0 . 0186 ) , but without the dendritic morphological response ( Fig 3E , S4B Fig ) . We conclude that the in vitro 8-br-cAMP/MPA–induced stress reaction is mimicked with the more physiological PGE2/MPA treatment . Furthermore , these data are consistent with a conserved role for PGE2 during pregnancy of therian mammals . In many cells , forkhead box class O ( FOXO ) TF family members generically function in stress response , counteracting oxidative stress , and apoptosis , as well as regulating gluconeogenesis and glycolysis [48] . FOXO1 also is an early acting TF in the differentiation of human DSCs [49] . Therefore , we sought to compare how FOXO1 mRNA and FOXO1 protein stability and subcellular localization are regulated in opossum ESFs . As observed in human ESFs , FOXO1 RNA is present in unstimulated MdESF , but FOXO1 protein is absent [16 , 50] , likely due to protein kinase B ( Akt ) -dependent proteasomic degradation [48] ( Fig 5A ) . In response to MPA treatment , FOXO1 accumulates in the cytoplasm ( Fig 5B ) , suggesting MPA alone can counteract the proteasomic FOXO1 degradation . Decidualizing stimuli and treatment with PGE2/MPA resulted in nuclear translocation of FOXO1 as well as cytoplasmic loading ( Fig 5C–5E ) , suggesting cAMP/PKA signaling controls FOXO1 nuclear localization . In response to induction of oxidative stress , FOXO1 behaved similarly to cAMP or PGE2 treatment , suggesting that post-translational modifications of FOXO1 act as a sensor of oxidative stress in MdESF ( Fig 5F ) . Moreover , immunofluorescence on uterine sections from pregnant M . domestica females in the late stages of gestation ( 11 . 5 days post coitus [d . p . c . ] ) found nuclear FOXO1 in uterine stromal cells near the luminal epithelium , suggesting that FOXO1 activation is part of the physiological role of MdESF during pregnancy ( Fig 5H ) . Strikingly similar results were obtained for FOXO1 in human DSCs ( S7A Fig ) , suggesting post-translational regulatory control of FOXO1 as found in human DSCs in response to decidualizing stimuli has been inherited from the ancestral paleo-ESF and ancestrally was part of a PGE2-induced cell stress response ( Fig 5G ) . In order to assess the functional role of FOXO1 activation in opossum ESF , we assayed oxidative stress and apoptosis by treatment with 2′ , 7′ dichlorodihydrofluorescein diacetate ( H2DCFDA ) ( to detect ROS ) and propidium iodide ( PI ) ( to detect early stages of apoptosis ) in stimulated and unstimulated MdESF ( Fig 6 ) . Apoptosis , i . e . , PI staining , was markedly elevated in MdESF treated with decidualizing stimuli ( Fig 6A and 6B; 4-fold increase , one-tailed t test p = 2 . 0 10−5 ) . That was also the case for PGE2/MPA but to a lesser degree ( 1 . 8-fold increase , one-tailed t test p = 0 . 015 ) . To determine if FOXO TFs function in this stress response , we transfected MdESF with siRNAs targeting FOXO1 and FOXO3 RNA transcripts and subsequently treated them with 8-br-cAMP/MPA or PGE2/MPA . We confirmed depletion of FOXO1 RNA ( as well as FOXO3 RNA ) by qPCR and depletion of FOXO1 protein by western blot ( S7B Fig , S7C Fig ) . siRNA-mediated knockdown ( KD ) of FOXO1 and FOXO3 increased signals for apoptosis ( Fig 6A and 6B ) ( ANOVA on log transformed fluorescence values , KD effects FOXO1 = 2 . 4-fold , FOXO3 = 1 . 8-fold , overall ANOVA p = 5 . 91 10−8 ) . Surprisingly , we did not find a significant interaction effect of FOXO1 and FOXO3 KD , suggesting they function additively in protecting against apoptosis ( Fig 6A and 6B ) . However , there is no significant effect of FOXO KD on ROS levels in our data , suggesting that ROS production in response to decidualizing stimuli is not regulated by FOXO proteins . FOXO1 KD resulted in marginally more cells positive for apoptosis than did FOXO3 perturbation ( Fig 6B; 1 . 31-fold , p = 0 . 045 ) . These results suggest that the ancestral function of FOXO genes in paleo-ESF was similar to the highly conserved , pan-metazoan roles of FOXO genes in classical stress response [51] . Therefore , we hypothesized that the regulatory linkages downstream of FOXO1 have diverged since the eutherian–metatherian split . From a list of genes in human DSCs that are positively regulated by FOXO1 ( i . e . , decrease in expression after FOXO1 KD and treatment with decidualizing stimuli ) , we selected seven genes that are also strongly up-regulated in MdESF stimulated with 8-br-cAMP/MPA or PGE2/MPA . Of the seven genes , one , IGFBP3 , significantly decreased in expression and all other genes either did not respond or increased in expression in response to FOXO1 KD in MdESF ( Fig 6C ) . This result suggests that FOXO1 transcriptionally regulates distinct sets of genes in MdESF and HsDSC . If we assume that the reproductive mode in MdESF is representative of the ancestral paleo-ESF , these data also suggest that the evolution of mammalian DSCs proceeded through modifying the target gene set of a largely conserved core GRN that includes FOXO1 .
Here we show that , whereas human ESF respond to decidualizing stimuli with a compensated physiological phenotype , the opossum ESF exhibit a classic stress phenotype . This difference was also found , though to a lesser degree , when we used a more physiological signal , PGE2 , instead of extracellular 8-br-cAMP . The responses of human and opossum ESFs were remarkably similar at the level of DSC regulatory gene expression , both in terms of transcriptional as well as post-translational regulation as in the case of FOXO1 . While post-translational activation of FOXO1 is necessary in human cells for the expression of DSC effector genes , e . g . , PRL , IGFBP1 , etc . , in opossum ESF , the functional role of activated FOXO1 is to protect the cells against apoptosis . We propose that the signaling pathway and large parts of the TF network are homologous and to some degree conserved between eutherian DSC and marsupial MdESF , suggesting that these components were also present in paleo-ESF , i . e . , prior to the evolutionary origin of DSCs . Furthermore , we hypothesize that there were at least two distinct molecular changes that led to the evolution of DSCs . On one hand , we find a small number of decidual TFs that are not expressed in stimulated MdESF , viz . members of the 5’ HoxD cluster HoxD12 , HoxD11 , and HoxD9 , as well as FOXM1 , TFAP2C , PRRX2 , and E2F8 . This suggests that one aspect of the evolution of the core GRN is the recruitment of additional TF genes through the evolution of cAMP response elements in existing or novel cis-regulatory elements . For example , in this list we find E2F8 , which functions in regulating polyploidization [35] , a derived feature of DSCs . On the other hand , we also find that DSC effector genes of the conserved TF network are different . Thus , another element by which the ancestral ESF cell type evolved into DSCs was the rewiring of gene regulation downstream of FOXO1 and other decidual regulatory genes , e . g . , CEBPB , PGR , HOXA10 , HOXA11 , and GATA2 ( Fig 7 ) . We tested this model by comparing the effect of FOXO1 KD on effector gene expression and found that in fact the regulatory role of human FOXO1 in these cells is extensively different from that in opossum ESF . Exactly how this “downstream reprogramming” was effected in evolution is not known and needs to be the subject of further investigation , although previous work suggests that recruitment of transposable elements may have been a key factor [52 , 53] . An alternative explanation for our results is that the decidual cell type may have evolved in the stem therian lineage , i . e . , before the most recent common ancestor of eutherians and marsupials , but has been lost in marsupials . Marsupial reproduction shares many plesiomorphic reproductive traits with the most basal branching mammals , the monotremes [5] . Although opossums do not lay eggs , along with the platypus , they have a relatively short gestation period and give birth to highly altricial young [54 , 55] . Furthermore , DSCs are necessary for maintaining pregnancy in species with the kind of invasive placentation that is only found in eutherian mammals [9] . For these reasons , it is likely that the DSC is truly a specific trait of eutherian mammals and not one with an older origin that was subsequently lost in the marsupial group . Our model of stress-derived decidual differentiation explains a number of otherwise puzzling facts . First , there is evidence that during physiological decidualization in humans the stromal cells produce intracellular ROS that mediate the decidualization process [56–59] . Here , we show that genes responsive to decidualization stimuli in a distantly related ESF cell type are also intimately linked to stress-related decidualization in eutherians [56] , e . g . , GPX3 , SOD1 , SOD3 , and CAT , and more interestingly these genes are also associated with a stress coping mechanism . Furthermore , we show that apoptosis-related genes known to play a role in decidualization , e . g . , GADD45A , TRAIL , and BCL2L11 [56 , 60] , are also up-regulated in MdESF . In the context of these stress-related genes , we also show that FOXO1 acts as an oxidative stress sentinel that counteracts apoptosis . Moreover , decidualization is associated with ER stress [61] , which we also observe through the expression of genes specifically associated with ER stress and UPR in opossum cells treated with 8-br-cAMP/MPA . Finally , a subpopulation of endometrial stromal cells undergoes cellular senescence in humans , a senescent phenotype that plays a critical role in implantation [62] . Thus , peculiar features of human decidualization , e . g . , redox signaling , ER stress , and cell senescence , are readily explained by our model , wherein decidual differentiation mechanisms arose in evolution from a pregnancy-related stress response that consequently activates many of the same regulatory genes and physiological processes as are activated in decidual cells during differentiation . Our results point to a model for the origin of a novel cell type , namely the modification of an ancestral cellular stress response . Very few studies have addressed the molecular mechanisms for the origin of novel cell types . However , two previous studies are of particular interest in regards to this . The study by Nagao and colleagues [63] investigated the leucophore pigment cell type in the perciform lineage . Experimental evidence suggests that the origin of the leucophore in the perciform lineage was achieved by a change in the functional interaction between TFs already expressed in a precursor cell type . Similarly , changes in the functional interactions of FOXD3 during the evolution of the neural crest cell type , a vertebrate novelty , is also especially noteworthy [64] . These results are broadly consistent with the model we propose for the evolution of the decidual cell type , in which similar TFs are expressed ancestrally and changes in the functional interactions between those TFs occurred that were critical for the evolution of the novel cell type . In fact , we have shown previously that , coincidental with the origin of decidual cells , the function of TF proteins , namely HOXA11 and CEBPB interacting with FOXO1 , has changed [65 , 66] . We note these genes are among those already responsive to decidualizing signals in opossum ESF . Hence , transcription factor protein evolution may be a common feature of the origin of novel cell types . In evolution , structural and developmental changes can result in cells being exposed to drastically altered or novel environments . In mammals , the evolution of pregnancy and in particular the evolution of extended gestation resulted in the endometrium functioning under exposure to a range of new stimuli and with new requirements for the success of reproduction . As these developmental changes occurred , it is reasonable to expect that exposure of cell types to novel tissue and developmental environments can be a source of cellular stress . These anciently conserved pathways can be a rich source of hardwired , modular components of stress GRNs . In this case , evolution can mitigate cellular stress in a variety of ways , including decreasing the stress-inducing stimulus , but also through co-option of the stress pathways into normal physiological function . Our data suggest that the decidual stromal cell type has evolved from a physiological stress response that is likely directly related to the invasion of trophoblast into maternal tissues , the condition seen in crown eutherians today . Whereas the evidence presented here pertains to the special case of the evolution of mammalian decidual cells , the recent discovery that ROS are important physiological signaling molecules during the differentiation of other cell types , e . g . , neurons [67 , 68] and mesenchymal cells [69] ( and functioning in tumorigenesis ) , may indicate a role for stress responses in the evolutionary derivation of novel cell types . Some cell types with novel physiological functions could therefore be understood as fulfilling physiological needs for which the ancestral body plan cannot compensate . The evolutionary rewiring of stress responses and functional changes in the interactions of transcription factors could be a few means within a suite of mechanisms involved in the evolutionary process of cell type origination to address physiological challenges .
All animal procedures were conducted under protocols approved by the Institutional Animal Care and Use Committee ( #11313 ) of Yale University . Opossum uterine tissue was collected from a M . domestica colony housed at Yale University . For ESF isolation , uterine tissue was harvested from a nonpregnant M . domestica female . For immunohistochemistry and ELISA , tissue from specific stages of the reproductive cycle were collected by following a standard breeding protocol outlined in Kin and colleagues [16] . Once collected tissue was stored for immunohistochemistry analysis in 4% paraformaldehyde in PBS 24 h , then washed in 50% ethanol for 1 h , then twice in 70% ethanol for 1 h then stored in 70% ethanol at −20°C . Tissue was stored for western blot and ELISA analysis by snap freezing in liquid nitrogen and then stored at −80°C . M . domestica ESFs were isolated as previously described [16] . Briefly , primary ESFs were harvested by enzymatic digestion and centrifugation combined with Percoll density gradient . The uterus of an adult female grey short-tailed opossum M . domestica was dissected , cut in half longitudinally , and cut into 2–3–mm fragments . These were digested with 0 . 25% trypsin-EDTA for 35 min at 37°C and digested in Dissociation Buffer ( 1 mg/ml collagenase , 1 mg/ml Dispase , 400 μg/ml DNaseI ) for 45 min at room temperature . Cell clumps were subsequently homogenized by passage through a 22-gauge syringe . Passage through a 40-μm nylon mesh filter removed remaining fragments . This lysate was used to generate a density gradient by centrifugation at 20 , 000 g for 30 min . A single cell suspension was generated from this lysate and was subsequently layered onto a Percoll gradient ( 1 . 09 g/cc Percoll , GE Healthcare Life Sciences ) and centrifuged at 400 g for 20 min to allow for cells to settle to their respective density layers . Using a 25-gauge needle , each 1-ml layer was removed working from low to high density and washed into 5 ml of 50-mM NaCl solution . As with previous iterations of this protocol in this laboratory , layers 6 through 8 generally contained a fairly homogeneous population of cells that outwardly exhibited fibroblast characteristics . Cells in these layers were pelleted , resuspended in growth media , and cultured in 24-well plates . To facilitate enrichment of fibroblasts versus epithelial cells , media was exchanged in each well after two hours in order to remove floating cells that had not yet attached . To validate our cell line , we conducted comparative qPCR and immunofluorescence for proteins that mark either epithelial or mesenchymal ( fibroblast ) cells . Transcription factors indicative of ESFs were enriched in RNA from Percoll layer 8 relative to layer 3 or RNA isolated from spleen ( S1 Table ) . Immunofluorescence on cells from Percoll layer 8 showed enrichment of the mesenchymal protein VIMENTIN and no epithelial contamination as judged by expression of CYTOKERATIN ( S1 Fig ) . Therefore , cells from layer 8 were used in these experiments and were propagated in a T75 culture flask by sub-passaging over a period of 2 months at 33°C . Once confluent , cells were subpassaged using Accutase ( AT104 , Innovative Cell Technologies ) or a cell scraper . The experiments detailed here were conducted with passages 12–20 . To isolate M . domestica skin fibroblasts from the animal , ethanol was applied to the skin , hair was removed , and a small sample was excised . The sample was transferred to PBS , and the subcutaneous tissue was removed by scraping the dermal side with a razor blade and forceps . The sample was cut into strips approximately 1 . 0 cm2 with a scalpel and was incubated in 0 . 3% trypsin-PBS for 30 min at 37°C . The epidermis was subsequently removed , and the sample was washed in ABAM-PBS with gentle shaking . The sample was transferred to a tissue culture dish and sliced into squares approximately 3 mm in size . Five to 10 of these squares were transferred to a 35-mm tissue culture dish , and a sterile 22-mm glass coverslip was placed over the samples . The samples were grown to confluency in growth media , which was refreshed every 3 days . MdESF were cultured in growth media with no antibiotic-antimycotic , containing ( per liter ) the following: 15 . 56 g DMEM/F-12 ( D2906 , Sigma Aldrich ) , 1 . 2 g sodium bicarbonate , 10 ml sodium pyruvate ( 11360 , Thermo Fisher ) , 1 ml ITS ( 354350 , VWR ) , and 100 ml charcoal-stripped fetal bovine serum ( 100–119 , Gemini ) . Media was replaced every 4 days unless otherwise stated . Over the duration of these experiments , MdESF were found to be mycoplasma free ( S8 Fig ) , as shown by periodic PCR assays for mycoplasma contamination ( 30-1012K , Universal Mycoplasma Detection Kit , ATCC ) . For decidualizing stimuli , MdESF were cultured in growth media supplemented with cAMP-analgoue 8-bromoadenosine 3′-5′-cyclic monophosphate sodium sale ( B7880 , Sigma Aldrich ) and progesterone-analgoue medroxyprogesterone 17-acetate ( MPA ) ( M1629 , Sigma Aldrich ) , at final concentrations of 0 . 5 mM and 1 μM , respectively . Growth media was supplemented with prostaglandin E2 ( 14010 , Cayman Chemicals ) at a final concentration of 10 μM . For RNA sequencing of unstimulated and stimulated MdESF , cells were grown to 70% confluency in T75 flasks and treated with the respective stimuli for two days prior to harvesting with Buffer RLT and subsequent processing with RNeasy Mini Kit ( 74104 , Qiagen ) following the manufacturer’s protocol . Illumina sequencing libraries were generated from RNA by Poly-A selection and sequenced by the Yale Center for Genome Analysis on the Illumina Hiseq 2500 system . For transcriptomic and GO enrichment analyses , see below under sub-heading Transcriptomic Analyses . Human ESF and DSC transcriptomic data used in these analyses were reported previously [8] , and FOXO1 KD RNAseq data in HsDSC have been deposited under GEO GSE115832 . MdESF or HsESF were grown in an 8-well chamber slide ( 12-565-18 , Fisher ) to 70% confluency . After treatment , cells were fixed with 4% paraformaldehyde in PBS for 15 min at room temperature . Cells were washed 2 times in ice-cold PBS , subsequently incubated for 10 min in 0 . 25% Triton X-100 in PBS , and finally washed 3 times for 5 min/wash in PBS . A blocking solution was applied with 1% bovine serum albumin ( BSA ) and 0 . 25% Triton X-100 in PBS for 30 min at room temperature . Cells were then incubated in blocking solution at 4°C overnight in the following primary antibodies: 1:200 rabbit anti-cytokeratin ( ab9377 , Abcam ) ; 1:200 mouse anti-vimentin ( sc-6260 , Santa Cruz ) ; mouse anti-FKHR ( FOXO1 ) ( sc-374427 , Santa Cruz ) . Cells were subsequently washed the next day 3 times for 5 min each in PBS , and secondary antibody incubation was for 1 h at room temperature in the dark . Secondary antibodies used in this study were as follows: 1:200 Alexa Fluor 555 goat anti-mouse IgG ( A21422 , Thermo Fisher ) ; 1:200 Alexa Fluor 488 goat anti-rabbit IgG ( A11008 , Thermo Fisher ) . Cells were then washed 3 times for 5 min each in PBS , and nuclei were stained with DAPI ( 10236276001 , Roche ) . Finally , cells were washed one time for 5 min in PBS and observed with an Eclipse E600 microscope ( Nikon ) equipped with a Spot Insight camera . Lastly , it should be noted that we tested two different antibodies targeting human FOXO3 in M . domestica . These antibodies were anti-FOXO3a/FKHRL1 ( EMD Millipore 07–702 ) and anti-FKHRL1 D-12 ( Santa Cruz , Sc-48348 ) . Both of these antibodies produced nonspecific bands on western blot and pervasive signal in MdESF . Therefore , we were not able to assess the protein localization dynamics of FOXO3 in MdESF . MdESF or HsESF were cultured in T75 flasks to 80% confluency . Cells were rounded up with Tryple and homogenized in RIPA buffer ( 89900 , Thermo Fisher ) supplemented with HALT Protease Inhibitor Cocktail ( PI87785 , Thermo Fisher ) for 15 min . Suspensions were centrifuged for 15 min at 13 , 000 RPM at 4°C . Protein concentrations were determined with Pierce BCA Protein Assay Kit ( 23225 , Thermo Fisher ) . Cell lysates were diluted to achieve a solution with 30–60 μg total protein , combined with an equal volume of 2× NuPage LDS Sample Buffer ( NP007 , Thermo Fisher ) with 2× NuPage Sample Reducing Agent ( NP0004 , Thermo Fisher ) , heated to 70°C , loaded in a NuPage 4%–12% Bis-Tris gel ( NP0321BOX , Thermo Fisher ) , and electrophoresed at 130 volts for 60–90 min . Proteins were transferred to polyvinylidene difluoride membranes with the iBlot Gel Transfer System ( Thermo Fisher ) . Membranes were subsequently incubated for 1 h at room temperature in blocking buffer ( 3% BSA in PBST ) and incubated with primary antibodies , listed above , overnight at 4°C . Primary antibody dilutions were the following: 1:200 FOXO1 , VIMENTIN , and CYTOKERATIN . After primary incubation , membranes were then washed 3 times for 5 min each in PBST and subsequently incubated with the corresponding HRP-conjugated secondary antibody , 1:5 , 000 of either goat anti-mouse ( sc-2005 , Santa Cruz ) or goat anti-rabbit ( sc-2054 , Santa Cruz ) . Signal was detected by incubating membranes in Clarity Western ECL substrate ( 1705060 , Bio-Rad ) in the dark for 5 min and visualized with a Bio-Rad Gel Doc System . Uncropped western blots are provided in S9 Fig and S10 Fig . Uterine tissue was dehydrated through a graded ethanol series , cleared in toluene , and then embedded in paraffin . We cut 7-μm cross sections on a microtome and mounted on Shandon polysine precleaned microscope slides ( 6776215Cs , Thermo Fisher ) . Sections were stored in the dark at room temperature until they were stained . We localized the expression of FOXO1 using immunohistochemistry . Slides were deparafinized in three successive washes of xylene ( 3 min each ) , then three successive washes of ethanol ( 3 min ) . Antigen retrieval was performed in citrate buffer ( 12 mM sodium citrate , pH 6 . 0 , 98°C , 1 h ) . Endogenous peroxidase activity was blocked with Dako Peroxidase Block ( Dako , 30 min ) . Slides were then incubated in primary antibody overnight . We used a goat polyclonal antibody generated against the N-terminal of human FOXO1 protein ( 1:5 , 000 dilution , FKHR antibody sc-9809 , SantaCruz ) . On day two , slides were incubated in a donkey anti-goat IgG-HRP secondary ( 1 h , 1:200 dilution , sc-2056 SantaCruz ) . Slides were then rinsed in PBS ( 5 min ) , PBS-BSA ( 0 . 1% , 5 min ) , then incubated for 5 minutes in TSA Plus Cyanine 3 system ( 1:50 NEL744001KT , PerkinElmer Inc . ) . Slides were again washed in PBS ( 5 min ) and PBS-BSA ( 0 . 1% , 5 min ) , counter stained in DAPI ( 1 time , 2 min , 10236276001; Roche ) , washed in deionized water for 5 min , and mounted in glycerol ( 50% ) . Snap frozen uterine tissue from adult female M . domestica was homogenized in extraction buffer ( 0 . 1 M phosphate , 1 mM EDTA , pH 7 . 4 ) containing indomethacin ( 10 μM final concentration ) using a mechanical homogenizer ( TissueRuptor , QIAGEN ) . Cellular debris was removed by centrifugation ( >13 , 000 RPM , 4 oC , 10 min ) . Tissue lysates were aliquoted into single use tubes and frozen at −80°C . Protein concentration was measured using Pierce BCA Protein Assay Kit ( 23225 , Thermo Fisher ) . After determining protein concentration , 1 mg of protein for each sample was used in the first round of ELISA against PGE2 following the manufacturer’s protocol ( 514010 , Cayman Chemical ) . Each sample was run in duplicate at two different dilutions . A dilution series of PGE2 standard provided by the manufacturer was included in each run , and PGE2 values in ng per mg protein were calculated from these standards . Due to the sensitivity of this assay , some samples contained excess PGE2 and therefore required additional dilutions in order to fall within the calculable range of the standard . For these samples with excess PGE2 , an additional ELISA plate was run with two additional dilutions . Samples were incubated in the provided PGE2 monoclonal antibody ELISA plate overnight at 4°C . The following day , the wells were washed 3 times and the staining reaction was allowed to proceed for 45 min with shaking at 400 RPM on an orbital shaker in the dark . The plate was read at 405 nm on a Viktor X multilight plate reader ( Perkin Elmer ) . MdESF were cultured in T25 culture flasks to 70% confluency and subsequently transfected with siRNAs as above . After two days , cells were treated either with 8-br-cAMP/MPA or with PGE2/MPA for four days . The change of media was accompanied by an additional round of siRNA transfection per above . At the time of RNA harvest , media was removed , cells were washed 2 times in PBS , and cells were lysed directly in the flask with Buffer RLT Plus plus beta-mercaptoethanol . RNA was harvested according to the manufacturer’s protocol ( 74034 , RNeasy Plus Micro Kit , Qiagen ) . Reverse transcription of 3 μg of RNA was carried out with iScript cDNA Synthesis Kit ( 1708891 , Bio-Rad ) with an extended transcription step of three hours at 42°C . For qPCR , all reactions were carried out with Power SYBR Green PCR Master Mix ( 4368708 , Thermo Fisher ) in triplicate with 40 ng of cDNA for template in each technical replicate reaction . Fold change was calculated by finding the ddCt values relative to the expression of TATA Binding Protein . All qPCR primer sets were validated by analysis of melting curves for 2 different sets of primers for the same gene . Primer sets used in this study are listed in S2 Table . MdESF at 70% confluency in 6-well culture plates were transfected with siRNAs targeting FOXO1 ( Mission custom siRNA , V30002 , Sigma Aldrich ) , FOXO3 ( Mission custom siRNA , V30002 , Sigma Aldrich ) , or negative control scrambled siRNA ( Silencer Negative Control No . 1 , AM4611 , Thermo Fisher ) . In preparation for transfection , siRNAs in 37 . 5 μl of OptiMem I Reduced Serum Media ( 31985 , Thermo Fisher ) were mixed with an equal volume of OptiMem containing 1 . 5 μl of Lipofectamine RNAiMax ( 13778 , Thermo Fisher ) , incubated at room temperature for 15 min , and subsequently added dropwise to cells in 3 ml growth media . Final concentration of siRNAs was 25 nM . In experiments involving stimulated media , an additional round of siRNA transfection was prepared and added dropwise after growth media with decidualizing stimuli was added . Two siRNAs were transfected for each gene . Custom siRNAs were synthesized by Sigma to target the mRNAs of M . domestica FOXO1 and FOXO3 , using the NCBI Reference Sequences XM_001368275 . 4 ( FOXO1 ) and XM_001368456 . 2 ( FOXO3 ) . Sense and antisense sequences are listed in S3 Table . siRNA-mediated KD of human FOXO1 in human decidual cells was carried out as previously described [70] . We confirmed depletion of both RNA and protein by qPCR and western blot . For FOXO1 , qPCR analyses showed that KD efficiency for these pooled siRNAs was >90% ( S7B Fig ) . This depletion in RNA led to a corresponding depletion in FOXO1 protein , as confirmed by western blot ( S10 Fig ) . We also desired to conduct a protein-level analysis for M . domestica FOXO3 . However , for M . domestica FOXO3 , we were able to confirm RNA depletion only ( S7B Fig ) , as we did not find a commercially available antibody that showed high specificity for FOXO3 in M . domestica . Cells were transfected as above with RNAi reagents and subsequently incubated in growth media in the presence of decidualizing stimuli for four days . For FACS analyses , conditioned media from each well was decanted into separate 15-ml conical centrifuge tubes , and cells were then washed in 2 ml PBS . PBS was decanted into the same 15-ml conical tube as the conditioned media , and 250 μl warm Tryple Express ( 12604 , Thermo Fisher ) was added to each well . To facilitate detachment , cells were placed in a 33°C incubator for 10 min . The detachment reaction was stopped by adding 700 μl growth media to each well . Cells were then transferred to their separate 15-ml conical tubes with conditioned media and PBS and subsequently pelleted by centrifugation at 400 RPM for 5 min . Supernatant was removed , and each cell pellet was resuspended in 1 ml pre-warmed Hank’s Balanced Salt Solution ( HBSS ) without Phenol Red , Ca2+ , Mg2+ ( 10–547 , Lonza ) containing freshly resuspended H2DCFDA ( Image-IT LIVE Green ROS Detection Kit , I36007 , Thermo Fisher ) to a final concentration of 25 μM . Cells were incubated at 33°C for 40 min . Just prior to FACS analysis , cells were placed on ice , and 0 . 5 ml HBSS containing 1 μg/ml propidium iodide was added to each tube . We utilized a doublet discrimination gating strategy on a BD Aria FACS instrument , wherein on average 93% of all cells were included in the analyses ( S11 Fig ) . Fluorescent signal detected in scrambled siRNA negative control cells were utilized to set the quadrant boundaries . Data for three replicates of each experiment were collected and mean percentages for each quadrant were calculated . Raw sequencing reads were mapped to opossum M . domestica genome assembly monDom5 with Ensembl annotation v86 , using Tophat2 v2 . 1 . 1 [71] and Bowtie2 v2 . 2 . 9 [72] . Read counts for all genes were calculated using HTSeq v0 . 6 . 1p1 [73] with Python ( v2 . 7 . 13 ) . Transcripts per million were calculated to estimate relative mRNA abundance [74] . We used Bioconductor package edgeR v3 . 16 . 5 [75] to assay for differential gene expression between unstimulated and stimulated MdESF . Genes that met the following criteria were considered to be significantly differentially expressed: ( 1 ) change in expression of at least 1 . 5-fold; ( 2 ) resulted in an adjusted P-value smaller than 10−6; and ( 3 ) expressed in at least one condition under comparison ( TPM ≥ 3 ) [76] . GO enrichment analyses were performed using Gorilla [77] , and the results were visualized using REViGO [78] . The data from the experiment testing the effect of 8-br-cAMP/MPA and PGE2/MPA , as well as FOXO1 and FOXO3 KDs on the presence of ROS and apoptotic cells , were analyzed as a two-factor ANOVA . The two factors were “stimulation” and “treatment , ” where “stimulation” had the levels , “growth media , ” 8-br-cAMP/MPA , and PGE2/MPA and “treatment” had the levels random siRNA , FOXO1 KD , and FOXO3 KD . The response variable was the fraction of cells showing either ROS or PI fluorescence . The analysis was performed with raw frequencies as well as with log-transformed response variables . For the PGE2 ELISA , the concentration of PGE2 for each sample was determined by standard curve . The values were subsequently log transformed and used in a one-tailed t test .
|
Animals consist of a wide variety of cells that serve different functions depending on their location in the body . Cells with similar functions , or cell types , in different animal species are related both by an evolutionary line of descent—similar to the relatedness of species themselves—and by a developmental line of descent in the embryo . Networks of interacting genes , or gene regulatory networks , control gene expression in the cell , thereby specifying cell type identity . Understanding how new cell types arise by changing gene regulatory networks is critical both to comprehending fundamental aspects of human biology and to manipulating cell types in the laboratory . We approached this question by studying endometrial stromal fibroblast ( ESF ) cells from the uterus of humans and opossums , two distantly related mammals . We showed that the distantly related cell type in opossum expresses a similar set of regulatory genes as the human cell , but in response to pregnancy-related signals , the opossum cells induce a stress response . In the human cells , these signals induce differentiation into decidual cells , a specialized cell type present in humans and closely related mammals . These results suggest that a gene regulatory network that modulated an ancestral , pregnancy-related stress response was hijacked and repurposed to function in differentiation and specification of the decidual cell type .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"cell",
"death",
"cellular",
"stress",
"responses",
"forkhead",
"box",
"evolutionary",
"biology",
"gene",
"regulation",
"cell",
"processes",
"hormones",
"developmental",
"biology",
"small",
"interfering",
"rnas",
"proteins",
"gene",
"expression",
"biochemistry",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"apoptosis",
"genetics",
"protein",
"domains",
"biology",
"and",
"life",
"sciences",
"lipid",
"hormones",
"prostaglandin",
"non-coding",
"rna",
"evolutionary",
"developmental",
"biology"
] |
2018
|
The mammalian decidual cell evolved from a cellular stress response
|
Translesion DNA synthesis ( TLS ) by specialized DNA polymerases ( Pols ) is a conserved mechanism for tolerating replication blocking DNA lesions . The actions of TLS Pols are managed in part by ring-shaped sliding clamp proteins . In addition to catalyzing TLS , altered expression of TLS Pols impedes cellular growth . The goal of this study was to define the relationship between the physiological function of Escherichia coli Pol IV in TLS and its ability to impede growth when overproduced . To this end , 13 novel Pol IV mutants were identified that failed to impede growth . Subsequent analysis of these mutants suggest that overproduced levels of Pol IV inhibit E . coli growth by gaining inappropriate access to the replication fork via a Pol III-Pol IV switch that is mechanistically similar to that used under physiological conditions to coordinate Pol IV-catalyzed TLS with Pol III-catalyzed replication . Detailed analysis of one mutant , Pol IV-T120P , and two previously described Pol IV mutants impaired for interaction with either the rim ( Pol IVR ) or the cleft ( Pol IVC ) of the β sliding clamp revealed novel insights into the mechanism of the Pol III-Pol IV switch . Specifically , Pol IV-T120P retained complete catalytic activity in vitro but , like Pol IVR and Pol IVC , failed to support Pol IV TLS function in vivo . Notably , the T120P mutation abrogated a biochemical interaction of Pol IV with Pol III that was required for Pol III-Pol IV switching . Taken together , these results support a model in which Pol III-Pol IV switching involves interaction of Pol IV with Pol III , as well as the β clamp rim and cleft . Moreover , they provide strong support for the view that Pol III-Pol IV switching represents a vitally important mechanism for regulating TLS in vivo by managing access of Pol IV to the DNA .
Despite the actions of several DNA repair pathways , lesions capable of blocking progression of the replicative DNA polymerase ( Pol ) persist in the DNA template . Translesion DNA synthesis ( TLS ) represents one evolutionarily conserved mechanism by which organisms cope with these replication-blocking lesions [1–3] . In contrast to repair functions , which either reverse or excise the damage , TLS acts to bypass the damaged site using one or more specialized Pol , allowing the replication fork to proceed past the lesion [1] . Depending on the TLS Pol used , the DNA lesion and its sequence context , bypass may be either accurate or inaccurate [1–3] . Furthermore , due to the fact that most TLS Pols lack intrinsic proofreading activity and possess an open active site , these Pols display a significantly reduced fidelity when replicating undamaged DNA . Thus , TLS Pols may cause mutations by catalyzing inaccurate TLS , or by gaining inappropriate access to undamaged DNA during normal replication . A growing body of evidence supports the view that mutations introduced by TLS Pols contribute to antibiotic resistance and adaptation of microbial pathogens [4–7] , as well as genome instability and cancer development in metazoans [8–11] . As a result , the actions of TLS Pols must be tightly regulated to limit unwanted mutations . Furthermore , TLS Pols replicate considerably slower than replicative Pols [12–14] . As a result , their unregulated access to the replication fork would significantly slow replication . The eukaryotic proliferating cell nuclear antigen ( PCNA ) and the bacterial β sliding clamp proteins play crucially important roles in managing the actions of TLS Pols , and in coordinating their activities with those of their respective replisomes via a process termed ‘Pol switching’ [12 , 15–20] . Pol-Pol interactions are also suggested to contribute to Pol switching [21–23] . However , several important questions regarding the mechanisms by which TLS Pols switch with replicative Pols , as well as the biological importance of the switching mechanism to regulation of TLS in vivo remain unanswered . E . coli contains five distinct Pols , which are named Pols I-V . Pols II , IV and V act in TLS [1 , 3] , while Pol I functions in DNA repair and Okazaki fragment maturation [24] . The 20-subunit Pol III holoenzyme serves as the bacterial replicase , and is composed of 2 homodimeric β sliding clamps and 3 core complexes ( Pol IIIαεθ ) , which are tethered together via interaction of Pol IIIα with the heptameric DnaX ATPase ( τ3δδ’ψχ ) that acts to load the β clamp onto primed DNA [25 , 26] . The Pol IIIαεθ core complex performs DNA synthesis functions . Within this complex , Pol IIIα catalyzes DNA polymerization , Pol IIIε functions in proofreading and Pol IIIθ modestly stimulates Pol IIIε proofreading activity [25 , 26] . With the possible exception of Pol I , each of the 5 E . coli Pols contains either a pentameric ( QLxLF ) or hexameric ( QLxLxL ) clamp-binding motif ( CBM ) that is required for biological activity [27–31] . The CBM interacts with a hydrophobic cleft located near the C-terminus of each β clamp protomer . The different Pols also contact non-cleft surfaces of the β clamp , and these interactions likewise contribute to Pol function and/or Pol switching [17 , 32–37] . To date , structural information regarding these non-cleft contacts is restricted to Pol IV . In the co-crystal structure of the complex consisting of the Pol IV little finger domain ( Pol IVLF; residues 243–351 ) bound to the β clamp , residues 303VWP305 of Pol IV interacted with positions E93 and L98 on the rim of the β clamp , while the C-terminal hexameric CBM ( 346QLVLGL351 ) of Pol IV extended over the β clamp dimer interface to interact with the β clamp cleft of the adjacent β clamp protomer [32] . Using a primer extension assay , we previously demonstrated that while only the Pol IV-β clamp cleft interaction was required for processive replication , both the β clamp rim and cleft interactions contributed to Pol III-Pol IV switching in vitro [12 , 17 , 38] . In contrast to our findings , Gabbai and colleagues , utilizing a different assay that may more accurately represent the structure and composition of the replisome , concluded that the Pol IV-β clamp rim contact stimulated but was not required for a Pol III-Pol IV switch in vitro [18] . In light of this finding , they suggested direct competition between Pol III and Pol IV for the β clamp cleft as an alternative mechanism for their switching . In addition to switching , Pol IV can be recruited directly to single strand ( ss ) DNA gaps generated by Pol III skipping over lesions in the template strand to continue replication downstream of the block [1 , 39] . In summary , recruitment of TLS Pols to lesions is suggested to occur by two different mechanisms: ( i ) β clamp may recruit TLS Pols post-replicatively to lesions within ssDNA DNA gaps generated by Pol III skipping , or ( ii ) TLS Pols may be recruited to the replication fork and access lesions after undergoing a switch with Pol III . However , the extent to which these proposed mechanisms are utilized in vivo has not yet been determined . Furthermore , the biological relevance of the Pol IV-β clamp rim interaction to the TLS function of Pol IV is also unknown . E . coli Pol IV catalyzes accurate bypass of N2-dG lesions induced by nitrofurazone ( NFZ ) , as well as alkylated adducts such as N3-methyladenine ( N3-mdA ) caused by methyl methanesulfonate ( MMS ) . As a result , E . coli strains lacking Pol IV function ( i . e . , ΔdinB ) display sensitivity to these agents [17 , 40–43] . In contrast to this protective role , overproduction of Pol IV is lethal to E . coli [34 , 44 , 45]; similarly , aberrant expression of Pol κ , the eukaryotic ortholog of dinB , promotes genome instability in human cells [46] . Lethality in aerobically cultured E . coli cells was suggested to result from toxic levels of double strand ( ds ) DNA breaks resulting from efforts to repair closely spaced 8-oxo-7 , 8-deoxyguanosine ( 8-oxo-dG ) adducts incorporated during replication of undamaged DNA by Pol IV [47] . However , sensitivity of a dnaN159 strain to ~4-fold higher than SOS-induced levels of Pol IV [34 , 44] , and of a dnaN+ E . coli strain to significantly higher than SOS-induced levels ( i . e . , ~70-fold; [45] ) , were both independent of Pol IV catalytic activity , suggesting that at least in these cases , lethality relied on one or more alternative mechanisms . The dnaN159 allele encodes a mutant form of the β sliding clamp that is deficient in regulation of proper access of the different E . coli Pols to DNA [34 , 35 , 38 , 44 , 48 , 49] . Thus , sensitivity of the dnaN159 strain to elevated levels of Pol IV was suggested to result from its enhanced ability to replace the bacterial Pol III replicase at the replication fork , thereby disrupting DNA replication [34 , 44 , 48] . Consistent with a Pol III-Pol IV switch underlying this lethal phenotype , mutations in Pol IV that disrupt its ability to interact with either the cleft ( Pol IVC; see Table 1 ) or the rim ( Pol IVR ) of the β clamp abrogated its ability to kill the dnaN159 strain [38] . Finally , SOS-induced levels of Pol IV modestly slowed the rate of DNA replication in vitro [13 , 14 , 45] , while overproduction of Pol IV severely impeded it , possibly by replacing Pol III [13 , 14 , 45] . The finding that Pol IVLF-β clamp interactions were dispensable when Pol IV was expressed at ~70-fold higher than SOS-induced levels suggested that Pol IV may interact with Pol III to effect its displacement from the β clamp [45] . Based on these results , Pol IV was suggested to act as a DNA damage checkpoint effector that acts to slow replication fork progression in response to DNA damage [13 , 45] . The goal of this study was to define the relationship between the physiological function of Pol IV in TLS and its ability to impede E . coli growth when overproduced . With this goal in mind , the hypersensitivity of the dnaN159 strain was exploited to identify 13 novel mutant Pol IV proteins that failed to confer lethality . Genetic and biochemical characterization of these Pol IV mutants strongly suggest that the ability of overproduced levels of Pol IV to inhibit E . coli growth is a consequence of its ability to gain inappropriate access to the replication fork via a switch that is mechanistically similar to that used under physiological conditions to coordinate the actions of Pol IV with Pol III . Importantly , further analysis of one of the mutants , Pol IV-T120P , revealed novel insights into the mechanism by which Pol IV gains access to DNA lesions in vivo . Specifically , Pol IV-T120P retained complete catalytic activity in vitro but , like Pol IVR and Pol IVC , failed to support Pol IV TLS function in vivo . Using a single molecule primer extension assay , we demonstrated that the T120P mutation abrogated a biochemical interaction of Pol IV with Pol III that was required for Pol III-Pol IV switching . Taken together , these results suggest that Pol III-Pol IV switching involves interaction of Pol IV with both Pol III and the β clamp rim and cleft regions , and provide strong support for the view that Pol III-Pol IV switching represents a vitally important mechanism for regulating TLS in vivo by managing access of Pol IV to the DNA .
Based on results of a quantitative transformation assay , the hypersensitivity of the dnaN159 lexA51 ( Def ) strain ( MS105 ) to ~4-fold higher than SOS-induced levels of Pol IV expressed from its native LexA-regulated promoter present in pRM102 was independent of Pol IV catalytic activity ( [38]; see Pol IV-D103N in S1 Table ) . Moreover , the ability of Pol IV to impede growth of MS105 was independent of dsDNA breaks stemming from the incorporation into nascent DNA of oxidized precursors , since lethality was observed regardless of whether MS105 was grown aerobically or anaerobically ( S2 Table ) . In contrast , lethality of the dnaN159 strain required the ability of Pol IV to interact with both the rim and cleft of the β clamp ( [38]; see Pol IVR and Pol IVC in S1 Table ) . Taken together , these results suggest that lethality was caused by inappropriate access of Pol IV to the replication fork , rather than its ability to incorporate oxidized precursors into nascent DNA [47] . With the goal of gaining insight into the mechanism by which elevated levels of Pol IV impeded growth , a genetic assay was used to select for novel Pol IV mutants that are unable to kill the dnaN159 strain . From a total of ~2 x 107 independent clones , 16 plasmid-encoded dinB mutants expressing a full length Pol IV protein were identified ( S1 Fig ) . These mutants corresponded to 13 unique dinB alleles: 12 contained a single missense mutation , while one contained two missense mutations ( Table 2 ) . Using our quantitative transformation assay referred to above [38] , we confirmed that each of these mutant Pol IV-expressing plasmids was unable to impede growth of the dnaN159 strain ( S1 Table ) . For comparison , the wild type Pol IV-expressing plasmid pRM102 was more than 1 , 000-fold less efficient at transforming MS105 relative to the pWSK29 control ( S1 Table ) . In addition to impeding E . coli growth , overproduced levels of Pol IV also promote –1 frameshift mutations within homopolymeric runs of dG or dA [50] , while SOS-induced levels confer UV sensitivity upon the dnaN159 strain [38 , 48] . Since these phenotypes appear to result from the ability of Pol IV to gain inappropriate access to the replication fork [34 , 44 , 48] , we hypothesized that the Pol IV mutations described above would likewise be impaired for these functions . Based on results of a lacZ–→LacZ+ reversion assay [51 , 52] , all 13 mutant dinB alleles were impaired for promoting –1 frameshift mutations ( S2 Fig ) . Likewise , these mutants were also impaired for conferring UV sensitivity upon the dnaN159 strain ( S3 Fig ) . Taken together , these results suggest that the mutations identified in Pol IV that prevented it from killing the dnaN159 strain served to impair its catalytic activity and/or its ability to gain access to the DNA template in vivo . As summarized in Fig 1A , the identified Pol IV mutations are distributed throughout all 4 structural domains of Pol IV . To gain insight into the possible defect ( s ) associated with each mutant Pol IV , the positions of the mutations identified in each of the 13 dinB alleles were represented on in silico models for the structure of the Pol IV-β clamp complex assembled on primed DNA in either a non-replicative ( meaning Pol IV is bound to both the β clamp rim and cleft , but not the DNA; see Fig 1B ) or a replicative mode ( meaning Pol IV is bound only to the β clamp cleft , as well as the DNA; see Fig 1C ) . Based upon these structural models , combined with our current understanding of E . coli Pol IV structure-function [32 , 53] , as well as published studies of the homologous Sulfolobus solfataricus P2 Dpo4 [54 , 55] , we inferred likely functions for several of the mutated residues in Pol IV ( Table 2 ) . Residues A15 , G52 , C66 and A143 likely contribute to either structural integrity or the hydrophobic core ( Fig 1A–1C ) , suggesting that mutations of these residues most likely alter overall Pol IV structure . Position D10 likely contributes to structural integrity and is adjacent to D8 , D103 and E104 ( Fig 1D ) , which together act to coordinate 2 Mg+2 ions to constitute the catalytic core of Pol IV , suggesting that substitution of D10 with glycine might affect the structure of the Pol IV catalytic center . Residue R75 resides in the fingers domain and appears to form a hydrogen bond with residue D20 in the palm ( Fig 1E ) , possibly contributing to the tertiary structure of Pol IV . Residues G183 , G219 , and R323 are all in close proximity to the DNA template and may be involved in Pol IV-DNA interactions ( Fig 1F–1H ) . Finally , H302 and Q342 of Pol IV are immediately adjacent to residues previously demonstrated by Bunting et al . to directly contact the β clamp rim ( 303VWP305 ) or cleft ( 346QLVLGL351 ) , respectively , suggesting that the H302Q and Q342K substitutions disrupt these interactions ( [32]; Fig 1B ) . While presumed functions could be assigned for many of the mutants based upon previous studies , possible defects of the Pol IV mutants bearing substitutions of residues A44 , T120 or A149 remain unclear ( Table 2 ) . In order to gain insight into the relationship between the abilities of elevated levels of Pol IV to impede growth of the dnaN159 strain [34 , 38 , 44] and overproduced levels of Pol IV to kill the dnaN+ strain [45] , a quantitative transformation assay was used to analyze the phenotypes of pBAD derivatives bearing the relevant Pol IV mutations ( see Table 1 ) . The ability of overproduced levels of full-length Pol IV ( pDB10 ) or the catalytic domain of Pol IV ( Pol IVCD; residues 1–230 expressed from pDB12 ) to impede growth of E . coli was independent of both its catalytic activity [45] and aerobic growth ( S4 Fig ) , similar to the situation discussed above for the dnaN159 strain ( S1 Table and S2 Table ) . Since overexpression of Pol IVCD was necessary and sufficient to impede growth ( [45]; see Fig 2 ) , we focused on Pol IV mutations mapping within the first 230 residues of Pol IV . Despite the fact that the mutant Pol IV proteins appeared stable when expressed from their native dinB promoter contained within a low copy number plasmid ( S1 Fig ) , 6 of the 11 mutants containing substitutions within the first 230 residues of Pol IV displayed either poor solubility or signs of extensive proteolysis following their overproduction from the T7 promoter while one mutant ( R75L ) was specifically unstable when cloned into Pol IVCD-expressing pBAD plasmid ( see Table 2 ) . These Pol IV mutants were not pursued further . Results for the other 4 Pol IV mutants are summarized in Fig 2 . The plasmids overproducing Pol IVCD-D10G ( pDB20 ) , Pol IVCD-C66S ( pDB21 ) , Pol IVCD-T120P ( pDB23 ) , or Pol IVCD-G183V ( pDB25 ) each transformed the dnaN+ strain with an efficiency comparable to that of the pBAD control , both in the presence or absence of arabinose ( albeit most exhibited tiny to small colonies ) , while the plasmid overproducing wild type Pol IV ( pDB10 ) or Pol IVCD ( pDB12 ) failed to transform in the presence of arabinose ( Fig 2 ) . However , in contrast to the other mutants , which formed tiny to small colonies in the presence of arabinose ( Fig 2 ) , the strain overproducing Pol IVCD-T120P displayed robust colonies that were indistinguishable from the strain bearing either the empty pBAD plasmid or overproducing Pol IVLF ( pDB14 ) . In contrast to the robust growth observed for the strain overproducing Pol IVCD-T120P , the strain overproducing the full length Pol IV-T120P ( pDB33 ) failed to grow in the presence of arabinose ( Fig 2 ) . We confirmed that the Pol IVCD-T120P mutant was expressed in a soluble form and at a level comparable to wild type Pol IVCD ( see legend to Fig 2 ) . Based on this observation and results discussed later in this report , the difference between the growth phenotype of the strain expressing Pol IVCD-T120P and that expressing full length Pol IV-T120P appears to be a result of the Pol IVLF domain , which contributes to the ability of Pol IV to impede E . coli growth [16 , 45] . Taken together , these findings suggest that a common mechanism underlies the ability of overproduced levels of Pol IVCD to impede growth of the dnaN+ strain and near-physiological levels of Pol IV to impede growth of the dnaN159 strain . Furthermore , the fact that Pol IVCD lacks the β clamp-binding Pol IVLF domain , yet is nevertheless able to displace Pol III from the β clamp in vitro [16 , 45] , suggests that Pol IVCD interacts physically with one or more subunit of Pol III holoenzyme . Thus , the ability of T120P to alleviate the lethal phenotype may be indicative of this mutant being impaired for a Pol III-Pol IV interaction . In order to gain insight into the mechanistic basis for the phenotypes of the mutant Pol IV proteins , recombinant forms of each were purified for biochemical analyses . As noted above , we were able to overproduce all 13 mutant proteins . However , 6 of the 13 were either partially proteolyzed or poorly soluble following their overproduction from the T7 promoter ( see Table 2 ) , suggesting that their substitutions may affect the tertiary structure of Pol IV . The ability of the other 7 mutant Pol IV proteins to catalyze DNA replication in vitro was analyzed using a primer extension assay [17 , 34 , 38 , 56] . The DNA template consisted of a 32P labeled 30-mer annealed near the middle of a 100-mer ( see depiction in Fig 3 ) . Using this template , replication activity of each mutant Pol IV alone , as well as in the presence of single stranded DNA binding protein ( SSB ) , β clamp and the DnaX ( γ3δδ’ ) clamp loader accessory proteins was analyzed . As controls , we examined Pol IV-D103N , which lacks catalytic activity [17 , 38 , 57] , as well as Pol IVR and Pol IVC , which are impaired for interaction with the β clamp rim or cleft , respectively [38] . Based on published in vitro studies , the Pol IV-β clamp rim interaction is required for Pol III-Pol IV switching , but is dispensable for Pol IV replication . In contrast , the β clamp cleft interaction is required for both Pol IV replication and the Pol III-Pol IV switch [17] . In the absence of accessory proteins , Pol IVR , Pol IVC , Pol IV-T120P and Pol IV-H302Q/Q342K exhibited replication activity roughly comparable to wild type Pol IV ( Fig 3A ) . In contrast , Pol IV-R323S was only modestly active , while Pol IV-D10G , Pol IV-C66S , Pol IV-R75L and Pol IV-G183V lacked detectable activity , similar to Pol IV-D103N ( Fig 3A ) . In the presence of SSB , β clamp and the DnaX complex , Pol IVR and Pol IV-T120P were again indistinguishable from wild type Pol IV ( Fig 3B ) . Pol IV-C66S , Pol IV-R75L , Pol IV-G183V and Pol IV-R323S each displayed modest replication activity ( Fig 3B ) , suggesting that the presence of accessory factors compensated in part for their intrinsic biochemical defects , possibly by helping to recruit the mutant Pol IV proteins to the primer/template junction , and/or by stabilizing an active conformation of the mutant Pol IV protein . Whereas Pol IV-H302Q/Q342K was indistinguishable from wild type Pol IV in the absence of accessory proteins ( Fig 3A ) , it was impaired for processive replication in their presence compared to wild type ( Fig 3B ) , suggesting that the Q342K mutation , which is adjacent to the Pol IV CBM ( see Fig 1 ) , interferes with the Pol IV-β clamp cleft interaction . Finally , Pol IV-D10G lacked detectable activity , similar to the D103N mutation , suggesting that D10 either participates directly in catalysis , or its substitution with glycine perturbs the structure of the catalytic center ( see Fig 1D ) . Taken together , these results demonstrate that with the exception of Pol IV-D10G , each of the mutant proteins retained at least partial catalytic activity in vitro . Remarkably , Pol IV-T120P supported replication activity and processivity that were each comparable to that of wild type Pol IV . In order to quantify the replication activity of Pol IV-T120P more rigorously , we measured its kinetic parameters and compared them to those of wild type Pol IV . As summarized in Table 3 , the catalytic efficiency ( kpol/Kd ) of Pol IV-T120P was ~2 . 5-fold higher than wild type Pol IV for incorporation of dC opposite template dG , and ~0 . 5-fold lower than wild type Pol IV for incorporation of dT opposite template dA . Both Pol IV and Pol IV-T120P were able to incorporate the other three dNTPs opposite a template dG or dA . However , in all cases the efficiency of misincorporation was significantly less ( <10% ) than that measured for correct incorporation . The small differences in catalytic efficiency between wild type Pol IV and Pol IV-T120P were attributable to effects of the T120P substitution on both dNTP binding ( Kd ) and Pol turnover ( kpol ) ( Table 3 ) . Thus , despite the fact that residue T120 is well removed from the catalytic center of Pol IV ( Fig 1 ) , its substitution with a proline nevertheless exerts a modest effect on Pol IV catalysis . These findings , taken together with those discussed above , confirm that Pol IV-T120P retains essentially wild type Pol activity when replicating undamaged DNA , despite its inability to impede E . coli growth when expressed at elevated levels . Whereas Pol IV plays an important role in tolerating MMS-induced DNA damage by accurately bypassing lesions including N3-mdA , Pol V ( umuDC ) contributes to MMS-induced mutations by mediating error-prone bypass of apurinic/apyrimidinic ( AP ) sites generated by either DNA glycosylases involved in the repair of alkylated bases , or their spontaneous decay [41 , 58] . Consistent with one published result [41] , loss of Pol IV function resulted in a ~5-fold increase in the frequency of Pol V-dependent MMS-induced mutagenesis ( Fig 4A ) . In contrast , expression of Pol IV at ~4-fold higher than SOS-induced levels from a low copy number plasmid ( pRM102 ) reduced the frequency of MMS-induced mutagenesis ~5-fold compared to the pWSK29 empty vector control ( Fig 4B ) . Taken together , we interpret these results to mean that Pol IV is limiting for accurate bypass of MMS-induced DNA lesions in vivo , and that when Pol IV is present , it leads to a reduction in the number of AP sites encountered by the replisome , thereby minimizing the Pol V-dependent mutator phenotype . In light of these findings , we asked whether any of the Pol IV mutants were able to reduce MMS-induced mutagenesis when expressed at ~4-fold higher than SOS-induced levels . In addition to the Pol IV mutants identified in the screen , we also analyzed Pol IVR and Pol IVC . As summarized in Fig 4B , overexpression of Pol IVR or Pol IVC failed to reduce the frequency of MMS-induced mutagenesis ( p<0 . 0001 based on Student’s t-test ) . Importantly , these results demonstrate a biologically important role for the β clamp rim and cleft in supporting Pol IV TLS function . Like Pol IVR and Pol IVC , each of the other Pol IV mutants were also unable to reduce the frequency of MMS-induced mutagenesis compared to the strain expressing wild type Pol IV from pRM102 ( p<0 . 0001 based on Student’s t-test ) . Taken together , these findings suggest that these mutant Pol IV proteins are impaired for gaining access to MMS-induced DNA adducts in vivo and/or mediating their bypass following recruitment , effectively shifting the bypass burden to Pol V . Since the Pol IV-T120P mutant retained complete catalytic activity while replicating undamaged DNA in vitro ( Fig 3 and Table 3 ) , but failed to accurately tolerate MMS-induced lesions in vivo when expressed at an elevated level ( Fig 4 ) , we more rigorously analyzed the TLS activity of Pol IV-T120P in vivo under physiologically relevant conditions . To this end , the dinB+ allele was replaced with dinB89 , which encodes the Pol IV-T120P mutation ( see Table 2 ) , and the ability of the resulting strain to tolerate MMS-induced DNA damage was measured . As controls , strains lacking dinB ( Pol IV ) and/or umuDC ( Pol V ) , as well as Pol IV-D103N ( dinB80 ) , which lacks detectable Pol activity were used . In addition , we also constructed and analyzed Pol IVR ( dinB82 ) and Pol IVC ( dinB81 ) strains to gain insight into the biological importance of these contacts to Pol IV TLS . We first examined MMS sensitivity by spotting serial dilutions of respective cultures onto plates supplemented with 0 , 3 or 4 . 5 mM MMS . Each of the Pol IV mutants displayed modest sensitivity to 3 mM MMS , with the ΔdinB , Pol IV-D103N , and Pol IVC strains being slightly more sensitive than the Pol IVR and Pol IV-T120P strains ( Fig 5 ) . At 4 . 5 mM MMS , the ΔdinB , Pol IV-D103N , and Pol IVC strains were between ~100- to 1 , 000-fold more sensitive than the wild type Pol IV strain . The Pol IVR and Pol IV-T120P strains were similar to each other , and were only slightly less sensitive than the ΔdinB strain ( Fig 5 ) . Taken together , these results demonstrate a biologically important role in Pol IV TLS for the β clamp rim as well as residue T120 of Pol IV . In contrast to ΔdinB , the ΔumuDC strain failed to display MMS sensitivity , or to exacerbate sensitivity of the ΔdinB strain , consistent with published reports [41] . Finally , MMS sensitivity of each of the Pol IV mutant strains was fully complemented by pRM102 , which expresses wild type Pol IV ( S5A Fig ) . We next measured the frequency of MMS-induced mutagenesis . The strain lacking Pol IV displayed a ~7-fold increase in Pol V-dependent MMS-induced mutagenesis ( Fig 6A ) , as expected [41] . Frequencies for the Pol IV-D103N ( dinB80 ) , Pol IV-T120P ( dinB89 ) , Pol IVC ( dinB81 ) and Pol IVR ( dinB82 ) strains were ~4- , ~5- , ~6- and ~3-fold elevated , respectively , relative to the wild type Pol IV control ( Fig 6B; p≤0 . 0001 based on Student’s t-test ) . These results demonstrate the importance of position T120 in Pol IV , as well as the ability of Pol IV to interact with the β clamp rim and cleft to carry out TLS in vivo . Importantly , the ability of Pol IV to inhibit mutagenesis by Pol V was not affected by the presence of the zaf-3633::cat cassette ( p = 0 . 7 based on Student’s t-test comparing the Pol IV+ strains MG1655 and MKS103 ) . As with MMS sensitivity , wild type Pol IV expressed from plasmid pRM102 restored the frequency of MMS-induced mutagenesis for these dinB mutants to the wild type Pol IV level ( S5B Fig ) . To determine if the MMS phenotypes of the Pol IV-T120P strain were due to a catalytic TLS defect which rendered Pol IV-T120P incapable of bypassing MMS-induced lesions , we used a primer extension assay to measure the ability of purified Pol IV-T120P to catalyze in vitro bypass of the model MMS-induced lesions O6-methylguanine ( O6-mdG ) , 3-deaza-3-methyladenine ( 3d-medA ) , which is a stable mimic of N3-mdA [59] , as well as an AP site . As summarized in Table 3 , both wild type Pol IV and Pol IV-T120P were able to bypass template O6-mdG and 3d-medA . While there were some differences in substrate binding ( Kd ) and/or turnover ( kpol ) , Pol IV-T120P was as efficient or more so than wild type Pol IV . Pol IV and Pol IV-T120P each incorporated either dC or dT opposite template O6-mdG with roughly equivalent efficiencies . In both cases , bypass was considerably less efficient than that observed for template dG , due to a reduction in both Kd and kpol , with Pol IV-T120P slightly outperforming wild type Pol IV . Both Pols were capable of incorporating low levels of dA or dG opposite template O6-mdG . However , the amount of incorporation was less than 10% compared to that for the insertion of dC or dT opposite the alkylated lesion . 3d-medA was easier for both Pol IV and Pol IV-T120P to bypass , again with Pol IV-T120 outperforming wild type Pol IV by a factor of ~2-fold , attributable in large part to stronger substrate binding ( Kd ) . Both Pols incorporated dA , dC or dG opposite template 3d-medA . The level of incorporation was significantly less than that measured for incorporation of dT . Finally , even though Pol IV-T120P was marginal in regard to its ability to bypass the AP site , inserting dA , it was nevertheless more efficient than wild type Pol IV ( Table 3 ) . Taken together , these results demonstrate that Pol IV-T120P is proficient in vitro for TLS past a variety of DNA adducts commonly induced by MMS , suggesting that the inability of Pol IV-T120P to cope with MMS-induced DNA damage in vivo was the result of its inability to gain access to the lesions . We previously utilized a single molecule primer extension assay to demonstrate exchange of Pol III and Pol IV on β clamp at the 3’ primer terminus in vitro [12] . The distinct polymerization rates of Pol III and Pol IV allowed us to unambiguously assign individual DNA synthesis events to each respective Pol and to measure their respective processivities when incubated alone or together ( Fig 7A ) . At 300 nM Pol IV , a 60-fold molar excess over Pol III that simulates levels found in SOS-induced cells [3] , Pol IV actively displaced Pol III from the DNA template as inferred from Pol III processivity measurements ( [12 , 16 , 17 , 38 , 45]; see Fig 7B ) . This ability of Pol IV to reduce Pol III processivity was dependent on the Pol IV CBM , arguing that Pol III displacement involves at a minimum a conformational exchange of the two Pols on the β clamp . Using this approach , we asked whether Pol IV-T120P was likewise able to displace Pol III from the β clamp , as inferred by a reduction in its processivity . As summarized in Fig 7B , a 60-fold molar excess of Pol IV-T120P over Pol III was as efficient as wild type Pol IV at inhibiting Pol III processivity . Together , these results suggest one of two possibilities: either ( i ) the T120P mutation does not impact the ability of Pol IV to displace Pol III from β clamp , or ( ii ) efficient recruitment of Pol IV to the Pol III-β clamp complex through its interactions with β clamp masks the Pol IV-T120P-dependent defect in Pol IV displacement of Pol III from β clamp . To distinguish between these two models , we analyzed the ability of Pol IVCD and Pol IVCD-T120P , both of which lack the Pol IVLF β clamp-binding domain , to impede Pol III processivity using the same single molecule assay . Furukohri and colleagues previously demonstrated that a ~900- to 1 , 800-fold molar excess of Pol IVCD over Pol III ( 890 nM Pol IV compared to 0 . 5–1 . 0 nM Pol III ) was able to disrupt the Pol III-β clamp complex assembled in vitro on a primed DNA substrate [16] . Similarly , we found that 900 nM Pol IVCD ( a 180-fold molar excess over Pol III ) disrupted Pol III synthesis , reducing its processivity to one-half of that observed in the absence of Pol IVCD ( Fig 7C , p <0 . 01 , determined using the Wilcoxon rank-sum test ) . This reduction in Pol III processivity most likely results from the ability of Pol IVCD to displace Pol III from the β clamp assembled on DNA [12 , 16 , 45] . Importantly , an equivalent concentration of Pol IVCD-T120P failed to reduce processivity of Pol III . Taken together , these findings support the view that residue T120 of Pol IV plays an important role in displacing Pol III from the β clamp , and demonstrate that the Pol IVLF domain contributes to this ability . Finally , these biochemical results are remarkably similar to the growth phenotypes observed for strains overproducing Pol IVCD-T120P ( pDB23 ) or full length Pol IV-T120P ( pDB33 ) from the arabinose promoter ( Fig 2 ) , which demonstrate the ability of the Pol IVLF domain to mask the phenotype of the T120P mutation in vivo .
With the goal of gaining new insights into the relationship between the physiological function of Pol IV in TLS and its ability when overexpressed to impede E . coli growth , we exploited the hypersensitivity of the dnaN159 strain to elevated levels of Pol IV to identify 13 novel Pol IV mutants that were unable to impede growth ( Table 2 ) . These Pol IV mutants were deficient in stimulating reversion of the CC108 lacZ –1 frameshift reporter when expressed at SOS-induced levels ( S2 Fig ) , and for conferring UV sensitivity upon the dnaN159 strain ( S3 Fig ) , indicating that they were unable to effectively compete with Pol III for access to the replication fork . Likewise , these mutations failed to impede growth of the dnaN+ strain when introduced into Pol IVCD and overproduced from the arabinose promoter ( Fig 2 ) . Finally , despite the fact that all but one of the mutant Pol IV proteins ( Pol IV-D10G ) retained appreciable catalytic activity in vitro ( Fig 3 ) , they were nevertheless impaired for tolerating MMS-induced lesions in vivo ( Fig 4 ) . These results , taken together with those discussed below , support the view that overexpressed levels of Pol IV impede E . coli growth by actively replacing Pol III at the replication fork via a mechanism that is similar to that used under physiological conditions to coordinate high fidelity processive Pol III replication with potentially mutagenic Pol IV TLS . In contrast to an earlier study [47] , we failed to observe an ability of overproduced levels of Pol IV to mediate cell death in either the dnaN159 or dnaN+ strains via excessive incorporation of oxidized precursors ( S2 Table and S4 Fig ) . Thus , Pol IV appears to be able to impede E . coli growth by either incorporating lethal levels of 8-oxo-dG or by displacing Pol III . This view is consistent with the finding that under the conditions used in this study lethality was independent of Pol IV catalytic activity [38 , 45] . However , our finding that several Pol IV mutants identified in this work were impaired for catalytic activity in vitro ( Fig 3 ) suggests that the ability of Pol IV to replace Pol III at the replication fork is dependent at least in part on residues in Pol IV that contribute to catalytic activity . Alternatively , the ability of Pol IV to persist at the replication fork after replacing Pol III likely contributes to the killing , and would rely on Pol IV processivity , which , with the exception of Pol IV-T120P , was impaired in the Pol IV mutants analyzed here . The Pol IV-T120P mutant was remarkable in that it was comparable to wild type Pol IV for replication of undamaged DNA in vitro , as well as for catalyzing bypass of 3d-medA , O6-mdG and an AP site ( Fig 3 and Table 3 ) , yet it was nevertheless unable to tolerate MMS-induced DNA lesions in vivo ( Figs 4–6 ) , presumably due to its inability to access these lesions . Consistent with this conclusion , the T120P mutation abrogated the ability of Pol IVCD to inhibit Pol III processivity in vitro ( Fig 7 ) . Based on previously published results [12 , 16 , 45] , inhibition of Pol III processivity is the result of Pol IV displacing Pol III from the face of the β clamp . Our finding that the Pol IV-T120P strain was impaired for tolerating MMS-induced DNA damage indicates that the ability of Pol IV to inhibit Pol III processivity is required for the TLS function of Pol IV in vivo . Furthermore , our finding that the strain expressing Pol IV-T120P ( dinB89 ) was almost as deficient as the isogenic ΔdinB , Pol IV-D103N ( dinB80 ) and Pol IVC ( dinB81 ) strains for tolerating MMS-induced lesions indicates that the ability of Pol IV to displace Pol III from the face of the β clamp is critical to Pol IV TLS function in vivo ( Figs 5 and 6 ) . TLS has been suggested to take place at either the replication fork via a Pol switch [3 , 12 , 15 , 17 , 18 , 20] , or in ssDNA gaps generated in part by Pol III skipping over DNA lesions to continue replication downstream of the blockage [18 , 39] . However , to date , the extent to which these two mechanisms are used in vivo was unknown . Our finding that the T120P mutation specifically interferes with the ability of Pol IV to switch with Pol III , taken together with its inability to cope with MMS-induced DNA damage in vivo ( Figs 4–6 ) , suggests that a significant fraction of Pol IV-mediated TLS in vivo relies on a Pol III-Pol IV switch . Consistent with this conclusion , SOS-induced levels of Pol IV slowed the rate of DNA replication in vivo by ~12% , arguing that Pol IV frequently replaces Pol III at the replication fork following SOS induction [14] , possibly via a Pol III-Pol IV switch . While it remains to be determined whether the reduced rate of replication in response to SOS induction represents a biologically important checkpoint effector function of Pol IV , as previously suggested [13 , 45] , the fact that strains lacking Pol IV function fail to exhibit enhanced sensitivity to agents that generate classes of DNA lesions other than those that Pol IV is capable of bypassing , such as UV photoproducts [60] , argues against such a model . Finally , Benson and colleagues [61] identified two Pol IV mutants ( V7G and F292Y ) based on their inability to impede growth of an E . coli strain expressing a mutant Pol IIIα allele ( dnaE915 ) . Both of these mutants retained the ability to bypass 3d-medA in vitro , but their abilities to cope with MMS-induced DNA damage in vivo was not examined . While V7 is in close proximity to T120 ( S6A Fig ) , neither it nor F292 is surface exposed ( S6B Fig ) , suggesting that these mutations may affect the structure of Pol IV . Regardless , it is possible that the V7G and/or F292Y mutations impair the Pol III-Pol IV switch . We previously described results supporting an important role for the Pol IV-β clamp rim interaction in mediating the Pol III-Pol IV switch in vitro [17 , 38] . In contrast to our findings , Gabbai and colleagues , utilizing a different assay that may more accurately represent the structure and composition of the replisome , concluded that the Pol IV-β clamp rim contact stimulated , but was not required for a Pol III-Pol IV switch in vitro [18] . In light of this finding , they suggested that direct competition between Pol III and Pol IV for the β clamp cleft represented an alternative mechanism for their switching . Previous efforts to define the role of the β clamp rim in Pol IV function in vivo utilized multi-copy plasmids expressing higher than physiological levels of Pol IVR and Pol IVC to complement the NFZ-sensitive phenotype of a ΔdinB strain [17 , 62] . Under these conditions , the Pol IVR strain was indistinguishable from the wild type Pol IV strain , suggesting the Pol IV-β clamp rim interaction was dispensable for Pol IV function in vivo . However , using strains expressing the Pol IVC or Pol IVR mutants from the chromosomal dinB locus , we confirmed an essential role for the β clamp cleft in Pol IV TLS , and provide compelling evidence for a biologically important role for the β clamp rim in contributing to Pol IV TLS ( Figs 4–6 ) . These findings , taken together with those discussed above regarding the T120P mutation , support the model that Pol IV TLS function in vivo relies on its ability to bind to both the rim and cleft of the β clamp , as well as its ability to inhibit Pol III processivity , possibly via a direct interaction of Pol IV with one or more subunits of Pol III . Since both the Pol IV-β clamp and the postulated Pol IV-Pol III interactions are required for Pol IV TLS function in vivo , it is conceivable that the postulated Pol III-Pol IV interaction could act to relax the requirement for the Pol IV-β clamp rim interaction in vitro , potentially explaining the apparent discrepancy between our published results and those of Gabbai and colleagues regarding the importance of the β clamp rim to the Pol III-Pol IV switch in vitro . Pol IV interacts physically with both UmuD and RecA [63–65] . These interactions are proposed to improve the fidelity of Pol IV by enclosing its open active site [64] . Interestingly , Pol IV-C66A was previously reported to bind more tightly to both RecA and UmuD [65] . It is conceivable that we identified the Pol IV-C66S with our genetic assay because it was affected for interactions with UmuD and/or RecA . Alternatively , we may have identified Pol IV-C66S due to its reduced stability ( [65]; see S1 Fig ) . In addition to C66 , residues P166 , F172 and L176 of Pol IV have also been demonstrated to interact with UmuD [64] . In contrast to C66 , these residues are in close proximity to T120 ( S6 Fig , panels C and D ) . Thus , it is possible that the T120P mutation also affects an interaction of Pol IV with UmuD . Finally , UmuD may contribute to the ability of Pol IV to switch with Pol III . Consistent with this possibility , UmuD interacts physically with the Pol IIIα and Pol IIIε subunits , as well as the β clamp [21 , 22 , 66] . However , the failure of Pol IVCD-T120P to impede Pol III processivity in vitro was independent of UmuD ( Fig 7C ) . Results discussed in this report provide several new insights into the mechanism by which the actions of Pol III are coordinately regulated with those of Pol IV , and when taken together with previously published findings [17 , 33 , 38 , 67] , support a new model for the role of Pol III-Pol IV switching in Pol IV-mediated TLS in vivo . In addition to its interactions with the β clamp , an interaction of Pol IV with Pol III also appears to play a biologically important role in recruiting Pol IV to lesions ( Figs 5 and 6 ) . Biochemical interaction of Pol IVCD with Pol III holoenzyme is sufficient to mediate displacement of Pol III from the face of the β clamp in vitro [12 , 13 , 16 , 45] . Our single molecule assay reproduced this finding , and further demonstrated that position T120 of Pol IV is important for this function ( Fig 7 ) . Residue T120 is one helical turn from the start of α-helix 5 ( see Fig 8 ) and its substitution with proline likely truncates the start of this helix . Thus , residue T120 , and/or residues in its vicinity , presumably mediates a physical interaction with one or more subunits of the Pol III holoenzyme . Both the α catalytic and the ε proofreading subunits of Pol III contain a CBM that interacts with the β clamp cleft [68 , 69] . Although the Pol III holoenzyme binds both β clamp clefts , a single β clamp cleft is sufficient to support processive Pol III replication , as well as Pol III-Pol IV switching [17 , 36 , 67] . Since the Pol IIIα-β clamp cleft interaction is required for Pol III function both in vitro and in vivo , while the Pol IIIε-β clamp cleft appears to be dispensable [17 , 29 , 36 , 67 , 69] , we suggest that a mechanism by which Pol IV initiates a switch with a stalled Pol III involves Pol IV first binding to the β clamp rim adjacent to the β clamp cleft that is bound by Pol IIIα . Based on an in silico model of the Pol III-β clamp-Pol IV complex , residue T120 of Pol IV is well positioned to contact Pol IIIα ( Fig 8A ) , but not Pol IIIε ( Fig 8B ) . Thus , Pol IV may be recruited to the replication fork through a combination of the Pol IVLF-β clamp rim and Pol IVCD-Pol III interactions . The Pol IVLF-β clamp rim interaction is likely too weak ( ~1 . 3 μM ) on its own to recruit Pol IV to the replisome in the absence of SOS-induction when Pol IV levels are ~330 nM [3 , 70] . However , if a Pol IV-Pol III interaction contributes to Pol IV recruitment , the affinity of Pol IV for the replisome may be sufficiently high to enable a Pol III-Pol IV switch irrespective of SOS-induction , which increases Pol IV levels from ~330 nM to ~3 . 3 μM [3 , 70] . Pol IV can only switch with a stalled Pol III [15 , 38] , suggesting that a conformational change in Pol IIIα contributes to the Pol III-Pol IV switch , possibly by unmasking a surface of Pol III that interacts with Pol IV leading to displacement of Pol III . It is currently unclear whether Pol IV is recruited to the replication fork in response to a specific conformation of the stalled Pol III replisome , or whether it is recruited through one or more Pol III-Pol IV interactions that are independent of a stalled Pol III . If a stalled Pol III replisome acts to recruit Pol IV , then a single Pol III-Pol IV interaction may be sufficient for both recruitment and Pol III displacement . However , if an actively replicating Pol III replisome recruits Pol IV irrespective of Pol III stalling , then distinct Pol III-Pol IV contacts would seemingly be required for Pol IV recruitment and Pol III-Pol IV switching . Finally , irrespective of the fact that the Pol IV-β clamp rim interaction is required for Pol IV-mediated TLS in vivo ( Figs 5 and 6 ) , the finding that Pol IVCD displaced Pol III from the face of the β clamp ( [16]; see Fig 7 ) suggests that Pol IV may not have to simultaneously bind the β clamp rim as well as one or more subunits of Pol III in order to displace Pol III from the face of the β clamp . Irrespective of the mechanism , once Pol III is displaced , the C-terminal 6 residues of Pol IV are able to bind the cleft of the β clamp that was previously bound by Pol III , ultimately granting control of both the β clamp and the replication fork to Pol IV for TLS ( Fig 8C ) . Although Pol IV displaces Pol III from the β clamp [12 , 16 , 45] , we suggest that Pol IIIα-DnaXτ-DnaB interactions act to retain Pol III within the replisome complex until such time as Pol IV relinquishes its control of the DNA template [71] , allowing Pol III to regain control of the replication fork after Pol IV leaves . However , when Pol IV is overexpressed , or when the replisome contains the mutant dnaN159-encoded β clamp protein , Pol IV repeatedly replaces Pol III on the clamp face . This repeated replacement could act to displace Pol III from the replisome , explaining the lethal phenotype observed for strains overexpressing Pol IV . While the Pol IV-β clamp rim interaction is presumably dispensable once Pol IV gains access to the β clamp cleft [17] , processive Pol IV replication requires contact with residues 148HQDVR152 of β clamp [33] . Inasmuch as residues H148 , Q149 and R152 of β clamp interact with the DNA template that it encircles [33 , 72] , Pol IV may have to compete with DNA to gain access to 148HQDVR152 of β clamp , which , in turn , may act to reposition the β clamp on the DNA , possibly exposing additional surfaces on the β clamp that stabilize the Pol IV-β clamp complex , and/or enhance its catalytic activity . Finally , our finding that the Pol IVR , Pol IVC and Pol IV-T120P mutant strains were severely impaired for tolerating MMS-induced DNA damage is consistent with the view that Pol III-Pol IV switching plays a pivotal role in regulating access of Pol IV to the DNA in vivo . Further studies are required to determine how long Pol IV maintains control of the replication fork after switching with Pol III , as well as whether Pol III and/or other factors play a role in displacing Pol IV from the replication fork .
Bacteria were cultured in either Luria Bertani ( LB; 10 g/l tryptone , 5 g/l yeast extract , 10 g/l NaCl ) , or M9 minimal medium ( 12 . 9 g/L Na2HPO4•7H2O , 3 g/L KH2PO4 , 0 . 5 g/L NaCl , 1 g/L NH4Cl ) supplemented with 0 . 1 mM CaCl2 , 2 mM MgCl2 , 5 μg/ml thiamine , 0 . 5% casamino acids and 0 . 5% glucose or 0 . 2% arabinose , as indicated . For anaerobic growth , 100 mM KNO3 was added to the growth to act as the terminal electron acceptor . When required , the following antibiotics were used at the indicated concentrations: ampicillin ( Amp ) , 150 μg/ml; tetracycline ( Tet ) , 10 μg/ml; kanamycin ( Kan ) , 40 μg/ml; chloramphenicol ( Cam ) , 20 μg/ml; rifampicin ( Rif ) , 50 μg/ml . E . coli strains were constructed using P1vir-mediated generalized transduction [73] , or λRed-mediated recombineering [74] , and are described in Table 1 . Strain genotypes were verified using either diagnostic PCR or nucleotide sequence analysis ( Roswell Park Biopolymer Facility , Buffalo , NY ) of respective PCR-amplified alleles . Strains were made competent for transformation using CaCl2 as described [48] . Bacterial plasmid transformation frequency [38] , UV sensitivity [38 , 48] and lacZ→Lac+ reversion [51 , 52 , 56] was measured as described in the indicated references . Plasmid DNAs are described in Table 1 . Standard techniques were used for cloning . Site-directed mutagenesis was performed using the Quickchange kit ( Stratagene ) . Synthetic oligonucleotide primers used for mutagenesis were purchased from either IDT or Operon , and their sequences are presented in S3 Table . All plasmid sequences were confirmed by nucleotide sequence ( Roswell Park Biopolymer Facility , Buffalo , NY ) . Mutant dinB alleles were subcloned from pWSK29 into pET11a ( Novagen ) by NdeI and BamHI ( Fermentas ) restriction , followed by ligation to the similarly prepared pET11a backbone using T4 DNA ligase ( Fermentas ) . Sensitivity to MMS ( Sigma ) was measured as described [41] . Spontaneous dinB mutations unable to impede growth of the dnaN159 lexA51 ( Def ) E . coli strain MS105 were identified by selecting AmpR transformants using 200 ng of plasmid pJH110 . This strain displayed a transformation efficiency of ~106 colony forming units/μg of supercoiled pWSK29 plasmid DNA . In instances where multiple colonies were obtained from a single transformation reaction , a single CFU was selected from the plate for further analysis to avoid sibling mutations . Plasmids were isolated using the Qiagen mini-prep kit as per the manufacturer’s recommendation . Purified plasmids were analyzed by agarose gel electrophoresis . Those of the appropriate size were retransformed into MS105 to verify their inability to impede growth . Those that transformed MS105 with an efficiency similar to that of the pWSK29 control plasmid were then analyzed by Western blotting using polyclonal anti-Pol IV antibodies as described [4 , 17] . Strains MKS103-MKS107 were constructed using λ recombineering as described [74] . Briefly , the 2 , 329 bp lafU’ zaf-3633::cat dinB80 yafN’ DNA cassette was PCR amplified from plasmid pMKS100 , pMKS101 , pMKS102 , pMKS103 or pMKS104 using primers P1 and P4 ( S3 Table ) , and electroporated into E . coli strain MG1655 containing pKD46 as described [75] . Chloramphenicol resistant colonies were selected on LB agar plates supplemented with chloramphenicol , and subsequently confirmed to contain the desired dinB allele by diagnostic PCR using primers MKS055 and MKS046 , which anneal 589 bp upstream of primer P1 and 498 bp downstream of primer P4 , respectively . The remaining primers listed in S3 Table were used for nucleotide sequence verification of the lafU’–zaf-3633::cat–dinB–yafN’ cassette from 91 bp upstream of the start of primer P1 to 163 bp downstream from the end of primer P4 , except for a 153 bp internal segment of the cat gene corresponding to amino acid residues L45-D96 , prior to using P1vir to transduce the linked zaf-3633::cat and dinB alleles into a fresh isolate of strain MG1655 . Cultures of LB media ( mock samples to measure spontaneous mutagenesis ) and LB media containing the indicated concentration of freshly added MMS ( 1 . 5 mM or 2 . 0 mM ) were inoculated with 200 μl of an exponential culture ( OD600 ~0 . 5 ) of the indicated strain . Cultures were incubated overnight at 37°C with aeration before plating appropriate dilutions onto LB media with or without Rif . Plates were incubated overnight at 37°C before counting colonies . MMS-induced mutation frequency was calculated as described [33] . Wild type and Pol IV mutant proteins [34] , SSB [76] , the γ3δδ’ form of the DnaX clamp loader [36] and β clamp [77] were purified as described in the indicated references . Primer extension assays were performed as described previously [17 , 34 , 56] using the 32P-radiolabeled PAGE purified 30-mer/100-mer DNA template . Briefly , reactions ( 20 μl ) contained replication buffer ( 20 mM Tris-HCl [pH 7 . 5] , 8 . 0 mM MgCl2 , 0 . 1 mM EDTA , 5 mM DTT , 1 mM ATP , 5% glycerol , and 0 . 8 μg/ml BSA ) , 1 nM 30-mer/100-mer template , 133 μM dNTPs ( Fermentas ) , 90 nM SSB , 1 nM γ3δδ’ DnaX clamp loader complex , 10 nM β clamp and 1 nM Pol IV . The reactions were pre-incubated for 3 min at 37°C to permit loading of β clamp prior to initiating replication by addition of dNTPs . Reactions were next incubated at 37°C for 5 min , then quenched by the addition of 25 mM EDTA and incubation at 95°C for 3 minutes . Aliquots of each reaction were then electrophoresed through an 8% UREA-PAGE at 60 watts for 3 , 332 volt hours , as described [56] . Replication products were visualized using a Bio-Rad imaging screen K and a Bio-Rad Personal Molecular Imager FX . Kinetic studies using wild type Pol IV or Pol IV-T120P were performed at 25°C in assay buffer ( 25 mM TrisOAc [pH 7 . 5] , 150 mM KOAc , 10 mM β-mercaptoethanol , 1 mg/ml bovine serum albumin , and 10 mM MgCl2 ) . The kinetic parameters ( kpol , Kd , and kpol/Kd ) for nucleotides were measured as previously described [78] . Briefly , a typical assay was performed by pre-incubating DNA substrate ( 200 nM ) with a 2-fold molar excess of DNA polymerase ( 400 nM ) in assay buffer . Reactions were initiated by adding variable concentrations of nucleotide substrate ( 1–500 μM ) . At variable times , 5 μl aliquots of the reaction were removed and immediately quenched by adding an equal volume of 200 mM EDTA . Polymerization reactions were monitored by electrophoresis through 20% sequencing gels as described [79] . Gel images were obtained with a Packard PhosphorImager by using the OptiQuant software supplied by the manufacturer . Product formation was quantified by measuring the ratio of 32P-labeled extended and un-extended primer . This ratio was corrected for substrate in the absence of polymerase ( zero point ) . Corrected ratios were multiplied by the concentration of primer/template used in the assay to yield total product . Observed rate constants were obtained using the following equation: y = A* ( 1-ekobs*t ) +C , where A is the burst amplitude in product formation , kobs is the observed rate constant of the reaction , t is the time , and C is the end-point in product formation . Data for the dependency of rate constant as a function of nucleotide concentration were fit to the Michaelis-Menten equation: kobs = kpol* ( dNTP ) / ( Kd+[dNTP] ) , where kobs is the rate constant of the reaction ( s−1 ) , kpol is the maximal rate constant of polymerization , Kd is the apparent dissociation constant for dNTP , and [dNTP] is the concentration of nucleotide substrate . Primer extension by Pol III and Pol IV was observed on single DNA molecules within custom microfluidic flow cells , as previously described [12] . Briefly , primed , single-stranded DNA substrates were derived from 7 . 2 kb phage M13mp18 DNA ( New England Biolabs ) end-labeled with digoxigenin and biotin . DNAs were bound to the streptavidin-coated flow cell surface on one end , and to anti-digoxigenin-coupled 2 . 8 μm-diameter beads on the other . Laminar flow through the flow cell exerted a constant ~3 pN force on the bead , and , by extension , uniformly throughout the tether . Conversion of entropically coiled ssDNA to extended dsDNA by a Pol at this constant force was observed as motion of the bead using dark-field microscopy . Bead positions were tracked by fitting beads to 2D Gaussians , and their motions were converted into DNA synthesis as a function of time . Resolution is determined by thermal fluctuations of the tethered bead ( σ ~70 bp ) and the choice of exposure time ( 0 . 5 s ) . All experiments were performed in replication buffer ( 50 mM Hepes-KOH [pH 7 . 9] , 12 mM Mg[OAc]2 , 80 mM KCl , 0 . 1 mg/ml BSA , 5 mM DTT ) supplemented with 5 nM Pol IIIαεθ , 30 nM β , 15 nM of the τ3δδ’ψχ form of the DnaX clamp loader complex , 760 μM dNTPs and 1 mM ATP . Pol IV , Pol IV-T120P , Pol IVCD and Pol IVCD-T120P were additionally included at the indicated concentrations . A cutoff of 45 bp/s was used to distinguish Pol III ( faster ) from Pol IV ( slower ) events . This cutoff captured ~95% of events in experiments with each polymerase alone . The Pol III replisome components used in the single molecule experiments were purified as previously described: β [80]; α , δ and δ’ [81]; ε and θ [82]; and τ and χψ [83] . The Pol III core αεθ and the clamp loader assembly with stoichiometry τ3δδ’χψ were then assembled and purified [83] .
|
Bacterial DNA polymerase IV ( Pol IV ) is capable of replicating damaged DNA via a process termed translesion DNA synthesis ( TLS ) . Pol IV-mediated TLS can be accurate or error-prone , depending on the type of DNA damage . Errors made by Pol IV contribute to antibiotic resistance and adaptation of bacterial pathogens . In addition to catalyzing TLS , overproduction of Escherichia coli Pol IV impedes growth . In the current work , we demonstrate that both of these functions rely on the ability of Pol IV to bind the β sliding processivity clamp and switch places on DNA with the replicative Pol , Pol III . This switch requires that Pol IV contact both Pol III as well as two discrete sites on the β clamp protein . Taken together , these results provide a deeper understanding of how E . coli manages the actions of Pol III and Pol IV to coordinate high fidelity replication with potentially error-prone TLS .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
A Genetic Selection for dinB Mutants Reveals an Interaction between DNA Polymerase IV and the Replicative Polymerase That Is Required for Translesion Synthesis
|
Convergent phenotypic evolution is often caused by recurrent changes at particular nodes in the underlying gene regulatory networks ( GRNs ) . The genes at such evolutionary ‘hotspots’ are thought to maximally affect the phenotype with minimal pleiotropic consequences . This has led to the suggestion that if a GRN is understood in sufficient detail , the path of evolution may be predictable . The repeated evolutionary loss of larval trichomes among Drosophila species is caused by the loss of shavenbaby ( svb ) expression . svb is also required for development of leg trichomes , but the evolutionary gain of trichomes in the ‘naked valley’ on T2 femurs in Drosophila melanogaster is caused by reduced microRNA-92a ( miR-92a ) expression rather than changes in svb . We compared the expression and function of components between the larval and leg trichome GRNs to investigate why the genetic basis of trichome pattern evolution differs in these developmental contexts . We found key differences between the two networks in both the genes employed , and in the regulation and function of common genes . These differences in the GRNs reveal why mutations in svb are unlikely to contribute to leg trichome evolution and how instead miR-92a represents the key evolutionary switch in this context . Our work shows that variability in GRNs across different developmental contexts , as well as whether a morphological feature is lost versus gained , influence the nodes at which a GRN evolves to cause morphological change . Therefore , our findings have important implications for understanding the pathways and predictability of evolution .
A major challenge in biology is to understand the relationship between genotype and phenotype , and how genetic changes modify development to generate phenotypic diversification . The genetic basis of many phenotypic differences within and among species have been identified [e . g . 1–15] , and these findings support the generally accepted hypothesis that morphological evolution is predominantly caused by mutations affecting cis-regulatory modules of developmental genes [16] . Moreover , it has been found that changes in the same genes commonly underlie the convergent evolution of traits [reviewed in 17] . This suggests that there are evolutionary ‘hotspots’ in GRNs: changes at particular nodes are repeatedly used during evolution because of the role and position of the gene in the GRN , and hence the limited pleiotropic effect of the change [18–21] . The regulation of trichome patterning is an excellent system for studying the genetic basis of morphological evolution [22] . Trichomes are actin protrusions from epidermal cells that are overlaid by cuticle and form short , non-sensory , hair-like structures . They can be found on various parts of insect bodies during different life stages , and are thought to be involved in , for example , thermo-regulation , aerodynamics , oxygen retention in semi-aquatic insects , grooming , and larval locomotion [23–27] ( Fig 1 ) . The GRN underlying trichome formation on the larval cuticle of Drosophila species has been characterised in great detail [reviewed in 21 , 22 , 28] ( Fig 1 ) . Several upstream transcription factors , signalling pathways , and tarsal-less ( tal ) -mediated post-translational proteolytic processing , lead to the activation of the key regulatory transcription factor Shavenbaby ( Svb ) , which , with SoxNeuro ( SoxN ) , activates a battery of downstream effector genes [29–37] . These downstream factors modulate cell shape changes , actin polymerisation , or cuticle segregation , which underlie the actual formation of trichomes [29 , 33] . Importantly , ectopic activation of svb during embryogenesis is sufficient to drive trichome development on otherwise naked larval cuticle , and loss of svb function leads to a loss of larval trichomes [38] . Regions of dorso-lateral larval trichomes have been independently lost at least four times among Drosophila species [39 , 40] . Recombination mapping and functional studies have shown that in all cases analysed , this phenotypic change is caused by changes in several svb enhancers , resulting in a loss of svb expression [6 , 10 , 39–42] . The modular enhancers of svb are thought to allow the accumulation of mutations that facilitate the loss of larval trichomes in certain regions without deleterious pleiotropic consequences . It is thought that evolutionary changes in larval trichome patterns cannot be achieved by mutations in genes upstream of svb because of deleterious pleiotropic effects , while changes in individual svb target genes would only affect trichome morphology rather than their presence or absence [19–21 , 29 , 33] . Given the position and function of svb in the larval trichome GRN , these data suggest that svb is a hotspot for the evolution of trichome patterns more generally because it is also required for the formation of trichomes on adult epidermis and can induce ectopic trichomes on wings when over expressed [38 , 43] . Therefore , one could predict that changes in adult trichome patterns are similarly achieved through changes in svb enhancers [20 , 21] . The trichome pattern on femurs of second legs also varies within and between Drosophila species [1 , 44] ( Fig 1 ) . In D . melanogaster , an area of trichome-free cuticle or ‘naked valley’ varies in size among strains from small to larger naked regions . Other species of the D . melanogaster species subgroup only exhibit larger naked valleys [1 , 44] . Therefore , trichomes have been gained at the expense of naked cuticle in some strains of D . melanogaster . Differences in naked valley size between species have previously been associated with differences in the expression of Ultrabithorax ( Ubx ) , which represses the formation of leg trichomes [44] . However , genetic mapping experiments and expression analysis have shown that naked valley size variation among populations of D . melanogaster is caused by cis-regulatory changes in miR-92a [1] . This microRNA represses trichome formation by repressing the svb target gene shavenoid ( sha ) , and D . melanogaster strains with small and large naked valleys exhibit weaker or stronger miR-92a expression , respectively , in developing femurs [1 , 45] . Therefore , while svb is thought to be a hotspot for the evolutionary loss of patches of larval trichomes , it does not appear to underlie the evolutionary gain of leg trichomes in D . melanogaster . Differences in GRN architecture among developmental contexts may affect which nodes can evolve to facilitate phenotypic change in different tissues or developmental stages . In addition , an evolutionary gain or loss of a phenotype may also result from changes at different nodes in the underlying GRN , i . e . alteration of a particular gene may allow the loss of a trait but changes in the same gene may not necessarily result in the gain of the same trait . Therefore , a better understanding of the genetic basis of phenotypic change and evaluation of the predictability of evolution require characterising the expression and function of GRN components in different developmental contexts , and studying how the loss versus the gain of a trait is achieved . Here we report our comparison of the regulation of trichome development in legs versus embryos . Our results reveal differences in expression and function of key components of the GRN between these two developmental contexts . These differences indicate that svb is likely unable to act as a switch for the gain of leg trichomes because it is already expressed throughout the legs in both naked and trichome-producing cells . Instead , regulation of sha by miR-92a appears to act as the switch between naked and trichome-producing cells in the leg . This shows that differences in GRNs between different developmental contexts can affect the pathway used by evolution to generate phenotypic change .
The embryonic expression , regulation , and function of many genes involved in larval trichome formation is well understood [for example , see 29 , 32–38 , 42 , 46] ( Fig 1A ) . To characterise the regulation of leg trichome development better we first carried out RNA-Seq of T2 pupal legs between 20 and 28 hours after puparium formation ( hAPF ) : the window when leg trichomes are specified [44] ( S1–S6 Files ) . We tested if genes known to play a role during larval trichome formation are also expressed in our samples and used a cut-off of 1 fragment per kilobase per million ( FPKM ) reads mapped to determine if a gene is most likely expressed or not . Note that we chose not to compare the actual expression levels of trichome genes in our leg data sets with those of previously published expression data for embryos because it is difficult to interpret what any quantitative differences in overall expression levels between these two heterogeneous mixtures of cells might mean with respect to trichome development . We found that key genes known to be involved in larval trichome formation are expressed in legs . These include Ubx , SoxN , tal , svb , and sha , as well as key components of the Delta-Notch , Wnt and EGF signalling pathways ( Fig 1 , and S1 Table ) . However , expression of several genes known to regulate larval trichome development [29 , 33 , 36] is barely detectable in legs ( i . e . below or around 1 FPKM ) . These include Dichaete , Arrowhead , and abrupt , which are also known to regulate svb expression during larval trichome development [34 , 42] ( Fig 1 and S1 Table ) . Furthermore , the expression of 28 of the 163 known targets of svb in embryos [29 , 33] is barely detectable in our dataset ( FPKM at or below 1 ) ( S2 Table ) . In addition , 12 out of the 43 genes thought to be involved in larval trichome formation independently of svb [33 , 36] are also expressed at levels of less than 1 FPKM in legs ( S3 Table ) . Therefore , our RNA-Seq data evidence key differences in both upstream and downstream components of the leg trichome GRN when comparing it to what is known for the embryonic GRN that specifies larval trichomes . Our leg RNA-Seq data also allowed us to compare expression between strains of D . melanogaster with different sizes of naked valley: Oregon R ( OreR ) which has a small naked valley and ebony4 , white ocelli1 , rough1 ( eworo ) which has a large naked valley ( Fig 1 ) . The size of the naked valley in these two strains is caused by differential expression of miR-92a [1] . We found that none of the known regulators of svb are differentially expressed between these two strains . In addition , we did not detect any significant differences in the expression of svb itself or most of its target genes including sha ( S1 , S2 and S3 Tables ) . However , we did find a trend towards higher expression of jing interacting gene regulatory 1 ( jigr1 ) in the large naked valley strain eworo , although this difference is not significant after p value correction for false discovery rate ( FDR ) ( S1 Table ) . Interestingly , miR-92a is co-expressed with jigr1 during neuroblast self-renewal [47] and it is located in one of its introns . Therefore higher expression of miR-92a may be indirectly detectable in eworo ( S1 Table ) . These results are consistent with miR-92a-mediated post-transcriptional regulation causing differences in naked valley size , and since this only occurs in a small proportion of leg cells , the effect on transcripts is likely to be difficult to detect using RNA-Seq . We next further examined the function of specific genes during leg trichome development compared to their roles in the formation of larval trichomes . It was previously shown that mutants of miR-92a have small naked valleys [48] , which is consistent with the evolution of this locus underlying natural variation in naked valley size [1] . We confirmed these findings using a double mutant for miR-92a and its paralogue miR-92b [47] , which exhibits an even smaller naked valley ( Fig 2 ) . Note that we did not detect any changes to the larval trichome pattern in these mutants compared to heterozygotes . We examined the morphology of the proximal leg trichomes gained from the loss of miR-92a compared to the trichomes found more distally . We found that the trichomes gained were indistinguishable from the other leg trichomes ( S1 Fig ) . This suggests that all of the genes required to generate leg trichomes are already transcribed in naked valley cells , but that miR-92a must be sufficient to block their translation . Indeed , we found that the extra trichomes that develop in the naked valley in the absence of miR-92a are dependent on svb because in a svb mutant background no trichomes are gained after loss of miR-92a ( Fig 2 ) . Furthermore , these results also show that trichome repression by Ubx in the naked valley requires miR-92a because trichomes in the miR-92a mutant develop in the region where Ubx is expressed [44] . Thus , our data confirm that Ubx plays opposite roles in the larval and leg trichome GRNs: in embryos Ubx activates svb to generate larval trichomes [46] , while we show that Ubx-mediated repression of leg trichomes [44 , 49] depends on miR-92a ( Figs 1 and 2 ) . The results above suggest that svb is expressed in the naked valley but is unable to induce the formation of trichomes because of the presence of miR-92a . To test this further we examined the expression of svb transcripts in pupal T2 legs using in situ hybridization . However , this method produced inconsistent results among legs and it was difficult to distinguish between signal and background in the femur . Therefore we examined the expression of a nuclear localising GFP inserted into a BAC containing the entire svb cis-regulatory region , which was previously shown to reliably capture the expression of this gene [43] . We detected GFP throughout T2 legs at 24 hAPF including in the proximal region of the posterior femur ( S2 Fig ) . This indicates that svb is expressed in naked valley cells that do not produce trichomes as well as in more distal trichome-producing cells . We next investigated the regulatory sequences responsible for svb expression in T2 legs . To do this we carried out ATAC-Seq [50 , 51] on chromatin from T2 legs during the window of 20 to 28 hAPF when leg trichomes are specified [44] . Embryonic expression of svb underlying larval trichomes is regulated by several enhancers spanning a region of approximately 90 kb upstream of the transcription start site of this gene [5 , 10] ( Fig 3 ) . Several of these larval enhancers also drive reporter gene expression during pupal development [43] . We observed that the embryonic enhancers DG3 , E and 7 contained regions of open chromatin according to our T2 leg ATAC-Seq data . However , we found additional accessible chromatin regions that do not overlap with known svb embryonic enhancers ( Fig 3 ) . Deletion of a region including the embryonic enhancers DG2 and DG3 [Df ( X ) svb108] results in a reduction in the number of dorso-lateral larval trichomes when in a sensitized genetic background or at extreme temperatures [5] . Moreover , Preger-Ben Noon and colleagues [43] recently showed that this deletion , as well as a larger deletion that also removes embryonic enhancer A ( [Df ( X ) svb106] , see Fig 3 ) , results in the loss of trichomes on abdominal segment A5 , specifically in males . We found several peaks of open chromatin in the regions covered by these two deficiencies in our ATAC-seq dataset ( Fig 3 ) and therefore tested the effect of Df ( X ) svb106 on leg trichome development . We found that deletion of this region and consequently enhancers DG2 , DG3 , Z and A did not affect the size of the naked valley or the density of trichomes on the femur or other leg segments of flies raised at 17°C , 25°C , or 29°C ( compared to the parental lines ) ( S3 Fig ) . This suggests that while this region may contribute to svb expression in legs , its removal does not perturb the robustness of leg trichome patterning . Next , to try to identify enhancer ( s ) responsible for leg expression , we employed all available GAL4 reporter lines for cis-regulatory regions of svb ( S4 Table ) that overlap with regions of open chromatin downstream of the above deficiencies ( Fig 3 ) . All 10 regions that overlap with open chromatin are able to drive GFP expression to some extent in second legs between 20 and 28 hAPF , as well as in other pupal tissues ( S4 Fig ) . While some of the regions only produce expression in a handful of epidermal cells or particular regions of the T2 legs , none are specific to the presumptive naked valley . Moreover , VT057066 , VT057077 , VT057081 , and VT057083 appear to drive variable levels of GFP expression throughout the leg ( S4 Fig ) . Note that the two regions overlapping with larval enhancers E and 7 ( VT057062 and VT057075 , respectively ) only drive weak expression in a few cells in the tibia and tarsus ( S4 Fig ) . To further test whether the expression of any of these regions is consistent with a role in trichome formation , we used them to drive expression of the trichome repressor miR-92a and the trichome activator sha-ΔUTR [1] . Intriguingly , driving miR-92a under control of only one of the fragments ( VT057077 ) caused the repression of trichomes on all legs ( Fig 3 and S5 Fig ) as well as on wings and halteres ( S5 Fig ) . Expressing miR-92a under control of seven fragments ( including VT057062 and VT057075 ) had no noticeable effect , and with two of the other fragments ( VT057053 , VT057056 ) only led to repression of trichomes in small patches along the legs consistent with the GFP expression pattern ( S4 and S5 Figs ) . Driving sha-ΔUTR with VT057077 is sufficient to induce trichome formation in the naked valley ( Fig 3 ) and on the posterior T3 femur ( S5 Fig ) . Driving sha-ΔUTR under control of any of the other nine regions did not produce any ectopic trichomes in the naked valley on T2 or on any other legs . These results indicate that a single enhancer , VT057077 , is able to drive svb expression throughout the second leg in both regions which normally produce trichomes and in naked areas . It was previously shown that miR-92a inhibits leg trichome formation by repressing translation of the svb target sha [1] . However , sha mutants are still able to develop trichomes in larvae , albeit with abnormal morphology [29] . These data suggest that there are differences in the functionality of svb and sha in larval versus leg trichome formation , and therefore we next verified and tested the capacity of svb and sha to produce larval and leg trichomes . As previously shown [38] , ectopic expression of svb is sufficient to induce trichome formation on normally naked larval cuticle ( Fig 4 ) . However , we found that ectopic expression of sha in the same cells does not lead to the production of trichomes ( Fig 4 ) . svb is also required for posterior leg trichome production [43] ( Fig 2 and S6 Fig ) , but over expression of svb in the naked valley does not produce ectopic trichomes ( Fig 4 ) . Over expression of sha on the other hand is sufficient to induce trichome development in the naked valley [1] ( Fig 4 ) . These results show that svb and sha differ in their capacities to generate trichomes in larvae versus legs . Interestingly , we observed that the ectopic trichomes produced by expression of sha-ΔUTR in the naked valley are significantly shorter than those on the rest of the leg ( S1 Fig ) . This suggests that although sha is able to induce trichome formation in these cells , other genes are also required for their normal morphology . We observed that another characterised svb-target gene , CG14395 [33] , is also a high-ranking predicted target of miR-92a [52]: its 3’UTR contains two conserved complete 8-mers corresponding to the binding site for this microRNA . We found that CG14395 is also expressed in pupal second legs according to our leg RNA-Seq data ( S2 Table ) and furthermore that RNAi against this gene resulted in shorter leg trichomes ( S7 Fig ) . Therefore it appears that miR-92a also represses CG14395 and potentially other svb target genes in addition to sha to block trichome formation . Svb acts as a transcriptional repressor and requires cleavage by the proteasome to become a transcriptional activator . This cleavage is induced by small proteins encoded by the tal locus [30–32] . We therefore tested if svb is unable to promote trichome development in the naked valley because it is not activated in these cells . We found that expressing the constitutively active form ovoB or tal in naked leg cells is sufficient to induce trichome formation ( Fig 4 ) , which is consistent with loss of trichomes in tal mutant clones of leg cells ( S6 Fig ) . Furthermore , it appears that tal , like svb , is expressed throughout the leg ( S6 Fig ) . It follows that svb and tal are expressed in naked cells but are unable to induce trichome formation under normal conditions because of repression of sha , CG14395 and possibly other genes by miR-92a . We hypothesise that over expression of tal on the other hand must be able to produce enough active Svb to result in an increase of sha transcription to overwhelm miR-92a repression .
The causative genes and even nucleotide changes that underlie the evolution of an increasing number and range of phenotypic traits have been identified [17] . An important theme that has emerged from these studies is that the convergent evolution of traits is often explained by changes in the same genes–so called evolutionary ‘hotspots’ [17 , 53] . This suggests that the architecture of GRNs may influence or bias the genetic changes that underlie phenotypic changes [18 , 19 , 21] . However , relatively little is known about the genetic basis of changes in traits in different developmental contexts and when features are gained versus lost [18] . It was shown previously that changes in the enhancers of svb alone underlie the convergent evolution of the loss of larval trichomes , while the gain of leg trichomes in D . melanogaster is instead mainly explained by evolutionary changes in cis-regulatory regions of miR-92a [1 , 6 , 10 , 39–41] . We investigated this further by comparing the GRNs involved in both developmental contexts and by examining the regulation and function of key genes . Our results show that there are differences between the GRNs underlying the formation of larval and leg trichomes in terms of the expression of components and their functionality . These changes are found both in upstream genes of the GRN that help to determine where trichomes are made , and in downstream genes whose products are directly involved in trichome formation ( Fig 1 ) . The latter may also determine the differences in the fine-scale morphology of these structures on larval and leg cuticle ( Fig 1 ) [29] . Furthermore , while the key evolutionary switch in embryos , the gene svb , is also necessary for trichome production on the posterior leg , over expression of this gene is not sufficient to produce leg trichomes in the naked proximal region of the T2 femur . This is because the leg trichome GRN employs miR-92a , which inhibits trichome production by blocking the translation of the svb target gene sha and probably other target genes including CG14395 . In the legs of D . melanogaster , miR-92a therefore acts as the evolutionary switch for trichome production , and consequently the size of the naked valley depends on the expression of this gene ( Fig 5 ) [1] . Interestingly , we observed that the ectopic trichomes produced by over expression of sha-ΔUTR in the naked valley are significantly shorter than those on the rest of the leg ( S1 Fig ) . Therefore , while sha is able to induce trichome formation in these cells , other genes , including CG14395 , are also required for normal trichome morphology . This suggests that GRNs may be able to co-opt regulators , in this case possibly miR-92a , that can act in trans to regulate existing components . Such changes can facilitate phenotypic evolution by phenocopying the effects of ‘hotspot’ genes in contexts where their evolution may be constrained . While trichomes can be lost as a result of the loss of svb expression but not loss of sha alone , interestingly , over expression of miR-92a is also able to suppress trichomes on other structures , including wings [1 , 45] , presumably through repression of sha and other genes like CG14395 . In contrast to larvae , it is unlikely that mutations in svb can lead to evolutionary changes in legs to gain trichomes and decrease the size of the naked valley . This is because this gene ( and likely all the other genes necessary for trichome production ) is already transcribed in naked valley cells . In addition , a single svb enhancer is able to drive expression throughout the legs including the naked valley . Although other enhancer regions of this gene are able to drive some expression in patches of leg cells , none of these is naked valley-specific . This suggests that evolutionary changes to svb enhancers would be unlikely to only affect expression of this gene in the naked valley . It remains possible that binding sites could evolve in this global leg enhancer to increase the Svb concentration specifically in naked valley cells . This could overcome miR-92a-mediated repression of trichomes similar to our experiments where tal and ovoB are over expressed in these cells , or when molecular sponges are used to phenocopy the loss of microRNAs [54] . However , this does not seem to have been the preferred evolutionary route in D . melanogaster [1] ( Fig 5 ) . Our study also corroborates that Ubx represses leg trichomes [44] whereas it promotes larval trichome development through activation of svb [46] . Moreover , our results indicate that Ubx acts upstream of miR-92a in legs because it is unable to repress leg trichomes in the absence of this microRNA . It is possible that Ubx even directly activates miR-92a since ChIP-chip data indicate that there are Ubx binding sites within the jigr1/miR-92a locus [55] . Intriguingly , there is no naked valley in D . virilis , and Ubx does not appear to be expressed in the second legs of this species during trichome development [44] ( Fig 5 ) . However naked valleys are evident in other species of the virilis and montana groups and it would be interesting to determine if these differences were caused by changes in Ubx , miR-92a , or even other loci ( Fig 5 ) . To the best of our knowledge , our study is the first to directly compare the expression and function of components of the GRNs underlying the formation of similar structures that have evolved in different developmental contexts . Our results show that the GRNs for trichome production in larval versus leg contexts retain a core set of genes but also exhibit differences in the components used and in their wiring . These differences likely reflect changes that accumulate in GRNs during processes such as co-option [e . g . 56] and developmental systems drift [57–59] , although it remains possible that the changes have been selected for unknown reasons . Importantly , we show that the differences in these GRNs may help to explain why they have evolved at different nodes to lead to the gain or loss of trichomes . This supports the suggestion that GRN architecture can influence the pathway of evolution and lead to hotspots for the convergent evolution of traits [17–19 , 21] . Indeed , such hotspots can also underlie phenotypic changes in different developmental contexts . For example , yellow underlies differences in abdominal pigmentation and wing spot pigmentation among Drosophila species [7 , 11 , 60 , 61] . However , we demonstrate that it cannot be assumed that evolutionary hotspots in one development context represent the nodes of evolution in a different context as a consequence of differences in GRN architecture . Our findings also highlight that the genes that underlie the loss of features might not have the capacity to lead to the gain of the same feature . Therefore , while evolution may be predictable in a particular context , it is very important to consider different developmental contexts and whether a trait is lost versus gained . Indeed , even when we map the genetic basis of phenotypic change to the causative genes it is important to understand the changes in the context of the wider GRN to fully appreciate how the developmental program functions and evolves . Since evolution is thought to favour changes with low pleiotropy [19 , 62–65] , the effects of genetic changes underlying phenotypic change should be tested more widely during development . Such an approach recently revealed that svb enhancers underlying differences in larval trichomes are actually also used in other contexts [43] . Interestingly , miR-92a is employed in several roles , including self-renewal of neuroblasts [47] , germline specification [48] , and circadian rhythms [66] . It remains to be seen if the evolutionary changes in this microRNA underlying naked valley differences also have pleiotropic consequences , and therefore if natural variation in naked valley size is actually a pleiotropic outcome of selection on another aspect of miR-92a function .
Fly strains used in this study are listed in S4 Table . GAL4 lines for analysis of svb expression and RNAi lines for analysis of CG14395 were obtained from the Vienna Drosophila Resource Center ( VDRC ) [67 , 68] . Flies were reared on standard food at 25°C unless otherwise indicated . Replacement of the P{lacW}l ( 3 ) S011041 element , which is inserted 5’ of the tal gene , by a P{GaWB} transposable element was carried out by mobilization in omb-GAL4; +/CyO Δ2–3; l ( 3 ) S011041/TM3 , Sb flies as described in [31] . Replacements were screened by following UAS-GFP expression in the progeny . The P{GaWB} element is inserted in the same nucleotide position as P{lacW}S011041 . Clonal analysis of tal S18 . 1 and svbR9 alleles were performed as previously described [69] . A transgenic line that contains the cis-regulatory region of svb upstream of a GFP reporter ( svbBAC-GFP ) [43] was used to monitor svb expression . Legs of pupae were dissected 24 h hAPF , fixed and stained following the protocol of Halachmi et al . [70] , using a chicken anti-GFP as primary antibody ( Aves Labs , 1:250 ) and an anti-chicken as secondary ( AlexaFluor 488 , 1:400 ) . Images were obtained on a confocal microscope with a 60X objective . SUM projections of the z-stacks were generated after background subtraction . A filter median implemented in ImageJ software [71] was applied . The proximal femur image was reconstructed from two SUM projections using Adobe Photoshop . For trichome length measurements , T2 legs were dissected , mounted in Hoyer’s medium/lactic acid 1:1 and imaged under a Zeiss Axioplan microscope using ProgRes MF cool camera ( Jenaoptik , Germany ) . Trichomes on distal and proximal femurs were measured and analysed using ImageJ software [71] . Statistical analyses were done in R version 3 . 4 . 2 [72] . Pupae were collected within 1 hAPF and allowed to develop for another 20 to 28 h at 25°C . Second legs were dissected in PBS from approximately 80 pupae per replicate and kept in RNAlater . RNA was isolated using phenol-chloroform extraction . This was done in three replicates for two different strains ( e4 , wo1 , ro1 and OregonR ) . Library preparation and sequencing ( 75 bp paired end ) were carried out by Edinburgh Genomics . Reads were aligned to D . melanogaster genome version 6 . 12 [73] using TopHat 2 . 1 . 1 . [74] . Transcripts were quantified using Cufflinks 2 . 2 . 1 and differential expression analysis conducted using Cuffdiff [75] ( S1–S7 Files ) . Genes expressed below or around 1 FPKM were considered not expressed . Raw sequencing reads are deposited in the Gene Expression Omnibus with accession number GSE113240 . Pupae were reared and dissected as described above . Dissected legs were kept in ice cold PBS . Leg cells were lysed in 50 μl Lysis Buffer ( 10 mM Tris-HCl , pH = 7 . 5; 10 mM NaCl; 3 mM MgCl2; 0 . 1% IGEPAL ) . Nuclei were collected by centrifugation at 500 g for 5 min . Approximately 60 , 000 nuclei were suspended in 50 μl Tagmentation Mix [25 μl Buffer ( 20 mM Tris-CH3COO- , pH = 7 . 6; 10 mM MgCl2; 20% Dimethylformamide ) ; 2 . 5 μl Tn5 Transposase; 22 . 5 μl H2O] and incubated at 37°C for 30 min . After addition of 3 μl 2 M NaAC , pH = 5 . 2 DNA was purified using a QIAGEN MinElute Kit . PCR amplification for library preparation was done for 15 cycles with NEBNext High Fidelity Kit; primers were used according to [50] . This procedure was carried out for three replicates for each of two strains ( e4 , wo1 , ro1 and OregonR ) . Paired end 50 bp sequencing was carried out by the Transcriptome and Genome Analysis Laboratory Göttingen , Germany . Reads were end-to-end aligned to D . melanogaster genome version 6 . 12 ( FlyBase ) [73] using bowtie2 [76] . After filtering of low quality reads and removal of duplicates using SAMtools [77 , 78] , reads were re-centered according to [50] . Peaks were called with MACS2 [79] and visualisation was done using Sushi [80] ( S8 and S9 Files ) . The reads have been deposited in the Gene Expression Omnibus with accession number GSE113240 .
|
A major goal of biology is to identify the genetic causes of organismal diversity . Convergent evolution of traits is often caused by changes in the same genes–evolutionary ‘hotspots’ . shavenbaby is a ‘hotspot’ for larval trichome loss in Drosophila , but microRNA-92a underlies the gain of leg trichomes . To understand this difference in the genetics of phenotypic evolution , we compared the expression and function of genes in the underlying regulatory networks . We found that the pathway of evolution is influenced by differences in gene regulatory network architecture in different developmental contexts , as well as by whether a trait is lost or gained . Therefore , hotspots in one context may not readily evolve in a different context . This has important implications for understanding the genetic basis of phenotypic change and the predictability of evolution .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"invertebrates",
"plant",
"anatomy",
"trichomes",
"medicine",
"and",
"health",
"sciences",
"legs",
"limbs",
"(anatomy)",
"animals",
"animal",
"models",
"developmental",
"biology",
"plant",
"science",
"model",
"organisms",
"drosophila",
"melanogaster",
"experimental",
"organism",
"systems",
"skeleton",
"drosophila",
"research",
"and",
"analysis",
"methods",
"musculoskeletal",
"system",
"gene",
"expression",
"evolutionary",
"genetics",
"insects",
"arthropoda",
"femur",
"eukaryota",
"anatomy",
"phenotypes",
"genetics",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"biology",
"organisms",
"evolutionary",
"developmental",
"biology"
] |
2018
|
Gene regulatory network architecture in different developmental contexts influences the genetic basis of morphological evolution
|
The inhibitor of apoptosis protein ( IAP ) family has been implicated in immune regulation , but the mechanisms by which IAP proteins contribute to immunity are incompletely understood . We show here that X-linked IAP ( XIAP ) is required for innate immune control of Listeria monocytogenes infection . Mice deficient in XIAP had a higher bacterial burden 48 h after infection than wild-type littermates , and exhibited substantially decreased survival . XIAP enhanced NF-κB activation upon L . monocytogenes infection of activated macrophages , and prolonged phosphorylation of Jun N-terminal kinase ( JNK ) specifically in response to cytosolic bacteria . Additionally , XIAP promoted maximal production of pro-inflammatory cytokines upon bacterial infection in vitro or in vivo , or in response to combined treatment with NOD2 and TLR2 ligands . Together , our data suggest that XIAP regulates innate immune responses to L . monocytogenes infection by potentiating synergy between Toll-like receptors ( TLRs ) and Nod-like receptors ( NLRs ) through activation of JNK- and NF-κB–dependent signaling .
The inhibitor of apoptosis ( IAP ) family of proteins plays a key role in cellular signaling , such as apoptosis , by binding to pro-apoptotic proteins , interrupting the intrinsic programmed cell death pathway and activating anti-apoptotic mechanisms [1]–[3] . In addition to modulating apoptosis , recent genetic studies have revealed that a Drosophila IAP protein , diap2 , acts as a regulator of anti-microbial immunity [4]–[7] . Innate immune signaling pathways are well conserved from Drosophila to humans , suggesting that IAP proteins may also play a role in mammalian innate immunity [8] . This hypothesis is consistent with a study demonstrating that cIAP2 exacerbates endotoxic shock in mice by controlling macrophage apoptosis [9] . Furthermore , a cohort of patients with X-linked lymphoproliferative syndrome ( XLP ) were found to have mutations in the gene encoding XIAP , resulting in a primary immunodeficiency [10] . XIAP , also known as BIRC4 and hILP , contains three baculoviral IAP repeat ( BIR ) domains , the characteristic protein-protein interaction domain of the IAP family [11] . XIAP also has a carboxy-terminal RING domain with E3 ubiquitin ligase activity that directs proteasomal degradation of target proteins [12] . Multiple signaling pathways can be modulated by XIAP , including NF-κB , MAP kinase and TGFβ signaling [13]–[16] . Moreover , XIAP can integrate cellular responses to diverse stimuli by interacting directly with ligands such as copper to regulate copper homeostasis [17] . XIAP has been predominantly characterized as an inhibitor of apoptosis , and interacts with many known mediators of programmed cell death , such as JNK , TAK1 , TAB1 , TRAF6 , and caspases-3 , -7 , and -9 [3] , [13] , [18] , [19] . However , XIAP-deficient mice do not appear to have striking defects in apoptosis , thus the role of XIAP in vivo is not yet clearly understood [20] . The innate immune response protects host organisms against invading pathogens prior to the onset of adaptive immunity . Pathogens stimulate innate immune signaling through pattern recognition receptors ( PRR ) , which recognize well-conserved pathogen-associated molecular patterns ( PAMPs ) [21] . PAMPs are detected at the host membrane by TLRs , and in the cytosol by the NLR and the RIG-I-like helicase ( RLH ) sensors [22] , [23] . Stimulation of either extracellular or intracellular PRR can result in activation of NF-κB and MAP kinase signaling pathways , leading to production of inflammatory mediators such as cytokines and costimulatory molecules [24] . Activation of TLRs and NLRs together can induce synergy between the signaling pathways , resulting in enhanced activation of innate and adaptive immunity [25] , [26] . Listeria monocytogenes is a cytosolic bacterial pathogen used extensively to probe aspects of innate and adaptive immunity [27] . L . monocytogenes is recognized by TLRs expressed on the surface of phagocytes [27] . After phagocytic uptake , L . monocytogenes escapes from host vacuoles by secreting a pore-forming toxin , listeriolysin O ( LLO ) [28] . Once in the cytosol , L . monocytogenes can trigger oligomerization and signaling by NOD1 and other NLRs [29] . Here we show that XIAP plays a protective role during infection by L . monocytogenes . We present evidence that amplifying JNK activation and subsequent pro-inflammatory cytokine production in response to cytosolic bacteria is one mechanism by which XIAP modulates innate immunity .
We first tested the hypothesis that XIAP contributed to anti-microbial immunity by infecting xiap+/y and xiap−/y mice with 1×105 L . monocytogenes and determining survival over time ( Figure 1A ) . At 7 dpi ( days post infection ) , 60% of the XIAP-deficient mice had succumbed to infection , whereas all wild-type mice survived . Similarly , at higher doses of L . monocytogenes more xiap−/y than xiap+/y mice succumbed to infection , although some xiap+/y mice also became moribund ( unpublished data ) . Depending upon the inoculum , morbidity and mortality of xiap−/y animals occurred between 2 and 5 dpi , prior to peak development of adaptive immunity , suggesting that XIAP had a protective effect during the innate response to bacterial infection . To better define the role of XIAP during innate immunity to intracellular bacterial infection , we infected wild-type and XIAP-deficient mice intraperitoneally with 5×105 L . monocytogenes , and harvested spleen and liver to enumerate bacterial burden at 24 , 28 and 72 hpi ( Figure 1B ) . By 48 h , xiap−/y mice had approximately 10-fold more L . monocytogenes in liver and spleen at 48 hpi compared to the xiap+/y mice , consistent with our observation of their decreased survival . At 72 hpi , the difference between the xiap+/y mice and the xiap−/y was even more pronounced , with the xiap−/y mice supporting 100-fold greater bacterial numbers . These results indicate that XIAP mediates innate resistance to L . monocytogenes infection . Mutations in XIAP have been associated with the human immunodeficiency syndrome , XLP [10] . One feature associated with this disease is an abnormally low number of natural killer T-cells ( NKTCs ) , although it is not yet clear how much this phenotype contributes to immunodeficiency . To determine if mice lacking XIAP exhibit a similar phenotype to XLP patients , we quantitated the percentage of NKTCs in the spleen of xiap+/y and xiap−/y mice ( Figure 1C ) . No significant difference in the number of splenic NKTCs was observed between xiap+/y and xiap−/y mice , indicating that survival of NKTCs in uninfected mice is not affected by a deficiency in XIAP , consistent with a previous report [10] . To determine if NKTC survival or activation was dependent on XIAP during L . monocytogenes infection , we infected animals and determined the number of splenic NK1 . 1+CD3+ NKTCs that expressed CD69 , a marker of activation ( Figure 1D ) . We observed similar numbers of activated NKTCs in xiap+/y and xiap−/y mice . These data suggest that XIAP does not play an important role in NKTC survival or activation in a murine model of listeriosis . We then tested the role of XIAP during infection of primary macrophages , an innate immune effector cell and a well-characterized host for L . monocytogenes replication . We infected unactivated bone marrow derived macrophages ( BMDMs ) , BMDMs activated with LPS and IFNγ or peritoneal macrophages with L . monocytogenes and measured intracellular bacterial growth over time ( Figure 2 ) . All types of xiap+/y and xiap−/y macrophages controlled L . monocytogenes infection equally well . We conclude from these data that XIAP does not contribute directly to restriction of L . monocytogenes growth in macrophages , even though XIAP-deficient mice exhibited an increased bacterial burden compared to wild-type mice . Taken together , our results demonstrate that XIAP is required for a protective immune response to L . monocytogenes infection in vivo . XIAP can activate NF-κB–dependent transcription in response to apoptotic stimuli [14] . In addition to regulating apoptosis , the canonical NF-κB p50/p65 heterodimer has a well-established role in proinflammatory cytokine transcription stimulated by TLR and NLR signaling [21] . Expression profiling of unactivated macrophages infected with L . monocytogenes did not reveal reproducible differences between wild-type and XIAP-deficient macrophages ( unpublished data ) . We then reasoned that activated macrophages might be a more relevant environment for studying XIAP function . We therefore investigated whether XIAP regulated NFκB-dependent processes during L . monocytogenes infection in activated macrophages by measuring translocation of p50 to the nuclear compartment . Activated BMDM were infected with wild-type L . monocytogenes , and translocation of the p50 subunit of NF-κB was analyzed by immunoblot ( Figure 3A ) . As early as 0 . 5 hpi , p50 was detected in the nuclear fraction of both xiap+/y and xiap−/y cells; however , in the presence of XIAP there was substantially more p50 in the nuclear fraction over time . We also measured DNA binding activity of the p65 subunit of the p50/p65 heterodimer in the nuclear fraction of uninfected and L . monocytogenes infected activated macrophages ( Figure 3B ) . At 1 and 2 hpi , infected xiap+/y macrophage nuclear lysates contained significantly more NF-κB DNA binding activity than infected xiap−/y nuclear lysates , suggesting that XIAP might enhance signaling of NF-κB–dependent pathways stimulated by bacterial infection . In some contexts , XIAP-dependent NF-κB activation can protect against apoptotic stimuli; therefore we tested if XIAP modulated apoptosis during L . monocytogenes infection . We first examined apoptosis in activated macrophages during L . monocytogenes infection by flow cytometry of infected cells using Annexin V ( AnnV ) , an indicator of apoptosis ( Figure 3C ) . A modest but reproducible increase in apoptosis was observed by 3 hpi in XIAP-deficient macrophages compared to wild-type macrophages , which remained consistent throughout infection ( Figure S1A ) . We also examined apoptosis in infected liver and spleen at sites of L . monocytogenes replication 48 hpi by performing TUNEL staining ( Figure 3D ) . Although the extent of apoptosis at foci of infection were heterogeneous , there did not appear to be any notable difference in the number or distribution of apoptotic cells per focus in xiap+/y compared to xiap−/y livers or spleens . We did not observe any XIAP-dependent difference in the numbers of AnnV+ T or B cells present in the spleens of mice at 48 hpi ( Figure S1B ) . In addition , caspase-3 cleavage in infected activated macrophages was not significantly altered ( unpublished data ) . While the infected xiap−/y macrophages exhibited a modest increase in cell death , we found no striking evidence for regulation of apoptosis by XIAP in the context of L . monocytogenes infection in vivo . Thus , XIAP regulates NF-κB activation during L . monocytogenes infection , but may enhance innate immunity by modulating cellular responses other than apoptosis in infected macrophages . In addition to NF-κB activation , TLR and NLR sensing of microbial infection stimulate MAP kinase phosphorylation , leading to activation [30] . Previous reports suggested that XIAP can promote JNK phosphorylation via interaction with TAB1 and the MAP3K , TAK1 [14] , [16] , [31] . To determine if XIAP affected JNK phosphorylation during L . monocytogenes infection , we performed immunoblot analysis of infected lysates from xiap+/y and xiap−/y activated macrophages using a phospho-JNK specific antibody ( Figures 4A and S2A ) . Upon infection with wild-type L . monocytogenes , JNK phosphorylation occurred as early as 0 . 5 hpi in both xiap+/y and xiap−/y cells . In the xiap−/y macrophages , JNK phosphorylation peaked at 0 . 5 hpi . However , in the presence of XIAP , enhanced JNK activation was prolonged up to 6 h . This suggests that XIAP augments JNK signaling during wild-type L . monocytogenes infection . To determine the contribution of XIAP to cytosol-specific signaling , we compared wild-type L . monocytogenes infection with a strain deficient in LLO or heat-killed L . monocytogenes ( HKLM ) , which both remain trapped in the vacuole . The LLO− bacteria and HKLM induced JNK phosphorylation at 0 . 5 hpi similarly to infection by wild-type bacteria , suggesting that this early JNK phosphorylation was linked to signaling from the vacuole , most likely through TLRs . However , JNK phosphorylation in response to vacuolar bacteria quickly diminished after 30 min , in contrast to the extended XIAP-dependent JNK activation observed during wild-type bacterial infection . To confirm that enhanced JNK phosphorylation in xiap+/y activated macrophages resulted in downstream signaling , we examined phosphorylation of c-jun , a target of JNK , by immunoblot ( Figures 4B and S2B ) [32] . Upon infection by wild-type L . monocytogenes , c-jun phosphorylation was prolonged in xiap+/y but not xiap−/y cells , similarly to JNK phosphorylation . Moreover , activation of c-jun upon infection by LLO− bacteria was considerably decreased compared to wild-type bacteria . To determine if XIAP also stimulated activation of other MAP kinase family members , we analyzed phosphorylation of p38 and ERK by immunoblot of infected macrophage lysates ( Figures 4C , 4D , S2C , and S2D ) . ERK1 and ERK2 were phosphorylated equivalently in xiap+/y and xiap−/y macrophages in response to infection by all L . monocytogenes strains . As previously shown , p38 phosphorylation was decreased during infection by vacuole-restricted bacteria compared to wild-type bacteria [33] . Phosphorylation of p38 upon infection with wild-type L . monocytogenes was not significantly affected by XIAP . These data demonstrate that XIAP prolongs JNK activation specifically in response to cytosolic L . monocytogenes . Since XIAP modulated JNK and NF-κB signaling in the context of infection , we hypothesized that induction of proinflammatory cytokines through these pathways would also depend on XIAP . Activated macrophages were infected with L . monocytogenes for 3 h , and RNA was analyzed by qRT-PCR to determine the expression of a subset of genes involved in innate immunity ( Figures 5A and S3 ) . Transcription of il6 , tnf , il10 , mip2 , and kc was strongly upregulated upon infection in the presence of XIAP , while induction of ifnb , il1b , ido , and inos was not significantly altered . To assess if XIAP-dependent gene expression correlated to increased protein production , we compared the secretion of IL-6 and TNF from uninfected and infected activated macrophages ( Figure 5B and 5C ) . Upon infection by wild-type L . monocytogenes , IL-6 and TNF secretion was induced to a greater extent in xiap+/y macrophages than in xiap−/y macrophages , while infection with the LLO− mutant induced little IL-6 and TNF secretion by either genotype . To determine if JNK activation was required for induction of IL-6 gene expression and secretion in response to wild-type L . monocytogenes infection , we treated activated macrophages with the JNK inhibitor SP600125 ( Figure 5D ) . IL-6 secretion by infected macrophages was markedly diminished by JNK inhibition , indicating that JNK activation is required for IL-6 induction by L . monocytogenes . Moreover , since LLO− mutant bacteria stimulated robust but temporally limited JNK phosphorylation and little IL-6 secretion , we infer that prolonged JNK activation is necessary for maximal IL-6 production during intracellular infection by L . monocytogenes . When L . monocytogenes infected cells were treated with an ERK-specific inhibitor , IL-6 secretion was similar to the untreated infected control cells . These results collectively suggest that the presence of XIAP enhances JNK activation in response to cytosolic bacteria , resulting in increased production of proinflammatory cytokines . To determine if XIAP enhanced proinflammatory gene expression in vivo , we performed qRT-PCR analysis on splenic RNA . RNA was isolated from splenocytes harvested from uninfected animals or animals infected with L . monocytogenes for 48 h ( Figure 6 ) . We examined the expression of several proinflammatory cytokines including IL-6 , TNF , and IFN-γ , produced during the innate immune response that are critical for clearing L . monocytogenes infection [34]–[36] . The expression of il6 and ifng were significantly enhanced in the presence of XIAP during infection , while expression of tnf and ifnb were not altered . We also examined the expression of il17 , a cytokine known to enhance expression of il6; we observed no reproducible differences in the expression of il17 [37] . These data confirm the results from our in vitro macrophage model; that XIAP promotes the expression of proinflammatory cytokine genes in response to L . monocytogenes infection . Innate immune signaling mediated by pattern recognition receptors , located on cellular membranes or in the host cytosol , stimulates transcription and secretion of proinflammatory cytokines . We used purified TLR and NLR ligands to better define a role for XIAP in innate immune signaling . Wild-type and XIAP-deficient activated macrophages were treated with TLR ligands , and secretion of IL-6 and TNF was measured after 24 h ( Figure 7A and unpublished data ) . While some PAMPS , such as the lipoprotein Pam3CSK4 , could induce high levels of IL-6 and TNF , we found no XIAP-dependent differences in proinflammatory cytokine induction . These results suggest that XIAP does not contribute to cytokine output in response to TLR stimulation alone . During a physiological infection , intracellular pathogens activate both extracellular and cytosolic innate immune pathways resulting in a coordinated immune response [27] . One well-characterized consequence of microbial sensing by cytosolic NLR proteins is activation of caspase-1 , which cleaves pro-IL-1β into its mature form [38] . Since XIAP can regulate the activity of some caspases , we tested whether XIAP contributed to IL-1β production , measured by ELISA , as an indicator of caspase-1 activation ( Figure 7B ) . Consistent with previous reports , IL-1β production was induced by cytosolic L . monocytogenes , but was not dependent upon XIAP [39] . We next examined the activation of NLR signaling using MDP , a ligand for NOD2 ( Figure 7C–7E ) . No differences in cytokine secretion were observed by treatment with MDP alone , however , during a physiological infection bacteria likely present both TLR and NLR ligands to an infected host cell . PAMPs contained by L . monocytogenes include lipoprotein , muramyldipeptide , bacterial DNA and flagellin [27] . To determine if XIAP enhanced synergy between TLRs and NLRs , we examined IL-6 , TNF and IL-1β secretion from xiap+/y and xiap−/y activated macrophages in response to the lipopeptide Pam3CSK4 , the NOD2 ligand MDP , or both ( Figure 7C–7E ) . When Pam3CSK4 and MDP were used in combination , we saw a substantial increase in IL-6 and TNF secretion by xiap+/y but not xiap−/y activated macrophages . We did not see any XIAP-dependent enhancement of IL-1β secretion in response to Pam3CSK4 and MDP in combination . To better deconstruct how XIAP might participate in integrating TLR and NLR signaling , we analyzed transcription of the il6 gene from xiap+/y and xiap−/y activated macrophages treated with MDP , Pam3CSK4 , or both ligands ( Figure 7F ) . Pam3CSK4 induced expression of the il6 gene in an XIAP-independent manner . Upon treatment with MDP , xiap+/y but not xiap−/y macrophages , responded by upregulating il6 transcript levels approximately 5-fold . When macrophages were treated with both ligands , xiap+/y macrophages exhibited enhanced expression of il6 compared to treatment of Pam3CSK4 alone , but xiap−/y macrophages did not . These results demonstrate that XIAP promotes synergy between the TLR and NLR pathways , resulting in increased production of pro-inflammatory cytokines .
Here we show that XIAP can regulate innate immunity to the bacterial pathogen , L . monocytogenes by modulating JNK and NF-κB signaling , resulting in enhanced cytokine production . We found little evidence to suggest that XIAP regulated apoptosis of bacterially infected cells in vitro or in vivo , but instead found that XIAP promoted synergistic inflammatory cytokine expression induced by extracellular and cytosolic innate immune signaling upon bacterial infection of activated macrophages . Specifically , XIAP amplified the cytosolic response to MDP or wild-type L . monocytogenes . These data identify XIAP as a regulator of cytosolic innate immune signaling . Notably , another IAP family member NAIP5 was found to mediate caspase-1 activation in response to cytosolic bacterial flagellin [40]–[42] . NAIP5 function in innate immunity could be attributed to the atypical domain structure of this IAP protein that exhibits similarities to the NLR family of cytosolic sensors [43] . However , these data taken together with our results lead us to speculate that regulation of innate immune signaling is an important role of mammalian IAPs . The IAP family appears to play multiple roles in mammalian biology , including protecting cells from apoptotic stimuli , regulating the cell cycle and modulating innate immune signaling . As a whole , these studies are consistent with genetic evidence in Drosophila demonstrating that dIAP1 primarily protects insect cells from programmed cell death , while dIAP2 is required for anti-microbial function of the Imd pathway [4]–[7] . The Imd pathway in Drosophila is activated by peptidoglycan recognition proteins ( PGRPs ) , while functionally analogous innate immune sensing of peptidoglycan in mammalian cells occurs in the cytosol by NOD1 , NOD2 , and NALP3 [44] . The Imd protein in Drosophila shares sequence homology with the mammalian RIP proteins , and a mammalian paralog , RIP2 , is an essential signaling adaptor for the cytosolic peptidoglycan sensors , NOD1 and NOD2 [8] , [45]–[47] . Thus , the Imd/RIP innate immune signaling module appears to have been co-opted for mammalian cytosolic surveillance for peptidoglycan . Genetic epistasis experiments in Drosophila place dIAP2 in parallel to TAK1 upstream of JNK and NF-κB signaling pathways [4] . Similarly , in mammalian cells , XIAP can modulate JNK and NF-κB signaling through TAK1 in endothelial cells and fibroblasts [13] , [48] . Activation of either NOD1 or NOD2 activates TAK1 , leading us to hypothesize that during bacterial infection , XIAP may facilitate this key association , linking cytosolic sensors to downstream signaling mediators [49] , [50] . During infection , microbial pathogens present multiple PAMPs recognized by the innate immune system , eliciting a coordinated protective response . This concept is illustrated by the paradigm of IL-1β processing , where TLRs mediate transcription of pro-IL-1β; however , cleavage and secretion are dependent upon activation of the caspase-1 inflammasome by cytosolic PAMPs [51] . However , IL-1β deficient mice are as resistant to L . monocytogenes infection as wild-type mice , suggesting that other inflammatory cytokines mediate innate immune control of this infection [52] . In contrast , IL-6- , TNF- and IFNγ-deficient mice are more susceptible to L . monocytogenes infection at 48 hpi than wild-type mice , demonstrating a requirement for IL-6 , TNF , and IFNγ in protection from this particular pathogen [34]–[36] , [53] , [54] . IFNγ is largely produced by innate immune effector cells other than macrophages , thus our observation that ifng transcription is decreased in the spleens of L . monocytogenes-infected XIAP mutant mice must be due to either a XIAP-dependent cell autonomous defect in a different cell type or a non-autonomous defect in an IFNγ producing cell resulting from a defect in macrophages [55] . Since XIAP is expressed in many different tissues , it is reasonable to suppose that XIAP may have pleiotropic effects in the innate immune response to L . monocytogenes [56] . However , macrophages are primary producers of IL-6 and TNF , and notably , NOD2 signaling is known to stimulate production of IL-6 and TNF [45] , [57] . The deficit in IL-6 and TNF production we observed in infected xiap−/y activated macrophages , and the defect in gene expression in vivo likely contributes to the enhanced susceptibility of XIAP-deficient animals to L . monocytogenes infection . Recent reports indicate that macrophages treated with LPS become tolerized to re-stimulation with TLR ligands [58] , [59] . Additionally , when macrophages are tolerized by LPS , the role of NOD1 and NOD2 in cytosolic surveillance becomes more critical during infection [60] . In our model , macrophages are activated with LPS and IFNγ prior to infection . When activated macrophages are infected with L . monocytogenes , the induction of proinflammatory cytokines is XIAP-dependent , indicating that XIAP plays a more critical role in regulating the innate immune response to cytosolic pathogens in macrophages where the TLR pathway may be tolerized and an inflammatory gene expression program initiated . We use these data to integrate XIAP into a cytosolic surveillance model whereby upon recognition of microbial ligands in the cytosol by innate immune sensors such as NOD2 , XIAP enhances association and function of signal transducers such as TAK1 and JNK [13] , [18] . Recruitment of signaling molecules by XIAP upon NLR stimulation would potentiate signaling pathways activated by TLRs , leading to maximal proinflammatory cytokine production . Apoptotic and microbial stimuli activate similar signaling pathways , but may lead to different outcomes . Macrophages as innate immune effector cells can control microbial infection by secreting cytokines and other pro-inflammatory molecules or by carrying out programmed cell death [61] . It has been hypothesized that when macrophages receive a strong inflammatory stimulus , they undergo apoptosis rather than secreting cytokines as a means of protecting the host [40] , [62] , [63] . Although previous data implicated XIAP in modulating apoptosis , our data demonstrate that XIAP also has an important role in proinflammatory cytokine production . However , we suggest that these two functions for XIAP may not be completely distinct , as the outcome of XIAP-dependent modulation of JNK and NF- κB pathways may depend on the quality and intensity of the stimulus [31] . Additionally , the ability of XIAP to regulate innate immunity is likely cell type and context dependent , as we did not see reproducible XIAP-dependent transcriptional regulation in unactivated macrophages . Future studies will determine which aspects of XIAP function contribute to immune signaling and elucidate the complex role of XIAP in the mammalian immune response .
Mice deficient in XIAP ( accession #U88990 ) were generated on a 129/Sv×129/SvJ background as previously described [20] . The XIAP-deficient mice were backcrossed onto the C57Bl/6 background for more than 10 generations . Six- to 12-week-old male XIAP-deficient mice or wild-type littermate controls were used for infection experiments . All animals received humane care as outlined by the Guide for the Care and Use of Laboratory Animals ( University of Michigan Committee on Use and Care of Animals ) . For cell culture infections , Listeria monocytogenes strains 10403S ( wild-type ) and hly− ( LLO− ) were inoculated into liquid brain-heart infusion ( BHI ) broth and incubated at 30°C overnight without shaking[64] . Prior to infection , L . monocytogenes cultures were washed and resuspended in PBS . HKLM was prepared by incubating bacteria at 70°C for 1 h . For animal infections , L . monocytogenes was grown to log-phase in BHI and aliquots were stored at −70°C . For each experiment , a vial was back-diluted and allowed to grow to OD600 0 . 5 . The bacteria were washed in PBS and diluted before injection . Mice were injected intraperitoneally with 5×105 L . monocytogenes equivalent to 0 . 5 LD50 for infection by the intraperitoneal route in C57Bl/6 mice [65] . The number of viable bacteria in the inoculum and organ homogenates was determined by plating 10-fold serial dilutions on Luria broth ( LB ) agar plates . For evaluation of survival , animals were infected with 1×105 or 5×105 L . monocytogenes , and observed every 24 h post-infection . For histology , the spleen and liver from infected mice were harvested at 48 hpi and fixed in 10% neutral buffered formalin . Paraffin sections were prepared and stained with ApopTag by the Cancer Center Research Histology and Immunoperoxidase Lab at the University of Michigan . Bone marrow macrophages were differentiated in DMEM supplemented with 20% heat inactivated FBS , 2 mM L-glutamine , 1 mM sodium pyruvate , 0 . 1% β-mercaptoethanol , and 30% L929 conditioned medium . Bone marrow cells were cultured at an initial density of 107 cells per 150 mm non-tissue culture treated dish for 6 d , with fresh medium added at day 3 . Cells were harvested with cold PBS without calcium and magnesium . BMDM were activated overnight in 10 ng/ml LPS ( Sigma #L6143 ) and 10 ng/ml ( 100 U/ml ) interferon-γ ( Peprotech #315-05 ) . Activated macrophages were infected with L . monocytogenes at an MOI of 10 , such that bacteria were observed in the cytosol in approximately 99% of the macrophages . Peritoneal macrophages were harvested by peritoneal lavage . Cells were pooled from two mice prior to plating . For L . monocytogenes growth curves , cells were plated on coverslips at a density of 1 . 7×105 cells/ml in 24-well plates . Macrophages were infected with L . monocytogenes for 0 . 5 h , washed 3 times with PBS , followed by addition of fresh medium with 50 µg/ml gentamicin . At each time point , 3 coverslips were lysed in water and plated on LB agar plates for to determine CFU . IL-6 ( R&D Systems ) , IL-1β ( R&D Systems ) , and TNF ( University of Michigan Cellular Immunology Core ) in the culture medium were measured by ELISA . Where indicated , cells were treated for 30 min with TLR ligands as follows: MDP 10 µg/ml ( Bachem #4009623 ) , Pam3CSK4 2 µg/ml ( Invivogen #tlrl-pms ) , poly ( I:C ) 10 µg/ml , LPS 10 ng/ml ( Sigma #L6143 ) , flagellin 10 ng/ml ( Invivogen #tlrl-flic ) , imiquimod 5 µg/ml ( Invivogen #tlrl-imq ) , CpG DNA 1 µg/ml ( IDT CpG F [5′-TCCATGACGTTCCTGACGTT] , CpG R [5′-AACGTCAGGAACGTCATGGA] ) . At 8 and 24 h post-treatment , supernatants were harvested for measurement of cytokines by ELISA . Inhibition experiments were conducted as described above , except cells were treated with 20 µM JNK inhibitor , SP600125 ( Sigma #S5567 ) , or 10 µM ERK inhibitor U0126 ( Cell Signaling #9903 ) for 1 h prior to infection . For nuclear and cytoplasmic fractionation , cells were lysed in NP-40 lysis buffer ( 50 mM Tris pH 8 . 5 mM EDTA pH 8 , 150 mM NaCl , 0 . 05% NP-40 [Igepal] , EDTA-free protease inhibitor cocktail [Roche] ) . Nuclei were pelleted by centrifugation at 1 , 000 rpm for 5 min; the cytosolic fraction was further clarified by centrifugation at 14 , 000 rpm for 10 min . Nuclei were washed and either resuspended in 2× SDS-PAGE lysis buffer for immunoblot or lysed for NF-κB ELISA by resuspension in nuclear lysis buffer ( 20 mM HEPES pH 7 . 9 , 400 mM NaCl , 1 mM EDTA , 10% glycerol , 0 . 1 mM DTT , EDTA-free protease inhibitor cocktail [Roche] ) and incubated at 4°C for 30 min . Nuclei were flash frozen and used for NF-κB p65 ELISA analysis ( Stressgen EKS-446 ) . BMDM were plated and activated overnight in 10 ng/ml LPS and 10 ng/ml interferon-γ . Cells were infected for 30 min at an MOI of 10 , bacteria were removed by 3 washes with PBS , and fresh medium containing 50 µg/ml gentamicin added . At 3 hpi , the medium was removed and spun to collect any non-adherent cells; the remaining cells were removed from the dish by incubating with ice-cold PBS without calcium and magnesium for 20 min at 4°C . Cells were stained with Annexin V and propidium iodide according to the manufacturer's protocol ( BD Biosciences #556420 ) . Splenocytes were harvested from uninfected or L . monocytogenes infected mice . BMDM were harvested from plates with ice cold PBS without Ca+ or Mg+ . Cells were blocked with Fc block ( BD Pharmingen 553142 ) for 15 min on ice . Cells were incubated in staining buffer ( PBS , 10% FBS ) with the indicated antibodies for 20 min on ice , followed by 3 washes in staining buffer . When necessary cells were incubated with secondary antibodies in staining buffer on ice for 20 min , and washed 3 times in staining buffer . Flow cytometric acquisition was performed on a FACSCanto . The data was analyzed using FlowJo software . The following antibodies were used: from BD Pharmingen; B220-PE ( 553089 ) , NK1 . 1-biotin ( 553163 ) , CD69-PE ( 553237 ) ; from Southern Biotech CD3 ( 1530-02 ) , Streptavidin-APC ( 7100-11L ) . Whole cell lysates were generated by adding 2× SDS-PAGE sample buffer directly to cell monolayers . Protein samples were separated by SDS-PAGE and transferred to PVDF . Blots were blocked in 5% BSA , incubated with primary antibodies , followed by a horseradish peroxidase conjugated secondary antibody . The following antibodies were used: β-actin ( Sigma #A5441 ) , NF-κB p50 ( Santa Cruz Biotechnology #8414 ) , USF-1 ( Santa Cruz Biotechnology #8983 ) , Phospho-JNK ( Cell Signaling 9251 ) , JNK1 ( Santa Cruz Biotechnology #571 ) , Phospho-p38 kit ( Cell Signaling 9210 ) , Phospho-c-jun ( Santa Cruz Biotechnology #822 ) , Phospho-ERK ( Cell Signaling 4377 ) , ERK-1 ( Santa Cruz Biotechnology #94 ) , goat anti Rabbit IgG-HRP ( MP Biomedical #67438 ) , goat anti-mouse IgG-HRP ( MP Biomedical #67429 ) . For RT-PCR , total RNA was harvested from infected or treated cells at 3 hpi with the RNeasy Mini Kit ( Qiagen ) . The RNA was used in a reverse transcriptase ( RT ) reaction with Moloney murine leukemia virus ( MMLV ) RT ( Invitrogen ) . cDNA obtained from the RT reaction was used for qRT-PCR amplification and quantitation by SYBR Green ( Stratagene MX3000p ) . Data was analyzed using the ΔΔCt method ( ΔΔCt = 2 ( ΔCt sample−ΔCt normalizer ) ) with β-actin used as a normalizer for in vitro experiments and gapdh used as a normalizer for in vivo experiments . Sequences for qRT-PCR primers are described in Table S1 . A two-tailed t-test was used for statistical analysis; p values of ≤0 . 05 were considered significant , while p≤0 . 001 were considered highly significant .
|
During a bacterial infection , the innate immune response plays two critical roles: controlling early bacterial replication and stimulating the adaptive immune response to clear infection . Host recognition of bacterial components occurs through pathogen sensors at the cell surface or within the host cell cytosol . Inhibitor of apoptosis proteins ( IAPs ) have been recently implicated in immune regulation , but how IAPs contribute to immunity is incompletely understood . Here , we show that X-linked IAP ( XIAP ) protects against infection by the cytosolic bacterial pathogen , Listeria monocytogenes , which causes severe disease in neonates and immunocompromised individuals . We found that XIAP enhanced MAP kinase signaling in L . monocytogenes infected macrophages , a key innate immune effector cell . Additionally , XIAP enabled synergy between cell surface and cytosolic bacterial sensors , promoting increased gene expression of proinflammatory cytokines . Our findings suggest that IAPs are integral regulators of innate immune signaling , coordinating extracellular and intracellular responses against microbial components to control bacterial infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/immunity",
"to",
"infections",
"microbiology/innate",
"immunity",
"immunology/innate",
"immunity",
"infectious",
"diseases/bacterial",
"infections",
"immunology/immunity",
"to",
"infections"
] |
2008
|
XIAP Regulates Cytosol-Specific Innate Immunity to Listeria Infection
|
We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum ( SFS ) computed on large genomic datasets . We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity , which cannot be tackled by other current likelihood-based methods . For simple scenarios , our approach compares favorably in terms of accuracy and speed with , the current reference in the field , while showing better convergence properties for complex models . We first apply our methodology to non-coding genomic SNP data from four human populations . To infer their demographic history , we compare neutral evolutionary models of increasing complexity , including unsampled populations . We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment , such as that recently released by Affymetrix to study human origins . Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations , our framework can correctly infer parameters of more complex models including the divergence of several populations , bottlenecks and migration . We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models . The two SNP panels lead to globally very similar estimates and confidence intervals , and suggest an ancient divergence ( >110 Ky ) between Yoruba and San populations . Our methodology appears well suited to the study of complex scenarios from large genomic data sets .
Reconstructing the past history of a given species is important not only for its own sake , but for disentangling demographic from selective effects [1] , [2] . Demography is indeed often estimated on a set of markers and the best neutral model is used as a null for evidencing markers under selection [3] , [4] or for finding global patterns of selection across the genome [e . g . 5] . Various methods have been proposed to estimate demography from genetic data , including full-likelihood methods [6]–[9] , summary-statistics likelihood based methods [10] , [11] , or different flavours of Approximate Bayesian Computation [12]–[16] . With some exceptions , these methods are relatively slow and do not scale up very well with new genomic data , as computation time increases with the number of loci . In contrast , recently developed composite-likelihood methods based on the site frequency spectrum [SFS , 17] have computing times that do not depend on the amount of available genomic data [18]–[21] , and several approaches have been proposed to estimate demographic parameters from the SFS [e . g . 11] , [17] , [20] , [21]–[24] . Among these latter methods , the most widely used is [21] , which estimates the expected joint site frequency spectrum for an arbitrary set of parameters by a diffusion approach . Whereas the estimation of the expected SFS is relatively fast , the optimization of the parameters is still time-consuming , which prevents to tackle models with more than three populations at the same time . While some methods can extract demographic information from single whole-genomes per population [25] , [26] , SFS-based methods , when applied to multiple individuals , do not require whole genome data because correct estimates of the SFS can be obtained from a few Mb [21] . However , with few exceptions [11] , the accuracy of SFS-based methods has not been properly assessed , and their ability to infer demographic parameters has been questioned [27] . One advantage of SFS-based inference methods is that they can handle large next generation sequencing ( NGS ) data sets [28]–[30] . However , the computation of the SFS from NGS data is not always trivial . An empirical Bayes approach has been proposed to estimate the joint 2D SFS from low coverage data [31] and an unbiased maximum likelihood approach has been developed to recover the SFS for a single population [32] . SFS obtained from low-coverage genomic data often show a deficit of rare alleles because a given allele needs to be observed in several individuals to exclude read errors [28] , [33] . These missing low frequency variants can lead to imprecisions and biases in population genetic inferences [34] . Several approaches have been proposed to correct for this bias [32] , [35] , either during the process of genotype calling itself [e . g . 31] , [36] , [37] or later by applying quality filters on called genotypes [e . g . 38] . Gravel et al . [28] have also proposed to predict the SFS from low-coverage data by using an overlapping subset of high quality data to derive a generalized correction of the SFS . It appears likely that SFS estimation will improve with higher coverage NGS data , and that such data will become increasingly available and used in the near future . As an alternative to deep sequencing , one could use information from a few tens of thousands SNP scattered over the whole genome to make demographic inference , but most SNP chips have complex and often unknown ascertainment schemes that bias the SFS if not properly taken into account [39]–[41] . However , a new SNP chip has recently been introduced [42] , [43] , which implements a known and simple ascertainment scheme where SNPs are selected at random from sites that are heterozygous in a single individual of a given population . Whereas this ascertainment scheme has no major effect on statistics designed to infer admixture [42] , it biases the site frequency spectrum [44] , [45] and thus potentially alters the estimation of other parameters . Using simple combinatorics , the SFS can be unbiased [44] in a single population , and this strategy could be extended to unbias joint SFS under complex models involving more populations . A diffusion approach has been recently proposed to estimate divergence times between two populations based on the fraction of SNPs having occurred recently in the ascertained population [45] , but this approach is currently restricted to the sole estimation of divergence time and cannot be applied if gene flow occurred between populations . In this paper , we introduce a flexible and robust way to estimate demographic parameters from the SFS inferred from sequence or SNP chip data that we implemented in the fastsimcoal2 software . Our method is based on Nielsen's approach [17] , which estimates the expected SFS from simulations under any demographic model . We compare the performance of this approach to [21] under a variety of evolutionary models with simulated data , and we show that it can successfully handle models including more than three populations . We also show how this approach can be extended to deal with ascertained SNP panels by explicitly modelling the ascertainment bias and computing likelihoods based on expected ascertained SFSs . We first apply our method to a large human genomic data set from which we estimate the demography of four populations , and then to two separate Affymetrix ascertained SNP panels [43] from which we estimate the demography of two African populations .
We performed parameter estimations for 10 data sets generated under each of the 3 evolutionary scenarios shown in Figures 1A–1C . We took two approaches for estimating demography: our new approach based on a composite multinomial likelihood where the expected SFS is obtained using coalescent simulations and [21] , which computes a composite Poisson likelihood where the expected SFS is obtained by a diffusion approximation . The two approaches have a very similar accuracy under a simple bottleneck scenario ( Figure S4 ) and under a scenario of population isolation with migration [46] ( IM model , Figure S5 ) . For both approaches we report the estimates leading to the maximum likelihood obtained among 50 independent runs . Under these conditions , leads to extremely accurate estimations for most data sets . However , in a few cases ( 1/10 for the bottleneck scenario , and 2/10 for the IM model ) , the best likelihood obtained from 50 runs led to very divergent estimates , which were not reported in Figures S4 , S5 . For those cases , the log likelihood appeared orders of magnitude smaller than those inferred for other data sets and could be easily spotted . Although it is possible to recognize that additional runs are necessary to get meaningful estimates , we did not follow this procedure here , as we wanted to allocate similar resources to the two programs and get results using an automated procedure not requiring further user tweaks . Contrastingly , fastsimcoal2 estimations seem to converge to correct values for all data sets in Figure S4 and S5 , even though the variances of the estimators are slightly larger than 's for those cases where both approaches agree on the correct demographic model . Parameter estimations under the more complex scenario of Figure 1C , mimicking a simple model of human evolution , are reported in Figure 2 . In this case , results obtained by fastsimcoal2 are again very accurate and close to the true values for all 10 data sets . With , we report results for only 8 data sets due to potential lack of convergence , as explained above . However , even for these 8 data sets , the best estimates can be quite far from the true parameters , especially for parameters related to the ancestral bottleneck . It suggests that for complex scenarios involving three populations and more than 5 parameters , needs to be run from many more than 50 initial conditions and that some iterative refinements of search ranges might be necessary to obtain correct solutions ( R . Gutenkunst , personal communication ) . Note that a lack of robustness of under certain conditions ( e . g . high migration rates between populations ) had already been reported before [11] , [24] . We have estimated parameters for the more complex hierarchical continent-island model shown in Figure 1D , involving samples from 10 different populations ( islands ) , a model that cannot handle . Continent-island models are equivalent to infinite islands models , and have been used to model recent spatial expansions [see e . g . 47] . This model could therefore represent two successive spatial expansions , the first one stemming from an ancestral refuge area , and the second one starting more recently from a single deme belonging to the first expansion wave . The parameters of interest are here the immigrations rates in each sampled deme , the timing of the spatial expansions and the ancestral population size . As shown in Figure 3 , all these parameters are extremely well estimated by fastsimcoal2 when we maximize the multiple pairwise composite-likelihood shown in eq . ( 7 ) . We note that we can also recover very well the immigration rate to the unsampled deme ( rightmost column in Fig . 3 ) from which the second expansion started . The accuracy of the immigration rate estimations is quite remarkable , given that they span over two orders of magnitude and that we specified the same search intervals covering four orders of magnitude for each parameter . We first applied our methodology to the problem of estimating the past demography of two African , one European and one African-American populations . The multidimensional SFS for these 4 populations was estimated from more than 220 , 000 non-coding SNPs , each located more than 5 Kb away from its closest neighbour , such as to minimize linkage disequilibrium between SNPs . We examined three evolutionary scenarios shown in Figure 4 to explain observed patterns of diversity . In the first and simplest scenario ( Figure 4A ) , the South Western African American population ( ASW ) was assumed to have been formed 16 generations ago ( around 1600 AD ) with initial input from one European ( CEU ) and two Niger-Congo speaking African populations ( Yoruba from Nigeria: YRI; Luhya from Kenya: LWK ) having diverged earlier . In order to calibrate the other parameters , we assumed that the European population diverged from the ancestral African population 50 Ky ago [28] , [48] . Under this scenario , we find that the ASW population would have initially received 16% ( CI95% = [15–17%] ) of its gene pool from the CEU population , 83 . 8% from the YRI population and almost nothing ( 0 . 2% ) from the LWK population ( see Table 1 , Model A ) . This European contribution is in line with previous estimates obtained from SNP-chip allele frequencies ( 17% for Southwest African Americans [49] ) . Under model A , the two Niger-Congo populations would have diverged very recently ( 70 generations ago , CI95% = [56–197] ) , and the CEU and YRI populations have the smallest effective population sizes ( around 4000 individuals ) , whereas the ASW population has the largest ( NASW = 170 , 000 individuals ) . The inferred human ancestral population size is relatively small ( about 8000 individuals ) and there is no real signal of an ancestral bottleneck since the estimated bottleneck size ( NBOT = 7083 ) is only 12% smaller than the ancestral size , in line with recent results showing no evidence for a strong Pleistocene bottleneck in humans [50] . Whereas model A captures some obvious features of the past demography of these populations ( see Table S1 ) , it seems relatively unrealistic for some other features ( i . e . a direct contribution of the CEU and YRI populations to ASW ) . We therefore investigated a more realistic but more complex and parameter-rich model involving several other unsampled populations , as shown in Figure 4B ( see Material and Methods for a complete description of this model ) . The multiple continent-island model B1 assumes that the ASW population was founded by migrants originating from a Niger-Congo and from a European metapopulations , from which the two Niger-Congo and the CEU populations currently receive migrants . It also assumes that the Niger-Congo and the European metapopulations passed through a bottleneck when they diverged from an ancestral African population . An even more complex scenario B2 includes a potential admixture of the Luhya population ( a Niger-Congo speaking population from Kenya ) with an unsampled ( potentially East-African ) population , which also diverged earlier ago from the ancestral African population . The model parameters estimates and their confidence intervals obtained by a parametric bootstrap approach are listed in Table 1 . The two models show overall very congruent values and overlapping 95% confidence intervals for their common parameters . The agreement is especially good for the human ancestral size ( NANC = 12–13 , 000 individuals ) , the ancestral African population size ( NAFR = 25–27 , 000 ) , the continental European size ( NEUR = 14 , 500–16 , 500 individuals ) , the European strong bottleneck intensity ( IBEUR = = 0 . 42–0 . 43 , where is the bottleneck duration , and is the bottleneck size ) , the Niger-Congo milder bottleneck intensity ( INC = 0 . 027–0 . 028 ) , the divergence time of the Niger-Congo metapopulation ( TNC = 793–797 generations ) , the time to the shift to the ancestral human population size ( TBOT∼10 , 000 generations ) , and the European contribution to the ASW population ( aE = 0 . 16–0 . 17 ) . The other parameters show different point estimates but all have overlapping confidence intervals . We have plotted the marginal SFS for each of the four populations in Figure S6 , to visualize the fit of the expected and observed SFS for each model . Whereas the expected population specific marginal SFSs show some discrepancies with the observation for the four populations under model A , the fit is much better for model B1 , except for LWK , which still shows an underestimation of singletons and doubletons . Model B2 , which allows for LWK admixture , leads to a much better fit for the LWK population , as shown by the cumulative distribution of differences between the expected and observed marginal SFS ( see 3rd row in Figure S6 ) . Under this model B2 , we estimate the LWK population to have 17% admixture from an unspecified but probably East African ( see e . g . Figure 1 in ref . [51] ) population . This East African population would have diverged from the ancestral African population more than 2200 generations ago ( 95% CI 1274–3586 ) , thus potentially before the out-of-Africa dispersal . Even though the different models can be conveniently compared on the basis of their marginal SFSs , these 1D SFSs only capture a small fraction of the total ( multidimensional ) SFS . Therefore the models are better compared on the basis of their likelihood . This is formalized here by a model comparison procedure based on AIC [52] , revealing that the relative likelihood of models A and B1 are almost 0 as compared to that of model B2 ( see Table S2 ) . We estimated the parameters of African past demographies shown in Figure 5 based on Yoruba and San samples for which we have independent SNP panels ( see Methods section ) . In model A ( shown in Figure 5A ) , we assumed that the Yoruba and San samples were taken from large populations that expanded after their divergence , and we allowed for a single pulse of gene flow between them at a given time Ta in the past . The model B ( shown in Figure 5B ) includes the divergence of two-continent island metapopulations , and assume that the sampled populations are each an island attached to these continents and that the two continents exchanged migrants some time ago in a single pulse of gene flow , like in model A , but also earlier in time ( see Figure 5B and material and methods for a complete description of the model ) . The point estimates of the two models and their associated 95% confidence intervals ( CI ) inferred from 100 parametric bootstraps are reported in Table 2 for both SNP panels . Overall , the two SNP panels show congruent point estimators and CI widths under the two models . There is only one parameter ( NAY ) for which the CI do not overlap under model A , which suggests that the two panels provide broadly compatible scenarios of African demography . Estimations from data simulated under the same model for parameter values similar to those inferred in Figure 5A show ( see Figure S8 ) that i ) both panels should perform very similarly for estimating parameters , ii ) all parameters of the model should be well estimated , except those related to a very recent expansion of one of the ascertained population , iii ) ancestral population sizes and divergence times are particularly well estimated , and iv ) the addition of a single Denisovan sequence allows one to recover the absolute values of the parameters . Concentrating on the parameters common to both models , we see in Table 2 that the ancestral size NANC shows very similar estimates across models and panels , with an estimated value around 9 , 000–9 , 500 individuals ( in line with estimates obtained with non-ascertained data set ) . The African population size is also consistently estimated to be around 18 , 000–28 , 000 individuals across models , and the ancestral Yoruban size appears smaller and between 5 , 500 and 13 , 000 individuals . These estimates fit well with previous Bayesian estimations of African demography from nuclear markers under slightly different models . Based on microsatellites , Wegmann et al . [13] estimated the ancestral size of Niger-Congo ( NC ) populations ( to which Yoruba belong ) to be 12 , 500 individuals and that of the ancestral African population to be 15 , 000 individuals . More recently , the analysis of 40 non-coding regions of 2 Kb [53] led to estimates of NC and African ancestral size to be 17 , 500 and 11 , 000 individuals , respectively , as well as a San effective size of the order of 20 , 000 individuals . The differences between these estimations and ours might be due to the fact that these previous analyses were based on slightly different models that assumed constant sizes for all current populations and the same population size before the split with Denisovans . In addition , we find evidence for some asymmetrical gene flow between San and Yoruba , around 500–600 generations ago ( 12 . 5–15 Kya ) under model A , and much more recently ( 60–80 generations ago ) under model B . Interestingly , this is the only parameter common to the two models that shows such drastic difference . Despite this disparity , which could be due to the fact that we allow for earlier migration between the two metapopulations in model B , we obtain very similar estimates for the admixture rates between populations both between panels and across models . Overall , we find a slightly larger extent of gene flow from Yoruba to San than the reverse , but the confidence intervals of the two parameters seem quite overlapping under both models . Under model A , the point estimates for the divergence time TDIV are much more different than what was obtained under our simulations ( Figure S8 ) , with a much younger divergence suggested by the San panel ( 2 , 600 generations or 65 Kya ) than for the Yoruba panel ( 4 , 700 generations or 117 . 5 Kya ) . Taking the middle of the overlap between the two CI would lead to a divergence time of 4 , 500 generations or 112 . 5 Kya ( Table 2 ) , in keeping with a recent estimate of the divergence of Khoisan populations obtained by an ABC approach [110 Ky , 53] , and compatible with the divergence time estimated between San and other West African population ( 65–120 Ky in [54] , or ∼100 Ky in [55] ) . Under model B , the two estimates obtained for panel 4 and 5 , show a similar discrepancy , but the estimated values are much higher ( 5 , 530 and 10 , 330 generations for panels 4 and 5 , respectively ) , which can also be due to the fact that we authorize some gene flow between the two metapopulations after their divergence . If we again take the middle of the overlap between the two CI , we obtain a value of 7 , 500 generations ( 180 Kya ) , substantially larger than the value obtained under model A ( 4 , 500 generations ) . An examination of the parameters restricted to model B suggests that the Yoruban continent expanded recently 170–300 generations ago ( 4250–7500 ya ) , from a relatively small population of 600–3600 individuals , and that the Yoruban island receives more migrants ( around 18 per generation ) than the San island ( 2–3 individuals per generation ) . The age of the expansion is slightly older than the divergence time between two Western Niger-Congo populations estimated previously ( 140 generations , [13] ) , and intermediate between the age of the Niger-Congo languages ( ∼10 Kya , [56] ) , and that of the Bantu expansion ( ∼5 Kya , [57] ) . The larger immigration rate seen in Yorubans is compatible with the fact that farmer populations generally maintain higher levels of gene flow with their neighbours than hunter-gatherers due to their larger effective size [47] . Note however that all parameter estimates mentioned above assume that the Denisova divergence time is correctly estimated at 16 , 000 generations or 400 Kya [58] , even though there is still a large uncertainty attached to this divergence time , which could range from 230 to 650 Kya [58] or even between 170 and 700 Kya in a more recent study [59] . Reported estimates and CI in Table 2 do not take this uncertainty into account , and should thus be rescaled if a different divergence time between Denisovans and Humans was proposed . Like in the case of non-ascertained data , we find that the more complex model is much better supported by the data . Even though this better fit is barely visible when considering the marginal 1D expected SFS ( see Figure S10 ) , this is more exactly quantified by an AIC analysis ( Table S3 ) revealing that the relative likelihood of model A is close to zero for both panels when compared to model B .
We have introduced a new and flexible simulation-based approach to estimating demographic parameters . For the tested scenarios , our composite-likelihood approach is as precise as [21] , which is the current standard in the field . Our approach seems more robust than since it is more likely to converge towards the correct solution when starting from the same number ( 50 ) of initial conditions ( see Figures 2 , 3 , S4 , S5 ) . In terms of computational speed , point estimates are very quickly obtained by for simple models ( on average 15 seconds and 6 minutes for models in Fig . 1A and 1B , respectively , compared to 15 minutes and 2h30 for fastsimcoal2 , respectively ) . However , fastsimcoal2 is much faster for more complex models with three populations and migration ( 4–5 h per run for fastsimcoal2 for model on Fig . 1C , compared to 34 h on average for ) . By maximizing the fit of two-dimensional SFS , fastsimcoal2 can also explore very complex models involving more than 10 populations with migration , which cannot be tackled by any other current method . Since fastsimcoal2 and use a very similar likelihood function ( see Figure S3 ) , it seems that the improved convergence of our approach lies in the use of the ECM optimization scheme , which compensates for the use of non-optimal approximate likelihoods . Note that our robust ECM maximization technique and the maximization of the product of pairwise composite likelihoods could also be used by methods deriving the SFS analytically or by a diffusion approximation ( like ) , thus potentially enabling the analysis of models as complex as those studied here . Also note that recent progress in the computation of joint SFS using coalescent or diffusion approaches [18] , [23] have led to the development of promising demographic inference methods applied to the study of relatively complex evolutionary models [see e . g . 24] . Even though different demographic trajectories can lead to exactly the same SFS in a single population [27] , we do not find any evidence of parameter non-identifiability in our investigated cases . This is probably because we restricted our search to a limited set of possible histories , defined by few-parameter models . Our results confirm that if the true history lies within the models considered , the parameters of relatively complex scenarios can be well recovered from the ( joint ) SFS . However , we must keep in mind that histories outside our model family might have identical likelihoods . One disadvantage of our method ( and of any other simulation-based method ) is that we are approximating the likelihood , implying that two runs from identical initial parameter values can results in different estimations ( see Figure S2 ) . Using more simulations for the estimation of the likelihood would lessen but not totally suppress this problem , but our results show that our maximization procedure leads to almost completely unbiased estimates and converges to correct values . Another disadvantage of our approach is its dependence on composite likelihoods . More powerful full likelihood approaches explicitly take into account linkage disequilibrium ( LD ) between sites [60] , and therefore might reveal useful to infer recent migration events ( see e . g . [61] ) . That being said , our applied data sets consist of SNPs randomly distributed across the whole genome , and so patterns of LD between sites are minimal . Whereas confidence intervals of demographic parameters based on composite likelihood ratios should in principle be too narrow ( see e . g . [21] , [60] , [62] , [63] ) , a study based on short stretches of DNA sequences has empirically shown that they were extremely similar to those obtained by explicitly modeling patterns of recombination [54] . This appears unlikely to be true in general , and certainly not if products of pairwise composite likelihoods were used ( as with eq . ( 7 ) , which was actually not used for our test cases ) . Similarly , the use of composite likelihoods in model tests based on AIC can overestimate the support for the most likely model [64] . However , the composite likelihoods in our test cases are quasi likelihoods due to the global independence between SNPs , and the differences in relative likelihood of alternative models are so huge ( see Tables S2 and S3 ) that some residual patterns of LD are unlikely to change our conclusions . As an alternative to our composite likelihood maximization approach , Garrigan [22] has proposed to integrate an approximate likelihood computed in a way similar to ours into an MCMC algorithm , allowing him to get posterior distributions and credible intervals . Whereas MCMC algorithms generally assume that the likelihood is computed accurately , it has been shown that MCMC procedure should lead to correct posterior distributions even if the likelihood is approximated , provided that there is no systematic error in its computation [65] , [66] . This Bayesian approach could be worth exploring as a possible extension of our likelihood maximization procedure . However , our current implementation has the advantage of quickly getting point estimates , around which CIs can be obtained later by repeating the estimation on bootstrapped samples . For instance , a point estimate for the IM model shown in Figure 1B is obtained in about 2h30 on a single core machine , whereas 40–80 h are necessary to get posterior distributions for the parameters of a similar IM model from a single MCMC run using a specialized coalescent program on a multi-core machine [see 22] . The additional versatility of our simulation-based likelihood approach is well exemplified by its handling of ascertained SNP chips , and the inference of several parameters from the SFS under complex demographic scenarios . Previous ways of handling ascertained SNP chips either consisted in removing the bias induced by the ascertainment [44] or taking it into account in the estimation procedure [39] , [45] . However , these methods are usually not as general as our implementation , as they are either restricted to models including a single population [44] , or to the case of the sole estimation of divergence time between two populations [45] . Contrastingly , our method can be applied to various types of demographic models including several populations , bottlenecks and migration . Our simulation results suggest that parameters of complex models can be correctly recovered when the ascertainment consists of randomly chosen SNPs heterozygous in a single individual ( Figures S8 and S9 ) . Interestingly , we find that some parameters of unascertained populations that diverged a long time ago either with ( Figure S8 ) or without ( Figure S9 ) admixture can also be quite well estimated when the model is well specified . This suggests that a given ascertainment panel of the GWHO Affymetrix chip could be used to infer parameters in several related populations . It is also worth noting that our calibration of parameters relied on the assumption that the divergence time with an outgroup population was known , but a different divergence time would only require a rescaling of the estimated parameters . The use of an outgroup species with fixed divergence time is a standard way to calibrate mutation rates ( as e . g . in [21] ) , but we note it could also be used within species for DNA sequence data when some uncertainty exist on mutation rates , which is currently the case in humans [67] , [68] . Most parameters inferred from real African populations have very similar estimates and confidence intervals irrespective of which SNP panel is used ( Figure 5 , Table 2 ) , which agrees with our simulation results ( Figures S8 , S9 ) . However , a few parameters seem to provide relatively divergent estimates , like the Yoruba and the African ancestral size , as well as the Yoruba-San divergence time , a discrepancy that is not really expected from the simulations . This discrepancy could stem from either an unknown source of ascertainment , from a misspecification of the model for one of the two ascertained population , or from an ascertained individual that is not representative of its population , the latter case being possibly due to inbreeding or admixture . It currently appears difficult to disentangle these cases , and the inclusion of additional parameters in model B only seems to marginally improve the fit of the expected SFS to the data . It suggests that our models still do not capture all aspect of the true demography of these populations , which might also affect our ability to reproduce the ascertained SFS , and have a negative impact on our estimations . We note however that previous estimates of African demography [e . g . 53] are more in line with those inferred from the Yoruba than from the San panel , which could suggest that our demographic models are more appropriate for the Yoruba than for the San population . Overall , our results nevertheless show that meaningful demographic estimates can be obtained from ascertained SNP chips , suggesting a useful and cheap alternative to large scale sequencing for demographic inference . Our methodology has the potential to infer demographic parameters from large scale genomic data under a much wider range of neutral evolutionary models than either the current implementation of , current Approximate Bayesian Computation ( ABC ) implementations [69] , summary statistics based approaches [11] , or other existing likelihood-based methods [22] . Whereas ABC has the potential to be applied to genomic data , it has rarely been done since it usually requires the simulations of data sets as large as those analysed , which is computationally very costly . Our approach could thus be seen as a powerful likelihood-based alternative to the study of complex evolutionary models , which are usually only tackled by ABC approaches [see e . g . 16] , [70] , [71] , with the additional advantage of not having to choose which summary statistics to use for the inference , which is often a problem in ABC [e . g . 13] , [72] , [73] , [74] . Our approach can indeed tackle complex evolutionary models with a relatively large number of populations ( see Figs . 1D , 4B and 5B ) . For instance , the model shown in Figure 4B includes 4 sampled populations , as well as four other unsampled populations , whose demography also needs to be reconstructed . AIC analysis reveals that the cost associated to increasing model complexity is rewarded by a much better fit to the data . One should however make a distinction between the inclusion of additional parameters for a given number of populations ( e . g . adding the possibility to have gene flow between populations ) , and the inclusion of additional populations . The addition of unsampled or ghost populations can not only modify parameter estimations but also alter our interpretation of the results ( see e . g . [75] , [76] ) . For instance , the inclusion of continents from which sampled populations received migrants ( which is an attempt at taking into account the spatial structure of African populations ) in Figure 4B improved the fit of expected SFS ( see Table S2 ) , without really modifying our estimation of the level of European admixture , but it radically changed our interpretation of the relationships between African Americans and extant African and European populations . As expected , the inclusion of a potential source of admixture for the Luhya population in model B2 improved the fit of the model and it allowed us to make inference about this ghost population , but it also modified estimated parameter values of this and other populations . These observations suggest that complex models are better studied by considering all populations simultaneously , and that a strategy consisting in estimating population-specific parameters and fixing them when incorporating additional populations would not be optimal . There are still some limits to the complexity of models that can be studied , and AIC-like approaches can be used to study which modifications sufficiently improve the model to be preserved . However , the question of whether our best model is the true model is not addressed by model comparisons such as likelihood ratios or AIC . One would ideally like to assess how well the model explains the data , which is usually done by some posterior predictive check in a Bayesian setting [77] , or by getting the data p-value under a frequentist approach . We have implemented such an approach , where the model p-value was evaluated by comparing an observed G-test statistic [3] , [62] to its model distribution . As expected , this approach leads to non-significant p-values when applied to simulated data sets ( Figure S11 ) . However , the p-values for all models shown in Figures 4 and 5 are highly significant ( p = 0 , Figures S12 and S13 ) suggesting that our implemented models of human evolution are still overly simplistic . This is not surprising given the high-dimensionality of the parameter space and the large amount of SNPs at hand giving us high power to reject inaccurate hypotheses . Since models are generally expected to be wrong , the question is at what point is a model so wrong that it is no longer useful [78 , p . 74] . The fact that the addition of plausible source of realism into our models significantly improves the fit to the data ( Tables S2 and S3 ) is reassuring in the sense that we have a methodology to refine our still imperfect evolutionary scenarios .
Nielsen [17] has shown that one could estimate the likelihood of a demographic model , where X is the site frequency spectrum , on the basis of coalescent simulations . This is because the probability of a given derived allele frequency i is simply a ratio of branch lengths of the coalescent tree expected under model as [17]: ( 1 ) where is the total length of a set of branches directly leading to i terminal nodes , and T is the total tree length . This probability can then be estimated with arbitrary precision on the basis of Z simulations as [62] ( 2 ) where is the length of the j-th compatible branch in simulation k ( see Figure S1A ) . Note that the estimator shown in eq . ( 2 ) implicitly weights simulations according to the probability that a mutation occurs on the simulated tree . Note that an estimator of the form ( as used by Garrigan [22] to estimate the expected SFS ) would give each tree the same weight and would thus give an excessive weight to genomic regions with shallow coalescent trees , which can be a problem for recently bottlenecked populations . If some simulated entries of the SFS were zero ( because ) , was set to an arbitrarily small values [as in 22] chosen here as . We have empirically checked that our procedure gives the correct SFS under two simple scenarios for which the expected SFS can be obtained exactly by the method developed by Chen [18] for cases involving up to two populations and no migration . These scenarios were ( i ) a bottleneck model ( as in Fig . 1A ) and ( ii ) a divergence model without migration ( as in Fig . 1B but without migration ) . We show in Figures S14 and S15 for scenarios i ) and ii ) , respectively , the fit of the SFSs entries ( estimated by our approach for different numbers of coalescent simulations ) to the true SFS entries . As expected the fit improves with the number of simulations , and the estimated SFS entries are distributed symmetrically around the true values without any visible bias for these two scenarios . Probabilities inferred from the simulations and eq . ( 2 ) can then be used to compute the composite likelihood of a given model as [20] ( 3 ) where is the SFS in a single population sample of size n , S is the number of polymorphic sites , L is the length of the studied sequence , and is the probability of no mutation on the tree , obtained as assuming a Poisson distribution of mutations occurring at rate . This formulation can be extended for the joint SFS of two populations as ( 4 ) and one can define a v-dimensional SFS for more than two ( v ) populations as ( 5 ) where is a composite index . However , when the number of populations in the model is larger than 2 and sample sizes are relatively large , the number of entries in the v-dimensional SFS can be huge , implying that most entries of the observed SFS will be either zero or a very small number and that the expected values for these low-count entries will be difficult to estimate precisely . In that case , we have chosen to estimate the v-dimensional by collapsing all entries with observed SFS less than a predefined threshold as ( 6 ) When v>4 , this approach will also prove computationally difficult , and in that case we have chosen to compute a composite composite-likelihood ( C2L ) obtained by multiplying all pairwise CL's , as ( 7 ) where is given by eq . ( 4 ) . As the likelihood is obtained by simulations , which incurs some approximation , we cannot use optimization methods based on partial derivatives . Even though other methods would be possible , we have chosen to use a conditional maximization algorithm [ECM , 79] , which is an extension of the EM algorithm where each parameter of the model is maximized in turn , keeping the other parameters at their last estimated value . The maximization of each parameter was done using Brent's [80 , Chapter 5] algorithm , which is a root-finding algorithm using a combination of bisection , secant and inverse quadratic interpolation [see e . g . 81] . We start with initial random parameter values , and perform a series of ECM optimization cycles until estimated values stabilize or until we have reached a specified maximum number of ECM cycles ( usually 20–40 ) . Unless specified otherwise , we used 100 , 000 coalescent simulations for the estimation of the expected SFS and likelihood for a given set of demographic parameters . Even though a higher precision could be reached with a larger number of simulations , especially for complex models , this number appears like a good compromise between computational efficiency and likelihood estimation accuracy ( see Figure S2 ) . Note that the imprecision on the likelihood estimation might also prevent an efficient optimization of our parameters , as a sub-optimal parameter might give by chance a better likelihood than the optimal one during an ECM cycle . Because the composite likelihood surface might have several local maxima and be difficult to explore [e . g . 60] , several independent optimizations are performed ( between 20 and 40 depending on the model and computation time ) , each starting from different initial conditions , and the overall maximum composite likelihood solution is retained . Coalescent simulations , estimation of the SFS , likelihood computations and its maximization were all done with fastsimcoal2 , a modified version of the fastsimcoal program [82] . fastsimcoal2 input file format and command lines arguments are briefly described in Supplementary Text S1 , and examples of input files are provided in Supplementary Text S2 . We have tested our program ability to recover demographic parameters from DNA sequence data in four relatively plausible but distinct scenarios of population differentiation involving one to ten populations with migration ( see Figure 1 ) . In all cases , we simulated with fastsimcoal2 400 , 000 unlinked regions of 50 bp , thus totaling 20 Mb of DNA sequences , assuming a mutation rate of 2 . 5×10−8 bp−1 per generation and an infinite-site model . Pseudo-observed SFS were also directly computed with fastsimcoal2 . Parameters were estimated independently from ten data sets generated under each model . For each data set generated under models with one to three populations , we performed 50 parameter estimations via ECM maximization , and each time retained the parameter set with maximum likelihood . For the model with 10 populations we only performed 20 estimations per data sets , and used 50 , 000 simulations instead of 100 , 000 for the other models to estimate the expected SFS due to long computation times . We describe the four tested models in Figure 1 , and the used parameter values are showed as red dots in Figures 2 , 3 , S4 and S5 . Absolute numbers ( generations , population sizes ) were obtained by assuming that the mutation rate of 2 . 5×10−8 bp−1 per generation was known . As a benchmark , we used to infer the demographic parameters in scenarios shown in Figure 1A–1C involving up to three populations . For each generated data set , we performed 50 parameter estimations using the Broyden-Fletcher-Goldfarb-Shanno ( BFGS ) optimization method implemented in , and we retained the parameters associated with the maximum likelihood . We followed 's manual specification to set reasonable upper and lower bounds of the search ranges of the parameter . In all cases , the expected SFS was estimated by extrapolating the SFS inferred from 3 grid sizes set to 40 , 50 and 60 , which are in all cases larger than our maximum samples sizes ( 30 in the IM model case ) . The composite likelihood was computed using 's multinomial model , which is in fact a product of Poisson likelihoods , where the expected model entries are scaled to sum up to 1 . This likelihood also ignores information about the expected and observed numbers of monomorphic and polymorphic sites used in our likelihood formulation ( as well as in [20] ) . Therefore , the ratio should be equal to showing that barring the terms , the two CLs differ by a single constant value . The difference between likelihoods computed with fastsimcoal and is illustrated in Figure S3 for the case of the bottleneck scenarios shown in Figures 1A . It shows that when monomorphic sites are not taken into account , fastsimcoal and indeed produce essentially identical likelihood profiles around true parameters . However , when monomorphic sites are used in the likelihood , the shape of the likelihood profiles differs , making it more or less peaky depending on the parameter . There is thus no clear advantage in using one or the other likelihood form for this scenario , but our use of monomorphic sites allows us to directly get absolute values of the parameters . We report in Figures 2 , S4 and S5 only the results obtained for data sets for which 's best log likelihood was less than 10% lower than the largest log-likelihood obtained with the other data sets , and we considered not to have converged for the discarded data sets . Recently , Affymetrix developed a new SNP array including ∼629 , 000 SNPs with known ascertainment scheme for population inference ( Axiom Genome-Wide Human Origins 1 Array , http://www . affymetrix . com/support/technical/byproduct . affx ? product=Axiom_GW_HuOrigin ) [43] . This array , abbreviated hereafter GWHO , is made up of SNPs defined in 13 discovery panels . In the first 12 panels , SNPs have been identified by comparing the two chromosomes of an individual from a known population , further quality checks and validation on a large population sample [43] . The 13th panel contains SNPs that are polymorphic when comparing the Denisovan sequence and a random San chromosome . Raw genotypes from 943 unrelated individuals from more than 50 worldwide populations are freely available on ftp://ftp . cephb . fr/hgdp_supp10/ . The ascertainment scheme of this array is simple and homogeneous over a given panel . However , the SFS inferred from this array is biased as only mutations that occur in the ancestry of the two compared chromosomes will be considered ( see Figure S1B ) . We show in Figure S7 the difference between the ascertained and non-ascertained SFS under a few basic demographic scenarios in a single population . The differences between the two SFS can be quite dramatic , implying that the estimation of demographic parameters on ascertained data sets without taking the ascertainment into account is bound to lead to biased estimates . Nielsen et al . [44] have shown how to correct the expected SFS within a given population under such a simple ascertainment scheme , and the ascertained joint SFS could be unbiased in a similar way by taking into account ascertainment probabilities in the ascertained populations . Rather than unbiasing the SFS , we have chosen here to incorporate the bias in the model and to infer demographic parameters directly from the ascertained ( joint ) SFS , a strategy similar in spirit to that used by Gravel et al . [28] to account for biases in the SFS obtained from low-coverage next-generation sequencing data . It implies we need to model the ascertainment scheme in the coalescent simulations such as to infer the expected ascertained SFS for a given demography . In order to estimate the SFS when SNPs are defined as being sites heterozygous in a given individual , we use the following procedure: 1 ) we perform conventional coalescent simulations under a given demography , 2 ) we choose two lineages at random in the ascertained population , 3 ) we identify the subtree relating the chosen lineages to their most recent common ancestor ( MRCA ) ( highlighted in blue in Figure S1B ) , 4 ) we update the numerator in eq . ( 2 ) by summing up branch lengths of the blue subtree that are ancestral to i1 lineages in population 1 , i2 lineages in population 2 , … , iv lineages in population v , 5 ) The denominator of eq . ( 2 ) is updated by summing up the total length of the blue subtree . Parameter optimization is then performed similarly to the unascertained case , except that the terms depending on the number of monomorphic sites ( ) in eq . ( 6 ) are removed from the likelihood since only polymorphic sites are reported on the ascertained chip , which implies that we cannot use a molecular clock . Therefore , parameter absolute estimation should be done relative to an arbitrarily fixed or known parameter ( e . g . population size , divergence time ) . Note however that a molecular clock could be used if the fraction of sites found heterozygous were known in ascertained individuals , as in this case the expected fraction of monomorphic sites would then simply be , where would be the total length of the expected ascertained tree ( shown in blue in Figure S1B ) . As mentioned in the next section on model test , it might be difficult to accept a simple model with a G-test based on tens of thousands of polymorphic sites , but in that case , it might be better to establish a procedure allowing one to improve models , by progressively adding some realism to simple models [89] . Our likelihood-based approach would in principle lend itself to model comparison through likelihood-ratios for nested models or through Akaike Information Criterion ( AIC , [52] ) for other model comparisons . However , we are here confronted with two distinct problems . The first one affects all composite likelihood approaches and due to the fact that the distribution of the composite likelihood ratio test ( CLRT ) is generally unknown . When the SFS is obtained from DNA sequences with relatively large levels of linkage disequilibrium , it has been proposed to obtain an empirical distribution of the CLRT by simulation of DNA sequences with recombination ( e . g . [54] , [90] ) . In the case of the AIC , Varin and Vidoni [91] have proposed to replace the number of parameter d of a given model in by an effective number of parameters that needs to be computed from a sensitivity matrix and Godambe Information matrix , which might be difficult to do in practice . We note however that in our two applied examples the SFS is computed from a collection of SNPs randomly distributed across the genome , such that we shall conservatively assume that the CL computed from the multidimensional SFS is close to a true likelihood . Note that this assumption would not be valid if one had computed a composite likelihood based on the product of pairwise composite likelihoods , like in eq . ( 7 ) . The second problem is linked to the fact that we estimate the likelihood with some error ( Figure S2 ) . As noted previously , this can prevent us to efficiently optimize our parameters , but it also means that the likelihood ratios or AIC statistics are imprecisely estimated . To address this problem , we have compared models on the basis of the maximum value of the likelihood obtained over 100 estimations performed for the ML parameters obtained by our optimization procedure . We then calculated the relative likelihood or the Akaike's weight of evidence in favor of the i-th model as ( see e . g . [89] ) where . Even though we can estimate parameters under any model , it can be useful to check how data fit the chosen model . To this aim we use an approach based on a likelihood ratio G-statistic [3] , [62] of the form , where CL0 is the observed maximum composite likelihood where the expected SFS is replaced by the relative observed SFS in eqs . 3 , 5 or 7 , and CLE is the estimated composite maximum likelihood computed using the procedure described above . We obtain the null distribution of the CLR statistic by the same parametric bootstrap procedure used to infer confidence intervals , where we generate by simulation a number of data sets using the estimated maximum-likelihood parameters of the model , and each time perform parameter estimations and estimate the CLR statistic . We can compute the p-value of the observed CLR statistic from this null distribution . Note that this type of G-test has been used before to find genomic regions under selection [3] , [62] . We report in Figure S11 the null distributions of the CLR and the p-values of two data sets generated under models shown in Figure 1A and 1B . In both cases , the p-values are not significant confirming that the data sets are compatible with the tested models . Note however that a non-significant p-value is not an absolute proof that the tested model is correct , as there could be a large number of models leading to similar SFSs , as was shown previously [27] , but it is an indication that the observed SFS is well explained by the model . However , in applied cases , we actually expect that this test leads to very significant values , since the true history of the populations is completely unspecified and our models are certainly overly simple and potentially far from reality .
|
We present a new likelihood-based method to infer the past demography of a set of populations from large genomic datasets . Our method can be applied to arbitrarily complex models as the likelihood is estimated by coalescent simulations . Under simple scenarios , our method behaves similarly to a widely used diffusion-based method while showing better convergence properties . In addition , our approach can be applied to very complex models including as many as a dozen populations , and still retrieve parameters very accurately in a reasonable time . We apply our approach to estimate the past demography of four human populations for which non-coding whole genome diversity is available , estimating the degree of European admixture of a southwest African American population and that of a Kenyan population with an unsampled East African population . We also show the versatility of our framework by inferring the demographic history of African populations from SNP chip data with known ascertainment bias , and find a very old divergence time ( >110 Ky ) between Yorubas from Western Africa and Sans from Southern Africa .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
|
Robust Demographic Inference from Genomic and SNP Data
|
The cuticular exoskeleton of insects and other arthropods is a remarkably versatile material with a complex multilayer structure . We made use of the ability to isolate cuticle synthesizing cells in relatively pure form by dissecting pupal wings and we used RNAseq to identify genes expressed during the formation of the adult wing cuticle . We observed dramatic changes in gene expression during cuticle deposition , and combined with transmission electron microscopy , we were able to identify candidate genes for the deposition of the different cuticular layers . Among genes of interest that dramatically change their expression during the cuticle deposition program are ones that encode cuticle proteins , ZP domain proteins , cuticle modifying proteins and transcription factors , as well as genes of unknown function . A striking finding is that mutations in a number of genes that are expressed almost exclusively during the deposition of the envelope ( the thin outermost layer that is deposited first ) result in gross defects in the procuticle ( the thick chitinous layer that is deposited last ) . An attractive hypothesis to explain this is that the deposition of the different cuticle layers is not independent with the envelope instructing the formation of later layers . Alternatively , some of the genes expressed during the deposition of the envelope could form a platform that is essential for the deposition of all cuticle layers .
The cuticular exoskeleton of insects provides multiple functions for the animal . It provides shape , protects the animal from the environment , limits water loss and provides the skeletal elements needed for locomotion . In different tissues of an individual animal the cuticle will have very different physical properties . For example , in one region the cuticle can be stiff and hard while in another region soft and elastic . The Young's modulus of different cuticles varies over about 8 orders of magnitude–a greater range than any other biological material [1] . The structure of the cuticle is as varied as its function and physical properties and is composed of multiple layers [1–3] . Cuticle is secreted in a sequential fashion by epidermal cells with the most external layer being secreted first . The number of layers and their thickness varies from one cuticle to another and insect cuticle has served as inspiration for a variety of human engineered material [4–6] . Insect cuticle contains proteins , chitin , lipids and water all of which are thought to be functionally important [1] . Some cuticle proteins have been identified by analyzing extracted soluble proteins from mature cuticle or cuticle in the process of synthesis [7–9] . Others have been identified by the analysis of RNA in cuticle synthesizing cells , by comparing the sequence of known cuticle proteins to genome sequences [10–13] or by serendipity [14] . The number of cuticle proteins encoded by the Drosophila genome is estimated to be around 150 and in some insect genomes the estimate is more than 200 [15] . A number of other proteins have been identified as being essential for the synthesis of normal cuticle such as chitin synthase [16 , 17] , chitinases [18 , 19] , Knk [20 , 21] and ZP domain proteins [18 , 22–24] . It is unclear if any of these ends up being part of the cuticle proper , although that has been suggested for ZP domain proteins [22] . The analysis of cuticle composition is complicated by the cross linking that is part of cuticle maturation [25–27] , the insolubility of some components and because other tissues and cell types become attached to the cuticle ( e . g . muscles ) as it is being synthesized by epidermal cells . The wing provides potential advantages as pupal wings can be dissected in a pure form without attached muscle or other tissues . We have taken advantage of this and characterized the pupal wing transcriptome of Drosophila by RNAseq [28 , 29] . We also examined pupal wings by transmission electron microscopy ( TEM ) over the period covered by the RNAseq experiments allowing us to correlate the dramatic changes in gene expression with the morphological process of cuticle deposition . The expression of many genes was almost completely restricted to a single time point making these genes candidates for taking part in the deposition of specific cuticle layers . Mutations in a number of genes expressed almost exclusively in 42 hr pupal wings are known to result in dramatic wing phenotypes ( e . g . miniature ( m ) , dusky ( dy ) [22] and dusky-like ( dyl ) [18] ) . This time point is in the middle of the period when the thin outermost envelope layer is deposited . It is known that wing procuticle , which is synthesized much later , is grossly abnormal in a m dy double mutant [22] and we here found that to also be the case for dyl . We also identified wing and cuticle phenotypes of additional genes primarily expressed at 42 hr and for two of these transmission electron microscopy established that there is a procuticle phenotype . To explain the extent of the mutant phenotypes seen with a loss or decrease in the function/expression of “42 hr genes” we suggest either that the successively laid down layers are not independent and that earlier layers instruct the deposition of later ones or that some of the “42 hr genes” form a platform or complex that mediates the deposition of all of the cuticle layers .
The Drosophila wing tissue undergoes a number of dramatic morphogenetic transitions during development [18 , 30] . The wing and the dorsal thorax are derived from the wing disc ( Fig 1A ) that proliferates throughout most of the larval period . The wing disc undergoes evagination after the start of pupariation producing a pupal wing that is a small version of the adult wing ( Fig 1B ) . All cell division in the wing ceases around 24 hr after white prepupae ( awp ) . The terminal differentiation of the Drosophila wing begins around 32 hr awp when the morphogenesis of the wing hairs ( trichomes ) starts [31] . The start of cuticle deposition is seen by the middle of wing hair outgrowth ( ~36 hr awp ) [31–33] . At this stage diffuse material and small patches of envelope are seen by transmission electron microscopy ( TEM ) . Around 40 hrs awp hair outgrowth is largely complete . Around 40–42 hr awp the wing cells begin to flatten and increase their apical surface area ( Fig 1C and S1A Fig ) ( 18 ) a process we refer as flattening/expansion . Since the wing is enclosed within a pupal cuticle sac it is forced to deform and bend back on itself ( Fig 1B–1E , S1A–S1E Fig ) ( 18 ) . In previous experiments we first observed chitin in hairs around 42 hr awp and later in wing blade cuticle [18 , 34] . Pigmentation of the wing was first obvious around 80 hr awp ( Fig 1D and 1E , S1C and S1D Fig ) and eclosion of the adult was around 96 hr awp . After eclosion the wing unfolds and extends to take on the shape of the adult wing ( S1E Fig ) . Early evidence for the beginning of cuticle deposition can be seen in 35–38 hr pupal wings . In these wings we observed disorganized relatively electron dense material close to the apical cell membrane ( S2 Fig , arrowheads ) . Over the wing blade region of cells it was rare to see evidence of the trilaminar envelope . It is possible the amorphous material is a precursor to the envelope . Cuticle deposition appeared somewhat more advanced over the hairs , as we often saw patches of envelope there ( S2 Fig , arrows ) . In wings 42 hr awp we observed the trilaminar envelope over much of the epidermal cells ( Fig 2A , arrows ) and hairs but it was not continuous and gaps were present ( Fig 2A , asterisks ) . Similar observations were made on 45 and 48 hr awp pupal wings although the gaps appeared less frequent . In 52 hr awp pupal wings the envelope was continuous and a layered epicuticle was seen ( Fig 2B ) . Pore canals were observed in the 52 hr wings ( Fig 2B , arrows ) and we often observed what appeared to be material being secreted from these pores . In both early stages and later ones we often observed protrusions of the cytoplasm in close juxtaposition to the cuticle ( S2A Fig and S3A and S3B Fig , arrows ) as has been seen previously ( e . g . [22 , 35] ) . These are thought to represent sites of cuticle deposition . Although they appear as rectangles in these sections they are likely rows of elevated cytoplasm equivalent to the undulae described for the deposition of the first instar larval cuticle [35] . The procuticle layer appeared relatively thick by 62 hrs with additional thickening over time ( Fig 2C–2F ) , although differences in the angle of sectioning can be misleading as to thickness . At later stages the layered nature of the envelope and epicuticle was less obvious and subtle layering in the procuticle was more obvious ( e . g . 96 hr awp ) ( Fig 2F ) . We refer to this cuticle maturation and it implies that some late gene expression modifies the structure of the cuticle layers deposited earlier . Filaments were seen in the procuticle and these are likely chitin . There was also a thin basal electron dense layer in 62 hr wings ( Fig 2C , asterisks ) that appears similar to the adhesion layer seen during the synthesis of the first instar larval cuticle [17 , 36] and perhaps what was called Schmidt’s layer in older studies on cuticle [37 , 38] . This putative adhesion layer thickened and became more electron dense over time and was very prominent in 88 and 96 hr wings ( Fig 2E and 2F , asterisks ) . This corresponds to the principal period of wing pigmentation and while there is relatively little pigmentation in the Drosophila wing blade compared to hairs it is possible that the material observed in 88 and 96 hr wings is pigmented procuticle . Thus , by morphology we can distinguish periods for envelope deposition ( 36–48 hr awp ) , epicuticle deposition ( 52 hr awp ) , procuticle thickening ( 62–88 hr awp ) , and the period of cuticle maturation and thickening of the adhesion layer/pigmentation of basal procuticle ( 88–96 hr awp ) . Due to possible differences in the angle of orientation we did not feel it justified to compare the thickness of cuticle from different sections or time points . In relatively low magnification TEM images ( 3–5 , 000X ) we could often observe both dorsal and ventral cuticle and cell layers ( Fig 2G ) . The two cuticle layers appeared to be relatively similar in thickness . However , when we measured the thickness of the layers ( see Methods ) we observed that one layer was reproducibly thicker ( ave ratio = 1 . 2 ( sd = . 11 ) ) ( S1 Table ) . We suspect the dorsal surface is the thicker one as dorsal hairs are larger than ventral ones . As noted above hair cuticle deposition appeared slightly advanced compared to wing blade cuticle . By 52 hrs awp the developing hairs were found on pedestals ( S3H Fig , arrow and had taken on their fluted shape ( S3D–S3H Fig ) . The pedestals are bulges of cytoplasm covered by cuticle that does not differ dramatically from that of the remaining wing blade cuticle ( 40 ) . One difference is the cuticle in the center of the pedestal often appears darker by TEM . This could be due to pigment as is seen in the hairs . The size of the hairs in the micrographs was dependent on where along the apical basal axis of the hair the section was cut . During the remainder of pupal development the internal morphology of the hair changed dramatically with both electron dense and lucent regions seen ( S3D–S3F Fig ) . At later stages ( e . g . 88 hr , 96 hr ) it was primarily electron dense , perhaps due to pigmentation of the hair ( S3 Fig ) . One difference between the formation of hair and wing blade cuticle is that distinct undulae were infrequently associated with hair cuticle ( see also [33] ) . Overall our TEM observations are consistent with those by others ( e . g . [33 , 39 , 40] ) although a complete TEM time series of wing cuticle deposition has not to our knowledge previously been presented . We characterized the transcriptome of pupal wing cells at 42 , 52 , 62 , 72 , 80 , 88 and 96 hrs awp by RNAseq ( S2 Table ) . The last time point was just prior to eclosion . 9 , 088 out of 17 , 243 genes were expressed in at least one of the time points using the criteria that anything with an FPKM ( Fragments Per Kilobase of exon per Million reads ) greater than 1 was expressed ( S2 Table ) . Clustering of the gene expression data based on mutual Jensen-Shannon divergence ( Fig 3A ) indicated that the 3 early time points clustered together as did the 4 later time points . The clustering of the 52 and 62 hr samples was surprising as morphologically the 62 hr wings appeared more similar to the 72 hr wings . There was a trend where the number of differentially expressed genes increased when we compared more distantly separated time points ( S3 Table ) . In many cases the changes in gene expression were quite large ( e . g . there were more than 100 cases where a 100 fold or larger difference was seen between neighboring time points—Table 1 ) . 5097 individual genes showed a statistically significant ( q<0 . 05 ) difference in expression between at least one pair of time points ( S4 and S5 Tables ) . The largest number of differences were seen between the 42 and 52 hr wings and the 88 and 96 hr wings ( Table 1 ) while the smallest number were seen between the 72 and 80 hr wings . As a control that the expression differences were not dependent on the approach we carried out RT-qPCR Relative Quantification ( RQ ) for a pair of ZP domain genes whose expression was highly stage specific ( dyl and CG10005 ) Our endogenous control was Xbp1 , a gene that was rather evenly expressed in our RNAseq data ( FPKM around 100 ) . A similar temporal pattern of expression was detected in both the RNAseq and RT-qPCR experiments ( Fig 4A ) . As is discussed later we also examined the data for changes in specific isoform usage . We took an alternative approach to identify genes expressed in a highly stage specific manner . For this we added up the FPKM values for each gene over the 7 time points and identified genes where expression was primarily seen at one time point ( S6 Table ) . The 42 and 96 hr samples stood out by having many genes ( 67 and 87 respectively ) where >90% of their total FPKM was found in that sample while there were no such genes in the 80 and 88 hr samples and only 1 in the 72 hr sample . The 5097 genes that showed expression differences were placed into 16 clusters ( Fig 3B and S5 Table ) . The clusters contained from 31–860 genes and each contained genes with a wide variety of absolute expression values . Ten of the clusters ( 3 , 5 , 7 , 8 , 11 , 12 , 13 , 14 , 15 and 16 ) displayed dramatic stage specific changes in expression patterns . These clusters were greatly enriched for genes where homologs are only found in arthropods ( average: 53% , range: 28–94% ) compared to the genes present in the other clusters ( average: 19% , range: 15–27% ) ( S7 Table ) . This difference was significant ( t-test , p = 1 . 25X10-4 ) . This suggests these clusters are enriched in genes involved in cuticle deposition . Among the clusters showing the most dramatic changes in gene expression were cluster 15 ( highly expressed only at 42 hrs ) and cluster 13 ( highly expressed only at 96 hr ) . We expected this set of 5097 genes contained many required for normal wing development . When we queried FlyBase for genes associated with wing phenotypes we found 2611 such genes . A majority of these ( 1469 ( 56% ) were present in the differentially expressed gene set ( S7 Table ) . The frequency of annotated wing phenotypes was not similar in all of the clusters ( S7 Table ) . Similarly , the frequency of genes identified in a genome wide RNAi screen [41] was not similar in all clusters S6 Table ) . We observed both under and overrepresentation of Gene Ontology associations in the clusters ( S8 Table ) . For example , clusters 4 , 5 , 6 , 9 and 10 contained a high proportion “housekeeping” genes taking part in cellular respiration , replication , translation and protein turnover . Cluster 4 was the only one enriched in “metabolic” genes and the only one in which “structural components of chitin-based cuticle” were underrepresented . The best structure was found in clusters 15 , 4 and 11 ( cluster silhouette >0 . 2 ) . In clusters 3 , 8 and 11–16 genes connected with chitin-based cuticle were overrepresented and there was a high proportion of arthropod-specific genes . We found 146 genes in fly genome annotated as encoding cuticle protein genes . Eighty three of these were expressed in the pupal wing using the criteria that there was at least one time point with an FPKM >1 ( S9 Table ) . Some of these genes were expressed at a very high level . For example , 25 had a total FPKM of greater than 1000 . There were a variety of expression patterns for these genes ( Fig 5A ) and some were expressed primarily at a single time point and others were expressed at a high level in all of the time points . For example , Cpr76Bc was expressed at a very high rate in 42 hr pupal wings but not at other stages , suggesting it might function in envelope morphogenesis . A recent RT-PCR analysis of selected cuticle proteins in Bombyx mori during pupal cuticle formation also found examples of very sharp expression peaks [42] . Over 97% of the total FPKM for annotated cuticle proteins was contributed by just 22 genes and more than 70% was contributed by just 3 ( CG34205 , Cpr64Ac and CG34461 ) . Thus , if cuticle protein content was reflected in mRNA content and the annotated cuticle proteins represented the true population of cuticle proteins , the protein content of wing cuticle on a mass basis would not be as complex as the number of cuticle protein genes suggests it might be . In addition to cuticle protein genes a number of other genes expressed at relatively high and varying levels in pupal wings are known to be important for normal cuticle deposition ( Fig 5B ) . These include genes such as krotzkopf verkehrt ( kkv ) ( [16 , 43] ( cluster 2 ) , which encodes chitin synthase and knickkopf ( knk ) [20 , 21 , 43] ( cluster 1 ) , which protects chitin from degradation . We also found 5 chitinases and a number of genes involved in pigmentation and sclerotization expressed . Among the most notable were pale ( ple ) [44] ( cluster 16 ) ) , ebony ( e ) [45] ( cluster 5 ) , Dopa decarboxylase ( Ddc ) [46 , 47] ( cluster 6 ) , and yellow ( y ) [48 , 49] ( cluster 3 ) . A number of ZP domain proteins are essential for cuticle formation in both the embryo and pupae [18 , 22–24] . Eighteen of the 20 fly ZP domain protein genes were expressed during wing cuticle deposition . The pattern of expression was dynamic and differed greatly between ZP domain genes . Several genes: dusky-like ( dyl ) , dusky ( dy ) , miniature ( m ) , trynity ( tyn ) , morpheyus ( mey ) , nyobe ( nyo ) were expressed at a much higher level at 42 hr than at any of the other time points ( Fig 5C ) . All of these genes function in the formation of denticles in the embryo [23] and dyl , dy and m mutants have dramatic wing phenotypes [18 , 22] . These data support the idea that these proteins function in the deposition of the cuticle envelope , as was suggested some years ago by Roch et . al . for m and dy [22] . Other ZP domain protein encoding genes are primarily expressed at other stages . CG10005 ( cluster 12 ) stood out because it was expressed at a much higher level in 52 hr wings than at any other stage , suggesting it functions in epicuticle deposition . Only the shorter transcript of this gene was expressed in pupal wings . Its associated polypeptide ( 182 aa ) consists almost entirely of the ZP-N , which is evolutionarily more conserved than ZP-C . Most ZP domain proteins contain both sub-domains . In experiments described previously we found that knocking down Cht6 expression produced a weaker version of the dyl mutant wing hair phenotype and these two genes showed a positive genetic interaction [18] . The expression pattern of Cht6 did not closely resemble that of dyl , however , we observed that Cht6 isoform RG had a very similar expression pattern to the principal dyl isoform ( also to overall dyl expression ) ( Fig 4B ) . We generated a spreadsheet that contained the 20 most highly expressed genes from each time point . Due to some genes being very highly expressed at multiple stages this was reduced to a set of 83 genes ( S10 Table ) that included genes with a wide variety of functions . For example , 9 of these encode known cytoskeleton proteins or effectors including actin5C and alpha-Tubulin84B . There were several enzyme encoding genes including Pxd [50] , which encodes peroxidase and Mmp1 , which encodes a metalloprotease [51] . Fourteen of the genes are annotated as cuticle protein genes in the fly genome and 2 others are homologs of genes annotated as cuticle protein genes in other insects ( but not in Drosophila ) . Three additional genes encode proteins similar to ones identified as cuticle proteins by Mass Spec analysis of proteins extracted from Anopheles gambiae ( Vectorbase ) . Eight of these 19 encode short proteins , less than 200 amino acids in length . In addition the amino acid content of many was unusual as is often seen in cuticle proteins [10 , 15 , 52] . Twelve lacked any Trp residues , 7 any Cys residues and there were also instances where proteins lacked any Arg , Asn , Asp , Gln or Glu . Four of the proteins contained greater than 20% Ala , 3 contained greater than 20% Val and there was one instance of greater than 20% Gln . An “average” protein does not possess more than 10% of any amino acid residue [53] . Three of the other most highly expressed genes ( dyl [23 , 24] , e [45] and pale [44] ) also play a role in cuticle formation . Thus , twenty two of the 83 genes are known to have a role in cuticle deposition . Among the remaining genes were 20 unstudied “CG” genes that are only found in arthropods . Interestingly , like the highly expressed annotated cuticle proteins 10 of the 20 encoded short ( <200 aa ) proteins and many also had a distinctive amino acid content . Nine did not contain any Trp , 5 did not contain any Asn , and 4 any Asp residues . There were also 3 instances of no Arg , 2 of no Cys , 2 of no Gln , 2 of no Glu , 2 of no Tyr , 1 of no His , 1 of no Phe and 1 of no Val residues . Two of the proteins contained greater than 20% Ala , 1 greater than 20% Val , 1 greater than 20% His and 1 greater than 30% Gly . We consider these genes candidates for encoding additional cuticle proteins or proteins that have specific functions in cuticle deposition . In addition , 7 members of the insect specific Osiris gene family [54] were among the 20 most highly expressed genes at 42 hr . The function of this family of genes is unknown . It is possible they function in cuticle formation or extracellular matrix secretion , assembly or modification . We hypothesized that part of the mechanism responsible for the changes in gene expression we observed involved changes in the abundance of transcription factors . We queried FlyBase and obtained a list of 957 Drosophila genes annotated as having DNA binding activity . This was combined with a list of 384 genes annotated as having RNA PolII regulatory activity . Duplicates were removed leaving a list of 1077 genes and this was further reduced by only considering genes that had that had an FPKM of 30 or greater in at least one time point . Sixty six of these 385 genes had FPKM values that differed at least 4 fold between neighboring time points ( see Fig 5D ) . We consider these genes to be good candidates for mediating the gene expression pattern for wing cuticle deposition . A number of genes were highly expressed in only one time point . For example , HR4 was expressed at a much higher level at 52 hrs than at any of the other time points . Several genes showed elevated expression at more than one time point . For example , aop was highly expressed at 52 and 96 hr but not in the intervening time points . We examined our data set for isoform changes and found 1362 cases of this ( S11 Table ) . For many of these ( 664 ) the isoforms changed 10 fold or greater in abundance . As described earlier we confirmed the isoform switching of a pair of Cht6 isoforms by RT-qPCR ( Fig 4A ) . Having established that the relative FPKM values for individual isoforms are accurate across samples , we searched the dataset for isoform switching events between neighboring time points . The search returned a small number of genes from the early time points ( 42/52 hr: 10 genes , 23 isoforms; 52/62 hr: 3 genes , 6 isoforms; 62/72 hr: 2 genes , 4 isoforms ) , and none from the later ones ( Fig 6A ) . Of particular interest was a pair of genes whose isoforms switched coordinately between 42 hr and 52 hr: mwh ( multiple wing hairs ) and CG14257 ( Fig 4C ) . mwh is a downstream component of the frizzled/starry night planar cell polarity pathway and mutations cause the development of multiple trichomes of abnormal polarity from individual wing epithelial cells [31 , 55 , 56] . Since the isoform FPKM values from RNA sequencing cannot be measured directly , but only inferred , we confirmed this result using RT-qPCR . These data show close co-expression of mwh-RA with CG14257-RC , and mwh-RB with CG14257-RB within the 42 hr—72 hr time frame ( Fig 4C ) . Visual examination of Fig 6 suggests there are more pairs of genes expressed in this pattern . The pale gene served as an internal control for the dataset . Its 3 . 7 kb transcript ( ple-RA ) is neuronal and the 3 . 2 kb transcript ( ple-RB ) is epidermal [59] . The vast majority of cells in the pupal wing are epidermal with only a relatively small number of neurons . pale was one of the most highly expressed genes in our dataset with a sharp peak at 96 hr awp . As expected our isoform analysis showed almost all of the expression was of the RB isoform ( Fig 6B ) . Our analysis yielded one promising candidate for a new gene , annotated automatically as CUFF . 2839 in locus: 3L:813 , 316 . 818 , 703 ( 3L:61D2 ) . Three potential transcripts were found for this gene , one containing 2 exons and two containing 3 exons . Open reading frame and polypeptide searches did not yield any homologs . Another newly annotated sequence of interest was CUFF . 3420 . 1 , which contained an ORF whose corresponding polypeptide was in part identical to UniProtKB Q6ILG5—a theoretical unknown protein . Among all novel transcripts in unannotated regions , the only candidate we consider likely to be a true positive was CUFF . 6 . 1 , which encoded the TART transposon reverse transcriptase . Its locus was on an unplaced genome fragment 211000022280091 . This transposon participates in the maintenance of Drosophila telomeres [60] . The results of the full search are presented S12 Table and the gene annotation file is provided in S2 File . S12 Table contains unedited Cuffcompare ( http://cole-trapnell-lab . github . io/cufflinks/cuffcompare/ ) output and complements the gene annotation file . The literature establishes that mutations in several genes expressed almost exclusively at 42 hr ( >90% of total FPKM values ) result in gross defects in wing morphology . These include the m ( 93% ) , dy ( 96% ) and dyl ( 93% ) genes [18 , 22] . For m and dy TEM examination showed that a loss of these two genes resulted in defects in cuticle structure that were obvious in the non-envelope cuticle layers [22] . For dyl we previously reported that chitin deposition was disrupted in hairs and bristles [18 , 24] , which would not be expected from an envelope defect . In the embryo mutations in a number of ZP domain genes including m , dy and dyl resulted in gross cuticle abnormalities in denticles [23] . We further characterized the dyl mutant phenotype and found that using ap-Gal4 or pnr-Gal4 to kd dyl led to dramatically abnormal cuticle pigmentation ( S13 Table ) ( Fig 7G ) . When ap-Gal4 was used to knock down dyl in the dorsal but not ventral cell layer the adult wing curled upward ( Fig 7G ) , consistent with the dorsal layer being smaller . There are several possible mechanisms that could give this result . It could be due to reduced proliferation or increased apoptosis in the dorsal layer . It could be due to a disruption of the flattening and expansion of the wing cells that starts around 42 hr , or it could be due to disruption of the unfolding and extension of the wing that happens right after eclosion ( Fig 1 and S1 Fig ) . We did not see any gross abnormalities in wing discs or in pupal wings prior to flattening/expansion ( Fig 1F , 1G , 1K , 1L , 1P and 1Q ) thus the curled phenotype is unlikely to be due to effects on proliferation or apoptosis . In wild type after flattening/expansion the pupal wing has a reproducible folding pattern . This was altered in the dyl kd wings ( Fig 1H–1J ) indicating that dyl is required for normal wing flattening/expansion . This suggests that the envelope , which is being deposited at 42 hr , plays a functional role in this process . Since the dyl kd wings were abnormal at the time of eclosion we were not able to determine if the process of wing unfolding/extension could also be contributing to the dyl phenotype . We next examined ap>dyl-RNAi pupal wings in the TEM . The 44 hr pupal wings were not dramatically abnormal although we often observed hairs that looked abnormal on one wing surface . We note that equivalent TEM sections of m and dy pupal wings also did not appear highly abnormal [22] . Several types of abnormalities were observed in the older dyl kd pupal wings . In 76–80 hr apf wings there were large gaps between the epithelial cells and the cuticle ( Fig 2H ) . This is reminiscent of what was observed in dyl mutant embryonic denticles [23] . It is worth noting that using ap-Gal4 to drive the kd allows us to use the ventral wing cells as a control . Thus , we can be confident that abnormalities observed consistently in only one cell layer are not due to artifacts such as poor fixation as that would affect both cell layers . In addition to the large gaps we also consistently observed that the procuticle on the mutant side was significantly thicker ( the ratio was 2 . 1 ( sd = 0 . 43 ) compared to 1 . 2 for wt , p = 0 . 0017 , t-test ) ( S1 Table ) , the procuticle appeared more variable with occasional “holes” and the adhesion layer appeared ragged . In some sections very abnormal procuticle was observed with large holes and irregularities ( Fig 2K and 2L ) . The pupal hairs were also highly abnormal with an atypical shape and very thin cuticle ( S3K Fig ) . Given how abnormal dyl mutant hairs are as observed by SEM , brightfield and confocal microscopy [18] this was not surprising . We previously found that knocking down ectodermal ( ect ) ( 99% of total FPKM at 42 hr ) function in the pupal wing led to wing hair defects [18] . When we kd ect using ap-Gal4 or pnr-Gal4 we observed notum abnormalities and frequently wings where the dorsal and ventral surfaces were not juxtaposed ( i . e . wing blisters ) ( S13 Table and Fig 7H ) . The ect kd wings also often appeared smaller than normal . As was the case for the dyl kd wings , we did not observe defects in ect kd wing discs or pupal wings prior to flattening/expansion ( Fig 1K and 1L ) , but such pupal wings showed a highly abnormal folding patterns after it ( Fig 1M–1O ) . Thus , it appears that ect also functions in wing flattening/expansion . We examined ap>ect-RNAi pupal wings in the TEM and observed several defects ( Fig 2I ) . As was the case for the dyl kd we also observed large gaps between the apical surface of the epithelial cells and the cuticle and the greater thickness of the dorsal vs ventral cuticle was enhanced ( ratio = 1 . 79 ( 0 . 22 ) ) compared to 1 . 2 for wt ( p = 8 X10-5 , t-test ) ( S1 Table ) . We did not , however , see the sort of holes in the procuticle seen with dyl . The ect pupal wing hairs also appeared abnormal as in proximal regions the hairs lacked the distinctive angular structure of wild type hairs ( S2M Fig ) . This fits the phenotype seen in adult ect kd wings [18] . As was the case for the dyl kd we did not see any dramatic phenotype in the 42 hr wing as assayed by TEM . We also chose 10 additional genes that were expressed primarily at 42 hr ( >90% of total FPKM ) and tested them to see if a kd would lead to wing and/or cuticle phenotypes . Seven gave a phenotype when kd with either ptc-G4 , ap-Gal4 or pnr-Gal4 although in many cases these were weak in terms of expressivity or penetrance ( S13 Table ) . Knocking down CG8213 with ap-Gal4 resulted in a severe curled upward wing phenotype as well as hair and notum bristle phenotypes ( Fig 7D ) . The bristle phenotypes included very short bristles , bristles with wispy distal regions ( Fig 7J ) and bristles with varying regions of increased and decreased pigmentation ( Fig 7J ) . This set of bristle phenotypes is not common but is also seen in knockdowns of dyl , Rab11 and exocyst components [24] . ap>CG2016-RNAi resulted in wings with a distinctive upward curl ( Fig 7F and 7L ) and ap>ImpE2-RNAi wings with a distinctive downward curl ( Fig 7E and 7M ) . These were not as strong as with CG8213 but they showed complete penetrance . We investigated the cg8213 kd phenotype further . As was the case for the dyl kd wings , we did not observe defects in wing discs or pupal wings prior to flattening/expansion ( Fig 1P and 1Q ) , but abnormalities were obvious after ( Fig 1R–1T ) . Thus , it appears that CG8213 also functions in pupal wing flattening/expansion . We also examined CG8213 kd pupal wings by TEM . As was the case for dyl and ect we observed large gaps between the cuticle and the apical surface of the dorsal epithelial cells ( Fig 2J ) . In contrast to the results with dyl and ect the cuticle on this side of the cell was also substantially thinner than that on the putative ventral side where the gaps were not seen . The V/D ratio was 2 . 29 ( sd = 0 . 54 ) on average , significantly different from the putative V/D ratio of 0 . 82 in equivalent wild type wings ( p = 2 . 9X10-5 , t-test ) ( S1 Table ) . The difference was significant even if we assume the thicker layer in our wild type images was the ventral surface ( p = 3 . 0 X 10−5 ) . We also observed occasional regions where the procuticle lost its normal organized layered morphology ( Fig 2M ) and hairs with an abnormal shape ( S2L Fig ) . Since we did not carry out RNASeq on wings younger than 42 hr the data presented does not establish if the expression of the 42 hr genes actually peaked at 42 hr or if it could have peaked earlier and be declining at 42 hr . To assess this we made use of an earlier study from our lab of gene expression at 24 , 32 and 40hr awp that used Affymetrix gene chips [61] . There were 48 “42 hr” genes with a total FPKM of over 100 and 45 of these were present in the chips used in our earlier study . Forty three of the 45 showed a higher level of expression at 40 hr than at 32 ( or 24 ) hr ( S14 Table ) . In most cases the increase in expression from 32 hr to 40 hr was quite large ( median 12 . 4 fold increase ) . We conclude that the vast majority of 42 hr genes show a peak in expression around that time .
Drosophila has served as a valuable model system for many molecular , cellular , developmental and behavioral problems . It has provided some important insights into cuticle synthesis , for example the role of ZP domain proteins in the process [22–24] . However , the number of such studies has remained limited and cuticle synthesis has remained poorly understood for a process so important for a large part of the biological world . The data set we describe here should provide a valuable resource for future studies on cuticle synthesis in Drosophila as well as other arthropods . Many of the previous studies and reviews concerning insect cuticle focused on molting behavior . While understanding this process is important , it is complicated by the shedding of the old cuticle and the influence of the environment . The pupa is a closed system , which presumably reduces the variability of cuticle formation due to external conditions and stimuli . Wings , as single-tissue organs , are ideal for transcriptomic or proteomic studies on the development of cuticle . It remains to be established how variable the program for cuticle deposition will be from one body region to another . The Drosophila genome is annotated with almost 150 cuticle genes [15] . Our expectation is that cuticle genes would be expressed at a high level as structural proteins are typically needed in stoichiometric amounts . We also expected that homologs of these genes would be found in insects and perhaps other arthropods but not in the genomes of the many types of animals that do not produce a cuticular exoskeleton ( e . g . vertebrates ) . Among the most highly expressed genes we identified 19 genes annotated as cuticle proteins either in the Drosophila genome or the genome of other insects . In this same set of very highly expressed genes were 20 unstudied genes that shared these properties . If the 20 unstudied genes prove to encode cuticle proteins it would more than double the number of highly expressed cuticle protein genes . Many annotated cuticle proteins are relatively short and many contain unusual amino acid content . Either or both of these properties were also true for many of these 20 genes consistent with the possibility that some may prove to be cuticle proteins . The 20 genes considered above were selected because they were among the most highly expressed genes . Our data also hint that there may be many other genes expressed in the pupal wing that have specific roles in wing cuticle formation . Of the 16 gene expression clusters 10 displayed patterns of gene expression that changed dramatically in a stage specific manner and these clusters were significantly enriched for arthropod specific genes . This suggests that the number of genes with specific roles in cuticle formation could be very large . The metamorphic behavior of insects is under hormonal control and at least indirectly changes in ecdysone titer almost certainly influence the pattern of gene expression we describe during wing cuticle deposition . It is not clear however that the large number of changes we described are a direct read out of hormone levels . Ecdysone levels increase rapidly during the start of the second day awp formation peaking at 30 hrs . Two less dramatic peaks were seen at 40 and 48 hr with generally declining levels from 40 hr to eclosion [62] . It is not obvious that this pattern can explain our data . For example , ecdysone levels are similar at around 88 and 96 hr but we detect dramatic changes in gene expression . Further , based on simple observations of pupae we know that the time course of pupal development differs substantially across the body ( e . g . abdominal development is delayed compared to thoracic development ) but as far as we are aware there is no evidence for a similar spatial variation in ecdysone levels . The literature provides validation that many genes we identified as being highly expressed in a dynamic fashion are essential for normal wing development and have phenotypes that implicate a role in cuticle formation or wing maturation . Several of the best examples are ZP domain proteins , which are known to organize apical extracellular matrix [23 , 63] . In insects this matrix becomes the cuticle . Previous studies had found that ZP domain proteins played important roles in cuticle formation [18 , 22–24] . In the embryo mutations in several ZP domain protein genes resulted in gaps between the apical surface of the epidermal cells and the cuticle [23] , a result also seen previously for m dy double mutant wings [22] and one that we observed in pupae for dyl mutants . In our experiments this set of ZP domain genes were almost exclusively expressed in 42 hr pupal wings suggesting they functioned in envelope formation . In dyl kd and mutants we first observed a hair morphology phenotype in 39 hr wings [18] . This is during the deposition of the envelope but prior to when we first detect chitin in hairs and before epicuticle deposition , suggesting dyl functions in envelope deposition . We did not detect a clearly abnormal envelope in TEM images of 42 hr dyl kd wings but this could be due to thinness of the envelope as even in wild type it sometimes appears diffuse . At later stages gross abnormalities in procuticle were seen . In their study of m and dy wings Roch et al . [22] detected disorganization and a delay in formation of the extensions of the apical cytoplasm that are thought to represent sites of envelope cuticle deposition . This led them to suggest that M and Dy were either structural components of the envelope or linked the cuticle and epithelial cells . Some of the early literature suggested envelope formation was mediated by polymerization [64] which is the presumed function of zona pellucida domain . CG10005 stood out due to being highly expressed only in the 52 hr sample . It is a candidate for mediating some aspect of epicuticle formation; as an example of a protein with only the ZP-N domain present ( most ZP-domain proteins contain ZP-N and ZP-C ) it might be a good candidate for studying the function of ZPD proteins in subdomain detail . The sclerotization of insect cuticle generally takes place very late in pupal development and shortly after eclosion . This process utilizes enzymes such as Tyrosine Hydroxylase , which is encoded by the pale gene [44] and Dopa Decarboxylase , which is encoded by the Ddc gene [46 , 47] . Loss of function mutations in these genes lead to lightly pigmented , weak and fragile cuticle . Consistent with this biological function pale displayed a dramatic peak in expression at 96 hr awp , just prior to eclosion . There was a more than 12000-fold increase in ple expression between 42 and 96 hr awp and an almost 500-fold increase in the 24 hrs between 72 and 96 hr awp . The expression of Ddc did not change as dramatically but it showed a strong peak at 88 hr and 92 hr awp with a 27 fold increase from 62 to 96 hr . Thus , the expression pattern of these two key sclerotization genes nicely fit with their known biological function . In our experiments the pigmentation of wing hairs was first obvious around 80 hr awp and it increased dramatically between then and 96 hr awp . There is a further increase during the first few hours of adult life . The expression of a number of genes known to be important for pigmentation varied during the period of cuticle deposition and provided further validation of our data set . Two of the most notable pigmentation genes are ebony ( e ) [45] and black ( b ) [65] . The expression of both of these showed a major peak at 96 hr awp . The yellow ( y ) gene is another key pigmentation gene and published data indicated it functioned in the middle of the pupal period [40] . Consistent with that y expression peaked at 52 hr awp in our experiments . The genetic analysis of cuticle proteins in insects has been relatively limited , although in recent years a number of interesting studies have been published ( e . g . [66–69] . In general these are based on examining the phenotype ( s ) associated with mutations that inactivate a single gene . It is important to distinguish between the mutant phenotype being a primary defect and not simply an end point that results from the lack of the gene product at an earlier execution point . The very sharp expression peaks that we found for some cuticle protein genes should facilitate such analyses . Understanding of the genetic basis for the complex structure of insect cuticle remains challenging . We believe exclusivity of some genes to arthropods is a clue to narrow down the gene search space . A reasonable hypothesis is that the thickness of individual cuticular layers is dependent on the level of expression of a few major cuticle proteins and on the density of protein packing . Knowing the details of the gene expression program allows one to design tests of this hypothesis . A likely hypothesis to explain the change from one cuticle layer to another ( e . g . envelope to epicuticle ) is that a change in the abundance of one or a few transcription factors leads to a change in the array of cuticle proteins expressed . Our analysis suggests candidates that could mediate such changes . For example , the large reduction in the expression of vri and CG14431 between 42 and 52 hrs and the steep increase in the expression of HR4 and Pdp1 at 52 hr could be responsible for the changes in gene expression that cause the transition from envelope to epicuticle . The shaven baby ( svb/ovo ) transcription factor was not detected as having a high level of expression at 42 hr . At first glance this might be considered surprising as it is known to regulate the formation of larval denticles [70–72] and wing hairs [18 , 73] and it functions to regulate the expression of a number of genes such as dyl and m whose expression was primarily found in 42 hr wings [14 , 70 , 72] . In previous experiments that utilized Affymetrix gene chips to examine pupal wing RNA at 24 , 32 and 40 hrs awp we found a peak of svb RNA at 32 hr with a sharp decline by 40 hr [61] . Thus , our previous results are consistent with the current ones . We suggest that Svb is required to turn on the expression of genes such as dyl and m but either it is not required to maintain their expression or Svb is stable enough so that protein synthesized at 32 hr can drive a high level of target gene expression at 42 hr . We analyzed a small collection of genes primarily expressed in 42 hr wings for their potential role in wing cuticle deposition . The results on CG8213 proved to be a particularly interesting . This gene encodes 3 large proteins ( range 1430–1693 aa ) each of which contains a serine protease domain [74] . A protein trap of CG8213 localized it to the perivitelline space in stage 11 and stage 15 embryos [75] , indicating the protein is secreted , which would place it in a subcellular location to function in cuticle deposition . A perivitelline space localization was also seen for several other proteins likely to function in cuticle deposition ( e . g Cda4 ( Chitin deacetylase 4 ) and Dsx-c73A ) . The bristle phenotype observed with kd CG8213 was reminiscent of that seen with kd of dyl and genes such as Rab11 that are needed for the secretion and/or plasma membrane insertion of Dyl [24] . This suggests the possibility that Dyl and CG8213 are functionally linked . The timing for the formation of the cuticle that covers wing hairs appears to differ from that of the cuticle that covers the wing blade . This could be a complicating factor in the analysis of our data . We previously found that chitin could first be detected in 42 hr hairs while it not detected in the wing blade until later [18] . Thus , it is possible that some 42 hr genes might in fact function in the formation of internal hair cuticle layers and not envelope , although we suspect this would not be the case for the most highly expressed genes . Indeed , the formation of internal hair cuticle appears to be more rapid , and the epi- and procuticle are less easily distinguished than in the wing blade cuticle . There is usually a strict temporal relationship between the deposition of the various cuticle layers [37] . From the literature it is unclear if the deposition of the later layers is independent of or dependent on the earlier ones . If the layers were independent then loss of function mutations in genes that are only involved in envelope deposition would be expected to produce an altered envelope with normal epi- and procuticles . Since the envelope is such a small part of the cuticle thickness , envelope defects would only be expected to alter the surface of the cuticle and not its overall structure . Such defects might be visible as a rougher surface and it would not be surprising if they had functional effects on the hydrophobicity of the cuticle and/or made the animals more sensitive to desiccation . However , by combining the data in this paper and the literature it appears that mutations in genes expressed almost exclusively during envelope deposition often result in highly abnormal procuticle . We suggest two classes of models to explain this . In the first we hypothesize that the early layers are important for and perhaps instruct the deposition of the later ones . The first step in cuticle deposition—the formation of the envelope presumably involves proteins such as Dyl , Dy and M along with other candidates such as CG8213 , Ect and Cpr76Bc . Dy and M have been hypothesized to be envelope components [22] , an idea that we find attractive . Interactions between these proteins and cellular constituents such as apical transmembrane proteins and the cytoskeleton could provide the spatial and patterning information needed to build the envelope . Once the envelope is complete it would then guide the deposition of the epicuticle . This could be mediated by outside in signaling from the envelope to the epidermal cells with this leading to the epicuticle components being deposited in the correct spatial pattern ( Fig 8 ) . Support for such signaling comes from observations of abnormal apical cytoskeletons in dyl , dy and m mutants [18 , 22 , 24] . Alternatively , the patterning of secreted epicuticle components could be mediated by their binding directly to envelope components ( Fig 8 ) . It seems likely that this would only work for the deposition of the epicuticle that was at the envelope-epicuticle transition boundary . At a later stage interactions between the patterned boundary layer epicuticle proteins and newly secreted epicuticle proteins could pattern the remainder of the epicuticle . Equivalent interactions between the epicuticle and procuticle would serve to instruct the initial deposition of the procuticle . It is worth noting that the late maturation changes observed in the envelope and epicuticle show that cuticle deposition is not a strict temporal hierarchy . While most studies have argued for such a hierarchy a study on the deposition of the first instar larval cuticle of Drosophila came to a different conclusion [35] . The instructive model is appealing in that it does not require a structure yet to be identified ( i . e . the platform ) and it can explain the altered apical cytoskeleton seen in mutants . However , it does not provide an obvious explanation for the large gaps seen between the apical surface and the cuticle in mutants , although that could be due to defects in outside-in signaling . In contrast the “platform model” can explain the gaps , however it does not provide an explanation for the changes in the apical cytoskeleton . Given the multiplicity of cuticle-related proteins it may be that these models are too simple and that a model that contains aspects of both will be correct . It will be important to determine if the proteins in question are incorporated into the envelope as predicted by the instruction models . The models suggested above assume that RNA levels are reflective of levels of synthesis of the relevant proteins . This is sometimes not precisely the case but in this system we find very large ( often 100 fold or greater ) changes in RNA levels so we doubt that changes in translational efficiency would be great enough to unlink RNA levels to general levels of protein synthesis . The model also assumes that the time of action of the relevant proteins is close to the time of their synthesis . Consistent with this are the cytoskeleton and other phenotypes seen in dyl mutants during the period of envelope deposition [18] , as well as the sharp increase of ple expression with simultaneous cuticle darkening . We also note that the Dyl protein accumulated on the apical surface and/or apical extracellular matrix during envelope deposition consistent with deposition and secretion being closely linked temporally to the presence of RNA in pupal wing and bristle forming cells [18 , 24] . The apical extracellular matrix is poorly understood compared to the basal extracellular matrix . Arthropod cuticle is not the only example of such a material as they are found in a wide variety of tissues , organs and organisms [76] . For example , the zona pellucida that surrounds mammalian oocytes [63] and the tectorial membrane of the cochlea [77 , 78] . Studies on the secretion and morphogenesis of insect cuticle could provide insights into more general questions about how cells can organize their apical extracellular matrix and how it influences tissue function and development . Our observations establish that mutations in a number of “42 hr genes” disrupt the process of wing cell flattening and expansion . This could be due defects in outside in signaling regulating the cytoskeleton or to a proper physical connection between the apical surface of wing cells and the developing cuticle being required for mechanical regulation of morphogenesis . The importance of the apical extracellular matrix for morphogenesis is not unique to the wing . Extensive studies have found defects in trachea lumen morphogenesis associated with mutations in genes required for the deposition of the cuticle that covers the apical surface the trachea cells [20] [79] [80] . Connections between the pupal cuticle and imaginal disc cells are important for disc evagination and overall appendage morphology [81–83] . Apical extracellular matrix has also been found to be important for a number of other morphogenetic events including mediating the anchoring of sensory neurons to cuticle in Drosophila [84] and to anchor dendritic tips during cell body migration during the formation of the amphid sense organs in C . elegans [85] . Apical extracelluar matrix is also important for morphogenesis in early sea urchin embryo [86] and in mammals the ZP domain protein hensin is important for kidney collecting tube development [87] [88] .
Oregon R flies were grown on standard fly food 25°C . White prepupae were collected and aged for varying lengths of time and pupal wings dissected in PBS ( phosphate buffered saline ) . At the starting time point of our analysis ( 42 hr awp ) , the wings are rigid enough to minimize contamination by other tissues . A detailed protocol can be found in S1 File ( Supplementary Methods ) . In a number of experiments we used apterous-Gal4 ( ap-Gal4- which drives expression in the cells that form the dorsal surface of the wing as well as the notum ( dorsal thorax ) , patched-Gal4 ( ptc-Gal4 , which drives expression in a stripe down the center of the wing and pannier-Gal4 ( pnr-Gal4 , which drives expression in a stripe down the mid line of the notum and dorsal abdomen ) . The RNAi inducing transgenes used for knocking down gene expression contain the UAS sequence that is recognized by Gal4 . Various lines came from either the VDRC or TRiP collections . The VDRC lines were obtained directly from the VDRC ( http://stockcenter . vdrc . at/control/main ) and the TRiP lines from the Bloomington Drosophila Stock Center ( http://flystocks . bio . indiana . edu/ ) . Those and other stocks obtained from the Bloomington Drosophila Stock Center ( NIH P40OD018537 ) were used in this study . Wings were fixed in 2% glutaraldehyde , 4% paraformaldehyde , rinsed in 0 . 1 M pH 7 . 4 cacodylate buffer , post fixed with OsO4 and then rinsed again . After rinsing with distilled water the samples were dehydrated and then in SPURR resin . Sections were stained with 4% uranyl acetate and 0 . 25% lead citrate and examined in a JEOL 1230 transmission electron microscope . The published images were assembled using Adobe Photoshop . To compare the thickness of the dorsal and ventral cuticle we used ImageJ to measure cuticle thickness ( 10 equally spaced measurements per sample point ) from juxtaposed dorsal and ventral regions in low magnification TEM images ( 3-5K ) . A t-test was used to compare the average thicknesses for at least 6 such averaged measurements . Details of the RNA isolation , manipulation and the RNASeq methods are provided in the S1 File ( Supplemental Methods ) . Briefly , RNA was isolated from wings and replicate libraries made using NEBNextR Ultra RNA library Prep kit for IlluminaR . Multiplexed samples were run on an Illumina MiSeq machine . Primers specific to all or selected transcripts from single genes were designed using Primer-BLAST76 and Geneious ( Biomatters Ltd . ) and obtained from Eurofins MWG Operon . Primer-BLAST provides analysis of potential spurious targets . The RT-qPCR was done on an Applied Biosystems 7500 Fast Real Time PCR System . Error bars in the figures presenting RT-qPCR data are standard deviations of triplicate sample RQ as reported by system software . Libraries from the first biological replicate were used for RT-qPCR reactions . The calibrator sample was an equimolar mixture of each library from the first biological replicate . The RQ value is expression in each sample relative to the calibrator ( which has the expression of 1 ) . Details of primers used and methods can be found in the S1 and S3 Files ( Supplementary Methods and MIQE data ) . The adapter sequences needed for sequencing on the Illumina MiSeq platform were automatically trimmed on the BaseSpace website . The Tuxedo RNAseq analysis suite was used as described by the authors of the suite [89] . Between 79 . 5 and 89 . 4% of the reads in the 14 samples were successfully mapped to the Drosophila genome indicating that the sequence was of high quality . Cross-replicate variability was assessed by plotting squared coefficient of variation ( CV2 ) against FPKM estimates for all quantified genes ( S4 Fig ) . The variability tends to decrease with increasing FPKM . The 42 hr dataset is slightly the most variable one and 72 , 80 , 88 hr show the lowest CV2 . Error bars for RNAseq data are 95% confidence intervals as reported by Cuffdiff [29] [90] . Downstream analysis of differential expression results , including production of heat map graphs , was done in R with the help of package cummeRbund and its dependencies . Gene clustering was done using a k-means algorithm ( partitioning around medoids ) . Other aspects of the analysis of differential expression were done in Excel . The reads were mapped to FlyBase genome version 6 . 02 , which contains 17234 genes and 33634 isoforms . Sequencing and mapping data are available for download from NCBI ( BioProject accession code: PRJNA259920 ) . For Fig 4 , the theoretical FPKM for each gene in the calibrator sample was calculated as an arithmetic mean of FPKM values in each sample . A more detailed description is provided in the S1 File .
|
Insects and other arthropods are an extremely successful group of animals . A unique and key feature of their lifestyle is their chitin containing cuticular exoskeleton , a complex layered material , which remains rather poorly understood for so prominent of a biological material . We have characterized the gene expression pattern of wing epithelial cells over the period of cuticle formation and also carried out transmission electron microscopy , which allows us to identify genes that likely play a role in the formation of different cuticle layers . Functional studies suggest that the deposition of the earliest layer influences the deposition of the later ones .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"chitin",
"invertebrates",
"animals",
"animal",
"models",
"drosophila",
"melanogaster",
"animal",
"anatomy",
"model",
"organisms",
"microscopy",
"materials",
"science",
"zoology",
"macromolecules",
"drosophila",
"materials",
"by",
"structure",
"research",
"and",
"analysis",
"methods",
"polymers",
"polymer",
"chemistry",
"proteins",
"gene",
"expression",
"chemistry",
"wings",
"insects",
"arthropoda",
"biochemistry",
"transmission",
"electron",
"microscopy",
"phenotypes",
"genetics",
"protein",
"domains",
"biology",
"and",
"life",
"sciences",
"electron",
"microscopy",
"physical",
"sciences",
"organisms"
] |
2016
|
The Gene Expression Program for the Formation of Wing Cuticle in Drosophila
|
Trypanosoma brucei gambiense is the causative agent of chronic Human African Trypanosomiasis or sleeping sickness , a disease endemic across often poor and rural areas of Western and Central Africa . We have previously published the genome sequence of a T . b . brucei isolate , and have now employed a comparative genomics approach to understand the scale of genomic variation between T . b . gambiense and the reference genome . We sought to identify features that were uniquely associated with T . b . gambiense and its ability to infect humans . An improved high-quality draft genome sequence for the group 1 T . b . gambiense DAL 972 isolate was produced using a whole-genome shotgun strategy . Comparison with T . b . brucei showed that sequence identity averages 99 . 2% in coding regions , and gene order is largely collinear . However , variation associated with segmental duplications and tandem gene arrays suggests some reduction of functional repertoire in T . b . gambiense DAL 972 . A comparison of the variant surface glycoproteins ( VSG ) in T . b . brucei with all T . b . gambiense sequence reads showed that the essential structural repertoire of VSG domains is conserved across T . brucei . This study provides the first estimate of intraspecific genomic variation within T . brucei , and so has important consequences for future population genomics studies . We have shown that the T . b . gambiense genome corresponds closely with the reference , which should therefore be an effective scaffold for any T . brucei genome sequence data . As VSG repertoire is also well conserved , it may be feasible to describe the total diversity of variant antigens . While we describe several as yet uncharacterized gene families with predicted cell surface roles that were expanded in number in T . b . brucei , no T . b . gambiense-specific gene was identified outside of the subtelomeres that could explain the ability to infect humans .
Trypanosoma brucei subsp . gambiense is the causative agent of Human African Trypanosomiasis ( HAT ) , or sleeping sickness , which is a vector-borne disease restricted to rural areas of sub-Saharan Africa . Trypanosomiasis in humans and livestock imposes substantial morbidity , representing a major impediment of agricultural production in the affected areas [1] , and is fatal where untreated . The World Health Organization estimated in 1998 that up to 60 million people are at risk in approximately 250 distinct foci [2] , although under-reporting has been estimated as high as 40% in some foci [3] . T . b . gambiense is the most clinically relevant sub-species , causing over 90% of all human disease . The gambiense disease is typically chronic , often lasting several years with few severe signs and symptoms until the late stage of nervous system involvement . T . b . gambiense is sensitive to treatment with pentamidine ( early stage ) and eflornithine ( late stage ) , drugs which are frequently ineffective against T . b . rhodesiense [4] , although the underlying biochemical reasons for these differences are unknown . Combination therapies against the late stage disease have performed encouragingly [5] but few drugs are available . Furthermore , unpleasant and in some cases severe side effects often result in poor patient compliance . Hence , new molecular targets are required to supply current drug discovery programmes [6] . T . brucei is subdivided into three subspecies based on infectivity to humans , pathogenicity and geographical distribution . T . b . gambiense and T . b . rhodesiense are human pathogens , causing Human African Trypanosomiasis ( HAT ) in West/Central and East Africa respectively . T . b . brucei cannot by definition infect humans and is found in a wide range of wild and domestic mammals . The human pathogens have also been found in various animal species and HAT caused by T . b . rhodesiense in East Africa is recognized as a zoonosis . T . b . gambiense comprises two groups; a genetically homogeneous group to which the majority of isolates belong ( group 1 ) , and a second represented by a handful of isolates from West Africa ( group 2 ) . Group 1 T . b . gambiense strains have the smallest genomes in the T . brucei species complex , having 71–82% of the highest DNA content measured for T . b . brucei [7]–[8] . Pulse-field gel analysis of T . b . gambiense chromosomes shows that few if any mini-chromosomes are present compared to the estimated 100 in T . b . brucei and T . b . rhodesiense , and the mini-chromosomes are also of a smaller size–average 25 kb in T . b . gambiense compared to 100 kb in T . b . brucei and T . b . rhodesiense [8]–[9] . Perhaps as a consequence of this reduced genome , T . b . gambiense also has a restricted repertoire of Variant Surface Glycoprotein ( VSG ) genes [8] , [10]–[12] . At any time , bloodstream form trypanosomes possess a surface glycoprotein coat formed through the expression of a single gene from a large archive of VSGs [13] . This coat obfuscates the host immune system by shielding the invariant surface epitopes from view and , when an immune response is inevitably raised against the VSG monolayer and the active VSG is replaced by another , it allows parasites expressing the novel variant to escape the immune response [13] . This periodic VSG ‘switching’ , or in situ activation , is facilitated by transposition of inactive VSG into a dedicated expression site at the telomeres by gene conversion [13]–[15] . Although VSG repertoire is clearly very large [16]–[17] , it is not known how VSG diversity accumulates over time and between strains . The SRA gene encodes a truncated VSG-like protein [18]; it is located within one specific VSG expression site and is expressed in Human serum-resistance clones of T . b . rhodesiense only [19] . Innate immunity to trypanosomes in Humans is conferred by a trypanolytic factor , apoL1 [20] and SRA has acquired a role in neutralizing the toxic effects of this protein [21] . Hence , when transcriptionally activated , SRA enables particular T . b . rhodesiense clones to infect Humans [22] . SRA is absent in T . b . gambiense [23] and the underlying basis for the trait of human infectivity here is as yet unknown . In T . b . gambiense , as yet the only example of a subspecies-specific gene is TgsGP , which encodes a 47 kDa VSG-like receptor protein , and is expressed in the flagellar pocket of bloodstream stage cells [24] . However , TgsGP is not associated with human infectivity in T . b . gambiense [25] . We produced an improved , high-quality draft genome sequence for T . b . gambiense DAL927 with the twin aims of identifying subspecies-specific genomic features that might contribute to our understanding of phenotypic variation and assessing the scale of genomic variation across T . brucei . This was achieved through comparison with the T . b . brucei 927 reference genome and we sought to evaluate the proficiency of this reference , ahead of the next generation of genome sequencing projects that will compare multiple isolates to scrutinize genetic divergence and genomic rearrangements in relation to disease . Our analyses show that the genome sequence of T . b . gambiense corresponds closely in gene order and content to the T . b . brucei 927 genome . Intraspecific genomic variation is largely associated with tandem or segmental duplications , among which we identify several subspecies-specific isoforms . Our final objective was to compare the VSG repertoires of T . b . brucei and T . b . gambiense , and so provide the first global perspective of how VSG diversity evolves on a genome scale . Details of the genome project describing the ‘Minimum Information for Genome Sequences’ are available online ( http://genomesonline . org/GOLD_CARDS/Gi00917 . html ) .
The sequence of the Trypanosoma brucei gambiense genome has been submitted to the EMBL database under accession numbers FN554964- FN554974 inclusive . The T . b . gambiense strain MHOM/CI/86/DAL972 was isolated from a patient in Côte d'Ivoire in 1986 and has been used routinely in laboratory studies since this time [26] . Bloodstream form trypanosomes were fed to tsetse in vitro and procyclics from infected midguts were established in culture and subsequently optically cloned . Procyclic form trypanosomes were grown in Cunningham's medium supplemented with 10% v/v heat-inactivated foetal calf serum , 5 µg/ml hemin and 10 µg/ml gentamycin at 27°C . High molecular weight DNA was purified by standard methods of phenol-chloroform extraction and alcohol precipitation . T . b . gambiense DNA was randomly sheared , size-selected DNA purified and subcloned into pUC19 plasmids ( 1 . 4 kb–4 kb inserts ) , as well as BAC vectors as previously described [27] . Inserts were sequenced by random sequencing using dye-terminator chemistry on ABI 3730 sequencing machines from both ends to generate paired end reads . There were 369 , 043 passed paired-end reads , producing roughly eight-fold coverage of the whole genome . Sequence reads were assembled using Phrap ( www . phrap . org; P . Green , unpublished ) . Automated in-house software ( Auto-Prefinish ) was used to identify primers and clones for additional sequencing to close physical and sequence gaps by oligo-walking . Manual base checking and finishing was carried out using Gap4 ( http://www . mrc-lmb . cam . ac . uk/pubseq/manual/gap4_unix_1 . html ) . Regions containing repeat sequences or with an unexpected read depth were manually inspected . The assembled contigs were iteratively ordered and orientated against the T . brucei 927 genome sequence , with manual checking . Aided by information from orientated read-pairs , together with additional sequencing from selected large insert clones , we re-examined regions with apparent breaks in chromosomal colinearity for potential assembly errors . The human-curated annotation of the T . b . brucei 927 reference genome was transferred to the assembled T . b . gambiense genome on the basis of BLASTp matches and positional information using custom perl scripts . Subsequently , gene structure and functional annotation were manually inspected and further edited , where appropriate , using the Artemis software [28] , as previously detailed [27] . The annotation of the T . b . gambiense genome can be viewed and searched via GeneDB ( http://www . genedb . org/ ) and comparative chromosome maps for T . b . brucei and T . b . gambiense are available at TritrypDB ( http://tritrypdb . org; [29] ) . Chromosomal sequences have been submitted to EMBL with the following accession numbers: FN554964-FN554974 inclusive . The T . b . gambiense capillary shotgun reads were aligned against the T . b . brucei 927 reference genome using SSAHA2 ( http://www . sanger . ac . uk/Software/analysis/SSAHA2/ ) . We discarded reads that mapped to more than one location on the reference genome , as well as pairs of reads that did not map in the correct orientation or to within 20% of the expected insert size of the library . In-house perl scripts were used to identify single nucleotide polymorphisms ( SNPs ) from the SSAHA alignments that adhered to a modified version of the Neighbourhood Quality Standard ( NQS , [30] ) ; we term this AltNQS . According to NQS , an acceptable SNP ( or fixed difference ) has a phred quality score of ≥23 and the 5 bases on either side of the SNP position have a quality score of ≥15 . However , these strict criteria do not allow for multiple mismatches within the 11 bp window . To accommodate the higher levels of polymorphism , our AltNQS adheres to the same rules as NQS but allows for multiple SNPs within the 11 bp alignment window as long as the base quality of each SNP has a phred score of at least 23 . To identify regions with significantly high SNP density on each chromosome , non-overlapping windows of 10 kb with at least 50% of read coverage were selected for analysis . For these windows , SNP density was calculated as the number of SNPs divided by the number bases covered in that 10 kb window . Using random sampling we estimated the mean and 97 . 5% confidence limit of mean SNP density . Regions with a value above the 97 . 5% quantile were identified as having significantly high SNP density values . Tandem gene arrays in the T . b . brucei 927 genome with >3 gene copies have previously been defined , and are known to contain polymorphism that is affected by recombination [31] . We assessed the variation among tandem gene duplicates to identify subspecies-specific genes . For each of these arrays , the coding and 3′ UTR sequences were gathered from the corresponding regions of the T . b . gambiense genome sequence . The downstream limit of the 3′ UTR was defined by the polypyrimidine termination motif [32] . All T . b . brucei and T . b . gambiense sequences were aligned in ClustalX [33] and manually adjusted . Those arrays showing no variation or only corresponding isoforms in both subspecies ( i . e . , simple orthology ) were discarded , leaving just those cases where a disparity in sequence diversity was apparent . To detect any ambiguity in phylogenetic relationships among sequences , each of these alignments was analyzed using SplitsTree v4 . 3 [34] , which applies a Neighbour-Net method [35] ) to estimate a phylogenetic network . Genetic distances were corrected for variation in base composition after excluding phylogenetically-uninformative characters . Each alignment was also analyzed using the pair-wise homoplasy index ( PHI ) test [36] that can detect multiple phylogenetic signals within an alignment and is robust in the presence of rate heterogeneity . A third method , the genetic algorithm for recombination detection ( GARD , [37] ) was applied to estimate the number and placement of recombination breakpoints along each alignment . 1258 predicted VSG protein sequences encoded in the T . b . brucei genome were compared with the T . b . gambiense 972 read library using pair-wise BLASTp searches . These included 36 VSG-related ( VR ) sequences that are structurally distinct from the bulk of canonical VSG [17] . Initially , all VSG-like sequences were extracted from the T . b . gambiense read library using BLASTx against whole VSG protein sequences . Each T . b . brucei VSG protein sequence was then individually BLAST-searched against this subset of VSG-like reads to determine its closest match in T . b . gambiense . A reciprocal comparison was carried out to confirm the relationship . To determine if a given gene was most closely related to a paralog in T . b . brucei or to an ortholog in T . b . gambiense , each T . b . brucei VSG protein sequence was also compared a combined database of VSG gene models and VSG-like reads using BLASTp . BioLayout Express 3D [38] was used to visualize the relative genetic distances between the 1258 T . b . brucei VSG sequences , using the BLAST scores derived from comparisons of each gene with all others , and a 70% cutoff to simplify the resulting network graph . To determine if VSG diversity is sub-structured according to life stage , nine VSG sequences known to be associated with metacyclic expression sites were BLAST-searched against all other ( bloodstream-expressed ) VSG and added to the network .
The draft genome assembly consists of 1768 contigs larger than 2 kb , amounting to 32 . 6 Mb of data . Of these , 281 contigs , totaling 22 . 1 Mb , were ordered and orientated against the T . b . brucei 927 reference genome . The remaining contigs encode additional copies of tandemly arrayed gene families as well as genes typically associated with subtelomeres such as expression site associated genes ( ESAGs ) , variant surface glycoprotein ( VSG ) genes and the ingi transposable element . The gene models and annotation of an initial set of 9898 coding sequences located on core chromosomes ( i . e . , not in subtelomeres ) were transferred to the T . b . gambiense genome on the basis of BLASTp matches and positional information using custom perl scripts . When compared , the T . b . brucei and T . b . gambiense genome sequences are very similar in terms of content , gene order and sequence identity . The absence of potentially gambiense-specific sequences was confirmed by examining a Phrap assembly of those capillary reads that did not map against the T . brucei 927 reference genome . Analysis of ∼40 , 000 unmapped sequence reads using BLASTx showed that among them were features homologous to VSG , ESAG and RHS genes , as well as ingi retrotransposons , but no additional coding sequences that were missing from T . b . brucei . We examined the divergence of coding sequences and a frequency histogram of percentage nucleotide identity ( Fig . 1 ) shows that 86 . 4% of genes vary by less than 1% from their T . b . brucei ortholog ( mean average nucleotide identity = 99 . 2% ) . Non-coding regions were more divergent , which is unsurprising given that they are probably under weaker purifying selection , but still remained 95 . 4% identical on average . However , against this general background of correspondence there are 69 pairs of orthologs that display significantly greater evolutionary change , ( i . e . , they are among the 5% most divergent orthologs with a nucleotide identity <95 . 2% ) . 35 of these gene pairs are VSG sequences; these surface glycoproteins are exposed to frequent gene conversion and evolve rapidly [16]–[17] , so naturally , they display lower sequence identities of ∼60–85% . However , they still display reciprocal top BLAST hits with T . b . brucei sequences . Also among these divergent gene pairs are 17 uncharacterized genes , 10 of which are predicted to encode cell-surface targeted proteins . For example , Tb927 . 5 . 4010/Tbg972 . 5 . 4300 ( 92 . 7% identical ) and Tb10 . 70 . 1280/Tbg972 . 10 . 6310 ( 93 . 7% identical ) are both located at strand-switch regions and encode hypothetical proteins with predicted signal peptides and GPI anchor sites . These genes , which appear to be evolving very quickly , are not found in either Leishmania major or T . cruzi , indicating that they are specific to African trypanosomes . A source of variation with potentially important functional consequences is allelic polymorphism . We detected high-confidence SNPs and fixed differences by mapping the T . b . gambiense reads to the T . brucei 927 reference sequence . Our analysis focused on the non-repetitive component of the genome as firstly , non-identical repeats can appear indistinguishable from SNPs and secondly , repeated regions may be subject to unusual selective pressures ( see below ) . After excluding these sequences , we identified a total of 224 , 568 putative fixed differences from 19 . 4 Mb of non-repetitive sequence , i . e . a diversity ( π ) of 0 . 0116 nucleotides per site . 92 , 794 of these differences were in coding regions , 49% of which were non-synonymous . To confirm that the variation identified when mapping the T . b . gambiense reads against the T . b . brucei 927 were not in fact false-positives due to heterozygosity within the T . b . brucei 927 reference sequence itself we also used the available capillary read data from the T . b . brucei 927 genome project to identify polymorphism within the published “haploid” consensus . Unfortunately , this was only possible for the four chromosomes ( 1 , 9–11 ) that were originally produced by shotgun sequencing , ( rather than a clone walking strategy ) , since these contain data from two homologous chromosomes at a given locus . From the SSAHA alignments , we identified 23 , 804 SNPs in 10 . 8 Mb of map-able sequence ( π = 0 . 0022 ) , of which 1 , 187 had the same heterozygous alleles in both the T . b . brucei 927 and the T . b . gambiense genome , indicating a false-positive rate of 5% . We identified 298 regions exhibiting higher than average diversity along the megabase chromosome . It is noteworthy , that this analysis excluded all telomere proximal regions owing to their highly repetitive nature . Whereas telomeres are well established in many species as sites of sequence variation and rearrangement [39]–[40] , the presence of interstitial regions of high diversity in addition to the sub-telomeres is striking . On rare occasions the otherwise consistent chromosomal colinearity is disrupted by sequence inversions and insertion-deletion events ( indels ) . In many cases indels coincided with sequence gaps , making it difficult to confirm genuine rearrangements . Nevertheless , chromosome 10 provides two examples , between 275–330 kb and 3250–3350 kb , of 55 and 110 kb segmental inversions respectively . Gene order within these inverted regions remains conserved . Typically , indels have two principal causes: transposable elements and internal VSG ‘islands’ . Transposable elements such as ingi and RIME sequences recombine in trypanosome genomes and are responsible for several rearrangements [27] . On chromosome 9 , a 7 kb insertion occurs in T . b . brucei due to an ingi element ( at 1 . 24 Mb ) not present in T . b . gambiense . Similarly , a 29 kb indel follows Tb11 . 02 . 5830 where an expression site-associated gene ( ESAG ) and a trans-sialidase gene have been inserted into T . b . gambiense at the corresponding position to a RIME sequence in T . b . brucei . By their nature , such rearrangements frequently occur in repetitive regions of the genome and , consequently , are difficult to resolve in genome assemblies . This therefore does not preclude that further events will be identified in the future . Another source of genomic variation concerns core chromosomal VSG and ESAG genes . VSG genes are predominantly found in subtelomeric arrays , on intermediate or mini-chromosomes [27] , [13] . In addition , VSG/ESAG genes are less commonly found non-telomerically as ‘islands’ , often on the opposing strand to neighbouring loci . These genes ( or pseudogenes ) may be: ( i ) atypical VSGs that do not encode all elements for accurate folding or post-translational modification; ( ii ) VR genes; or ( iii ) canonical VSG genes , imported from the subtelomere or mini-chromosome through segmental duplication . An example of the latter is a segmental insertion including 8 VSG genes that affects chromosome 9 in T . b . gambiense ( Tbg972 . 2 . 570–640 ) , since the VSG sequences are unrelated to each other and therefore , have not resulted from recent tandem duplications . In total , 17 such VSG/ESAG islands were noted in both genomes , only 6 of which were unique to one subspecies or other , including a segmental duplication in T . b . brucei of an atypical VSG combined with an insertion or deletion of ESAGs ( Supplementary Fig . S1 ) . Clearly , VSG/ESAG islands are among the more dynamic features of core chromosomes , yet where they are conserved between T . b . brucei and T . b . gambiense they contain orthologous gene sequences , indicating that they not exposed to frequent gene conversion processes like VSGs elsewhere . Beyond transposable elements and VSG ‘islands’ , other differences in gene order are caused by a class of small , putative coding sequences of unknown function ( 103 cases ) . These genes encode hypothetical proteins with a predicted length of 151–274 amino acids and which have no database matches to any experimentally characterized protein . Transcriptomic data ( George Cross , Rockefeller University , unpublished data; Veitch et al . , University of Glasgow , submitted ) suggest that some of these putative genes are at least transcribed , although no product has yet been identified in proteomic assays to date ( Aswini Panigrahi , SBRI , pers . comm . ) . Regardless of which genome encodes the putative gene , homologous sequences of high identity are found in the other genome at the corresponding positions , but without the open reading frame . Hence , they may be non-coding RNA genes or other non-coding conserved elements of undiscovered function . These features are annotated to ensure completeness , and they may yet reveal functional importance , but our view is that they are unlikely to produce proteins and will not be considered further . Our comparative analysis identified only a single coding sequence , a putative iron-ascorbate oxidoreductase ( Tb09 . 211 . 4990 ) , which is absent from the genomic repertoire of T . b . gambiense . We did not identify the TgsGP locus , which is known to be unique to T . b . gambiense [24] because it is located in the subtelomere and these regions were not fully assembled . However , sequence identical to TgsGP was identified among the unassembled reads . Thus , it is possible that other subspecies-specific genes exist within the subtelomeres that are not recorded here . Tb09 . 211 . 4990 is preceded upstream on chromosome 9 by a strand-switch region and downstream by both retrotransposon-like proteins and the splice-leader RNA tandem array . This region is conserved in T . b . gambiense , but the oxidoreductase is absent . The gene is absent from the more distantly related kinetoplastids Leishmania major and T . cruzi , as well as 9 out of 11 other T . b . brucei strains and a representative group 2 T . b . gambiense ( STIB 386 ) that we examined with PCR primers specific to this oxidoreductase ( data not shown ) . When compared phylogenetically with other iron-ascorbate oxidoreductases in T . brucei , ( principally the tandem gene array at the right-hand terminus of chromosome 2 , e . g . Tb927 . 2 . 6180 ) , this protein is clearly structurally distinct ( only 80% amino acid identity ) and constitutes an evolutionarily old lineage . This suggests that Tb09 . 211 . 4990 is gained and lost at the population level , and that it provides additional functionality to T . b . brucei 927 and two other T . b . brucei strains in which it has been found . The comparison of gene content did not identify widespread subspecies-specific loci , and found no obvious differences that could explain the distinct phenotypes of T . brucei subspecies . For example , ornithine decarboxylase , the target of eflornithine to which T . b . gambiense is uniquely sensitive , is present in single , diploid copy in both genomes and displays only a single non-synonymous substitution ( N137I ) . We did , however , detect substantial variation within families of certain uncharacterized genes that could have important functional consequences . Such differences in co-linearity involve either the expansion of a single-copy gene in one subspecies to a tandem pair in the other , or a difference in the number of duplicates where there is a tandem array in both subspecies . Current methods of genome assembly tend to detect the first scenario ( i . e . , single copy vs . many ) but have limitations in accurately quantifying copy number and in distinguishing between copy number and allelic variation . In fact , while the number of repeat units assembled can be arbitrary , the variation among tandem gene duplicates can be accurately assessed from genome sequence data for the two subspecies . In 20 cases , a single-copy feature ( be it a single gene or chromosomal segment ) in T . b . gambiense exists in multiple , tandem copies in T . b . brucei , while 8 cases of the converse were observed ( Table 1 ) . For the majority of these cases , the tandem duplicates were identical and the duplication did not result in any novel , unique sequence . But in 8 cases in T . b . brucei , the extra duplicates contained sequence variation that might represent subspecies-specific isoforms . In four additional cases , the would-be unique sequences were found among sequence reads of the apparently single-copy subspecies , indicating that it had been omitted from the assembly ( marked by an asterisk in Table 1 ) . The genes involved in these T . b . brucei-specific segmental duplications are as yet uncharacterized , but their features suggest that they are potential sources of subspecies-specific factors and interesting opportunities for further research . They are evolutionarily novel since they are not conserved in either T . cruzi or L . major; several encode proteins with predicted cell surface roles; and some are among the fastest evolving of all T . brucei genes . For example , a tandem gene array of hypothetical genes encoding cysteine-rich secretory proteins is shown in Supplementary Fig . S2; these are homologous to a single gene ( Tbg972 . 3 . 6170 ) at the corresponding position on chromosome 3 in T . b . gambiense . From the relative strength of BLAST hits between homologs , it is clear that the first gene in the array and the singleton in T . b . gambiense are orthologs , while the additional copies in T . b . brucei ( absent from the T . b . gambiense read library ) are unique paralogs . Indeed , they have evolved considerably , sharing only 55 . 1% amino acid identity with the upstream orthologs . Similarly , Figure 2 shows a single segment on chromosome 9 in T . b . gambiense ( Tbg972 . 9 . 4160 , 4140 and 4130 ) that corresponds to five tandem repeats in T . b . brucei . Among gene duplicates of the second and third coding sequences , which encode hypothetical transmembrane and GPI-anchored proteins respectively , there is considerable sequence variation ( average nucleotide identities of 51 . 2% and 59 . 1% respectively ) . As in Supplementary Fig . S2 , the 5′-most segment in T . b . brucei is orthologous to the T . b . gambiense genes , but the downstream copies are structurally divergent . A third example of segmental duplication with subsequent divergence of tandem copies occurs on chromosome 6 and concerns a hypothetical protein with a predicted signal peptide and GPI anchor ( Supplementary Fig . S3 ) . Such segmental duplications provide rare examples of subspecies-specific gene paralogs or isoforms . It remains to be seen how common , and how ephemeral , such copy number variation is among T . brucei strains generally . But these cases are especially interesting because they do not simply concern gene dosage . In fact , with divergence in protein sequence often between 30–40% among paralogs , the effects on protein function could be considerable . Not only have these genes multiplied in number in very recent evolutionary time , this has been accompanied by rapid structural divergence in their predicted cell surface gene products , suggesting a role for adaptive change . Such protein isoforms could contribute to the observed differences between group 1 T . b . gambiense and other T . brucei isolates in the host-parasite relationship , both in the mammalian and insect hosts . Tandem gene arrays in the T . b . brucei genome usually contain sequence variants and analysis of tandem duplicate variation using T . b . brucei sequences alone showed that divergence frequently results in sequence mosaics and concerted evolution within genomes [31] . After discounting the minority of invariant tandem arrays in T . b . gambiense , 35 tandem gene arrays that contained sequence variation were compared with their T . b . brucei homologues , demonstrating that 27 arrays contained subspecies-specific gene copies ( Table 2 ) . In 5/49 instances subspecies-specific copies displayed unique sequence motifs , suggesting differential assortment of the ancestral gene repertoire between the daughter subspecies . Elsewhere , subspecies-specific copies were recombinants of other duplicates . Tests for recombination carried out on multiple alignments of gene copies from both subspecies demonstrated that sequence mosaics occurred in 31/35 data sets as exemplified by the array of invariant surface glycoproteins on chromosome 2 ( ISG; Tb927 . 2 . 3270–3320 ) ( Fig . 3 ) . The ISG array comprises 6 and 12 gene copies in T . b . brucei and T . b . gambiense , respectively . GARD analysis detected at least five recombination breakpoints ( Fig . 3a ) and the recombinant nature of ISG is reflected in a highly reticulated phylogenetic network ( Fig . 3b ) . This also identifies potential subspecies-specific recombinants , for instance , the proximity of ‘Tbg7’ to ‘Tbg10’ reflects the overall similarity of these copies , but closer inspection shows that small sections of homology exist with other copies , i . e . , ‘Tbg8/9’ ( Fig . 3c ) . Similarly , the intermediate position of Tbg1 reflects its affinities with multiple , unrelated sequences ( Fig . 3d ) . Some of the hardest genome regions to reliably assemble are subtelomeres , since they usually contain numerous high-copy gene families , as well as simple and complex sequence repeats . The fluidity of subtelomeric assemblies perhaps reflects some reality about the true mutability of subtelomeric regions , since they are known to vary widely in length between trypanosome strains [41] . In comparing ∼1 . 3 Mbp of subtelomeric sequence immediately contiguous to the chromosomal cores between the two subspecies , it is clear that they are highly similar in composition and gene order . In both T . b . brucei and T . b . gambiense the largest component of subtelomeric genes comprises VSGs ( 67 . 8% and 44 . 4% , respectively ) , followed by ESAGs ( 13 . 4% , 15 . 8% ) , and transposable element-related genes ( 7 . 6% , 13 . 5% ) . Adenylate cyclases ( 2 . 2% , 3 . 0% ) and glycosyltransferases ( 1 . 1% , 1 . 5% ) are also prominent features in both genomes . Beyond these subtelomeric regions , previous comparisons of telomeric VSG expression sites in various T . brucei strains and subspecies have established that the essential components are ubiquitous [42]–[43] . Hence , although T . brucei telomeres are known to evolve rapidly and display widespread karyotypic variation , the composition of regions beyond core chromosomes remains consistent across the species . As the relative divergence ( Fig . 1 ) and antigenic variability of different T . brucei strains is of diagnostic and clinical importance , we investigated the diversity between the VSG repertoires in the two subspecies by comparing all of the 1258 VSG sequences annotated to date in the T . b . brucei 927 genome with the unassembled sequence reads from the T . b . gambiense genome . Hence , it should be noted that we are comparing whole genes from T . b . brucei with fragments from T . b . gambiense . Among VSG genes with reciprocal top hits in the T . b . gambiense read library , the average amino acid identity is 43 . 3% , but with substantial variation ( SD = 21 . 35 , n = 692 ) . Clearly , the substitution rate affecting VSG nucleotide sequences is relatively high , due either to positive selection , or a relaxation of purifying selection . Yet VSGs do not evolve so quickly as to abolish detectable orthology between subspecies; 692 VSG genes had a reciprocal top BLAST hit with a T . b . gambiense sequence read , indicating that 55% of the T . b . brucei repertoire ( or parts thereof ) were conserved in T . b . gambiense . Furthermore , 1061 VSG genes ( 84% ) had reciprocal BLAST hits or very close matches , ( i . e . , within the top three BLAST hits for the matching T . b . gambiense read ) . The network representation emphasizes the global perspective of the VSG repertoire in T . b . brucei 927 relative to T . b . gambiense ( Fig . 4 ) . 197 VSG without close matches to T . b . gambiense reads are distributed throughout the network , indicating that they do not share a common origin and represent losses in T . b . gambiense . As the T . b . brucei 927 subtelomeric sequences are incomplete , its VSG set is partial , and therefore further T . b . brucei-specific VSG sequences may be identified in future . That said , our analysis consistently demonstrated that VSG genes have corresponding sequences in both subspecies , though 787 ( 63% ) were better related to other T . b . brucei genes than any T . b . gambiense read , suggesting that a gene duplication or gene conversion event had occurred since separation of the subspecies . We sought to identify phylogenetic structure , or discernable subsets , among VSG to establish limitations on gene conversion . The structural distinction between canonical VSG and VR proteins is already established [17] and is consistent with the location of VRs outside of the subtelomeres and their lack of pseudogenes . Accordingly , the VRs cluster together ( yellow shading ) in the network . They contrast with the otherwise diffuse arrangement of canonical VSG; sequences do not cluster by chromosomal location or by developmental stage expression since metacyclic-VSG are distributed throughout . VSG specific to T . b . brucei ( red shading ) do not belong to a single lineage , and equally , there is no evidence for evolutionary expansion of a particular subset . Instead , these data indicate that gene conversion has occurred frequently among subtelomeric VSG in the recent past , unlimited by genomic location and resulting in occasional gene loss . The complement of variant surface glycoproteins in T . b . gambiense has previously been reported as being smaller , or more restricted , than that of T . b . brucei , based on a smaller overall genome size and reduced subtelomeric components [8] . This could indicate fewer discrete VSG sequence types or just fewer copies of an equal number of types . The data presented here suggest that if T . b . gambiense had a smaller VSG repertoire , this is likely to reflect quantity rather than sequence types . Hutchinson et al . [44] reported that , while protein sequences had diverged consistent with subspecies-specific adaptation , 14 expressed VSG genes in the T . b . brucei Tororo strain all had close homologs in both T . b . brucei 927 and T . b . gambiense . Our data support the idea that the VSG repertoire is relatively stable across T . brucei subspecies , but that VSG genes have an inherently high substitution rate resulting in rapid sequence divergence relative to other genes . Corresponding VSG sequence types are thus likely to be found in any T . brucei strain , making it realistic to catalogue all VSG types and to monitor their expression in the field . Thus , the apparent lack of genetic hypervaribility concerning VSG in T . brucei seems simpler than other systems of antigenic variation , such as the var surface glycoproteins in Plasmodium falciparum , where frequent switching of expressed antigens is combined with genetic hypervariability and there is minimal overlap between repertoires between isolates [45]–[47] . Like P . falciparum , T . brucei ‘shuffles its deck’ with every infection , but , unlike Plasmodium , it always uses the same pack of cards . T . b . gambiense is the most important human-infective form of T . brucei and currently endemic throughout central Africa . In producing a draft genome sequence for T . b . gambiense this study attempted to identify genetic causes for human infectivity in T . b . gambiense , as well as assess the scale of intraspecific genomic variation . Genomic conservation between T . b . gambiense and the T . b . brucei validates the use of T . brucei brucei as a model for studying the unculturable T . b . gambiense . Specifically , intraspecific genomic divergence is typically <1% in coding regions; gene gain and loss is associated with rare segmental duplications; indels are few and generally caused by transposable elements or VSG/ESAG ‘islands’; and 84% of surface antigens are represented whole or in part in both subspecies . The VSG repertoire is essentially conserved at the level of modular protein domains , which are reorganized by gene conversion into novel mosaics in each strain . Therefore our data are likely to anticipate the archive present in the genomes of other strains , and a definition of total VSG diversity should be achievable through the addition of further sequences in the near future . Comparative genomics has identified species-specific genes in other eukaryotic pathogens that display interspecific pathological variations , including Leishmania spp . [48]; Candida albicans and C . dubliniensis [49]; and Plasmodium knowlesi and P . vivax [50] . In applying a similar rationale here , we found no obvious candidate for a gene analogous to SRA that could account for human-infectivity in T . b . gambiense . However , since both SRA and TgsGP are homologous to VSG genes and subtelomeric , such a gene might not be apparent from comparison of the core chromosomes and could still exist within the subtelomeres of T . b . gambiense . Alternatively , rather than differences in gene content per se , phenotypic variability could be due to individual SNPs or indels , or to differences in gene expression . Given that innate immunity to trypanosomes in Humans is based on the uptake of high-density lipoprotein particles , which contain apoL1 and stimulate trypanolysis [20]–[21] , perhaps the likeliest cause of phenotypic variation relates to this process . Indeed , it is possible to select for resistance to trypanolysis by down-regulating TbHpHbR [51] , which encodes an haptoglobin-haemoglobin surface receptor that normally scavenges haem from the host , but also facilitates the uptake of trypanolytic particles [52] . However , this gene is present in T . b . gambiense ( Tbg972 . 6 . 120 ) and differs from its T . b . brucei counterpart ( Tb927 . 6 . 440 ) by only 5 amino acid replacements ( L210S , A293V , E369G , G370A and M398I ) . Hence , the basis for human infectivity in T . b . gambiense remains debatable , and we must now consider that features shared by both subspecies have been modified in structure or expression in T . b . gambiense to provide the genotypic basis for resistance to trypanolysis . This issue apart , several putative cell-surface protein families that include subspecies-specific members have been identified in T . b . brucei . These proteins are previously unrecognized elements of the trypanosome surface that display both recent gene duplications and accelerated evolutionary rates and we speculate that they may have acquired novel functions . We also suggest the presence or absence of such hypothetical genes varies on a population scale , and might yet contribute to phenotypic variability in host range within T . brucei .
|
Sleeping sickness , or Human African Trypanosomiasis , is a disease affecting the health and productivity of poor people in many rural areas of sub-Saharan Africa . The disease is caused by a single-celled flagellate , Trypanosoma brucei , which evades the immune system by periodically switching the proteins on its surface . We have produced a genome sequence for T . brucei gambiense , which is the particular subspecies causing most disease in humans . We compared this with an existing reference genome for a non-human infecting strain ( T . b . brucei 927 ) to identify genes in T . b . gambiense that might explain its ability to infect humans and to assess how well the reference performs as a universal plan for all T . brucei . The genome sequences differ only due to rare insertions and duplications and homologous genes are over 95% identical on average . The archive of surface antigens that enable the parasite to switch its protein coat is remarkably consistent , even though it evolves very quickly . We identified genes with predicted cell surface functions that are only present in T . b . brucei and have evolved rapidly in recent time . These genes might help to explain variation in disease pathology between different T . brucei strains in different hosts .
|
[
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] |
[
"genetics",
"and",
"genomics/gene",
"discovery",
"genetics",
"and",
"genomics/comparative",
"genomics",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"evolutionary",
"biology/genomics",
"genetics",
"and",
"genomics/genome",
"projects",
"infectious",
"diseases/protozoal",
"infections"
] |
2010
|
The Genome Sequence of Trypanosoma brucei gambiense, Causative Agent of Chronic Human African Trypanosomiasis
|
Certain bacterial adhesins appear to promote a pathogen's extracellular lifestyle rather than its entry into host cells . However , little is known about the stimuli elicited upon such pathogen host-cell interactions . Here , we report that type IV pili ( Tfp ) -producing Neisseria gonorrhoeae ( P+GC ) induces an immediate recruitment of caveolin-1 ( Cav1 ) in the host cell , which subsequently prevents bacterial internalization by triggering cytoskeletal rearrangements via downstream phosphotyrosine signaling . A broad and unbiased analysis of potential interaction partners for tyrosine-phosphorylated Cav1 revealed a direct interaction with the Rho-family guanine nucleotide exchange factor Vav2 . Both Vav2 and its substrate , the small GTPase RhoA , were found to play a direct role in the Cav1-mediated prevention of bacterial uptake . Our findings , which have been extended to enteropathogenic Escherichia coli , highlight how Tfp-producing bacteria avoid host cell uptake . Further , our data establish a mechanistic link between Cav1 phosphorylation and pathogen-induced cytoskeleton reorganization and advance our understanding of caveolin function .
The primarily extracellular obligate human pathogen Neisseria gonorrhoeae ( P+GC ) is the causative agent of the sexually transmitted disease gonorrhoea , affecting over 60 million people every year worldwide [1] . It is a type IV pili ( Tfp ) -producing bacteria that colonizes mucosal epithelia of the human urogenital tract [2] . Tfp are proteinaceous filaments that play a crucial role in pathogenesis by mediating the initial attachment to host cell receptors and are expressed on the surface of a variety of bacterial pathogens such as Gram-negative N . meningitidis , Pseudomonas aeruginosa , and enteropathogenic E . coli ( EPEC ) as well as Gram-positive Streptococcus sanguis and Clostridium perfringens [3] . A growing body of evidence suggests that adhesins such as Tfp are key pathogenesis factors facilitating not only attachment but soliciting the necessary host cell cytoskeletal rearrangements and signaling cascades that promote an extracellular lifestyle [4] . Tfp-expression and cytoskeletal remodeling allows N . meningitidis to resist shear stress possibly encountered in the bloodstream [5] , and mechanical forces generated by pilus retraction of P+GC lead to cytoprotection [6] . However , details of the elicited signaling cascades within the host cell upon attachment of Neisseria remain patchy and require clarification . The early stages of infection with P+GC are characterized by Tfp-mediated attachment to host cells [7] . This is followed by retraction of pili in a force-generating depolymerization process [8] and formation of microcolonies on the surface of host epithelial cells [2] . Cortical actin and various signal transducing proteins are then recruited to the site of bacterial attachment [9] . As infection proceeds , the phase-variable opacity associated ( Opa ) proteins are expressed , allowing occasional entry and transcytosis of individual bacteria through epithelial cells to reach underlying tissues [10] . Several signaling proteins that are recruited to P+GC microcolonies have also been found to be associated with lipid rafts and caveolae , cholesterol-enriched microdomains of cell membranes [11] , suggesting that these or associated proteins play an essential role in this initial infection step . The major structural protein of plasma membrane caveolae , caveolin-1 ( Cav1 ) , is also known to localize to subcellular compartments and to the cytoplasm [12] . Cav1 has been shown to inhibit signal transduction by binding to numerous target proteins with its scaffolding domain [12] , but it can also promote signaling events through phosphorylation on tyrosine 14 ( Tyr14 ) [13] , [14] . We speculated therefore that Cav1 could play an important role during P+GC infection . Here we provide evidence that during the early stages of infection , P+GC triggers a phosphotyrosine-dependent Cav1-Vav2-RhoA signaling cascade that elicits cytoskeletal rearrangements and effectively impedes bacterial uptake into host cells .
To assess the role of Cav1 in the Tfp-mediated binding of P+GC to host cells , we began by monitoring the cellular localization of Cav1 in ME-180 cells , a human epidermoid carcinoma cell line , immediately following infection . We found that endogenous Cav1 localized close to P+GC microcolonies after 2 h of infection ( Figure 1A ) . Using live-cell imaging , we likewise observed a substantial accumulation of Cav1-GFP at sites of bacterial infection , which was induced even by single diplococci and within seconds after P+GC attachment ( Figure 1B , lower panel ) . Cav1-GFP recruitment occurred throughout the early stages of infection , resulting ultimately in a conspicuous accumulation of the protein ( Figure 1B , upper panel and Video S1 ) . By contrast , using a non-piliated isogenic GC strain ( P− Opa57+GC is a non-piliated isogenic strain of P+GC that produces an Opa57 adhesin specific for CEACAM receptors ) , we found no recruitment of endogenous Cav1 in ME-180 cells ( Figure S1A ) . Interestingly , despite the observed recruitment of Cav1 after P+GC infection , microarray and Western blot analysis failed to detect any increase in Cav1 expression ( unpublished data ) . This rather points to a cellular Cav1 reorganization leading to Cav1-accumulation than de novo synthesis . The functional role of Cav1 in bacterial infection was then explored by downregulating Cav1 levels in ME-180 cells using RNA interference ( RNAi ) . Reduced Cav1 levels resulted in efficient internalization of P+GC by ME-180 cells , as demonstrated by gentamicin protection assays ( Figure 1C ) and confocal microscopy ( Figure 1D ) . Next , we turned to the human gastric carcinoma cell line AGS , which , like many other malignant cell lines , is devoid of detectable levels of Cav1 [15] . Stably transfected cell lines , AGS-Mock and AGS-Cav1 , were infected with P+GC for 2 h and bacterial uptake was monitored by gentamicin protection assay . In contrast to mock-transfected cells , Cav1-expressing AGS cells inhibited P+GC internalization ( Figure 1C ) . As before , siRNA-mediated downregulation of Cav1 in AGS-Cav1 cells restored bacterial uptake ( Figure 1C ) . The total number of cell-associated bacteria was similar in the AGS and ME-180 cells and was unaffected by Cav1 expression . In addition , inhibition of bacterial uptake was Tfp-specific , as Opa-mediated bacterial uptake remained unaltered by Cav1 expression ( Figure S1B ) . Finally , bacterial uptake was independent of pilus retraction , as indicated by the use of an isogenic , non-retractile PilT-deficient GC mutant ( Figure S1C ) . Interestingly , in Cav1-negative AGS cells the observed epithelial cell entry resulted in a drastic decrease over time in the viability of internalized bacteria ( Figure 1E ) . Taken together , these results demonstrate that Cav1 plays a pivotal role in preventing internalization of P+GC by host epithelial cells , thus promoting bacterial survival . To broaden our findings , we also investigated Cav1 recruitment in EPEC infection , as initial adherence of EPEC to intestinal epithelial cells is conducted by type IV bundle-forming pili [16] . To avoid super-imposition with the actin-recruiting function of the EPEC type III secretion system ( TTSS; [17] ) , we used a TTSS deficient mutant that still expressed Tfp . Interestingly , we found that the infection of epithelial cells by EPEC also induced an accumulation of Cav1 beneath the bacteria , similar to our observations with P+GC ( Figure S2A ) . Most importantly , Tfp-producing EPEC entered Cav1 deficient AGS cells more rapidly compared to cells producing Cav1 ( Figure S2B ) . This pronounced effect was even observed in the presence of the EPEC TTSS , thus emphasizing a generalized role of Cav1 in blocking cell entry of Tfp-producing bacteria . Together , our data suggest that Cav1 accumulation is a Tfp-specific and immediate cellular response to bacterial attachment that occurs throughout the early stages of infection . Cav1 has also been shown to localize to non-caveolar cellular regions , where it participates in transport of signaling proteins via phosphorylation on Tyr14 [18]–[20] . We hypothesized that Cav1 recruited to P+GC microcolonies localizes outside caveolae and plays a role in protein trafficking or signaling . To address this question , we mapped the location of Cav1 in infected host cells using 3D-reconstruction of confocal images and immunogold staining . We found that Cav1 accumulates in the vicinity of P+GC but not directly at the plasma membrane ( Figure 2A and Figure S3 ) . Analysis of horizontal sections of confocal image stacks revealed F-actin structures in infected cells localized between the plasma membrane and endogenous Cav1 ( Figure 2B ) . This is consistent with previous observations on the assembly of F-actin structures in epithelial cells following the attachment of P+GC [9] , corroborating an association between F-actin and Cav1 . Indeed , treatment of ME-180 cells with either cytochalasin D ( Cyt D ) or latrunculin A ( Lat A ) , which disrupt actin filaments , prevented Cav1 accumulation ( Figure S4A ) and induced bacterial internalization as shown by confocal imaging ( unpublished data ) and gentamicin protection assays ( Figure S4B ) . Thus , both recruitment of Cav1 and inhibition of bacterial internalization require a functional actin cytoskeleton . Cav1 has previously been reported to bind cytoskeletal components such as the actin-crosslinking protein filamin and intermediate filaments [21] , [22] . Moreover , relocation of Cav1 to the caveolae-free front of migrating cells requires its distribution along cytoskeletal structures and is dependent on the presence of Tyr14 [23] . To determine if the Cav1-cytoskeleton association observed here was dependent on Tyr14 of Cav1 , we purified the cytoskeletal fraction from AGS cells that had been transfected either with an epitope-tagged version of wild-type Cav1 ( Cav1-HA ) or with a phosphorylation-defective mutant ( Y14F-Cav1-HA ) . Similar to endogenous Cav1 in ME-180 cells , Cav1-HA was completely recovered from the cytoskeletal fraction of transfected AGS cells , whereas only 45% of the total Y14F-Cav1-HA was detected in this fraction ( Figure 2C ) . However , after P+GC infection no significant differences in the Cav1-cytoskeleton association were observed expressing either Cav1 construct ( unpublished data ) . Thus , it is likely that phosphorylation of Cav1 on Tyr14 promotes its association with the cytoskeleton . To understand the importance of Cav1 phosphorylation during infection with P+GC , we monitored the phosphorylation status of Cav1 during infection ( 2 h ) of serum starved ME-180 cells . In addition , we blocked Src kinases and Abl kinases using chemical inhibitors previously reported to phosphorylate Cav1 [24] , [25] . The Src family kinase inhibitor PP2 [26] and Abl tyrosine kinase inhibitor STI571/Imatinib [27] were added ( 10 µM each ) , individually and in combination , 1 h prior to infection . Western blot analysis of phospho-Tyr14-Cav1 levels ( Figure 2D , upper panels ) and quantification of data ( Figure 2D , lower panel ) showed P+GC were able to elicit Cav1 phosphorylation at Tyr14 . In control-treated ME-180 cells , Cav1 phosphorylation levels were increased 1 . 5-fold after 5 min of infection . Phosphorylation levels then reached a plateau phase before rising again after 30 min , increasing up to 4-fold after 90 min of infection . In accordance with previous reports [24] , [25] , this phosphorylation depended on active Src- and Abl-kinases . Compared to untreated control cells , stimulation of phosphorylation by P+GC in PP2- and STI571-treated cells was markedly reduced during the whole infection period . Strikingly , ME-180 cells treated with both inhibitors exhibited minimal levels of phosphorylation and stimulation by P+GC was negligible . Next , we determined the cellular localization of phospho-Tyr14-Cav1 using confocal imaging ( Figure 2E ) . Despite the low levels of immunostained phosphorylated Cav1 , phospho-Tyr14-Cav1 was detected in the vicinity of attached P+GC , as observed previously with non-phosphorylated Cav1 , suggesting a direct link between P+GC infection and Cav1 phosphorylation . Hence , we infected AGS cells expressing either wild-type Cav1-HA or Y14F-Cav1-HA with P+GC . In contrast to wild-type Cav1 , Y14F-Cav1 was not recruited to bacterial attachment sites and did not impede internalization ( Figure 2F ) , strongly suggesting that Cav1 phosphorylation at Tyr14 is induced or enhanced by P+GC , enabling Cav1 recruitment , Cav1-mediated prevention of bacterial uptake , and a strong association of Cav1 with the cytoskeleton . Taken together , our data suggest that Cav1 phosphorylation plays a role in downstream signaling , linking Cav1 with cytoskeletal rearrangements . To identify signaling proteins that could interact with Cav1 upon phosphorylation at Tyr14 , we synthesized two fluorescently labeled peptides with sequences corresponding to residues 5-22 of Cav1 , one phosphorylated on Tyr14 and the other not phosphorylated . We then used these peptides to probe protein microarrays comprising virtually every Src homology 2 ( SH2 ) and phosphotyrosine binding ( PTB ) domain encoded in the human genome , as previously described [28] . In order to obtain quantitative information , we probed the arrays , in duplicate , with eight concentrations of each peptide , ranging from 10 nM to 5 µM . We then fit the resulting fluorescence data to an equation that describes saturation binding [28] , enabling us to obtain equilibrium dissociation constants ( KDs ) for the binding of each peptide to each recombinant domain ( Figure 3A ) . Previous studies with other phosphopeptides have shown that >90% of the SH2 and PTB domains on these arrays are active [28] and hence non-interactions should be viewed as reliable information as well . In total , the arrays highlighted six SH2 domains that recognized the Cav1 phosphopeptide with high affinity ( KD <2 µM ) : Abl2 , Vav2 , Phospholipase Cγ1 ( PLCγ1 ) , SH2D3C , Grb10 , and Abl1 . Although strong interactions with the SH2 domain of Abl2 are frequently observed ( this is a particularly promiscuous domain ) , we were intrigued by the high affinity interaction with the SH2 domain of RhoA GEF Vav2 ( KD = 220 nM ) . To investigate the physiological relevance of this biophysical interaction , we performed the following biochemical experiments: First , we incubated biotin-labeled peptides with sequences corresponding to residues 7–21 of Cav1 , one phosphorylated on Tyr14 and the other not phosphorylated , with cellular lysates derived from ME-180 cells and precipitated the peptides with streptavidin-coated beads . Consistent with the microarray data , Vav2 co-purified exclusively with the phosphorylated peptide ( Figure 3B ) . Moreover , 40% more Vav2 was recovered from infected cell lysates than from uninfected cells . Similarly , PLCγ1 , another important binding partner identified by the protein microarray , showed a vastly increased binding affinity to the phosphorylated peptide . However , levels of PLCγ1 were similar in both lysate types . Next , we immunoprecipitated full-length , endogenous Cav1 protein from either untreated ME-180 cells or from cells that had been pretreated with the tyrosine phosphatase inhibitor pervanadate to trigger elevated levels of Cav1 phosphorylation . Western blot analysis revealed binding between Vav2 and full-length Cav1 in pervanadate-treated cells ( Figure 3C ) . To better understand the molecular mechanism of the observed phospho-Tyr14-Cav1–Vav2 interaction we expressed different Vav2 constructs ( Figure S5 ) in ME-180 cells and immunoprecipitated them using antibodies against the respective tags [29] , [30] . First , we expressed the full-length Vav2 coupled with GFP in pervanadate- and control-treated cells . We were then able to precipitate GFP-Vav2 and control GFP from transfected cells using a GFP antibody; however , phospho-Tyr14-Cav1 co-precipitated exclusively with pervanadate-treated GFP-Vav2 expressing cells ( Figure 3D ) . This further demonstrated the phospho-specificity of the phospho-Tyr14-Cav1–Vav2 protein-protein interaction . Since only the SH2 domain of the Vav2 protein had been spotted on the protein microarray , we assumed the Vav2-Cav1 interaction was SH2-specific . To verify this , we expressed truncated Vav2 coupled to FLAG in pervanadate- and control-treated cells . Truncated Vav2 consists solely of the C-terminal SH3-SH2-SH3 domains of the protein ( Figure S5 ) . Similar to full-length Vav2 , we were able to precipitate truncated Vav2 from transfected cells using a FLAG antibody . Again , phospho-Tyr14-Cav1 co-precipitated exclusively with pervanadate-treated , truncated Vav2 expressing cells ( Figure 3E ) , demonstrating the relevance of the remaining Vav2 domains for the observed phospho-Tyr14-Cav1–Vav2 interaction . Taken together , these results identify the RhoA GEF Vav2 as a novel interaction partner of tyrosine-phosphorylated Cav1 . Next , we assessed the role of Vav2 and PLCγ1 in P+GC infection by knocking down their function in ME-180 cells using RNAi . Confocal microscopy revealed that , as with Cav1 , reducing Vav2 levels using siRNA resulted in efficient internalization of P+GC by ME-180 cells ( Figure 4A ) . By contrast , shRNA-mediated downregulation of PLCγ1 in ME-180 cells did not result in P+GC internalization ( Figure S6A ) . This shows that Vav2 plays a role in preventing bacterial uptake , possibly by participating in cytoskeletal reorganization through its function as a GEF for the Rho/Rac family of GTPases . Alternatively , Vav2 could function in a manner independent of its GEF activity simply by physically linking signaling molecules to the actin cytoskeleton [31] . To test for the involvement of RhoA in impeding bacterial internalization , we treated ME-180 cells with low concentrations of the Rho-specific inhibitor CT04 , a cell permeable form of the C3 transferase from Clostridium botulinum , and subsequently infected the cells with P+GC . Interestingly , treatment with CT04 led to a strong uptake of bacteria ( Figure 4B ) . Since Vav2 also activates Rac1 [32] , we also tested the relevance of Rac1 for P+GC internalization . ME-180 cells were treated with the Rac1-specific chemical inhibitor NSC23766 [33] and then infected with P+GC . In contrast to Rho inhibition , treatment with NSC23766 did not affect bacterial entry ( Figure 4C ) . These results were also confirmed in ME-180 and HeLa epithelial cells using different inhibitor concentrations ( Table S1 ) . Partial siRNA-mediated knockdown of RhoA further demonstrated the relevance of this small GTPase in impeding cellular uptake of P+GC . Interestingly , downregulation of other small GTPases such as Cdc42 and Rac1 did not enhance P+GC internalization ( Figure S6B and Table S2 ) . Together , these findings highlight the importance of RhoA in preventing bacterial entry , probably by forming a cytoskeletal barrier . To investigate the direct impact of Cav1 expression on RhoA activation , we compared RhoA activation in control ME-180 cells expressing a luciferase shRNA with Cav1 shRNA knockdown cells in response to infection with P+GC . Control cells showed a strong increase in the levels of the GTP-bound state of RhoA within the first 5 min of infection , followed by a decline to basal levels over the next 10 min . By contrast , Cav1 knockdown cells did not activate RhoA throughout the early stages of infection ( p<0 . 05 , Figure 5A ) . These data support a model in which a signaling cascade involving Cav1 , Vav2 , and RhoA act to inhibit the internalization of P+GC by host cells . Finally , to delineate the sequence of the Cav1-Vav2-RhoA signaling cascade , we infected both Vav2-knockdown ME-180 cells and CT04-treated ME-180 cells with P+GC and monitored the recruitment of Cav1 by confocal microscopy ( Figure 5B ) . Cav1 recruitment was not affected by either treatment , indicating that Cav1 lies upstream of Vav2 and RhoA . Taken together , our data strongly suggest that P+GC infection induces a Cav1-Vav2-RhoA signaling cascade in host cells . Phosphotyrosine-dependent Cav1 recruitment to sites of bacterial attachment induces the recruitment of Vav2 and RhoA in response to infection , which functions to prevent bacterial internalization , probably via RhoA-dependent cytoskeletal rearrangements . Our findings highlight a compelling anti-invasive strategy of pathogenic bacteria , uncover Vav2 as a novel Cav1 signaling partner , and suggest ways in which tyrosine-phosphorylated-Cav1 could mediate cytoskeletal rearrangements . In addition to identifying Vav2 as a Cav1 interaction partner , our protein microarrays highlighted five other proteins , all of which have been implicated in modulating the cytoskeleton through small GTPases: SH2D3C is an integrin-associated signaling pathway component , proposed to regulate the actin cytoskeleton via its GEF-like domain which binds Ras family GTPases [34]; PLCγ1 exhibits mitogenic activity by acting as a GEF for the small GTPase PIKE ( phosphatidylinositol-3-OH kinase ( PI ( 3 ) K ) enhancer ) [35] and is also capable of directly activating Rac1 [36]; Grb10 putatively binds small GTPases of the Ras superfamily [37]; and the Abl kinases are known to link various cell surface receptors to signaling pathways involved in cytoskeletal reorganization and to regulate the activation of Rac and Rho GTPases [38] , [39] . It remains to be determined if these additional proteins play a role in Cav1-mediated signaling . Recently , Cav1 has been demonstrated to interact with RhoA and Rho-associated kinase 1 ( ROCK1 ) to promote Rho activation through inhibition of the Src-p190RhoGAP pathway . Cav1 has also been shown to serve as a target of ROCK1 signaling , indicating the presence of a positive feedback loop [40]–[42] . Furthermore , phospho-Tyr14-Cav1 orders microdomains within focal adhesions and stabilizes focal adhesion-associated kinase [43] , [44] . Here , we report a direct link between RhoA activation and tyrosine-phosphorylated Cav1 through the RhoA GEF Vav2 . Phosphorylation of Cav1 on Tyr14 may serve as a crucial switch between the activated and inactivated state of RhoA . Thus , the direct interaction of tyrosine-phosphorylated Cav1 with Vav2 might support increased activation of RhoA in cellular compartments where tyrosine-phosphorylated Cav1 accumulates , such as focal adhesions or at sites of P+GC attachment . Together , our data reveal an immediate early anti-invasive activity of P+GC , dependent on tyrosine-phosphorylated Cav1 host-cell signaling , that facilitates the establishment and maintenance of this pathogen's extracellular niche . This process , triggered by Vav2-mediated activation of RhoA , elicits cytoskeletal rearrangements that may function as a physical barrier to prevent internalization of attached bacteria . Cav1 is known to participate in protein trafficking [18] , and we have observed a drastic , instantaneous , and RhoA-independent recruitment of Cav1 at sites of P+GC attachment . Thus , Cav1 is the presumptive central element of the identified Cav1-Vav2-RhoA signaling cascade , playing a crucial role in the localization of its signaling partners to the site of infection . The resulting prevention of premature uptake seems to be beneficial for these bacteria as P+GC are rapidly killed inside host cells . P+GC , and probably other Tfp-producing bacteria including EPEC , could use this initial extracellular phase to adapt and prepare for the subsequent steps of infection . For example , GC employ variation of the Opa invasins to prepare individual bacteria for deliberate cell entry [10] and transcytosis [45] . EPEC , on the other hand , may use their Tfp as an immediate block to cell entry even before the TTSS is placed or used in delivering its anti-invasive effectors [46] . Taken together , this Tfp-triggered mechanism extends our understanding of how P+GC use pili to elicit cytoprotective effects and modulate the host cell for their own benefit , as described previously [5] , [6] . By investigating P+GC colonizing its host , we have exploited these bacteria as a tool to identify an anti-invasive bacterial strategy as well as a novel Cav1-dependent signaling cascade leading to RhoA activation .
The human cervix carcinoma cell line ME-180 ( ATCC HTB33 ) was grown in McCoy's 5A medium ( Gibco-Invitrogen , Carlsbad , CA , USA ) supplemented with 10% FCS ( Biochrom , Berlin , Germany ) . The human gastric adenocarcinoma cell line AGS ( ATCC CRL-1739 ) was grown in RPMI 1640 medium ( Gibco-Invitrogen , Carlsbad , CA , USA ) supplemented with 10% FCS . For microscopy , cells were seeded on acid-washed glass coverslips . The GC strains used in this work were derived from N . gonorrhoeae strain MS11 [47] . Strain P+GC was selected for observable piliated phenotype ( P+Opa− ) . P+GC , P+GCΔpilT [48] and non-piliated Opa57-expressing GC [49] strains were resuspended in cell culture medium and added to cell monolayers in serum-free medium at a multiplicity of infection of 100 . EPEC strains E2348/69 ( O127:H6 ) and EPEC 2348/69 CVD452 , a mutant defective in type III-dependent secretion , were grown overnight at 37°C in LB broth without shaking . The following day cultures were diluted 1∶100 in serum free DMEM and grown without shaking under previously described conditions known to stimulate TTSS expression for 3 . 5 h to create so-called preactivated cultures [50] . Consequently EPEC cultures were added to cell monolayers for 2 h . Cells were treated with either 100 µM pervanadate ( Sigma Aldrich , St . Louis , MO , USA ) , 1 µM Cyt D ( Sigma Aldrich , St . Louis , MO , USA ) or 100 nM Lat A ( Biomol , Hamburg , Germany ) for 30 min , 10 µM PP2 ( Calbiochem , San Diego , CA , USA ) , 10 µM STI571 ( LC Labs , Woburn , MA , USA ) , or 20 µM , 100 µM , and 300 µM NSC23766 ( Calbiochem , San Diego , CA , USA ) for 1 h or 50 ng/ml , 100 ng/ml , and 250 ng/ml cell permeable Rho inhibitor CT04 ( Cytoskeleton , Denver , CO , USA ) for 4 h before infection . Experimental treatments had no effect on bacterial viability . Quantification of bacterial binding and entry into host cells was performed using standard gentamicin-based assays with dilution plating to recover viable bacteria . Cell confluency at infection time was 70% . Cells were washed three times in serum-free RPMI 1640 medium prior to infection and incubated in serum-free RPMI 1640 medium ( with indicated chemicals ) for 30 min . Bacteria were added to the cells at a multiplicity of infection of 100 . Cells were then incubated in RPMI 1640 medium at 37°C , 5% CO2 for 2 h . 100 µg/ml gentamicin ( Sigma Aldrich , St . Louis , MO , USA ) was then added for an additional 2 h to kill extracellular bacteria . Cells were washed , and 1% saponin ( Serva , Heidelberg , Germany ) was added to permeabilize cells followed by plating of appropriate dilutions of the lysate on GC agar . To quantify adherent bacteria , lysis with saponin was done prior to gentamicin treatment . Intracellular gentamicin-protected ( GmP ) bacteria were determined as a percentage of total cell-associated bacteria . Assays were conducted in triplicate wells , yielding the given mean and the standard deviation . Each experiment was repeated at least three times . Data were tested for significance using Student's t test . Representative experiments are shown . The coding region of human caveolin-1 ( cav1 ) was amplified from total cDNA of the ME-180 cell line and cloned into the expression vector pcDNA3 ( Promega , Madison , WI , USA ) , which also encodes an N-terminal HA-tag . For live-cell microscopy , cav1 was cloned into the vector pEGFP-N1 ( Invitrogen , Grand Island , NY , USA ) . AGS cells were transfected with the vector pcDNA3 alone or the pcDNA3-cav1 construct ( Cav1-HA ) , then stable clones were isolated and maintained in RPMI supplemented with 10% FCS and 500 µg/ml G418 ( PAA Laboratories , Linz , Austria ) . The point mutation Y14F-Cav1-HA was generated by changing tyrosine 14 of cav1 in Cav1-HA to phenylalanine using the QuikChange site-directed mutagenesis kit ( Stratagene , La Jolla , CA , USA ) according to the manufacturer's protocol . The accuracy of the mutation was confirmed by DNA sequencing . Full-length GFP-Vav2 cloned into pEGFP-C2 was a gift of Dr . László Buday ( Semmelweis University , Budapest , Hungary ) and truncated Vav2 ( consisting of the C-terminal SH3-SH2-SH3 domains of Vav2 ) cloned into pcDNA3 . FLAG was a gift of Dr . Daniel D . Billadeau ( Mayo Clinic , Rochester , MN , USA ) . All transfections were performed using Lipofectamin™ 2000 ( Invitrogen , Grand Island , NY , USA ) according to the manufacturer's protocol . The cells were further analyzed 24 h post-transfection . The siRNA duplexes targeting human Cav1 ( Cav1A: GCAGTTGTACCATGCATTA , Cav1B: ATTAAGAGCTTCCTGATTG ) , RhoA ( TAGGCTGTAACTACTTTATAA ) , Rac1 ( ATGCATTTCCTGGAGAATATA ) , Cdc42 ( TTCAGCAATGCAGACAATTAA ) , and firefly luciferase ( AACUUACGCUGAGUACUUCGA ) were purchased from Qiagen ( Hilden , Germany ) . The siRNA duplexes targeting lamin A ( CCTGGACTTCCAGAAGAACA ) and Vav2 ( ON-TARGET Plus SMART pool containing the following siRNAs: CUGAAAGUCUGCCACGAUA , UGGCAGCUGUCUUCAUUAA , GUGGGAGGGUCGUCUGGUA , and GCCGCUGGCUCAUCGAUUG ) were synthesized by Dharmacon Research ( Lafayette , CO , USA ) . The transfection of siRNAs was carried out using Hiperfect transfection reagent ( Qiagen , Hilden , Germany ) according to the manufacturer's instructions . Briefly , ME-180 and AGS-Cav1 cells were transfected with 50 nM siRNA duplex and used for experiments 72 h after transfection . shRNA-expressing vectors were constructed by cloning computed shRNA oligonucleotides ( Metabion , Martinsried , Germany ) into the pLVTHM vector . The sequences of the targets of the shRNAs are as follows: human Cav1 , 5′-CAGCAACAATTTATGAATTGA-3′; human Vav2 , 5′-GCATGACTGAAGATGACAAGA-3′; human PLCγ1 , 5′-GGACTTTGATCGCTATCAAGA-3′; firefly luciferase , 5′-AACTTACGCTGAGTACTTCGA-3′ . All constructs were verified by sequencing . Viruses carrying the shRNAs were produced by transfecting 293T cells with the generated pLVTHM constructs together with viral packaging vectors ( psPAX2 , pMD2G , kindly provided by D . Trono , Ecole Polytechnique Fédérale de Lausanne , Switzerland ) by calcium phosphate transfection . Viruses were harvested from the supernatant 48 h after transfection , filtered , and applied to ME-180 cells for lentiviral infection in the presence of polybrene ( 5 µg/ml , Sigma-Aldrich , St . Louis , MO , USA ) . Pools of GFP-positive cells were selected and validated for their ability to knock down protein expression of target genes by more than 70% in comparison with luciferase control cells . Cells were grown on coverslips ( 12 mm diameter ) and processed for immunofluorescence as described previously [51] . Differential staining of intra- and extracellular bacteria was achieved by double staining of bacteria , primarily without permeabilization of cells and subsequently after cell permeabilization . For labeling , the following antibodies were used: anti-Cav1 ( N20 , Santa Cruz Biotechnology , Santa Cruz , CA , USA ) , rabbit anti-Neisseria gonorrhoeae ( USBiological , Swampscott , MA , USA ) , and anti-pilus ( m346 , monoclonal mouse , generated at the Max Planck Institute for Infection Biology ) . All antibodies were used at a 1∶100 dilution . Filamentous actin was detected with Alexa 546-conjugated phalloidin ( Invitrogen , Grand Island , NY , USA ) . All secondary antibodies were purchased from Jackson Immuno Research Laboratories ( West Grove , PA , USA ) . Samples were analyzed by confocal laser scanning microscopy using a Leica TCS SP microscope , equipped with an argon/krypton mixed gas laser source ( Leica , Solms , Germany ) . Image stacks were further processed using Photoshop ( Adobe Systems , San Jose , CA , USA ) or Imaris ( Bitplane , Zürich , Switzerland ) . ME-180 cells were transfected with the described pEGFP-N1-Cav1 construct and grown in 3 . 5 cm2 glass-bottom dishes ( MatTek , Ashland , MA , USA ) overnight under standard conditions . Fresh serum-free RPMI without phenol red ( Gibco-Invitrogen , Carlsbad , CA , USA ) was added , and cells were placed in a humidified incubation chamber at 37°C and 5% CO2 . Images were obtained with the VT-Infinity system ( Visitron Systems , Munich , Germany ) . Briefly , the system consists of an Olympus IX81 ( Olympus , Tokyo , Japan ) , VT-Infinity galvo scanner confocal head ( Visitron Systems , Munich , Germany ) , and a Hamamatsu C9100-02 CCD camera ( Hamamatsu Photonics K . K , Tokyo , Japan ) . Bright field images were acquired with a 63× phase contrast objective ( NA1 . 25 oil , Olympus , Tokyo , Japan ) and a high-speed shutter system . Fluorescent images were acquired with a 488 nm laser beam with an intensity of 250 mW using the 488 nm emission filter set ( Chroma Technology , Brattleboro , VT , USA ) . Images were collected and processed using MetaMorph ( Universal Imaging Corporation , West Chester , PA , USA ) and Imaris ( Bitplane , Zürich , Switzerland ) software . Pervanadate-treated and untreated ME-180 cells were lysed in 1× Cell Lysis Buffer ( Cell Signaling , Boston , MA , USA ) containing PhosStop Phosphatase Inhibitor and Complete™ Protease Inhibitor ( both: Roche Diagnostics , Mannheim , Germany ) . The lysates were pre-cleared for 4 h with protein G-agarose beads ( Calbiochem , San Diego , CA , USA ) and incubated with 2 µg anti-Cav1 antibody ( rabbit , BD Transduction Laboratories , Franklin Lakes , NJ , USA ) overnight . Protein G-agarose beads were subsequently added for 4 h to precipitate antigen-antibody complexes . After extensive washing , the precipitate was eluted by heating to 95°C in SDS loading buffer and the individual proteins separated by SDS-PAGE . Western blotting was used to assess the precipitate using the following antibodies: anti-Cav1 ( N20 , Santa Cruz Biotechnology , Santa Cruz , CA , USA ) , anti-phospho-Tyr14-Cav1 ( clone 56 , BD Transduction Laboratories , Franklin Lakes , NJ , USA ) and anti-Vav2 ( C64H2 , Cell Signaling , Boston , MA , USA ) , and HRP-conjugated secondary antibodies ( Amersham Biosciences , Pittsburgh , PA , USA ) . Western blot was developed using ECL reagent ( ICN Biomedicals , Aurora , OH , USA ) . Blots were quantified using ImageJ software ( v1 . 44a ) . Fluorescently labeled peptides with sequences corresponding to residues 5–22 of Cav1 were synthesized , one phosphorylated on Tyr14 and the other not phosphorylated as previously described [28] , purified to >95% by preparative reverse phase HPLC , and quality controlled via mass spectrometry and analytical HPLC ( Thermo Electron , Karlsruhe , Germany ) . Human SH2 and PTB domains were expressed and purified as previously described [28] , and protein microarrays were fabricated and probed as more recently reported [52] . Peptides were designed as 15-mers ( residues 7–21 of Cav1 ) bearing an N-terminal biotin . Peptides were synthesized as pairs , one phosphorylated on Tyr14 and the other not phosphorylated , purified to >95% by preparative reverse phase HPLC , and quality controlled via mass spectrometry and analytical HPLC ( Thermo Electron , Karlsruhe , Germany ) . For affinity pull-downs , 10 nmol of immobilized peptide was added to ∼2 mg of cell lysate . ME-180 cells were lysed in 1× Cell Lysis Buffer ( Cell Signaling , Boston , MA , USA ) containing 2 mM sodium orthovanadate , as a phosphatase inhibitor , and Complete™ Protease Inhibitor ( Roche Diagnostics , Mannheim , Germany ) . The lysates were pre-cleared for 1 h with streptavidin agarose beads ( Invitrogen , Grand Island , NY , USA ) and equal amounts of lysate were incubated overnight at 4°C with streptavidin agarose beads , pre-saturated with the respective biotinylated peptides . After extensive washing , the streptavidin precipitate was eluted by heating to 95°C in SDS loading buffer and the individual proteins separated by SDS-PAGE . Western blotting was used to assess the precipitate using anti-Vav2 ( C64H2 , Cell Signaling , Boston , MA , USA ) antibody . AGS and ME-180 cells ( 2×107 ) were washed with PBS at 4°C and then incubated in lysis buffer ( 1 mM EGTA , 4% PEG 6000 , 100 mM PIPES pH 6 . 9 , 0 . 5% Triton X-100 ) for 5 min at 4°C to stabilize the cytoskeleton . Supernatant containing cytoplasmic and compartmental proteins were removed and the remaining cytoskeletal proteins washed once , harvested in lysis buffer by scraping , and pelleted by centrifugation ( 14 , 000×g , 5 min ) . Pellets were washed once with 1 ml wash buffer ( 1 mM EGTA , 4% PEG 6000 , 100 mM PIPES pH 6 . 9 ) , then collected in SDS loading buffer and analyzed by Western blotting . Using an enzyme-linked immunosorbent assay-based RhoA activation assay kit ( Cytoskeleton , Denver , CO , USA ) active RhoA was determined according to the manufacturer's protocol . Briefly , to synchronize Rho activity , cell monolayers exhibiting 60% confluency were grown in culture medium with 0 . 5% FCS for an additional 24 h and then serum-starved for another 16 h . After infection , cells were lysed at the indicated time points , aliquots snap-frozen in liquid nitrogen , and the protein concentration determined using Precision Red Advanced Protein Assay ( Cytoskeleton , Denver , CO , USA ) . Cell lysate ( 37 . 5 µg protein ) from each sample was incubated in microwells coated with the isolated Rhotekin Rho-binding domain . Active RhoA was subsequently measured using immunodetection followed by a colorimetric reaction measured by absorbance at 490 nm . Assays were conducted in triplicate microwells , yielding the given mean and the standard deviation . Data were tested for significance using Student's t test . Cells were fixed in 2% PFA/1% acrolein in PBS for 2 h at RT . After washing with PBS , the cells were overlaid with warm gelatine ( 10% PBS ) and scraped off the plate . After gelling at 4°C , the specimens were cut into small blocks , post-fixed in 2% PFA , and infiltrated with a sucrose/PVP solution . Specimens were mounted on a stub , frozen in liquid nitrogen , and 60 nm sections were produced using a RMC MTX/CRX cryo-ultramicrotome ( Boeckeler Instruments , Tucson , AZ , USA ) . Sections were thawed , blocked , and incubated with anti-Cav1 antibody ( rabbit , BD Transduction Laboratories , Franklin Lakes , NJ , USA ) . After washing , bound antibody was detected using anti-rabbit secondary antibodies coupled to 6 nm colloidal gold . The samples were analyzed on a Leo 906E transmission electron microscope ( Carl Zeiss , Jena , Germany ) equipped with a Morada digital camera ( Silicon Integrated Systems , Hsinchu , Taiwan ) . P+GC pili were purified as described previously [53] . Purified pili were utilized for immunizing BALB/c mice for the generation of monoclonal antibodies following standard poly-ethylene glycol ( PEG ) fusion protocol . Briefly , 6–8-wk-old Balb/c mice were primed with 50 µg of purified pili in Freund complete adjuvant followed by two boost injections on day 20 and 40 in Freund incomplete adjuvant . Spleen cells were fused with P3X63Ag8 myeloma cells . Positive hybridomas were screened by standard ELISA against purified pili . Anti-pilin antibody producing hybridomas were subcloned three times by limited dilution .
|
Like many bacterial pathogens , successful attachment of Neisseria gonorrhoeae—the causative agent of the sexually transmitted disease gonorrhoea—to its host cells depends on specialized structures on the bacterial surface called type IV pili ( Tfp ) . Pathogen attachment induces changes within host cells that may facilitate and promote infection . In this study , we identify some of the earliest cellular signals elicited by N . gonorrhoeae during infection , which , in this case , prevent the organism from entering the cell precociously . After attachment to host cells the bacteria form microcolonies on the cell surface . Underneath these microcolonies , so-called cortical plaques form within the host cell—these contain the cytoskeleton protein actin and a range of signaling proteins . We show that N . gonorrhoeae recruits a host cell protein called caveolin-1 to the cell membrane where the bacteria are attached; here , caveloin-1 effectively impedes uptake of the bacteria by activating a signaling cascade that involves its phosphorylation on a tyrosine residue and subsequent interactions with proteins that regulate the cytoskeleton . Thus , these proteins play a pivotal role in maintaining N . gonorrhoeae in the extracellular milieu . By extrapolating our findings to another Tfp-producing bacterium , the enteropathogenic Escherichia coli , we argue that the establishment and maintenance of this extracellular state benefits certain pathogens by giving them time to express proteins required for subsequent steps of infection .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"cell",
"biology/cytoskeleton",
"molecular",
"biology",
"cell",
"biology/cell",
"signaling"
] |
2010
|
Tyrosine-Phosphorylated Caveolin-1 Blocks Bacterial Uptake by Inducing Vav2-RhoA-Mediated Cytoskeletal Rearrangements
|
Following the widespread use of genome-wide association studies ( GWAS ) , focus is turning towards identification of causal variants rather than simply genetic markers of diseases and traits . As a step towards a high-throughput method to identify genome-wide , non-coding , functional regulatory variants , we describe the technique of allele-specific FAIRE , utilising large-scale genotyping technology ( FAIRE-gen ) to determine allelic effects on chromatin accessibility and regulatory potential . FAIRE-gen was explored using lymphoblastoid cells and the 50 , 000 SNP Illumina CVD BeadChip . The technique identified an allele-specific regulatory polymorphism within NR1H3 ( coding for LXR-α ) , rs7120118 , coinciding with a previously GWAS-identified SNP for HDL-C levels . This finding was confirmed using FAIRE-gen with the 200 , 000 SNP Illumina Metabochip and verified with the established method of TaqMan allelic discrimination . Examination of this SNP in two prospective Caucasian cohorts comprising 15 , 000 individuals confirmed the association with HDL-C levels ( combined beta = 0 . 016; p = 0 . 0006 ) , and analysis of gene expression identified an allelic association with LXR-α expression in heart tissue . Using increasingly comprehensive genotyping chips and distinct tissues for examination , FAIRE-gen has the potential to aid the identification of many causal SNPs associated with disease from GWAS .
The proliferation of genome-wide association studies ( GWAS ) has achieved considerable advances concerning the identification of novel genetic loci associated with phenotypic traits and diseases , and also confirmed many established genetic associations . Following GWAS , the next objective in genetics will be identification of the causal variants marked by current GWAS , and determination of the molecular mechanisms altered by these genetic variants . This step will be another major milestone towards realisation of the fundamental goal for GWAS , in developing novel drug targets based on this new genetic information . Only a small percentage of GWAS hits are themselves non-synonymous coding SNPs , with their expected causality by changing protein structure and function . The majority of GWAS hits occur within intronic and intergenic regions of the genome and are likely to exert their effects at the level of gene regulation [1] . Due to the complex nature of gene regulation [2] , with regulatory elements commonly occurring up to 100 kb from a transcription start site ( TSS ) , identifying the causal SNP from potentially hundreds of other SNPs that are simply in near or complete linkage disequilibrium ( LD ) with one identified from GWAS , is a challenging undertaking . The ENCODE project has significantly increased our understanding of the location of regulatory elements throughout the genome [3] . Using techniques such as chromatin immunoprecipitation followed by sequencing ( ChIP-seq ) , we now know the genomic binding sites for some of the key transcription factors ( TF ) involved in gene regulation in a number of experimental tissues . This technique relies on the existence of a ChIP-grade antibody to recognise each DNA-bound transcription factor , and is the major limitation towards the complete characterisation of all human TF binding sites [4] . A more widespread use of ChIP-seq has been the annotation of the genome for histone methylation signatures , such as H3K4me1 and H3K4me3 , strong markers of enhancers and promoters [5] . Other sequencing techniques have been used to map the genome for open chromatin , including DNase I hypersensitivity ( DNase-seq ) [6] and formaldehyde-assisted isolation of regulatory elements ( FAIRE-seq ) [7] . These regions of open chromatin correlate extremely highly with both histone methylation signatures and TF ChIP-seq , but in contrast to ChIP-seq , are able to identify regulatory regions without prior knowledge of a specific transcription factor involved . If a non-coding SNP associated with gene regulation were to be functional , it would be expected to alter not only transcription factor binding , but also histone methylation signatures and chromatin accessibility . We have applied this hypothesis to identify the functionality of SNPs on a larger scale than has previously been possible , using gene chip technology . In this paper we describe a method for allele-specific FAIRE using gene chip technology , we term FAIRE-gen , to identify possible candidate functional SNPs in loci related to cardiovascular disease .
To examine the potential to use gene chips to assess allele-specific FAIRE , three lymphoblastoid cell lines were examined following IL-1β stimulation to induce cell proliferation [8] . Subsequent to cell fixing , chromatin extraction and sonication , the fragmented chromatin was divided into two groups for each cell line: a control DNA and a FAIRE-enriched DNA sample . For the control DNA , the crosslinks were reversed and the DNA purified; for the FAIRE-enriched DNA , the chromatin underwent three rounds of phenol:chloroform extraction to enrich the sample for open chromatin , followed by reversal of crosslinks and DNA purification . Both samples were standardised to 50 ng/µl and genotyping performed using the Illumina CVD BeadChip , a custom-designed chip containing 49 , 094 SNPs from gene loci selected to play a potential role in cardiovascular disease ( Figure 1 ) . Genotyping call frequencies for sonicated control DNA were comparable to non-fragmented DNA ( 97 . 2% vs 98 . 1% ) ; whereas those for FAIRE-enriched DNA were significantly lower ( 56 . 7% ) . Using an existing lymphoblastoid FAIRE-seq dataset , the level of enrichment at the location of the CVD BeadChip SNPs was compared with the FAIRE-gen samples . The logR ratio output from the Illumina GenomeStudio was used to indicate the level of allelic amplification and therefore FAIRE-gen enrichment , compared to the respective control samples . A strong association of mean FAIRE-gen-enriched allelic intensity with FAIRE-seq peak intensity was observed ( p = 2 . 34×10−82 , Figure 2 ) . The reduced amplification of alleles outside of open chromatin results in decreased genotype clustering and a lower call-rate in the FAIRE-enriched samples . Following FAIRE , an allelic effect on open chromatin would enrich one allele over the other in a heterozygous individual . To examine whether this small dataset was large enough to identify an allele-specific effect on open chromatin , each sonicated control sample and its respective FAIRE-enriched sample was examined using the B allele frequency ( BAF ) , which measures the proportion of the genotype from an individual attributed to the B allele ( often the minor allele ) . To ensure a consistent allelic effect was found , only SNPs that were heterozygous in all three cell lines were examined . This reduced the number of SNPs under analysis to 3 , 129 . These 3 , 129 heterozygous SNPs were examined for allelic enrichment , where the control BAF and FAIRE-enriched BAF were compared for each cell line . One SNP showed a statistical significant difference with all three cell lines after applying the Bonferroni correction: rs7120118 ( Figure 3 ) , where the C allele was enriched in open chromatin . The fact that only a single association was identified was not unexpected for such a genotyping chip , where the SNP coverage per gene is low and concentrated within coding regions , where the majority of genes covered do not overlap with eQTLs or GWAS studies , and considering the very small number of cell lines examined . Despite only one SNP reaching the Bonferroni cut-off , there was overall enrichment in the study for p-values<0 . 05 ( Figure S1 ) , highlighting the potential for a greater number of significant results with a larger sample . The SNP that did show statistical significance is located within intron 6 of NR1H3 , coding for LXR-α . Examining genomic annotations for this SNP on the UCSC Genome Browser , it can be seen that not only is this SNP located in a region of open chromatin by DNase I-seq [9] , [10] , FAIRE-seq [7] , [11] and with enhancer-specific histone methylation signatures [5] , [12] ( Figure 4 ) , it has also been identified as a GWAS SNP for HDL-C levels [13] . To confirm the effects seen using the Illumina CVD BeadChip on rs7120118 with allele-specific FAIRE , the study was replicated using the Illumina Metabochip , a consortia custom-designed genotyping chip , containing 196 , 726 SNPs to primarily examine associations identified by GWAS for cardiometabolic traits and diseases , those in strong LD , and also a number of rare variants . The Metabochip contains rs7120118 , and seven out of the eight further SNPs identified as in complete LD with rs7120118 from the CEU panel in the 1000 Genome Project . A total of 20 lymphoblastoid cells were examined , including new FAIRE preparations for the original three cell lines . 6 additional cell lines were heterozygous for rs7120118 , excluding the three previously examined . Comparing BAF between sonicated controls and FAIRE DNA for these 6 cell lines , the C allele was again enriched in the FAIRE sample ( control BAF = 0 . 44 , FAIRE-enriched BAF = 0 . 67 , p = 0 . 0036 ) . The seven SNPs in complete LD with rs7120118 were examined by the same analysis from the Metabochip using all 9 heterozygous cell lines . Unlike the original Illumina CVD BeadChip assay , Metabochip FAIRE-gen was performed on both unstimulated and IL-1β-stimulated lymphoblastoid cell lines , allowing a direct comparison of IL-1β stimulation on allele-specific open chromatin . The results for all analyses are shown in Table 1 . The rs7120118 C allele was enriched with and without IL-1β stimulation by 15 . 5% ( p = 0 . 008 ) and 4 . 4% ( p = 0 . 022 ) , respectively . No other SNPs from the seven in complete LD with rs7120118 in the IL-1β-stimulated cell lines showed allelic enrichment . From the stimulated cell lines there was a trend towards BAF enrichment from the adjacent SNP rs2279239 ( 11 . 3% , p = 0 . 01 , Figure 5 ) , contained within the same region of open chromatin , but this did not reach statistical significance when correcting for multiple comparisons . Examining the unstimulated cell lines , two further SNPs showed modest allelic imbalance following FAIRE: rs2167079 ( exon 1 of ACP2 ) , with a 10 . 3% reduction in BAF ( p = 0 . 003 ) and rs326222 ( intron 8 of DDB2 ) with a 4 . 9% reduction in BAF ( p = 0 . 003 ) . To confirm the allele-specific enrichment from the C allele of rs7120118 , genotyping of the 20 sonicated control and FAIRE samples was carried out using the TaqMan platform for allelic discrimination . Allelic ratios were determined by extrapolation from a standard curve of the vic/fam ratio from samples of known genotype . The allelic ratios do not differ significantly from the Metabochip data , confirming the ability for gene chips to provide a suitable high-throughput method for FAIRE-gen ( Figure 6 ) . The SNP showing the greatest and most consistent allelic effect for open chromatin , and confirmed in two subsequent genotyping platforms , rs7120118 , has been identified using GWAS as being associated with plasma HDL-C levels [13] . The SNP was associated with a beta coefficient of 0 . 04 ( 0 . 0073 SE , p = 6 . 7×10−8 ) , but this finding has not been replicated in further GWAS , and not reported in a recent meta-analysis of lipid traits comprising >100 , 000 individuals [14] . To confirm the original association , we examined this SNP in a prospective UK cohort of 4724 individuals from the Whitehall II study . Baseline characteristics of the study are shown in Table 2 . This data replicated the reported association with an HDL-C raising effect from the C allele ( beta = 0 . 016 , p = 0 . 0059 ) . No other SNPs in strong LD with this SNP ( r2>0 . 5 ) showed significantly greater effect sizes ( Table 3 ) . An additional cohort , the Copenhagen City Heart Study ( CCHS; n = 10 , 322 , baseline characteristics shown in Table 2 ) was genotyped for rs7120118 , and this also showed a similar effect size ( beta = 0 . 015 , p = 0 . 041 , Table 4 ) . Combining the two datasets in a meta-analysis using a fixed-effects model did not alter the effect size ( beta = 0 . 016 ) although increased the significance ( p = 0 . 0006 ) . As there is an association between gender and HDL-C levels in the general population , we also carried out stratification for gender . This showed a similar direction of effect in both studies , showing that the effect seen with rs7120118 functionality is unlikely to be gender-specific . This correlates with the functional in vivo findings , where the rs7120118 C allele is associated with open chromatin in cells from both male and female origin ( data not shown ) . To determine if the association of rs7120118 with both HDL-C levels and open chromatin was also associated with an intermediate phenotype of NR1H3 gene expression , this SNP was examined in five tissue samples from 316 patients undergoing aortic valve surgery . A significant allele-specific effect was observed in heart tissue ( p = 0 . 0127 ) ( Figure 7 ) , with a trend towards significance in aortic adventitia ( P = 0 . 154 ) . In both cases the C allele of rs7120118 was associated with an upregulation of NR1H3 expression .
We have examined the possibility of using high-throughput gene chips to examine the allele-specific nature of open chromatin using FAIRE ( illustrated in Figure 1 ) . The study identified a functional SNP , rs7120118 , where the minor C allele is enriched in open chromatin and associated with increased HDL-C . Although the level of significance for HDL-C levels was adequate for a SNP with an a priori hypothesis , this would be much lower than required for genome-wide significance , highlighting the importance of combining functional studies with GWAS to identify candidate SNPs for disease or trait associations , particularly those with lower effect sizes , rare SNPs or small cohorts . Indeed , examining a recent meta-analysis of lipid traits in >100 , 000 individuals , rs7120118 did show a strong association with HDL-C levels ( p = 1 . 297×10−14 , Figure 8 ) although this was not reported as significant in the study [14] , perhaps due to the strong LD in the region , with the association signals covering >29 genes . We have shown that the minor allele is associated with increased NR1H3 gene expression in heart tissue and aortic adventitia , adding to a previous genome-wide study revealing a significant association with rs7120118 and gene expression of NR1H3 and ACP2 in lymphoblast cells [15] . From this data it can be postulated that rs7120118 directly affects a long-range regulatory element ( >15 kb from NR1H3 TSS ) in a non-tissue-specific manner , altering gene expression and HDL-C levels . The principle of allele-specific FAIRE was previously applied by Gaulton et al to examine the functionality of a single type II diabetes ( T2D ) GWAS SNP in TCF7L2 [16] . The authors used FAIRE-seq to determine global tissue-specific regions of open chromatin in pancreatic tissue , followed by TaqMan allelic discrimination to ascertain the effect of a single putative functional SNP on open chromatin . They found that the allele conferring increased risk of T2D and higher gene expression was also associated with enrichment for open chromatin . Although successfully demonstrating the use of FAIRE to identify a causal SNP from a GWAS , the use of TaqMan would not be applicable for examining a large number of potentially functional SNPs . FAIRE-gen , in contrast is only restricted by the number of SNPs that can fit on a genotyping chip . The action of IL-1β on chromatin structure , a cytokine known to induce proliferation of EBV-transformed lymphoblasts [8] , was examined in this study to reveal further potential allele-specific differences in open chromatin under different environmental conditions . For rs7120118 , an allele-specific effect was observed in both unstimulated and IL-1β-stimulated cell lines , although the effects were stronger in the IL-1β stimulated samples . The action of IL-1β activates NF-κB , potentially altering expression of transcription factors that bind to the regulatory region surrounding rs7120118 . Indeed , a nearby cluster of transcription factor binding sites determined by ChIP-seq includes a site for c-Jun binding ( Figure 4 ) ; the JUN promoter contains several NF-κB binding sites ( UCSC Genome Browser hg19/NCBI37 ) [3] , which may explain this enhanced effect . It could be hypothesised that the C allele that favours open chromatin allows for preferential access for known , or as yet uncharacterised , transcription factors , which would act as an enhancer for NR1H3 gene expression , and increased HDL-C levels . In contrast , a potential allelic effect was observed with the promoter SNP rs2167079 ( in complete LD with rs7120118 ) , only in unstimulated cells . IL-1β is known to reduce expression of NR1H3 in HK-2 cells [17] , and it could be postulated that IL-1β may lead to chromatin remodelling and a decrease in open chromatin at the NR1H3 promoter in lymphoblasts , accounting for the lack of allelic effect in the IL-1β-stimulated cells . Alternatively , the modest allele-specific chromatin effects from the unstimulated cell lines could simply represent false-positive findings . Haplotype structure may also affect local chromatin , particularly where more than one SNP occurs in the same region of open chromatin . We have examined the variation surrounding rs7120118 using HapMap-derived genotypes for the lymphoblasts used in the Metabochip study . No further SNPs at the locus provided additional haplotypic information for the effects on open chromatin , suggesting that rs7120118 , rather than a haplotype , is responsible for this observation . To assess the reproducibility of the FAIRE-gen methodology , the two Metabochip datasets were examined , considering the second IL-1β-treated study as a replicate . Examining the SNPs showing an allele-specific effect on open chromatin from the untreated samples , following Bonferroni correction ( p<5 . 2×107; n = 127 ) , 100% were replicated in the treated sample with significance set at p<0 . 05 , ( 91% replicated with Pc<3 . 9×10−4; n = 116 ) , indicating the sensitivity of the assay . The sensitivity and specificity of the assay to identify true functional variants can only be accurately determined by further functional analysis of each putative SNP . The smallest detectable difference in allele-specific open chromatin for the SNPs reaching genome-wide Bonferroni cut-off in the Metabochip was 10% ( rs75106522 ) . One limitation with FAIRE-gen , as opposed to FAIRE-seq is the dependence of the gene chip to contain all relevant SNPs for the trait under examination . For the recent custom-designed chips which contain dense markers and aim to include all SNPs that tag GWAS-identified markers for diseases and related traits , such as the Illumina Metabochip and Immunochip , this is less of a problem . Future genotyping chips containing all common SNPs associated with diseases/traits could potentially resolve this drawback . For determining the location of potential causal SNPs from a number of SNPs acting as proxies , FAIRE-gen is only able to identify single allele-specific SNPs if other proxies are not located within the same region of open chromatin . This can be illustrated for rs7120118 , where a nearby SNP , rs2279239 , is located only 4 . 6 kb away , and close to the same region of open chromatin ( Figure S2 ) . This SNP shows a similar trend for allelic-specificity , although somewhat reduced due to the distance from the putative causal SNP . Since the assay includes data from SNPs that are not present in open chromatin , there may also be a number of false-positive associations from the methodology , where amplification from background ( non-open ) chromatin may , in theory , preferentially occur for one allele . For this reason , replication using FAIRE-gen or FAIRE-seq in a separate study , and in vitro methodologies would be desirable to confirm functionality . In conclusion , FAIRE-gen shows promise as an economical , high-throughput method to enable targeted unbiased detection of allele-specific regulatory elements , which may help to refine GWAS disease-association signals to identify disease-causing variants .
The Whitehall II study was approved by the UCL Research Ethics Committee , and participants gave informed consent to each aspect of the study . The CCHS was approved by institutional review boards and Danish ethical committees , and conducted according to the Declaration of Helsinki . Written informed consent was obtained from all participants . 20 EBV-transformed lymphoblastoid cell lines , derived from the Centre d'Etude du Polymorphism Humain ( CEPH ) panel ( Coriell Cell Repositories , identifiers listed in table S1 ) , were cultured in RPMI 1640 ( PAA ) with 2 mM L-glutamine and 15% fetal bovine serum ( PAA ) at 37°C , 5% CO2 . Cell viability was verified using the ADAM-MC cell counter ( Digital Bio ) , and minimum cell viability for experiments was ≥99% . Stimulation of cells was carried out by an overnight incubation in serum-free media , and addition of 5 ng/ml IL-1β , two hours prior to cell fixing . 1×108 cells were cultured for each experiment and incubated with 1/10 volume of fresh 11% formaldehyde for 20 min . 1/20 volume of 2 . 5 M glycine was added to quench formaldehyde . Cells were washed 3 times in PBS and resuspended in 10 ml lysis buffer 1 ( 50 mM HEPES-KOH , pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 10% glycerol , 0 . 5% NP-40 , 0 . 25% Triton-X-100 , 1× protease inhibitors ) for 10 min . After centrifugation , the supernatant was discarded and pellet resuspended in 10 ml lysis buffer 2 ( 10 mM Tris-HCL , pH 8 . 0 , 200 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 1× protease inhibitors ) for 10 mins . The nuclei were pelleted and resuspended in 3 . 5 ml lysis buffer 3 ( 10 mM Tris-HCL , pH 8 . 0 , 100 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 0 . 1% NA Deoxycholate , 0 . 5% N-lauroylsarcosine , 1× protease inhibitors ) . Sonication was performed using the Bioruptor sonicator ( Wolflabs , York , UK ) and optimized to produce maximum enrichment of fragments 100–1000 bp , prior to downstream analysis . 1/10 volume of 10% Triton X was added to the sonicated sample , the sample centrifuged at 20 , 000 g and the lysate stored on ice . Following chromatin fixing , isolation and sonication , the sheared lysate was subject to three rounds of phenol:chloroform extraction , followed by a final chloroform extraction . The DNA was ethanol precipitated and the pellet resuspended in TE buffer . The DNA solution was treated with 0 . 2 mg/ml RNase A and incubated at 37°C , and 0 . 2 mg/ml proteinase K at 55°C for two hours . Samples were incubated at 65°C overnight to remove crosslinks . The samples were subjected to a further phenol:chloroform extraction and ethanol precipitation and standardised to 50 ng/ml for Illumina genotyping chips . For each respective control sample , 10% of the fixed and sonicated chromatin was reverse-crosslinked at 65°C overnight , treated with 0 . 2 mg/ml RNase A and incubated at 37°C for two hours and 0 . 2 mg/ml proteinase K at 55°C for 2 hours . The samples underwent 3 rounds of phenol:chlororom extraction followed by ethanol precipitation and standardisation to 50 ng/ml for Illumina genotyping . Genotyping was carried out using the Illumina CVD BeadChip and Illumina Metabochip . Genotype calls for control samples were generated using Illumina GenomeStudio software . Call rates for control and FAIRE samples are described in the Results . DNA was extracted from whole blood . Genotyping for 6 , 156 samples and laboratory analysis of has been described previously [18] . 5529 samples were genotyped using the Illumina CVD BeadChip [19] and 3 , 413 samples were genotyped using the Illumina Metabochip . Genotype calls were generated using Illumina GenomeStudio software . After filtering for duplicates , cryptic relatedness , ambiguous gender , self-reported non-Caucasians , outliers based on the genome-wide identity-by-state analysis implemented in PLINK , sample call rate>80% and SNP call rate>98% , 5059 CVD BeadChip and 3126 Metabochip genotyped samples were available for analysis . The CCHS [20] , [21] is a prospective study of the Danish general population initiated in 1976–78 with follow-up examinations in 1981–84 , 1991–94 , and 2001–03 . Individuals were randomly selected to represent the Danish general population aged 20 to 80+ years . We included 10 , 322 participants who gave blood for DNA analysis at the 1991–94 and/or 2001–03 examinations . The study was approved by institutional review boards and Danish ethical committees , and conducted according to the Declaration of Helsinki . Written informed consent was obtained from all participants . Plasma levels of total cholesterol , LDL cholesterol , HDL cholesterol , and triglycerides were measured using standard hospital assays ( Konelab , Helsinki , Finland , and Boehringer Mannheim , Mannheim , Germany ) . LDL cholesterol was calculated using the Friedewald equation if the triglyceride level was less than 4 mmol per liter ( 354 mg per deciliter ) and was measured directly for higher triglyceride levels . Follow-up studies of rs7120118 in the samples from Copenhagen were performed using an ABI PRISM 7900HT Sequence Detection System ( Applied Biosystems Inc , Foster City , California , USA ) and a TaqMan-based assay . Tissue biopsies ( mammary artery , ascending thoracic aorta and liver ) were taken from patients undergoing aortic valve surgery as part of the Advanced Study of Aortic Pathology ( ASAP ) study [22] . Aortic biopsies were divided into intimal-medial and adventitial halves . Peri-aortic fat was removed from the adventitial specimens where present . RNA from the tissue biopsies was hybridized to Affymetrix ST 1 . 0 Exon arrays and obtained scans were RMA normalized and log2 transformed . eQTL analysis was performed with an imputed genotype from circulating blood DNA ( Illumina 610w-Quad BeadArrays ) . The full methods for this study have been described previously [22] . Comparison of the GM12878 lymphoblast FAIRE-seq data track was obtained from the UCSC Genome Browser ( http://hgdownload . cse . ucsc . edu/goldenPath/hg18/encodeDCC/wgEncodeChromatinMap/wgEncodeUncFAIREseqZinbaGm12878 . narrowPeak . gz ) and compared to ( mean log R ratio of SNPs following FAIRE-enrichment ) - ( mean log R ratio for the respective control SNPs ) . The mean SNP log R ratios stratified by strength of FAIRE-seq signal were compared by ANOVA . A paired two-sided t-test was used to compare the control BAF with the respective FAIRE-enriched BAF . Visualisation of Manhattan plots and data management from the UCSC Genome Browser was carried out using Galaxy software [23]–[25] . In WHII , linear regression analysis of log-transformed HDL-C with SNPs using an additive model was performed using PLINK 1 . 0 . 7 . Analysis was carried out in all individuals and stratified by gender . Regression analysis was performed unadjusted for covariates as well as gender ( only in analysis of all individuals ) and age added as covariates . Stata software , version 10 ( Stata Corp , College Station , Texas ) was used for all analyses in the CCHS . Trend tests were by Cuzick's nonparametric test for trend . Linear regression was used to determine per-allele β-coefficients . For trend tests and linear regression analysis , rs7120118 TT , TC and CC genotypes were coded as 0 , 1 , and 2 , respectively . Statistical analysis of gene expression was carried out using R-2 . 13 . 0 and Bioconductor 2 . 8 [26] . Association between gene expression and genotype was calculated using an additive linear model as implemented in the lm-function in R .
|
The identification of genetic variants associated with complex diseases has rapidly grown through lowering costs of genome sequencing and the use of large-scale genotyping chips based on this sequencing data . There have not been corresponding advances in the identification of causal genetic variants compared to variants simply associated with diseases or traits . Most of these causal variants are thought to be located not within regions coding for proteins , but within genomic regions that regulate the level of protein . We have combined the use of large-scale gene chips with functional analysis , to determine regions of the genome that confer a greater potential for controlling gene regulation dependent on the genotype of that individual . Combining this data with population data and gene expression data , we identify a potential causal variant that alters regulation of LXR-α , a key mediator in lipid metabolism , and show that this variant is associated with HDL-C levels . This methodology provides a model for future analyses to identify further causal variants for disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome-wide",
"association",
"studies",
"genome",
"expression",
"analysis",
"genomics",
"functional",
"genomics",
"gene",
"expression",
"genetics",
"molecular",
"genetics",
"biology",
"human",
"genetics",
"population",
"genetics",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics"
] |
2012
|
Use of Allele-Specific FAIRE to Determine Functional Regulatory Polymorphism Using Large-Scale Genotyping Arrays
|
Cyclic AMP-activated intestinal Cl− secretion plays an important role in pathogenesis of cholera . This study aimed to investigate the effect of diclofenac on cAMP-activated Cl− secretion , its underlying mechanisms , and possible application in the treatment of cholera . Diclofenac inhibited cAMP-activated Cl− secretion in human intestinal epithelial ( T84 ) cells with IC50 of ∼20 µM . The effect required no cytochrome P450 enzyme-mediated metabolic activation . Interestingly , exposures of T84 cell monolayers to diclofenac , either in apical or basolateral solutions , produced similar degree of inhibitions . Analyses of the apical Cl− current showed that diclofenac reversibly inhibited CFTR Cl− channel activity ( IC50∼10 µM ) via mechanisms not involving either changes in intracellular cAMP levels or CFTR channel inactivation by AMP-activated protein kinase and protein phosphatase . Of interest , diclofenac had no effect on Na+-K+ ATPases and Na+-K+-Cl− cotransporters , but inhibited cAMP-activated basolateral K+ channels with IC50 of ∼3 µM . In addition , diclofenac suppressed Ca2+-activated Cl− channels , inwardly rectifying Cl− channels , and Ca2+-activated basolateral K+ channels . Furthermore , diclofenac ( up to 200 µM; 24 h of treatment ) had no effect on cell viability and barrier function in T84 cells . Importantly , cholera toxin ( CT ) -induced Cl− secretion across T84 cell monolayers was effectively suppressed by diclofenac . Intraperitoneal administration of diclofenac ( 30 mg/kg ) reduced both CT and Vibrio cholerae-induced intestinal fluid secretion by ∼70% without affecting intestinal fluid absorption in mice . Collectively , our results indicate that diclofenac inhibits both cAMP-activated and Ca2+-activated Cl− secretion by inhibiting both apical Cl− channels and basolateral K+ channels in intestinal epithelial cells . Diclofenac may be useful in the treatment of cholera and other types of secretory diarrheas resulting from intestinal hypersecretion of Cl− .
Transepithelial Cl− secretion is an essential transport process in intestine and plays an important role in determining intestinal fluid secretion [1] . Chloride secretion creates a negative electrical potential , which in turn provides a driving force for transport of Na+ and water into intestinal lumen . Stimulation of Cl− secretion by secretagogues ( e . g . hormones , neurotransmitters , and enterotoxins ) occurs mostly via cAMP or Ca2+-mediated pathways [2] . The Cl− secretory process requires coordinated functions of several types of transport proteins located in both apical membrane ( i . e . Cl− channels ) and basolateral membrane ( i . e . Na+-K+-Cl− cotransporters , Na+-K+ ATPases , and K+ channels ) of enterocytes ( Fig . 1A ) [2] , [3] . Both cAMP- and Ca2+-mediated Cl− secretion require Na+-K+-Cl− cotransporters ( NKCC1 ) and Na+-K+ ATPases to take up Cl− and maintain their driving force , respectively . In contrast , the apical chloride channels and basolateral K+ channels involved in the cAMP and Ca2+-mediated pathways are of distinct types . Cystic fibrosis transmembrane conductance regulator ( CFTR ) Cl− channels and KCNQ1/KCNE3 K+ channels are involved in cAMP-activated Cl− secretion , whereas Ca2+-activated Cl− channels ( CaCC ) and KCa3 . 1 K+ channels are involved in Ca2+-activated Cl− secretion [3]–[6] . Interestingly , a recent study using human intestinal epithelial ( T84 ) cells suggested that inwardly rectifying Cl− channels ( IRC ) provided an alternative route for apical Cl− exit during cAMP-activated Cl− secretion [7] . Importantly , abnormal Cl− secretion has been implicated in the pathogenesis of diseases . For example , decreased intestinal Cl− secretion is associated with constipation in cystic fibrosis , while increased intestinal Cl− secretion causes secretory diarrhea in cholera and Traveler's diarrhea ( caused by enterotoxigenic Escherichia coli ) [8] . Cholera is a severe type of secretory diarrhea resulted from intestinal infection with Vibrio cholera and kills hundreds of thousand people per year [9]–[11] . At present , the mainstay therapy of cholera is the use of oral rehydration solution ( ORS ) , which is effective only in 80% of cholera cases [9] . However , ∼20% of cholera patients require intravenous fluid replacement because their intestinal fluid loss is too severe to be replenished by ORS [9] , [12] . Diarrhea in cholera is known to result mainly from the pro-secretory effect of cholera toxin ( CT ) produced by V . cholera on enterocytes [12] . After internalization into enterocytes , cholera toxins induce an elevation of intracellular cAMP and subsequent CFTR-dependent Cl− secretion , resulting in intestinal fluid secretion and fluid loss [12] . With an attempt to develop anti-secretory therapy of cholera , several classes of CFTR inhibitors have been identified and demonstrated to effectively reduce CT-induced intestinal fluid secretion in both rats and mice [13]–[16] . Interestingly , a recent study using a V . cholerae infection model in adult mice confirmed CFTR as a major host factor determining intestinal fluid secretion in cholera [17] . Accordingly , CFTR is regarded as a promising drug target for cholera . Non-steroidal anti-inflammatory drugs ( NSAIDs ) , a group of commonly used drugs exerting their anti-inflammatory action via inhibition of cyclooxygenases , have been shown to be functional modulators of both cation and anion channels in various types of tissues [18] . Interestingly , ibuprofen and fenamates such as flufenamic acid have been shown to inhibit CFTR in respiratory epithelial cells and in Xenopus oocytes , respectively [19] , [20] . However , the effects of another widely used and better-tolerated cyclooxygenase 2 ( COX-2 ) -selective NSAID , diclofenac , on epithelial Cl− channels including CFTR remain unexplored . Indeed , this drug has been shown to directly inhibit several types of cation channels including acid sensing ion channels ( ASIC ) , voltage-sensitive sodium channels , and transient receptor potential ( TRP ) channels [18] , [21] . Since diclofenac shares similarity in chemical structure and spectrum of activity against some ion channels ( especially ASIC and TRP channels ) with flufenamic acid and ibuprofen , we hypothesized that diclofenac may inhibit CFTR and reduce cAMP-activated Cl− secretion in intestinal epithelia . Therefore , this study was performed to investigate the effect of diclofenac on cAMP-activated intestinal Cl− secretion and its underlying mechanisms using T84 cell monolayers as a model of intestinal epithelia . In addition , potential utility of diclofenac in the treatment of cholera was investigated in vivo using the two mouse closed-loop models of cholera induced by CT and by V . cholerae .
This study has been approved by the Institutional Animal Care and Use Committee of the Faculty of Science , Mahidol University ( permit number MUSC56-022-284 ) . This study was performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the US National Institutes of Health . CFTRinh-172 was obtained from Calbiochem ( San Diego , California , USA ) , cholera toxin was from List Biological Laboratories , Inc . ( Campbell , California , USA ) whereas trypsin-EDTA , fetal bovine serum , penicillin and streptomycin were from HyClone ( Logan , Utah , USA ) . Other chemicals were obtained from Sigma-Aldrich ( St . Louis , Missouri , USA ) . T84 cells and Calu-3 cells were obtained from American Type Culture Collection ( Manassas , Virginia , USA ) . T84 cells were cultured in 50% Dulbecco's Modified Eagle's medium and 50% Ham's F-12 medium supplemented with 10% fetal bovine serum , in the presence of 100 U/ml penicillin and 100 µg/ml streptomycin . Calu-3 cells were cultured in Eagle's Minimum Essential Medium supplemented with 10% fetal bovine serum , 100 U/ml penicillin , and 100 µg/ml streptomycin . Both types of cells were maintained at 37°C in a humidified incubator under an atmosphere of 95% O2/5% CO2 . For electrophysiological analysis , T84 cells and Calu-3 cells were plated onto Snapwell inserts at a density of 5×105 cells/well , and grown in a humidified incubator with daily replacement of culture media for 14 days . In these experiments , inserts ( with transepithelial electrical resistance >1 , 000 Ω . cm2 as measured by EVOM2 volt-ohm meter ( World Precision Instruments , Sarasota , Florida , USA ) ) were mounted in Ussing chambers . For short-circuit current ( Isc ) measurements , both apical and basolateral hemichambers were filled with Kreb's solution containing ( pH 7 . 3 ) 120 mM NaCl , 25 mM NaHCO3 , 3 . 3 mM KH2PO4 , 0 . 8 mM K2HPO4 , 1 . 2 mM MgCl2 , 1 . 2 mM CaCl2 and 10 mM glucose . For Isc analysis of mouse intestine , a sheet of mouse ileum was prepared without muscle stripping and mounted in Ussing chambers filled with Kreb's solutions containing indomethacin ( 10 µM in both apical and basolateral solutions; to prevent prostaglandin-induced Cl− secretion ) and amiloride ( 10 µM in apical solution; to prevent current contributed by Na+ absorption ) . For apical Cl− current measurements , apical and basolateral hemichambers were filled with low Cl− and high Cl− solutions , respectively , to create a basolateral-to-apical Cl− gradient . High Cl− basolateral solution contained ( pH 7 . 3 ) 130 mM NaCl , 2 . 7 mM KCl , 1 . 5 mM KH2PO4 , 1 mM CaCl2 , 0 . 5 mM MgCl2 , 10 mM Na-HEPES and 10 mM glucose . In low Cl− apical solution , 65 mM NaCl was replaced with 65 mM sodium gluconate , and the concentration of CaCl2 was increased to 2 mM . To induce basolateral membrane permeabilization , amphotericin B ( 250 µg/ml ) was added into basolateral solutions and incubated for 30 min prior to apical Cl− current measurements . For basolateral K+ current measurements , apical and basolateral hemichambers were filled with high K+ and low K+ solutions , respectively , to establish an apical-to-basolateral K+ gradient . High K+ apical solution contained ( pH 7 . 3 ) 142 . 5 mM K-gluconate , 1 . 25 mM CaCl2 , 0 . 40 mM MgSO4 , 0 . 43 mM KH2HPO4 , 0 . 35 mM Na2HPO4 , 10 mM Na-HEPES and 5 . 6 mM glucose . In low K+ basolateral solution , concentration of K-gluconate was reduced to 5 . 4 mM and 136 . 9 mM N-methyl-glucamine was added . Before basolateral K+ current measurements , apical membrane of T84 cells was permeabilized by amphotericin B ( 250 µg/ml ) and ouabain ( 1 mM ) was added into basolateral solution to prevent current contributed by Na+-K+ ATPase . In the measurement of Ca2+-activated basolateral K+ channel activity , BaCl2 ( 5 mM ) was added into the basolateral solution to prevent current contributed by cAMP-activated basolateral K+ channels . For determination of Na+-K+ ATPase activity , both apical and basolateral hemichambers were filled with Kreb's solutions . After initiating Isc measurements , cells were treated with DMSO ( control ) or diclofenac ( added into basolateral solution ) , followed by apical membrane permealization by amphotericin B ( 250 µg/ml ) . After stabilization of the amphotericin B-induced rise in Isc , ouabain ( 1 mM ) was added into the basolateral solution and the values of ouabain-sensitive current were used to represent the activity of Na+-K+ ATPases . Kreb's solutions were bubbled continuously with 95% O2/5% CO2 whereas Cl− and K+ solutions were bubbled with 100% O2 . All solutions were maintained at 37°C . Short-circuit current/apical Cl− current/basolateral K+ current/Na+-K+-mediated current was recorded using a DVC-1000 voltage-clamp ( World Precision Instruments , Sarasota , Florida , USA ) with Ag/AgCl electrode and 3 M KCl agar bridge . Intracellular cAMP levels in T84 cells were measured using cAMP immunoassays ( R&D Systems , Minneapolis , Minnesota , USA ) . T84 cells were seeded on 24-well plates at a density of 106 cells/well and grown for 24 h in a humidified 5% CO2/95% O2 incubator at 37°C . Then , cells were washed three times with PBS before 1-h treatments with DMSO ( vehicle ) , diclofenac ( 200 µM ) , forskolin ( 20 µM ) , or forskolin ( 20 µM ) plus diclofenac ( 200 µM ) . Thereafter , cells were lysed with cell lysis buffer and level of intracellular cAMP was measured according to the manufacturer's instructions . N+-K+-C1− cotransporter ( NKCCl ) activity in T84 cells was measured using Thallium ( Tl+ ) influx-based fluorescent assays ( Invitrogen , Carlsbad , California , USA ) with some modifications [22] . Briefly , T84 cells were seeded on 96-well plates at a density of 105 cells/well and grown for 48 h in a humidified 5% CO2/95% O2 incubator at 37°C . Forty-eight hours later , culture media were removed and cells were incubated for 2 h with Cl−-free buffer containing fluorogenic Tl+-sensitive dye and probenecid ( an organic anion inhibitor used to inhibit transporter-mediated dye efflux ) . Cells were then washed twice with Cl−-free buffer and incubated for 15 min with Cl−-free buffer containing Thallium sulfate ( Tl2SO4 , 5 mM ) and clotrimazole ( an inhibitor of basolateral K+ channels; 30 µM ) with or without diclofenac ( 200 µM ) . Bumetanide ( 100 µM ) was used as a positive control . For measurements of NKCC1 activity , fluorescent intensity ( excitation wavelength = 490 nm; emission wavelength = 520 nm ) was recorded 15 s before automated addition of NaCl solution ( final [NaCl] = 135 mM ) and thereafter for 30 s using a Wallac Victor2 microplate reader ( Perkin Elmer , Waltham , Massachusetts , USA ) . NKCC1 activity was analyzed from the slope of linear increase in fluorescent intensity within 15 s following NaCl addition . T84 cells plated on 6 well-plates were incubated for 20 min with vehicle ( DMSO ) , ATP ( 100 µM ) or ATP ( 100 µM ) plus diclofenac ( 20 µM ) . Proteins were extracted using lysis buffers containing ( pH 7 . 4 ) 1% Triton X-100 , 50 mM Tris-HCl , 150 mM NaCl , 1 mM EDTA , 1 mM NaF , 1 mM Na3VO4 , 1 mM PMSF and protease inhibitor ( PI ) cocktail . Protein concentration was determined using a Lowry method . Equal amounts of proteins were loaded on sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a nitrocellulose membrane . The membrane was blocked with 0 . 5% milk for 90 min at room temperature before incubation overnight at 4°C with antibodies against phosphorylated Ca2+/calmodulin-dependent protein kinase II ( CaMKII ) or β-actin ( Cell signaling technology , Denver , Colorado , USA ) . The secondary antibodies were horseradish peroxidase-conjugated anti-rabbit IgG antibodies . The immunoblot was visualized using a chemiluminescence detection method . T84 cell viability was measured using 3- ( 4 , 5-dimethyl-2-thiazolyl ) -2 , 5-diphenyl-2H-tetrazolium bromide ( MTT ) assays . In brief , T84 cells were plated on 96-well plates at a density of 1×105 cells/well and grown overnight , followed by 24-h incubation with culture media containing DMSO ( control ) or diclofenac at various concentrations . After removal of culture media , cells were treated with MTT reagent ( 5 mg/ml ) for 4 h at 37°C and the reaction was stopped by addition of an aliquot of DMSO ( 100 µl ) into each well . Thirty minutes later , an absorbance at 540 nm was determined using a spectrophotometer . Barrier function of T84 cell monolayers was measured using fluorescein isothiocyanate ( FITC ) -labeled dextran ( molecular weight ∼4 . 4 kDa ) flux assays . Briefly , T84 cells were plated on Transwell membrane support ( Costar , Cambridge , Massachusetts , USA ) at a density of 5×105 cells/well and grown for 14 days , when transepithelial electrical resistance was >1 , 000 Ω . cm2 . Cells were then treated for 24 h with DMSO ( control ) , diclofenac ( 20 µM and 200 µM ) , and EGTA ( 3 mM ) ( added to both apical and basolateral sides ) . To measure FITC-dextran flux , FITC-dextran ( 1 mg/ml ) was added to apical side , and an hour later , basolateral media were sampled for measuring concentrations of FITC-dextran using Wallac Victor2 microplate reader ( Perkin Elmer , Waltham , Massachusetts , USA ) . To investigate the in vivo effect of diclofenac on CT- and V . cholerae-induced intestinal fluid secretion , mice ( 30–35 g , ICR strain; The National Laboratory Animal Center , Salaya , Nakornpathom , Thailand ) were fasted for 24 h before experiments . After anesthesia by an intraperitoneal injection of thiopenthal sodium ( 50 mg/kg ) , an abdominal incision was made and 3 closed ileal loops ( 2–3 cm in length ) were made by ligations and then instilled with 100 µl of phosphate-buffered saline ( PBS ) or PBS containing CT ( 1 µg ) or V . cholerae ( classical O1 569B strain of V . cholerae at 107 CFU/loop ) . This strain of V . cholerae was used since it has been known to produce large amounts of CT and cause consistent intestinal fluid secretion in adult mouse closed-loop models [17] . Body temperature of mice was maintained at 36–37°C for the entire period of operation using heating pads . After abdominal closure by sutures , mice were intraperitoneally administered with DMSO ( control ) or diclofenac ( 30 mg/kg ) , and allowed to recover from anesthesia . Four hours ( for experiments using CT ) or 12 hours ( for experiments using V . cholerae ) later , mice were anesthetized again , abdomen was opened and ileal loops were removed for measurements of intestinal fluid secretion from loop weight/length ratios . Then , mice were euthanized with an injection of thiopenthal sodium ( 150 mg/kg ) . To determine the effect of diclofenac on intestinal fluid absorption , mouse ileal loops were instilled with PBS , with or without intraperitoneal administration of diclofenac ( 30 mg/kg ) . Ileal loops were removed for measuring loop weight/length ratios at 20 min and 40 min after PBS instillation . Results are presented as means ± S . E . M . Statistical differences between control and treatment groups were evaluated using Student's t test or one-way ANOVA followed by Bonferroni's post hoc test , where appropriate , with p value<0 . 05 being considered statistically significant .
Cyclic AMP-activated Cl− secretion across human intestinal epithelial ( T84 ) cell monolayers was investigated using Isc measurements . First , we investigated the relative contribution of CFTR to cAMP-activated Cl− secretion in T84 cells . As shown in Fig . 1B , CFTRinh-172 , a CFTR-specific Cl− channel blocker , completely abolished the cAMP-activated Cl− secretion elicited by forskolin ( an adenylate cyclase activator ) , indicating that CFTR provided a primary route for apical Cl− exit upon cAMP stimulation in these cells . Next , the effect of diclofenac on cAMP-activated Cl− secretion was investigated . As depicted in Fig . 1C , diclofenac , concomitantly added into both apical and basolateral solutions , inhibited cAMP-activated Cl− secretion in a concentration-dependent fashion , with an IC50 of ∼20 µM and almost complete inhibition at 200 µM . In addition , the polarity of inhibition by diclofenac was determined using a protocol of sequential additions of the drug . In this experiment , diclofenac was sequentially added into basolateral and apical solutions , respectively . As demonstrated in Fig . 1D , basolateral or apical additions of diclofenac ( at final concentrations of 20 µM and 200 µM ) produced a similar degree of inhibitions , suggesting that diclofenac equally affected both apical and basolateral transport processes . Since diclofenac is metabolized by intestinal cytochrome P450 ( CYP ) enzymes to hydroxylated diclofenac metabolites and reactive intermediates [23] , we then investigated whether the inhibitory effect of diclofenac required metabolic activation by CYP enzymes . Figure 1E shows that pretreatment with 1-aminobenzotriazole ( 1-ABT; 1 mM ) [24] , an inhibitor of CYP enzymes , had virtually no effect on the inhibition of cAMP-activated Cl− secretion in T84 cells , indicating that the effect of diclofenac did not require metabolic activation . To investigate the effect of diclofenac on CFTR Cl− channel activity , apical Cl− current measurements were performed in T84 cells . In this experiment , basolateral membrane was permeabilized by amphotericin B and a Cl− gradient was established using asymmetrical Cl− buffers ( [Cl−] in basolateral solution >[Cl−] in apical solution ) . Apical Cl− current induced by CFTR agonists under this experimental condition , therefore , indicates CFTR Cl− channel activity . As shown in Fig . 2A , apical Cl− current induced by forskolin ( 20 µM ) , CPT-cAMP ( cell-permeable cAMP; 100 µM ) , and genistein ( direct CFTR activator; 20 µM ) were inhibited by diclofenac in a dose-dependent manner , with IC50 of 8 µM , 8 . 5 µM and 10 µM , respectively , and with almost complete inhibition at 100 µM . These results suggest that diclofenac inhibited cAMP-activated Cl− secretion in T84 cells , at least in part , by inhibiting CFTR Cl− channel activity . To examine the effect of diclofenac on another cell type expressing human CFTR , apical Cl− current analysis was performed using monolayers of Calu-3 cells , a human airway epithelial cell line . It was found that diclofenac also inhibited CFTR-mediated apical Cl− current induced by CPT-cAMP in this cell line with IC50 of ∼10 µM and with near complete inhibition at 100 µM ( Fig . 2B ) . In addition , the reversibility of diclofenac inhibition of CFTR Cl− channel activity was investigated using apical Cl− current measurements in T84 cells . The inhibitory effect of diclofenac on CFTR-mediated apical Cl− current disappeared after removing diclofenac ( 20 µM ) from bathing solutions ( Fig . 2C ) , suggesting that the effect is reversible . Of note , the recovery of apical Cl− current was inhibited by CFTRinh-172 , confirming that the current was indeed CFTR-mediated Cl− current . Based on the finding that , in T84 cells , CFTR-mediated apical Cl− current was effectively inhibited by diclofenac , subsequent experiments were performed to investigate the mechanisms by which CFTR Cl− channel activity was suppressed in these cells using apical Cl− current analysis . Figure 3A demonstrates the mechanisms of CFTR regulation in T84 cells . In general , CFTR Cl− channel activity is regulated by cAMP-dependent protein kinase A ( PKA ) and AMP-activated protein kinase ( AMPK ) [25] . Phosphorylation at CFTR's R-domain by PKA and AMPK causes activation and inhibition of CFTR Cl− channel activity , respectively [26] , [27] . Levels of PKA phosphorylation at CFTR's R domain depends on activities of protein phosphatases [28] , which dephosphorylate CFTR , and intracellular cAMP level , which stimulates PKA activities . On the other hand , intracellular cAMP level in T84 cells depends on the activities of adenylate cyclase ( which generates cAMP ) , phosphodiesterase ( PDE; which degrades cAMP ) and multidrug resistance-associated protein 4 ( MRP4; which mediates cAMP efflux ) [12] , [29] . Because the potencies of diclofenac inhibition of forskolin- and CPT-cAMP-stimulated apical Cl− current in T84 cells were comparable ( Fig . 2A ) , we hypothesized that the targets of diclofenac might be downstream to cAMP generation or involve AMPK activation . To investigate whether diclofenac indirectly inhibited CFTR by decreasing cAMP levels via activation of PDE or MRP4 , activating AMPK , or stimulating protein phosphatases , dose-inhibition studies were performed in the presence or absence of inhibitors of these regulatory proteins . As demonstrated in Fig . 3B , the potency of inhibition of forskolin-induced Cl− current in the presence of IBMX ( PDE inhibitor; top-middle current tracing ) was not different from that of control ( CFTR Cl− channel activity stimulated by forskolin; top-left current tracing ) , indicating that the inhibitory effect was not due to stimulation of PDE . Likewise , pretreatment with MK571 ( MRP4 inhibitor; Fig . 3B , top-right current tracing ) , compound C ( AMPK inhibitor; Fig . 3B , bottom-left current tracing ) or Na2VO3 ( protein phosphatase inhibitor; Fig . 3B , bottom-right current tracing ) did not alter the potency of diclofenac . Range of the agonist-induced apical Cl− current in these experiments was ∼40–60 µA/cm2 . The summary of dose-inhibition studies is shown in Fig . 3C . Furthermore , the effect of diclofenac on intracellular cAMP contents was investigated in T84 cells using cAMP immunoassay kits . As depicted in Fig . 4 , diclofenac at 200 µM , a concentration found to fully inhibit cAMP-activated Cl− secretion in T84 cells , had virtually no effect on intracellular cAMP levels under both basal and forskolin-stimulated conditions . All together , the results suggest that the inhibition of CFTR Cl− channel activity by diclofenac in T84 cells was not due to indirect mechanisms including an alternation of intracellular cAMP levels , AMPK activation , and CFTR dephosphorylation . Since additions of diclofenac into apical and basolateral solutions produced similar degree of inhibitory effects on cAMP-induced Cl− secretion in T84 cells , we hypothesized that this drug may affect basolateral transport proteins , namely cAMP-activated K+ channels , Na+-K+ ATPases and Na+-K+-Cl− cotransporters ( NKCC1 ) . The effect of diclofenac on cAMP-activated basolateral K+ channels was , therefore , investigated using basolateral K+ current analysis . In this method , apical membrane was permeabilized by amphotericin B ( 250 µg/ml ) and a K+ gradient ( [K+]apical>[K+]basolateral ) was established using asymmetrical K+ buffers in the presence of ouabain in basolateral solutions ( to prevent current generated by Na+-K+ ATPases ) ( Fig . 5A , inset ) . The basolateral K+ current elicited by an addition of cell-permeable cAMP ( e . g . CPT-cAMP ) would , therefore , reflect the activity of cAMP-activated K+ channels located in the basolateral membrane of T84 cells ( i . e . KCNQ1/KCNE3 K+ channels ) . As shown in Fig . 5A ( left ) , clotrimazole ( 30 µM ) , a known inhibitor of basolateral K+ channels , markedly inhibited the CPT-cAMP-induced current , validating this method for assessing the activity of cAMP-activated basolateral K+ channels . Interestingly , the cAMP-activated basolateral K+ current was dose-dependently inhibited by diclofenac with an IC50 of ∼3 µM and almost complete inhibition at 20 µM ( Fig . 5A ( right ) ) . In addition , the effect of diclofenac on Na+-K+ ATPase was investigated using a protocol designed to specifically measure Na+-K+ ATPase activity . In this protocol , intracellular Na+ loading by amphotericin B-induced permeabilization of apical membrane stimulated Na+-K+ ATPase activity , resulting in an increase in Isc ( Fig . 5B , inset ) . Values of Isc sensitive to ouabain ( 1 mM; added into basolateral solution ) , an inhibitor of Na+-K+ ATPases , were used as indicators of Na+-K+ ATPase activity . As depicted in Fig . 5B , pretreatment with diclofenac ( 200 µM ) had no effect on ouabain-sensitive Isc , indicating that diclofenac had no effects on Na+-K+ ATPase activity . Subsequently , the effect of diclofenac on NKCC1 activity was investigated in T84 cells using Thallium ( Tl+ ) influx-based fluorescent assays [22] . In this experiment , cells were loaded with Tl+-sensitive dye and bathed in Cl− -free bathing solution containing Tl+ and clotrimazole ( 30 µM; to prevent influx of Tl+ through K+ channels ) ( Fig . 6A ) . To measure NKCC1 activity , NaCl solution , which triggers NKCC1-mediated Tl+ uptake , was added into each well resulting in an increase in fluorescent intensity . NKCC1 activity was deduced from the slope of increased fluorescent signal within 15 s after NaCl addition . As depicted in Fig . 6B , pretreatment with diclofenac ( 200 µM ) had no effect on Tl+ influx into T84 cells compared to control , whereas bumetanide ( a known inhibitor of NKCC1; 100 µM ) completely prevented Tl+ influx into these cells . These results indicate that the inhibitory effect of diclofenac on cAMP-activated Cl− secretion was not through the inhibition of NKCC1 . In addition to CFTR , Cl− transport across apical membrane of T84 cells is mediated by two other types of Cl− channels including Ca2+-activated Cl− channel ( CaCC ) and inwardly rectifying Cl− channel ( IRC ) [12] . We next determined the effects of diclofenac on these two apical Cl− channels using apical Cl− current analysis . To determine the effect of diclofenac on CaCC , T84 cell monolayers were pretreated with CFTRinh-172 ( to prevent CFTR-mediated Cl− transport ) and CaCC-mediated apical Cl− transport was stimulated by ATP ( 100 µM ) . As shown in Fig . 7A ( left ) , diclofenac inhibited CaCC-mediated apical Cl− current in a concentration-dependent manner with an IC50 of ∼1 µM and almost complete inhibition at 20 µM . Since the activation of CaCC is mediated by Ca2+/calmodulin-dependent protein kinase II ( CaMKII ) in T84 cells [30] , we determined whether diclofenac interfered with the steps of ATP-induced CaMKII activation ( starting from ATP-P2Y receptor binding to Ca2+ elevation-induced CaMKII phosphorylation ) by performing immunoblot analysis of phosphorylated CaMKII , which is an indicator of CaMKII activation . As depicted in Fig . 7A ( right ) , 20-min treatment with ATP ( 100 µM ) induced CaMKII phosphorylation , which was unaffected by co-incubation with diclofenac ( 20 µM ) . The results suggest that the inhibition of CaCC-mediated apical Cl− current by diclofenac is not due to the interference with ATP-induced CaMKII activation . Next , we determined the effect of diclofenac on IRC , a recently identified apical Cl− channel activated via cAMP-exchange protein directly activated by cyclic AMP 1 ( Epac1 ) -Ca2+-mediated pathways [7] . To measure IRC activity , T84 cells were pretreated with CFTRinh-172 ( to exclude the contribution by CFTR ) prior to IRC stimulation by forskolin . As depicted in Fig . 7B , IRC-mediated apical Cl− current was concentration-dependently inhibited by diclofenac with an IC50 of ∼5 µM and near complete inhibition at 100 µM . Furthermore , the effect of diclofenac on Ca2+-activated basolateral K+ channels ( i . e . KCa3 . 1 ) was investigated using basolateral K+ current analysis in T84 cells . In this experiment , cells were pretreated with BaCl2 ( 5 mM; to prevent current contributed by cAMP-activated basolateral K+ channels ) before stimulation of Ca2+-activated basolateral K+ channels by ATP ( 100 µM ) . As depicted in Fig . 7C , basolateral K+ current induced by ATP was dose-dependently inhibited by diclofenac with near complete inhibition at 200 µM . The results indicate that diclofenac suppressed the activities of CaCC , IRC and Ca2+-activated basolateral K+ channel in T84 cells . To evaluate potential intestinal toxicity of diclofenac , the effects of diclofenac on T84 cell viability and barrier function were investigated . Cell viability was determined by MTT assays . As illustrated in Fig . 8A , 24-h exposure to diclofenac , at concentrations from 10 µM to 200 µM , did not affect T84 cell viability . To assess the integrity of intestinal barrier function , flux of fluorescein isothiocyanate ( FITC ) -dextran ( molecular weight 4 . 4 kDa ) , a fluorescence-tagged paracellular marker , across T84 cell monolayers was determined after 24-h treatment with diclofenac . As shown in Fig . 8B , diclofenac at concentrations of 20 µM and 200 µM did not change FITC-dextran flux across T84 cell monolayers compared with control . On the other hand , treatment with EGTA ( 3 mM ) , a Ca2+-chelating agent known to disrupt epithelial tight junctions , markedly increased the FITC-dextran flux serving as a positive control for this experiment . These experiments suggest that diclofenac , at concentrations found to inhibit transepithelial Cl− secretion , is not cytotoxic to intestinal epithelial cells . Since cAMP-induced Cl− secretion plays an important role in the pathogenesis of secretory diarrheas especially cholera [12] , we investigated the potential application of diclofenac in the treatment of cholera using both in vitro and in vivo models . As demonstrated in Fig . 9A , diclofenac inhibited cholera toxin ( CT ) -induced Cl− secretion in T84 cells with an IC50 of ∼10 µM and >95% inhibition at 100 µM . Likewise , diclofenac inhibited forskolin-induced Cl− secretion in mouse intestinal sheets , although with lower potency compared to the studies in T84 cells ( Fig . 9B ) . Of particular importance , antidiarrheal efficacy of diclofenac was investigated in mouse closed-loop models of cholera induced by either CT or V . cholerae . In this experiment , loop weight/length ratio was used as an indicator of intestinal fluid secretion and diclofenac was intraperitoneally administered at a dose of 30 mg/kg . Based on the principle of body surface area-based dosage conversion [31] , this dose of diclofenac in mice is equivalent to 2 mg/kg of diclofenac in human , which is the dose for treatments of pain and inflammation . Interestingly , concomitant intraperitoneal administration of diclofenac ( 30 mg/kg ) significantly inhibited both CT- and V . cholerae-induced fluid secretion by ∼70% ( Fig . 9C and Fig . 9D ) . Further , the effect of diclofenac alone on intestinal fluid absorption was determined using a mouse closed-loop model . In this experiment , closed ileal loops were instilled with PBS with or without intraperitoneal administration of diclofenac ( 30 mg/kg ) and , 20 min or 40 min later , the ileal loops were removed for loop weight/length ratio measurements . As shown in Fig . 9E , the ileal loop weight/length ratio of diclofenac-treated groups was not significantly different from that of control at both 20 min and 40 min after PBS instillation , indicating that diclofenac had no effect on intestinal fluid absorption . Data of ileal loops at 1 min after PBS instillation ( without diclofenac treatment ) and ileal loops without PBS instillation ( -PBS , empty loop ) were included for comparisons .
In this report , we demonstrated the inhibitory effect of diclofenac , a commonly used NSAID , on cAMP-activated Cl− secretion in human intestinal epithelial cell line ( T84 cells ) . Functional analyses of membrane transport processes involved in Cl− secretion indicate that the effect of diclofenac involves inhibition of CFTR Cl− channels and cAMP-activated basolateral K+ channels . Interestingly , diclofenac also inhibited two other types of apical Cl− channels , namely , Ca2+-activated Cl− channels ( CaCC ) and inwardly rectifying Cl− channels ( IRC ) , and suppressed Ca2+-activated basolateral K+ channels T84 cells . More importantly , this study revealed a potential utility of diclofenac in the treatment of cholera using both in vitro and in vivo models . Cyclic AMP-activated Cl− secretion by enterocytes requires coordinated functions of several transport proteins located in both apical and basolateral membranes . Chloride anion is uptaken into enterocytes via Na+-K+-Cl− cotransporters and subsequently transported into intestinal lumen via cAMP-activated apical Cl− channels , namely CFTR and IRC . However , our previous and present studies ( Fig . 1B ) indicate that cAMP-activated apical Cl− efflux is mainly via CFTR , and that IRC plays a significant role only after inhibition of CFTR [15] . Additional transport proteins required for cAMP-activated Cl− secretion are cAMP-activated basolateral K+ channels and Na+-K+-ATPases , which are required for recycling K+ and Na+ back to serosa , respectively , and thus play crucial roles in maintaining electrochemical driving force for apical Cl− efflux into intestinal lumen . Inhibition of one of these transport proteins can stop the whole process of cAMP-activated Cl− secretion [4] , [32] , [33] . Because inhibition of cAMP-activated Cl− secretion was observed when diclofenac was added into either apical or basolateral solutions , we hypothesized that diclofenac may target either apical or basolateral transport proteins . In order to prove this hypothesis , the effects of diclofenac on functions of individual transport proteins ( i . e . CFTR , cAMP-activated basolateral K+ channel , Na+-K+ ATPase and NKCC1 ) were investigated . The underlying mechanisms of diclofenac inhibition of CFTR-mediated Cl− transport were investigated in T84 cells using apical Cl− current measurements . Results showed that IC50 of diclofenac was ∼8 µM–10 µM regardless of the mechanisms of CFTR activation ( increases in cAMP levels by forskolin , direct stimulation of PKA by CPT-cAMP , or direct CFTR activation by genistein ) . In agreement with this result , we found that the inhibitory effect of diclofenac on CFTR-mediated apical Cl− current was unaffected by pharmacological inhibition of PDE , MRP4 , protein phosphatase or AMPK , all of which are negative regulators of CFTR Cl− channel activity . In addition , the levels of intracellular cAMP were unaffected by diclofenac . These results indicate that , in T84 cells , the inhibition of CFTR by diclofenac is not via indirect mechanisms including decreasing cAMP levels ( by inhibition of adenylate cyclase or activation of PDE or MRP4 ) , dephosphorylation of CFTR ( by protein phosphatase ) , or phosphorylation by AMPK . We speculate that diclofenac may inhibit CFTR Cl− channel activity by acting directly on the channel . Interestingly , the inhibitory effect of diclofenac on CFTR function was also observed in Calu-3 cells , human airway epithelial cells endogenously expressing CFTR , suggesting that the effect of diclofenac is not cell line-specific . In addition to the inhibitory effect on CFTR , diclofenac blocked the cAMP-activated basolateral K+ channels in T84 cells . Of note , the potency of diclofenac on the inhibition of cAMP-activated Cl− secretion ( IC50∼20 µM ) is lower than that on the inhibition of CFTR ( IC50∼10 µM ) and cAMP-activated basolateral K+ channels ( IC50∼3 µM ) . This may be due to the intracellular negative membrane potential which impedes the entry of negatively charged diclofenac into the cells , resulting in lower intracellular concentration of diclofenac in intact cells ( in short-circuit current analysis ) than in permeabilized cells ( in apical Cl− and basolateral K+ current analysis; membrane potential is ∼0 mV ) at any given concentrations of diclofenac in bathing solutions . Furthermore , we found that diclofenac inhibited other types of ion channels involved in intestinal Cl− secretion including IRC , CaCC and Ca2+-activated basolateral K+ channels without affecting Na+-K+ ATPase and NKCC1 activities . Therefore , all of the data obtained from functional analysis of individual transport proteins indicate that diclofenac inhibits Cl− secretion mediated by both cAMP and Ca2+-dependent pathways . Of particular interest , diclofenac inhibited ATP-induced CaCC-mediated Cl− transport without any effects on ATP-induced CaMKII phosphorylation , indicating that diclofenac may directly inhibit CaCC . Indeed , CaCC-mediated Cl− secretion by enterocytes plays pivotal roles in driving intestinal fluid secretion in rotavirus diarrhea , the most common cause of infectious diarrhea in children under 5 years of age [34] , [35] . Accordingly , diclofenac or related compounds may be of particular benefit in the treatment of rotavirus diarrhea . Furthermore , since clinical use of diclofenac is known to be associated with constipation [36] , [37] , our findings may provide a mechanistic insight into the cellular events underlying constipation in patients taking diclofenac . To date , several classes of potential antidiarrheal therapeutics for cholera have been identified , with CFTR inhibitor being recognized as the most promising candidate [12] . In support of this notion , CFTRinh-172 , a small-molecule CFTR inhibitor identified by high-throughput screening , has been shown to reduce both cholera toxin ( CT ) - and live V . cholerae-induced intestinal fluid secretion in mice by >90% [17] , [38] . Until now , several classes of CFTR inhibitors have been identified and shown to exhibit antidiarrheal efficacy in animal models of cholera [12] , [14]–[16] . However , the development of these CFTR inhibitors into new antidiarrheal therapy has progressed slowly , probably , due to the limited financial incentives for the investment in research and development of drugs for cholera , which is prevalent in developing countries . Therefore , it may be more reasonable to develop antidiarrheal therapy of cholera by extending clinical applications of known drugs that are found to inhibit CFTR-mediated Cl− secretion . In the present study , we found that diclofenac effectively abrogated CT-induced Cl− secretion in T84 cells with an IC50 of ∼10 µM , which is lower than its potency on the inhibition of Cl− secretion induced by other CFTR agonists including forskolin , CPT-cAMP and genistein . Higher potency on the inhibition of Cl− secretion induced by CT compared to other CFTR agonists indicates that diclofenac may have other beneficial pleiotropic effects against CT intoxication in T84 cells . Importantly , we demonstrated that diclofenac at a dose of 30 mg/kg inhibited CT- and V . cholerae-induced intestinal fluid secretion by 70% in mouse closed-loop models . Based on the body surface areas of mice and humans [31] , this dose of diclofenac ( 30 mg/kg ) could be converted into the human equivalent dose of ∼2 mg/kg , which is the dose recommended for treatments of pain and inflammation in human . Furthermore , diclofenac had no cytotoxicity in T84 cells , as revealed by cell viability assay and measurements of barrier function , and had no effects on basal intestinal fluid absorption , both of which are prerequisite properties of an antidiarrheal therapy . These results indicate that diclofenac represent a class of known drug that may have potential utility in the treatment of cholera . Future studies will be required to determine antidiarrheal efficacy of diclofenac in the treatment of cholera in humans . In conclusion , this study reveals diclofenac as an inhibitor of Cl− secretion across human intestinal epithelial cells . The mechanisms of inhibition involve blockades of apical Cl− channels ( CFTR , CaCC and IRC ) and basolateral K+ channels ( KCNQ1/KCNE3 and KCa3 . 1 ) ( Fig . 10 ) . Our findings may lead to the successful development of diclofenac or related compounds into an inexpensive and effective therapy of secretory diarrheas resulting from either cAMP or Ca2+-activated Cl− secretion including cholera and rotavirus diarrheas .
|
Diarrhea in cholera results from stimulation of cAMP-mediated intestinal Cl− secretion by cholera toxin ( CT ) . This study demonstrates that diclofenac , a widely used non-steroidal anti-inflammatory drug ( NSAID ) , inhibited cAMP-activated Cl− secretion in human intestinal epithelial ( T84 ) cells by inhibiting both apical Cl− channels ( i . e . CFTR ) and cAMP-activated basolateral K+ channels ( i . e . KCNQ1/KCNE3 ) . The mechanism by which CFTR was inhibited did not involve changes in intracellular cAMP levels and activation of negative regulators of CFTR activity including AMP-activated protein kinase ( AMPK ) and protein phosphatase . In addition , diclofenac suppressed two other types of apical Cl− channels , namely , Ca2+-activated Cl− channels and inwardly rectifying Cl− channels , and Ca2+-activated basolateral K+ channels ( i . e . KCa3 . 1 ) without affecting Na+-K+ ATPase and Na+-K+-Cl− cotransporter activities . Of particular importance , diclofenac at 30 mg/kg , which is the human equivalent dose for treatment of pain and inflammation ( ∼2 mg/kg in human ) , exhibited anti-secretory efficacy in mouse closed-loop models of cholera induced by either CT or V . cholerae . This study provides a rational basis for further development of diclofenac and related compounds as anti-diarrheal therapy for cholera and other types of diarrheas resulting from Cl− transport-driven intestinal fluid secretion .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"gastroenterology",
"and",
"hepatology",
"medicine",
"and",
"health",
"sciences"
] |
2014
|
Inhibition of cAMP-Activated Intestinal Chloride Secretion by Diclofenac: Cellular Mechanism and Potential Application in Cholera
|
Challenges in maintaining high effectiveness of classic vector control in urban areas has renewed the interest in indoor residual spraying ( IRS ) as a promising approach for Aedes-borne disease prevention . While IRS has many benefits , application time and intrusive indoor applications make its scalability in urban areas difficult . Modifying IRS to account for Ae . aegypti resting behavior , named targeted IRS ( TIRS , spraying walls below 1 . 5 m and under furniture ) can reduce application time; however , an untested assumption is that modifications to IRS will not negatively impact entomological efficacy . We conducted a comparative experimental study evaluating the residual efficacy of classically-applied IRS ( as developed for malaria control ) compared to two TIRS application methods using a carbamate insecticide against a pyrethroid-resistant , field-derived Ae . aegypti strain . We performed our study within a novel experimental house setting ( n = 9 houses ) located in Merida ( Mexico ) , with similar layouts and standardized contents . Classic IRS application ( insecticide applied to full walls and under furniture ) was compared to: a ) TIRS: insecticide applied to walls below 1 . 5 m and under furniture , and b ) Resting Site TIRS ( RS-TIRS ) : insecticide applied only under furniture . Mosquito mortality was measured eight times post-application ( out to six months post-application ) by releasing 100 Ae . aegypti females /house and collecting live and dead individuals after 24 hrs exposure . Compared to Classic IRS , TIRS and RS-TIRS took less time to apply ( 31% and 82% reduction , respectively ) and used less insecticide ( 38% and 85% reduction , respectively ) . Mortality of pyrethroid-resistant Ae . aegypti did not significantly differ among the three IRS application methods up to two months post application , and did not significantly differ between Classic IRS and TIRS up to four months post application . These data illustrate that optimizing IRS to more efficiently target Ae . aegypti can both reduce application time and insecticide volume with no apparent reduction in entomological efficacy .
Vector control is the principal approach for managing Aedes aegypti and reducing transmission of Aedes-borne diseases ( ABD; e . g . , dengue , chikungunya , Zika ) . Implementation of vector control targeting ABDs has primarily been in response to reports of virus transmission , using methods such as truck-mounted ultra-low volume spraying ( ULV ) /thermal fogging , source reduction and larviciding [1 , 2] . Recent assessments of the public health value of these reactive interventions , triggered by the need to contain Zika transmission and prevent the devastating congenital malformations attributed to infection of pregnant woman , has highlighted the dearth of data supporting the role of vector control tactics in preventing ABDs [3–5] . Multiple factors challenge the efficacy and coverage of existing vector control tactics , including rapid urbanization leading to widespread Ae . aegypti distribution [6] , the occurrence of cryptic larval habitats [7 , 8] , the rapid rise of insecticide resistance [9] and the multiplicity of virus transmission locations generated by fine-scale human mobility patterns [10 , 11] . Given these challenges , management of Ae . aegypti requires highly effective , innovative approaches that can be implemented across epidemiological settings and within integrated vector management strategies [4] . Adult Ae . aegypti in urban settings typically rest indoors , where they feed frequently and almost exclusively on human blood [12–14] . This endophilic and anthropophilic behavior partially explains why outdoor space spraying ( e . g . , truck-mounted ultra-low volume spraying ) has very limited efficacy against Ae . aegypti and ABD transmission [15] . Vector control methods that deliver insecticides indoors are more promising because they can exert a direct impact on resting adult mosquitoes [5] . The principal methods of applying insecticides indoors are indoor space spraying ( ISS; application of insecticides with a droplet size of < 50 μm that kill adult vectors upon contact [5] ) and indoor residual spraying ( IRS; the application of aqueous formulations of insecticides with longer term residual efficacy on the walls and ceilings of houses that kill the adult vectors landing on these surfaces [16] ) . In terms of application and performance , ISS and IRS are very different . Indoor space spraying can be deployed rapidly , particularly during epidemics , because it can be applied quickly ( < 10 min ) , but ISS can require up to three application cycles to achieve maximum efficacy and has a short-lived insecticidal effect , as it only targets flying mosquitoes making contact with the transient insecticidal cloud . Indoor residual spraying can provide longer-term protection after a single application; however , application time can be lengthy if all furniture and belongings need to be removed from the spray area . Despite field evidence pointing to significant epidemiological impacts of IRS in preventing dengue [5 , 10 , 17] , and recent modeling work forecasting significant long-term reductions in disease burden after its implementation [18] , the perceived labor-intensive nature of IRS ( in comparison to ISS ) and issues of community acceptance [19] have hindered its adoption for urban vector control targeting Ae . aegypti . To overcome the time-consuming aspects of IRS and account for Ae . aegypti-specific behaviors , several modifications to the ‘classic’ IRS strategy intended to control vectors of malaria or Chagas disease ( i . e . , full house spraying , movement of furniture and treatment of all walls and ceiling ) have been proposed . In Cairns , Australia , IRS is performed targeting Ae . aegypti resting sites , and insecticide is applied to exposed low walls ( below 1 . 5 m ) , under furniture , inside closets and on any dark and moist surface where Ae . aegypti may be found resting [10] . This modified IRS was implemented in Cairns after the detection of local dengue transmission and dramatically reduced IRS application time and resulted in the successful containment of multiple outbreaks [10 , 17 , 20] . One of the untested assumptions of the modifications introduced to the classically-applied IRS is that there is no negative impact on entomological efficacy . Using a novel experimental house setting , we conducted a comparative study to evaluate the residual efficacy of classically-applied IRS against two novel IRS application methods using a non-pyrethroid insecticide against a locally-derived , pyrethroid-resistant strain of Ae . aegypti . For each IRS application method , the application time and volume of insecticide used were measured . Entomological impact over time was compared among the IRS application methods . We hypothesized that the two novel IRS application methods would provide similar levels of entomological efficacy as classically-applied IRS , but would be applied faster and use less insecticide . Furthermore , we hypothesized that the efficacy of a non-pyrethroid insecticide , specifically a carbamate insecticide ( bendiocarb ) , would be similar between the two novel IRS application methods and classically-applied IRS .
Within a replicated system of nine experimental houses , we tested the residual efficacy of three IRS application methods on free flying , field-derived Ae . aegypti . The experimental houses were located in Caucel , a neighborhood at the periphery of the subtropical city of Mérida , México , and were rented long-term by the Universidad Autónoma de Yucatán ( UADY ) after explaining the purpose and extent of the study to the owners . Mérida is the capital of the state of Yucatán , has a population of roughly one million and experiences a rainy season from May through October . Dengue is endemic and transmission occurs throughout the year , although peak transmission occurs between July and November and corresponds with the rainy season [18 , 21 , 22] . Average dengue sero-prevalence rate in the population is 73 . 6% [23] . Since 2016 , Chikungunya and Zika viruses also circulate within Merida , impacting the public health system and vector control operations [22] . Local management tactics for Ae . aegypti include ISS with either pyrethroids ( e . g . , deltamethrin ) or organophosphates ( e . g . , malathion ) and ULV with organophosphate insecticides ( e . g . , chlorpyrifos and malathion ) [24] . Resistance to pyrethroids ( both type I and type II ) occurs in local Ae . aegypti populations , however these populations are still presently susceptible to carbamates [24–26] . Distance between experimental houses ranged from 0 . 3 to 2 km . The houses were similar in floor plan and design; all were concrete , single-story and had one or two living rooms , two bedrooms , one bathroom and one kitchen ( Fig 1 ) . Houses were on average 57 . 8 ± 2 . 8 m2 ( mean ± SEM ) and uniformly had walls 2 . 5 m in height . Construction characteristics were that of subsidized middle to low-income housing in Mérida , typical of areas with high ABD transmission [22] . To prevent any mosquitoes used in the experiments from escaping from the houses , all windows and doors were screened on both the outside and inside of each house before the study began . Additionally , a double screened-door vestibule was built into the main entrance of each house to allow personnel to enter and exit while preventing mosquitoes from escaping ( Fig 1 ) . Sinks , drains and toilets were also sealed with window screening . Existing furniture within houses was removed , and where furniture could not be removed ( e . g . , built-in kitchen or closet cabinets ) it was sealed with window screening . Houses were then refurnished with standardized furniture and clothing that represented typical elements found within houses ( Fig 1 ) . Furniture within in the living room ( or split between two living rooms ) included two black plastic tables and four plastic chairs . Within each bedroom was a bed made out of PVC tubing and black cloth , a black plastic night stand and six articles of clothing ( 3 black and 3 white ) hung within the closet . Additionally , four plastic buckets ( 1 L ) were half filled with water and a dark cloth and placed throughout each house to provide moisture into the environment and reduce mosquito mortality due to desiccation . Ant baits ( Antex Gel , Allister de México ) were placed next to each door or any other location where ants were observed to enter the experimental houses . The house layout was carefully designed to mirror elements and surface materials found in regular homes , but making sure that they were standardized in a way that allowed replication and comparability between replicates . Insecticide was applied within experimental houses on 3 July 2017 . A manual compression sprayer ( Hudson 93793 X-Pert ) fitted with flat nozzles and a flow control valve ( model CFV . R11/16SYV . ST , CFValue , Gate LLC ) was used to spray houses at a flow rate of 550 mL / min . Bendiocarb ( Ficam 80% WP , Bayer CropScience; 125 g sachet / 7 . 5 L water ) , a carbamate insecticide , was applied at a dosage of 0 . 375 g active ingredient / m2 as recommended by the WHO [16] . Bendiocarb was used because of the known susceptibility of local Ae . aegypti populations that were resistant to synthetic pyrethroids [24] . Additionally , a previous RCT in Mérida found high community acceptance of bendiocarb , with no reported adverse reactions , when it had been applied within homes [24] . The same individual applied insecticide for each of the nine experimental houses . Houses were randomly assigned to one of three different IRS application methods: 1 ) Classic IRS- insecticide applied to walls and under furniture ( n = 3 houses ) , 2 ) Targeted IRS ( TIRS ) - insecticide applied to walls below 1 . 5 m and under furniture ( n = 3 houses ) or 3 ) Resting Site TIRS ( RS-TIRS ) - insecticide only applied under furniture ( n = 3 houses ) . Furniture was not removed from experimental houses during the insecticide application and insecticide was not applied to clothing or the plastic buckets with water . Duration of application was measured for each house , starting when the applicator entered the house and ending when the applicator exited . To estimate the volume of insecticide applied within each house , the insecticide within the sprayer was measured using a graduated cylinder before and after each application . To test the residual efficacy of each IRS application method , a total of 100 Ae . aegypti females were released within each experimental house . The strain used ( San Lorenzo strain ) was locally derived , had a high level of resistance to pyrethroids and full susceptibility to carbamates [24 , 26] . The San Lorenzo strain was reared and maintained at the insectaries of the Unidad Colaborativa para Bioensayos Entomológicos , UADY , Mérida , México . Mosquitoes released into houses were three to seven days old from the F4 generation , before release had only been provided sugar solution and were non-bloodfed . Post-insecticide application , mosquitoes were released into the experimental houses eight times over a six month period; 1 ) +1 day , 2 ) +14 days , 3 ) +1 month , 4 ) +2 months , 5 ) +3 months , 6 ) +4 months , 7 ) +5 months and 8 ) +6 months . To facilitate mosquito recovery , all experimental houses were vacuumed and swept clean of any debris on the floor one day prior to mosquito release . After 24 hrs exposure , a team of four field technicians entered each house and searched for live mosquitoes using a Prokopack aspirator [27] and searched by hand for dead mosquitoes . Searching for Ae . aegypti ceased when either 100 mosquitoes were collected or > 20 minutes elapsed after the last mosquito was collected ( circa 30–40 min / house ) . Natural mortality within experimental houses was measured by placing three unsprayed control cups ( 250 mL ) within each house , with each cup containing 10 San Lorenzo strain females . Control cups were placed within experimental houses simultaneously during the main release of mosquitoes during the +4 , +5 and +6 months post-application evaluations . After searching for released Ae . aegypti ceased , the number of live and dead Ae . aegypti within control cups were counted . For each sampling period , mortality was calculated per house by dividing the number of dead individuals by the number of individuals released . Missing individuals were assumed to be dead . Mortality was compared between IRS application methods using mixed-model analysis of variance ( ANOVA ) in R 3 . 2 statistical software ( https://www . r-project . org/ ) . Sampling date , IRS application method , and their interaction were classified as fixed effects and experimental house was classified as a random effect . When significant differences were detected , pairwise comparisons were made using LSMEAN package and alpha levels were adjusted for multiple comparisons using the Tukey correction . Additionally , regression analysis was used to assess the relationship between application time and volume of insecticide applied among the three IRS application methods . This was an experimental study , and because mosquitoes were released into uninhabited houses rented on long-term contracts , we did not require an Institutional Review Board .
Compared to Classic IRS , TIRS reduced application time on average by 5 . 8 min / house ( 31 . 3% reduction ) , whereas RS-TIRS reduced application time on average by 15 . 2 min / house ( 82 . 0% reduction ) ( Table 1 ) . Similarly , compared to Classic IRS , TIRS used on average 2 . 02 L / house less insecticide ( 37 . 9% reduction ) , while RS-TIRS saved on average 4 . 53 L / house ( 84 . 8% reduction ) ( Table 1 ) . Compared to TIRS , RS-TIRS reduced both application time by 9 . 40 min / house ( 73 . 8% reduction ) and insecticide volume by 2 . 50 L / house ( 75 . 5% reduction ) ( Table 1 ) . Reductions in both application time and insecticide volume were significantly linear ( F = 140 . 1; df = 1 , 7; P < 0 . 0001 ) , indicating consistent insecticide application among IRS application methods . A total of 7 , 200 Ae . aegypti females were released within the experimental houses throughout the trial . Mosquito recovery averaged 96 . 9 ± 0 . 82% ( Mean ± SEM; n = 72 releases ) . Based on pilot data , we attribute high recovery to pre-cleaning the floors of experimental houses the day before mosquitoes were released and to effective management of ants using baits . Mortality within control cups average 4 . 4 ± 1 . 3% , 1 . 5 ± 0 . 7% and 5 . 0 ± 1 . 7% ( Mean ± SEM ) for evaluations from +4 , +5 and +6 months post-application , respectively , indicating high Ae . aegypti survival within the experimental house environments . There was a significant interaction between IRS application method and sampling time post application ( F = 6 . 3; df = 14 , 42; P < 0 . 0001 ) ( Fig 2 ) . Almost complete mortality of all released mosquitoes was observed up to two months post-application ( ranging from 97 . 3 to 100% ) ; there were no significant differences in mortality among the three IRS treatments within the first 4 sampling periods . At three months post-application , mortality of Ae . aegypti dropped significantly in houses treated with RS-TIRS ( from 97 . 3% at +2 months to 48 . 1% at +3 months ) compared to Classic IRS and TIRS houses , where mortality remained high ( 99 . 7% and 94 . 5% , respectively ) . At four months post-application , mortality of Ae . aegypti from Classic IRS and TIRS treated houses dropped to 79 . 8% and 74 . 2% , respectively , but were both significantly greater compared to mortality of Ae . aegypti from RS-TIRS houses , which dropped to 19 . 7% . Mortality in experimental houses with Classic IRS remained high five months post-application ( 78 . 4% ) and was significantly greater compared to both TIRS ( 25 . 5% ) and RS-TIRS ( 10 . 8% ) , which did not differ from each other . Efficacy of all three treatments was greatly reduced six months post-application ( one month beyond the expected residual duration of bendiocarb ) . Mortality in Classic IRS treated houses was reduced to 39 . 2% , yet was significantly greater compared to RS-TIRS ( 10 . 4% ) , although neither treatment differed significantly from TIRS ( 16 . 6% ) ( Fig 2 ) .
We compared the residual efficacy of Classic IRS against two novel IRS application methods , TIRS and RS-TIRS , in experimental houses , and hypothesized that the two novel IRS application methods would be as efficacious as Classic IRS . Furthermore , we hypothesized that the efficacy of a non-pyrethroid insecticide , bendiocarb , would be similar among the two novel IRS application methods and Classic IRS . Although both TIRS and RS-TIRS took less time to apply and used less insecticide compared to Classis IRS ( Table 1 ) , these data support our hypotheses , as pyrethroid-resistant Ae . aegypti mortality did not differ among the three IRS application methods up to two months post-application and did not differ between Classic IRS and TIRS up to four months post-application ( Fig 2 ) . Using bioassays within experimental houses that closely simulate typical living conditions , this study provides important information that can help improve the mode of IRS application and cost-effectiveness within the urban context of ABD transmission . Improvements in IRS efficiency and application are key for increasing scalability and adoption of this management tactic [28] . Recent and rapid scaling-up of IRS for malaria control illustrate the potential public health benefits of this approach [29] , but also point to the difficulties of reaching and sustaining high coverage levels due to IRS’s labor-intensive nature [30] . If IRS were to be widely adopted for urban Ae . aegypti management , lessons from IRS scale-up for malaria vector control should be taken into consideration to better frame the operational conditions and approaches for intervention delivery . Field observational studies from Central and South America have found that Ae . aegypti primarily rest indoors and below 1 . 5 m , particularly on or near dark places such as behind or under furniture , under beds , on clothing and on lower parts of walls [13 , 27 , 31] . This low-resting behavior has also been observed in experimental hut studies using an Ae . aegypti strain from Thailand [32] . Modifying IRS to account for key Ae . aegypti resting behaviors resulted in important reductions in application time and insecticide volume ( Table 1 ) without sacrificing entomological efficacy for two to four months post application ( Fig 2 ) . The fact that we detected high mortality with no statistical difference between Classic IRS and TIRS methods show that Ae . aegypti are not avoiding treated locations by shifting resting behaviors above 1 . 5 m . Additionally , RS-TIRS was applied only to common resting sites ( beds , chairs and other furniture ) and resulted in to up to 2 months of full protection , providing further evidence of the remarkable preference of Ae . aegypti for specific resting locations . Duration of protection differed between TIRS and RS-TIRS applications . Although RS-TIRS could be completed on average in 3 . 3 min / house ( Table 1 ) , the protection provided ( using > 80% mortality as a threshold ) by this approach lasted two months , or half the duration of Classic IRS or TIRS ( Fig 2 ) . One of the challenges of RS-TIRS when applied in real households ( which would likely be more cluttered and full of personal items than our experimental houses ) is that it may entail the treatment of personal belongings that are preferentially used by Ae . aegypti as resting sites ( e . g . , suitcases , clothes , etc . ) . Applying insecticide to personal belongings could potentially lead to community disapproval of the methodology , as well as potentially result in unanticipated exposure to insecticides [19] . As such , while there are significant reductions in application time and insecticide volume , performing RS-TIRS may be more challenging than performing TIRS . Given that TIRS provides longer-term protection ( up to 4 months ) compared to RS-TIRS , we see the former as a methodology highly suitable for implementation within the context of urban Ae . aegypti management . A randomized controlled trial evaluating the entomological impact of Classic IRS using bendiocarb against pyrethroid-resistant populations of Ae . aegypti in Mérida , México , demonstrated a 65–75% reduction in adult Ae . aegypti abundance in treatment clusters , compared to controls , up to three months post-application [24] . Furthermore , the application time of Classic IRS from this trial averaged approximately 30 min / house [24] . Our experimental study demonstrated that an application of TIRS required roughly 12 min to complete but resulted in a 4-month protection of treated houses . The residual effects observed were driven by the insecticide used ( bendiocarb residuality is expected to last between 3 and 5 months ) , and its interaction with treated substrates ( in our case , painted walls , cloth , wood and plastic ) . Given the recent development of new residual insecticide formulations for malaria , which extend residual duration out to 6–8 months and are effective against pyrethroid-resistant mosquitoes [33 , 34] , there is potential for extending residual power of TIRS beyond the 4-month mark . Despite the higher cost of novel insecticide formulations , applying novel insecticides via TIRS would not only reduce application time but also potentially increase cost-effectiveness . Furthermore , extending residual duration can provide a longer window of protection and shift IRS application from reactive ( in response to reported clinical cases , as in [10] ) to pro-active ( performed prior to the transmission season [18] ) . A recent analysis of historical dengue , chikungunya and Zika cases geocoded to the household level found a significant level of spatial overlap of the three pathogens within specific geographic units that accumulated more than half of all cases [22] . The pro-active ( pre-season ) deployment of high-quality interventions such as TIRS within hot-spot areas could offer additional protection to areas that consistently report high rates of ABD transmission [22 , 35] . An insecticide with residual duration that lasts more than 5 months could protect a household for an entire transmission season ( which lasts 5 to 6 months ) using a single TIRS application . Additionally , using insecticides pro-actively should be coupled with insecticide-resistance monitoring and insecticides used for TIRS changed when resistance is first detected . Previous studies have demonstrated that fitness costs associated with pyrethroid resistance in Aedes aegypti do exist and that susceptibility can be regained in the absence of selection [36] . While the efficacy of such pro-active TIRS implementation in preventing ABD will require further evaluations with proper epidemiologic endpoints [37] , the findings presented here provide clear evidence for how IRS applications could be optimized for urban Aedes management . However , larger field studies with epidemiologic endpoints are needed to further assess the efficacy of these modified TIRS techniques .
|
Vector control is the primary strategy for managing Aedes aegypti and reducing transmission of Aedes-borne diseases; however , the indoor resting behavior of Ae . aegypti and the evolution of insecticide resistance reduces the effectiveness of many vector control tactics . Indoor residual spraying ( IRS ) is effective against Ae . aegypti , but lengthy application time makes IRS difficult to scale within urban environments . We compared the application and entomological efficacy of Classic IRS against two novel Aedes-targeting IRS application methods ( Targeted IRS [TIRS]- insecticide applied to walls below 1 . 5 m and under furniture and Resting Site TIRS [RS-TIRS]- insecticide applied only under furniture ) within experimental houses using a carbamate insecticide . Both TIRS and RS-TIRS took less time to apply and used less insecticide compared to Classic IRS . Mortality of pyrethroid-resistant Ae . aegypti did not differ among treatments out to two months post-application , and there was no difference in mortality between Classic IRS and TIRS out to four months post-application . These data provide evidence that IRS application methods can be improved to take less time and insecticide yet not lose entomological efficacy , making TIRS more scalable within urban environments . However , larger field studies with epidemiologic endpoints are needed to further assess the efficacy of these modified TIRS techniques .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"chemical",
"compounds",
"carbamates",
"tropical",
"diseases",
"parasitic",
"diseases",
"animals",
"organic",
"compounds",
"infectious",
"disease",
"control",
"insect",
"vectors",
"zoology",
"public",
"and",
"occupational",
"health",
"agrochemicals",
"infectious",
"diseases",
"aedes",
"aegypti",
"chemistry",
"disease",
"vectors",
"insects",
"agriculture",
"arthropoda",
"insecticides",
"mosquitoes",
"eukaryota",
"organic",
"chemistry",
"entomology",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"physical",
"sciences",
"malaria",
"organisms"
] |
2019
|
Efficacy of novel indoor residual spraying methods targeting pyrethroid-resistant Aedes aegypti within experimental houses
|
Extensive cell-to-cell variation exists even among putatively identical cells , and there is great interest in understanding how the properties of transcription relate to this heterogeneity . Differential expression from the two gene copies in diploid cells could potentially contribute , yet our ability to measure from which gene copy individual RNAs originated remains limited , particularly in the context of tissues . Here , we demonstrate quantitative , single molecule allele-specific RNA FISH adapted for use on tissue sections , allowing us to determine the chromosome of origin of individual RNA molecules in formaldehyde-fixed tissues . We used this method to visualize the allele-specific expression of Xist and multiple autosomal genes in mouse kidney . By combining these data with mathematical modeling , we evaluated models for allele-specific heterogeneity , in particular demonstrating that apparent expression from only one of the alleles in single cells can arise as a consequence of low-level mRNA abundance and transcriptional bursting .
Gene expression in genetically identical individual cells often deviates from that of the cell population average [1] , which in mammals can impact cell fate and development [2–5] , response to environmental stimuli [6–9] and disease [10–13] . Over the past few years , it has emerged that at least some of this variability arises due to random fluctuations in the biochemical processes that underlie transcription and translation . In the case of transcription , a primary source of fluctuations is so-called transcriptional bursting , where a gene alternates between an active state , during which RNA is produced , and an inactive state , where no RNA is transcribed . Because both the time of onset of these bursts and the amount of RNA produced in a single burst are random , this process can lead to cell-to-cell variability [14–16] . An additional nuance to the effects of bursting on cellular variability is that diploid mammalian cells carry two sets of chromosomes ( one from each parent ) , which means that they also have two copies of each individual gene . It is typically assumed that for most genes both copies , called alleles , are capable of being expressed , thus providing protection through redundancy if one of them is mutated [17 , 18] . Recent studies , however , which made use of crosses between distantly related mouse strains and high-throughput sequencing , uncovered that there can be extensive differences in the relative expression levels of the two alleles [19–23] . Additional work then showed that even if transcripts from both alleles are detected at the population level , there may be substantial variation in the degree of allelic imbalance in single cells . For example , while for most genes individual cells express RNA from both alleles , for other genes the population can be a mixture of cells expressing RNA from only one or the other allele . This latter expression pattern has been termed random monoallelic expression , and certainly , genes with such an expression profile exist: most X-linked genes are expressed only from one X chromosome due to random X-chromosome inactivation [24–26] , and a similar pattern has also been shown for some autosomal genes , such as olfactory receptors or antigen receptors [27–29] . Understanding random monoallelic expression is of particular interest given that quantitative cell-to-cell differences or spatial heterogeneity in allele-specific gene expression have the potential to modify phenotypic outcome if the two alleles harbor different functional variants , as has been described for both X-linked ( eg . [30–34] ) and autosomal traits [35 , 36] . Beyond these prototypic cases , it has been proposed that many more autosomal genes may be subject to random monoallelic expression [37–43] , but some key properties of this extended class sets them apart from the more established examples . Similarly to the initial group , these genes were classified as displaying random monoallelic expression because cells with only transcripts from one or the other gene copy were observed , with both types of cells present in the same experiment . In addition , some of these genes maintain their monoallelic expression status over multiple passages in clonal cell lines [40 , 43] , so a specific , heritable mechanism could limit transcription to only one allele , as is the case for X chromosome inactivation . However , thus far such a mechanism remains elusive [40 , 41] . Moreover , unlike previously established cases , many genes in this extended set are not expressed exclusively monoallelically , and typically a subset of cells or clones with transcripts from both alleles can also be detected [37–41] . This suggests that if a specific mechanism does exist to regulate monoallelic expression , it is limited to only a subset of cells . To resolve this question of mechanism , Reinius et al proposed an elegant scenario ( so-called dynamic random monoallelic expression ) , whereby the many genes with random monoallelic expression may arise not by differential cell- and allele-specific regulation , but instead monoallelic expression may arise by chance [43 , 44] . In this scenario infrequent transcriptional bursting would lead to cells that contain only RNA from one of the two gene copies . This observed monoallelic expression of mRNA would be temporary and the allelic state of a cell could change over time . The authors confirmed this model in clonal cell lines [43] , but whether the same is true in tissues is still an open question . Different groups have deployed single-cell transcriptomics to determine the degree of cell-to-cell allelic imbalance [42 , 43 , 45 , 46] , but technical limitations inherent to low abundance RNA quantification , as well as parameter choice can impact the interpretation of allele-specific sequencing data [47 , 48] . Thus , it has been hypothesised that the level of monoallelic expression , especially at single-cell level , could be an overestimate [45 , 49] . This absence of precise quantitative data has made it difficult to definitively answer if random monoallelic expression observed in vivo requires a dedicated mechanism or if it could arise as a consequence of transcriptional bursting . In this study , we adapted a previously described single-molecule RNA fluorescent in situ hybridization technique that is sensitive to single-nucleotide differences between RNAs for the analysis of transcripts in snap-frozen , cryosectioned tissues from different mouse strains and their hybrids [50–52] . This allowed us to determine the allelic origin of individual RNAs in single cells , while preserving both their spatial context and their in vivo expression levels . We used this method to measure allele-specific expression of multiple autosomal genes and of Xist , a gene for which it is well-documented that individual cells randomly and exclusively transcribe either the maternal or the paternal copy . Quantitative analysis of the data enabled us to answer whether the autosomal genes we investigated were expressed from one or both gene copies in single cells in tissue . While we observed monoallelic expression in some cells , mathematical modeling showed that this pattern was compatible with random transcriptional events ( including transcriptional bursting ) from the two alleles producing low levels of RNA , rather than an explicit mechanism governing random monoallelic expression .
Our goal in tissues was to quantitatively measure the amount of cell-to-cell variability in transcript abundance from either the maternal or paternal allele of a gene to determine the degree of imbalance between transcripts arising from the two alleles . To make these measurements , we modified a protocol previously developed by our group [50] that enables the detection of single nucleotide variants ( SNVs ) on individual RNA molecules in situ in cultured cells ( Methods , Fig 1A ) . We applied this method to mouse kidney tissues from C57BL/6J ( BL6 ) and JF1/Ms ( JF1 ) mice and their F1 progeny , since these two strains harbor a large number of genomic differences , allowing us to target most genes with multiple SNV-specific probes . To demonstrate our ability to detect expression from the BL6 vs . JF1 allele using our technique in tissue , we first examined the expression of Xist , a prototypical example of random monoallelic expression [25 , 53] . In female cells Xist is transcribed exclusively from the ( randomly chosen ) inactivated X chromosome , is expressed at high levels , and contains a large number of SNVs between the two substrains , making it an ideal test case for our method . We collected kidney tissues from mice at day 4 of postnatal development , snap-froze them in liquid nitrogen or on aluminium blocks in dry ice and cryosectioned them at 5 μm thickness . We then applied our in situ hybridization protocol using allele-specific probes . As expected , we observed the appropriate sex-specific expression pattern: no fluorescent signal in male tissues ( S1 Fig ) , whereas in female tissues , our guide probes clearly labelled nuclear Xist foci , which colocalized with signal from the strain-specific probes . For SNV-targeting probes , controls in homozygous tissues confirmed that only the appropriate probes bound , with little binding from the probes targeting the other strain ( S1 Fig ) . Whereas in heterozygous samples the two strain-specific probes each labelled a subset of the nuclei in the anticipated mutually exclusive pattern ( Fig 1B ) . To further analyse the data , we developed a pipeline to computationally identify Xist RNA foci , and automatically classify them as BL6 or JF1 within an entire scanned kidney section ( Methods , Fig 1B ) . This algorithm identified tens of thousands of Xist foci per section ( mean 22k , min 8 . 2k , max 30k ) , and—in agreement with our manual inspection of the data—predominantly identified Xist foci of the correct identity in the homozygous samples ( S1 Table ) , while the overall population ratio of BL6:JF1 foci ranged from 45:55 to 65:35 in heterozygous samples ( Fig 1C , S2A Fig ) . Having verified that we could correctly measure the allelic origin of clusters of Xist RNA accumulated on the X chromosome in mouse tissues , we next ascertained whether the method would also work for monodisperse spots corresponding to single RNA molecules , which is how most mRNAs appear in the cell . We considered this challenging , because single , punctate RNA spots would be both smaller and considerably dimmer compared to Xist RNA , which accumulates multiple copies on the inactivated X chromosome . Thus , to test our protocol for use on single RNA molecules , we designed probes for 8 autosomal genes that contained at least one polymorphism in BL6 versus either JF1 or the C7 strain ( which carries both copies of chromosome 7 from the M . musculus castaneus strain in a BL6 background ) . These genes were selected to represent genes with ( Aebp1 , Churc1 , Lyplal1 ) and without ( Aqp11 , Mpp5 , Podxl , Prcp , Stard5 ) putative random monoallelic expression [38–41] and with different expression levels , patterns and chromosomal locations . Some of the chosen genes were also linked to specific kidney disease phenotypes ( Aqp11 [54] , Mpp5 [55 , 56] , Prcp [57] ) ( Methods , S4 Table ) . As expected , these genes were typically expressed at much lower levels than Xist and punctate individual mRNA spots could not be as readily observed in low magnification scans . We therefore combined whole tissue scans in a single plane at low magnification with random sampling of the tissue at higher magnification , where we imaged the entirety of the section . This approach allowed us to identify individual mRNA spots within the context of the whole tissue section in the 60x scan , while the additional data collected from the 100x z-stacks facilitated our ability to precisely determine colocalization between the guide probes and the strain-specific probes ( Fig 1D ) . Colocalization between the strain-specific signal and the mRNA probes allowed us to determine from which allele a given mRNA originated . Accordingly , this could be used as a key readout for SNV-specific single molecule RNA FISH , and we characterized the quality of the experiments using colocalization rate ( i . e . what percentage of the guide spots colocalized with allele-specific spots ) . When we assayed the overall colocalization rates for these autosomal genes in kidney sections , we found that 4 out of 8 genes had mean colocalization rates >50% ( S4 Fig , S4 Table ) , which is comparable to colocalization rates previously observed in cultured cells [50] , showing that we were able to perform quantitative SNV-specific single molecule RNA FISH directly in tissue . We also observed an apparent trend between colocalization rate and the number of SNV probes , where genes with fewer SNV probes had lower colocalization rates than those with more SNV probes . We tested a series of parameters that could affect probe binding ( base composition , GC content , probe secondary structure and folding energy ) , but found no parameter that differentiated between the probes with high and low colocalization rates ( S4 Fig ) . However , it should be noted that SNV probes were only tested as full sets ( i . e . all SNV probes for a given gene were tested together ) , and we therefore do not know the binding behaviour of individual probes . Collectively , these results showed that we could directly visualise and assign strain-specific identities to both focally localised RNA ( Xist ) and single molecule RNA spots in the context of whole tissue sections . This motivated us to ask if we could directly quantify cell-to-cell heterogeneity in the allelic origin of RNAs in tissue . To determine how the chromosomal origin of RNAs contributes to cell-to-cell heterogeneity in tissue , we focused on two different questions . For Xist , we investigated the spatial clustering of cells based on their X inactivation choice , i . e . to what extent cells expressing Xist from either the JF1 vs BL6 X chromosome intermix . For the autosomal genes , we quantified their allelic imbalance in single cells , i . e . whether individual cells expressed RNAs from the two chromosomes at different ratios . In the case of Xist , we measured spatial clustering of allele-specific expression because each cell is randomly and fully committed to expressing RNA from only the BL6 or the JF1 chromosome . Thus , allelic imbalance in tissue is not due to quantitative expression differences between the two alleles in single cells . Instead , it can arise either as a consequence of overall skewing of X chromosome inactivation rates or due to uneven spatial distribution of cells with a given inactivated X chromosome . Such spatial partitioning can arise through the local expansion of cells in which X chromosome inactivation “choice” has already been fixed , resulting in extended patches of cells carrying the same inactive X chromosome [58–61] . Because we had observed fairly balanced expression from the BL6 and JF1 chromosome in our initial analysis of heterozygous samples ( see previous section , Fig 1C ) , we next determined whether cells expressing Xist from either the BL6 or JF1 allele segregated spatially . Although visual inspection of the sections revealed no extended regions where cells expressed Xist foci with the same strain-specific identity , computational modelling could potentially reveal a more precise view of spatial patterning . We therefore developed a metric that characterized the distribution of cells expressing BL6 Xist RNA ( Methods and S2 Fig ) and then compared this to either completely randomized BL6 and JF1 assignments or randomizations where we introduced different sized clusters of cells . We found that in all tissues BL6 cells were less evenly distributed than the random assignments ( S2C Fig ) , suggesting that cells cluster together more than expected in a completely random scenario . Our subsequent comparison with the clustered assignments further supported and refined this interpretation: it showed that our data was most similar to simulations with smaller cluster sizes . The closest matching seed size was different , depending on what scale we assessed spatial partitioning , but centered around a cluster size of 2–4 cells ( mean cluster size: 3 . 2 , standard deviation 0 . 7 ) ( S2 Table , Fig 2A and S2D Fig ) . Thus , cells expressing Xist from the same chromosome clustered together in small patches in tissue sections , resulting in a spatially fairly mixed population of cells and showed no evidence of extended patches with the same allele-specific expression . For the autosomal genes we wanted to know how much the allelic imbalance differed between cells . Visual inspection of our data indicated a range of allelic ratios , including BL6 monoallelic , JF1 monoallelic , as well as biallelic mRNA expression . To quantify this , we focused on three autosomal genes ( Aebp1 , Lyplal1 , Mpp5 ) where >50% of guide spots colocalized with signal from SNV probes ( we excluded Podxl , because the high expression levels of Podxl mRNA in podocytes precluded separating individual RNA spots ( S7 Fig ) ) . We selected this cutoff based on testing in cultured mouse fibroblasts , where we had found that in cells with >50% colocalization rate , our method consistently identified the correct allele ( Methods , S5 Fig ) . First , we considered that the observed chromosomal origin ( BL6 vs . JF1 ) could either be due to true biological variability or to technical error ( as seen when we detected RNA from the “incorrect” strain in homozygous tissues ) . To distinguish these , we determined the BL6 and JF1 signal for these genes in kidney tissue from both BL6 and JF1 homozygous mice , as well as in tissue from reciprocal heterozygous crosses . For all three genes , we counted only a few mRNAs in the majority of cells ( mean number of RNA spot counts per cell: 3 . 5 for Aebp1 , 3 . 2 for Lyplal1 and 2 . 6 for Mpp5 ) . These RNA counts measured by RNA FISH were higher than those observed via single-cell RNA sequencing of the kidney [62] . The mean RNA count by single-cell sequencing ranged between 1 . 0–1 . 78 UMI/cell for Aebp1 , 1 . 0–1 . 2 UMI/cell for Lyplal and 1 . 0–1 . 63 UMI/cell for Mpp5 , depending on the cell type , considering only those cells where transcripts for these genes were detected ( S3B Fig ) . Given the higher detection rate of our method , it may be particularly useful for assessing allele-specific expression for these lowly expressed genes . We predominantly detected the correct allele in the homozygous kidney samples , both in bulk and at the single cell level ( Fig 2B , 2D and 2F ) . In heterozygous samples we observed a more balanced presence of both BL6 and JF1 mRNAs , with the reciprocal crosses showing similar results ( heterozygous data in Fig 2B , 2D , 2F and 2G , and S5 Table ) . These results indicated that our technical error ( false positive rate ) was less than the biological variability and that we could use our method to measure quantitative single-cell differences . Moreover , when we compared the BL6 allelic ratios in homozygous and heterozygous cells with >2 mRNA , we found some heterozygous cells with allelic ratios similar to those of the homozygous samples , but also a subset of cells with an allelic ratio that was intermediate to that of homozygous cells ( Fig 2G ) . We obtained similar results when we switched the fluorescent dyes conjugated to the SNV detection probes used to label the BL6 and JF1 mRNA ( Methods , S6 Fig ) . The observation that single cells had transcripts from both the BL6 and JF1 allele were particularly intriguing for Aebp1 and Lyplal1 , because these two genes had been previously identified as genes with putative random monoallelic expression in other tissues [38–41] . Still , given that we did in fact observe cells with either BL6 or JF1 monoallelic mRNA expression in heterozygous tissue , akin to the random monoallelic expression pattern , we wanted to know which transcriptional models our quantitative single-cell allelic imbalance data was compatible with , in order to explain how this expression pattern could arise . Our results showing that individual cells could have mRNA from either one or both alleles motivated us to assess whether existing models of transcription were sufficient to explain the observed cell-to-cell variability in allelic imbalance . To evaluate these models , we first used the correlation coefficient between the BL6 and JF1 mRNA expression in our population as a simple metric that captures the joint behaviour of the two alleles: a correlation coefficient of zero represents no coordinated expression between the two alleles , positive values indicate coordinated expression , while negative correlation could be indicative of anti-correlation or repulsion between the two alleles . The correlation between BL6 and JF1 mRNA counts were 0 . 41 for Aebp1 , 0 . 29 for Lyplal1 and 0 . 35 for Mpp5 suggesting somewhat coordinated expression . To better understand how these values compare to the expectations from different models of transcription , we initially considered two extreme cases: an “all-or-none” scenario in which every cell has transcripts exclusively from one or the other gene copy , and a “coin flip” scenario in which the allelic origin of every individual transcript is essentially indistinguishable from random coin flipping ( Fig 3A ) . The former scenario could suggest the existence of regulatory mechanisms that limit transcription to only one gene copy per cell ( an extreme form of random monoallelic expression , as is the case with Xist ) , whereas the latter corresponds to a null model with no distinct allele-specific transcriptional regulation . We used computational modeling to simulate these two scenarios and to discriminate between them . We first checked if our data were similar to those expected in the “all-or-none” scenario , in which the transcripts in each individual cell were either solely from one or the other gene copy . Looking in heterozygous cells , it seemed qualitatively apparent that ( as noted ) many cells have mRNAs from both gene copies , which was seemingly incompatible with this scenario . However , it was still formally possible that cells in reality only had transcripts exclusively from one of the gene copies and that the apparent transcripts from the other copy were technical artifacts due to false detection events . We used homozygous tissue to measure the rate at which these false detection events occur , and thereby estimated the expected false detection rate in heterozygous cells . We then computationally simulated hypothetical “all-or-nothing” heterozygous cell populations taking into account these false detection rates , and found that the RNA counts and distributions observed in such a simulated all-or-none population were still inconsistent with our data ( compare Fig 3B with heterozygous data in Fig 2B , 2D and 2F ) . Next , we calculated the correlation of the BL6/JF1 counts for the “all-or-nothing” simulations , which showed much lower correlation values than we had observed in the real data ( Fig 3C ) . Thus , the two alleles in our simulated data were uncorrelated or anti-correlated , as would be expected in the case of random monoallelic expression , and were unlike the positively correlated BL6 and JF1 mRNA we had observed our measurements . In addition , the probability of observing the strain-specific mRNA counts we measured versus simulated cell populations was also different ( S8 Fig ) . Collectively , these results indicate that in the kidney , none of the three genes we interrogated displayed “all-or-none” expression . In our alternative scenario , it is possible that the two copies of the gene transcribe RNA independently and each random transcription event produces just a single RNA , thus leading to the “coin-flipping” model in which most cells would have RNAs from both alleles in them , but with some statistical noise about this population average ( Fig 3A ) . We modeled the outcome of such a scenario and found that the real versus simulated single-cell RNA distributions looked very similar for all three genes ( compare Fig 3D with heterozygous data in Fig 2B , 2D and 2F ) . We also saw that the correlation between the BL6 and JF1 allele in our modeled data was similar to our measured correlation in the case of Lyplal1 and Mpp5 ( Fig 3E ) . Statistical analysis showed that when we treated the strain-specific RNA counts per cell as a series of independent coin-flips , the probability of the observed distributions for both Lyplal1 and Mpp5 fell within the distribution of likelihoods from our simulated model ( S8 Fig ) . Together , these results demonstrated that the allelic imbalance observed for Lyplal1 and Mpp5 was compatible with a simple coin-flipping null model of transcription from the two alleles . In the case of Mpp5 , where we had found false detection rates to differ most depending on which fluorescent dyes were coupled to the allele-specific probes , we collected data from heterozygous tissues swapping the dyes used on the BL6 and the JF1 probes and repeated our analysis and simulations . We obtained similar results , showing that a model for all-or-none expression did not recapitulate the allelic counts observed in our data , whereas the coin-flip model did ( S6B and S6C Fig ) . We also tested whether the detection efficiency of ~50% influenced our conclusions about the “all-or-none” vs . “coin flip” scenarios by repeating our simulations assuming 100% detection efficiency and then randomly downsampling to the measured detection rate . The results were essentially indistinguishable from those obtained without downsampling , further verifying that our results were not due to the technical noise introduced by the low detection efficiency ( S5C and S5D Fig ) . All analyses of our simulations therefore supported the conclusion that allelic imbalances for Lyplal1 and Mpp5 were compatible with a simple coin-flipping null model of transcription . Aebp1 , however , showed higher levels of imbalance per cell than could be explained by this model . This was not clearly visible on a scatterplot of the simulated RNA counts per cell ( Fig 3D ) , but the measured BL6 and JF1 counts were less correlated than those in the simulated dataset ( Fig 3E ) . Yet this gene did not exhibit the all-or-none behavior either . We therefore considered a third , intermediate scenario motivated by the phenomenon of transcriptional bursting [14–16] . Transcriptional bursts refer to the fact that most mammalian genes are transcribed in short pulses during which multiple transcripts are synthesized , interspersed between periods during which the gene remains inactive . When there are two copies of a gene , each bursting independently [63] , the expected result would be that some cells may have more transcripts from one of the copies than expected by the coin flipping model above; bursting would be akin to getting several heads or tails in a row every time one flipped a coin . To test whether transcriptional bursting could explain the observed data , we first wanted to confirm that Aebp1 was indeed transcribed in a burst-like fashion . To verify this , we measured Aebp1 transcriptional activity directly in kidney cells by using intronic probes ( Fig 3F ) , which , owing to the extremely short half-life of introns , detect almost exclusively nascent transcripts at the site of transcription [63] . This showed that 19% of cells with Aebp1 mRNA were also actively transcribing Aebp1 , and that these transcription sites contained more than 1 RNA based on their fluorescence intensity relative to cytoplasmic RNA spots ( average 1 . 6x higher fluorescence intensity in 3 independent experiments , S9 Fig ) . This data also showed that the majority of actively transcribing cells had only one Aebp1 transcription site , although a small subset ( 6 out of 55 ( 11% ) cells with Aebp1 transcription sites ) showed simultaneous expression from both alleles . This corroborated that cells indeed produced Aebp1 in transcriptional bursts and that it was possible for individual cells to transcribe RNA from both alleles simultaneously . Next , we turned to simulations to assess the RNA distributions that we would expect in a scenario where the two alleles transcribed RNA independently from each other and in bursts . Initially , we estimated the expected burst size ( average number of RNAs that were transcribed together in a single burst ) and burst frequency for the two alleles based on the observed RNA counts for each allele independently and used these parameters as inputs for our model ( see methods for details ) . BL6 and JF1 RNA counts simulated this way closely matched our measurements , which was also reflected by the likelihood of the real data falling within that of the simulated data for the two alleles separately ( Fig 3G ) . We then wanted to see whether the degree to which there was allelic imbalance in single cells could be explained by the two alleles bursting independently; i . e . , whether for per-cell RNA counts from the two alleles was more or less correlated than one would expect by chance . To simulate the null hypothesis of no interaction between alleles we randomly paired up the modeled BL6 and JF1 counts , mimicking cells that contain RNA from both alleles . When we compared this simulation to the real data we consistently observed that the modeled per cell BL6 and JF1 counts were less correlated than the real pairwise measurements ( Fig 3G and S10 Fig ) . Thus , while in our measurements cells with high BL6 expression typically also expressed JF1 at higher levels ( compare heterozygous data in Figs 2B and 3G top panel ) , in the modeled data BL6 and JF1 counts showed little correlation . This was also true when we incorporated false detection events in our model to account for possible incorrect allelic assignment , as we had done in the “all-or-none” model ( S11 Fig ) . Together , our transcription site measurements and simulations showed that the observed allele-specific single-cell RNA counts for Aebp1 were compatible with transcriptional bursting of the two gene copies individually and that expression from the two alleles was correlated . Thus , for Aebp1 , Lyplal1 and Mpp5 the observed monoallelic expression in some single cells can likely be explained by low levels of transcription occurring randomly from the two gene copies without having to invoke a special mechanism that limits expression to one of the alleles .
There has been great interest in recent years to precisely measure expression from the two alleles of a gene in diploid cells , ideally directly in tissue and at the single-cell level . The RNA fluorescence in situ hybridization method described here is a step in this direction: by visualizing endogenous SNVs it enables the assignment of single RNA molecules to their allele of origin in single cells and in the context of whole tissue sections . We now provide quantitative information about cell-to-cell heterogeneity with single transcript resolution , which is an extension of our previous work , where we used this method for a more qualitative assessments ( i . e . presence-absence ) of parental origin of mRNA in tissue [52] . When we applied our method to autosomal genes we observed that individual cells in heterozygous tissue spanned the entire range from all RNAs originating from the BL6 chromosome through various more mixed populations to all RNAs originating from the JF1 chromosome . These observed allelic imbalances were not due to the parental origin of the gene copies , because reciprocal crosses ( BL6xJF1 vs JF1xBL6 ) showed similar results . We therefore asked what model could explain the observed single-cell allelic imbalance pattern and combined our data with computational simulations to address this question . We found that the observed allelic distributions could be recapitulated by a model where transcription occurred randomly from the two alleles , perhaps with moderate transcriptional bursting ( e . g . in the case of Aebp1 ) . Thus , we did not have to invoke a special mechanism that restricted expression to only one allele to explain the presence of cells with either BL6 or JF1 monoallelic expression status . Our results suggest that cells with RNA expression from only one of the alleles occur due to the low levels of expression and thus the limited number of random sampling events from the two gene copies . Because transcription is a dynamic process this state is likely transient so that while a cell may have mRNA from only one allele at a particular time , it may gain mRNA from the other allele a short time later if another transcriptional event occurs . For Mpp5 , this conclusion is in line with the fact that the reported haploinsufficient phenotype is thought to be caused by overall reduced dosage [55 , 56] rather than by a special subpopulation of cells with monoallelic expression . More generally , this scenario links the observation of transcriptional bursting with that of random monoallelic expression , as put forward by Sandberg et al [43 , 44] , and explains why we observed the co-occurrence of cells with one and two transcription sites in the same population . In the case of Aebp1 and Lyplal1 , which had previously been identified to display random monoallelic expression , our data suggest that no additional mechanism is needed to explain the presence of cells with monoallelic expression in kidney tissue . However , we cannot extrapolate these results to other tissues and mouse strains , given that random monoallelic expression is tissue specific . Thus , we cannot conclude whether mechanisms for allele-specific regulation or for maintenance of monoallelic status are present in the mouse strains and cell types used in the studies that originally identified genes with random monoallelic expression [37–41] . Interestingly , though , those studies also observed that many genes with monoallelic expression in one clonal cell line are expressed biallelically in others and that the expression of a given gene is often lower in cell lines with monoallelic expression than in those with biallelic expression [38–40] , which is consistent with our results . In addition to the data on Aebp1 , Mpp5 and Lyplal1 , we also demonstrated our ability to distinguish Xist expression from the BL6 vs JF1 chromosome and to assess the spatial relationship of cells expressing different parental alleles . We detected a spatially fairly mixed population in the kidney , where cells expressing Xist from the same chromosome clustered together in small patches in transverse sections . This is contrary to other tissues , such as intestinal crypts or the skin [58 , 59 , 61 , 64] , and suggests either a larger number of kidney precursor cells or extensive cell migration during development . Because our method only provides a snapshot in time we cannot easily distinguish between these scenarios ( lineage vs . migration ) , especially given that the kidney is a complex organ , composed of cells originating from different embryonic lineages that undergo extensive migration during development even after birth [65 , 66] . Regardless of the developmental mechanism , however , our data indicate that there is likely no major spatial segregation due to X chromosome inactivation in this tissue , and in the case of mutations , any phenotypic effects would be fairly evenly distributed . Through the examples detailed above we have shown how our method can be used to directly quantify cell-to-cell differences that arise due to differential expression from the two alleles in diploid cells . Our approach overcomes multiple limitations imposed by previous methods: First , because this method enables sensitive SNV-specific detection of even single mRNA molecules it provides more information than RNA FISH measurements that rely solely on quantifying the number of transcription sites in individual cells [36 , 37 , 40 , 41 , 67] . Second , although our approach tests only single genes in a given experiment and thus has much lower throughput than single-cell sequencing-based methods , it relies on direct detection of transcripts and is therefore not subject to subsampling and dropout , which complicate the interpretation of sequencing-based cell-to-cell variability results [44 , 46 , 68–70] . Finally , by making use of pre-existing endogenous SNVs it eliminates the need for genetic manipulation , for example to label the gene of interest with fluorescent tags , as has been done to measure X chromosome inactivation choice [61 , 71] or to monitor the transcription of autosomal genes from the two chromosomes [9 , 72] . Moreover , because the breeding history for classical inbred mice has lead to extended regions of shared ancestry ( and shared SNVs ) between different strains [73 , 74] , a probeset developed for one strain can often easily be adapted for another strain . For example , while we measured Xist expression from the BL6 and JF1 allele , the probes were designed so that they should distinguish equally well between the 129-strains and CAST/EiJ , which are also commonly used to study strain-specific expression . In addition , while our quantitative analysis focused solely on genes with a relatively high number of SNVs and high mean colocalization rates ( >50% ) , it should be noted that we did not systematically explore the relationship between SNVs and colocalization rates , and also that lower colocalization can be sufficient to address specific questions , as was demonstrated in a recent single cell in situ analysis of A-to-I RNA editing [75] . It is therefore likely that depending on the scientific question , less stringent cutoffs can be applied to colocalization rates and/or the number of SNVs required . In addition to the number of SNVs/allele-specific probes , the applicability of our method also depends on the expression level of the gene of interest . Based on the genes tested here , our method is applicable for genes with low-to-moderate expression levels , corresponding to approximately 2–25 FPKM in bulk sequencing data ( see Methods ) . The lower boundary for this estimate is set by Churc1 , a gene that is consistently expressed at low levels in all cell types of the kidney ( 0 . 1–1 UMI/cell , ~2 FPKM ) . It is technically possible to determine colocalization events at this mRNA density , though a large number of cells needs to be screened for robust quantification . The upper boundary is based on the observation that a high density of transcripts makes it impossible to precisely assign colocalization . In our experiments only Podxl in podocytes showed such high expression levels . Based on the relatively low proportion of podocytes in the kidney we determined that this matched an expression level of >800FPKM in these cells ( based on bulk sequencing data ) and an average count of 6 . 9 UMI/cell in single-cell sequencing data . Importantly , given the higher detection efficiency of RNA per cell , allele-specific single-molecule RNA FISH provides a valuable complementary tool to single-cell transcriptomics for low-to-moderate expression level genes , and will likely be useful in confirming findings made by sequencing . In conclusion , we demonstrated how quantitative measurement of allele-specific expression in tissue could be used to directly determine the level of allelic imbalance in single cells . By combining these measurements with modeling , we showed that random monoallelic expression could arise in vivo by chance alone . Beyond this application , our methods could have a number of additional uses . Similar analyses could be performed in other tissues and , for example , could enable the evaluation of genetic variants directly in the tissue believed to be affected if there are genic SNVs in linkage with those variants or to study mutations thought to lead to haploinsufficiency . Furthermore , with single cell resolution , our method allows for the interrogation of particular cellular subtypes within a tissue . In concert with recent genome-wide association studies in single cells [76 , 77] , this technique provides a useful tool for quantitative assessment of allele-specific genetic effects .
C57BL/6J and JF1/Ms founder mice were obtained from Jackson Laboratories . All mouse work was conducted in accordance with the University of Pennsylvania Institutional Animal Care and Use Committee . For tissue collection we used either homozygous C57BL/6J or JF1/Ms pups , or F1 heterozygotes from both C57BL/6J x JF1/Ms or JF1/Ms x C57BL/6J crosses . We dissected pups at postnatal day 4 using standard techniques , and mounted tissues in Tissue-Plus O . C . T . compound ( Fisher Healthcare ) , flash-froze them in liquid nitrogen or on an aluminum block in dry ice , and then stored tissues at −80°C . We determined sex of the animals by visual inspection and verified this by SRY-specific PCR on DNA extracted from a tail sample , collected during dissection . Tissues were cryosectioned at 5 μm using a Leica CM1950 cryostat . We adhered tissue samples to positively charged Colorfrost plus slides ( Fisher Scientific ) , washed slides in PBS , fixed them in 4% formaldehyde for 10 min at room temperature , then washed again in PBS two times . Fixed slides were stored in 70% ethanol at 4°C . We shortlisted genes that had previously been identified in studies of random monoallelic expression , genes with known function and/or phenotypes in kidney and genes with documented expression in different cell types in the kidney . We then filtered shortlisted genes for expression levels of >20 TPM in publicly available bulk kidney sequencing data [78] , accessed via Expression Atlas ( https://www . ebi . ac . uk/gxa/ ) . We later established that this corresponded to expression levels of >10FPKM in bulk and 0 . 1–1 UMI/cell ( in the tissue ( s ) with highest expression; S3 Fig and S9 Table ) using additional bulk [79] and single-cell [62] transcriptome data , respectively . The only gene with lower expression was Churc1 ( TPM <10 , FPKM ~2 . 2 ) , for which we were nevertheless able to robustly detect transcripts using the guide probes ( in agreement with a count of 0 . 1-1UMI/cell in all cell types of the kidney ) , and identify colocalization events above random ( pixel-shifted ) controls . The only gene with higher expression was Podxl with a UMI of 6 . 9 per cell in podocytes . To identify exonic SNVs between the C57BL/6J and JF1/Ms strains we used the NIG Mouse Genome Database ( http://molossinus . lab . nig . ac . jp/msmdb/index . jsp ) [80] . For Aebp1 and Lyplal1 we confirmed these SNVs through PCR amplification and sequencing of exonic sequences of genomic JF1/Ms DNA . All guide probes and the Aebp1 intron probe set were designed using the Stellaris probe designer ( Biosearch Technologies ) , SNV-specific probes were designed as specified in Levesque et al . [50] and mask oligonucleotides were selected to leave a 7-11bp overhang ( toehold ) sequence ( all probe sequences available in S6 Table ) . Guide probes were purchased labeled with Cal fluor 610 ( Biosearch Technologies ) , while SNV-specific probes and intron probes were ordered with an amine group on the 3′ end . For these latter probes we pooled the oligonucleotides for each probe set and coupled them to either NHS-Cy3 or NHS-Cy5 ( GE Healthcare ) for the allele-specific probesets , or NHS-Atto488 or NHS-Atto700 ( Atto-Tec ) for the intronic probes . We purified dye-coupled probes by high-performance liquid chromatography . Mask oligonucleotides were used unlabelled . DNA was extracted from tail biopsies using a quick-lyse protocol: 100μl of Solution A ( 25mM NaOH and 0 . 2mM EDTA ) were added to the tissue and kept at 95°C for 30 min , before adding an equal volume of Solution B ( 40mM Tris , pH = 8 ) . Samples were then spun at 6000 rpm for 10 min and 100μl of the top layer was transferred to a fresh tube . 1μl of this solution was used as template for PCR . To verify the presence of reported SNVs in Aebp1 and Lyplal1 , we designed primers for the exonic segments of these genes ( primer sequences available in S7 Table ) , and PCR-amplified genomic DNA using AmpliTaq Gold ( ThermoFisher ) with buffer II and 0 . 25mM MgCl2 according to the manufacturer's instructions . PCR amplicons were purified with ExoSAP-IT ( ThermoFisher ) and submitted for sequencing to the University of Pennsylvania DNA sequencing facility . For sex-specific genotyping of pups we used Sry-specific primers ( S7 Table ) , since this gene is located on the Y chromosome and thus amplicons can only be detected in male tissues . PCR was performed as for sequencing , and the presence-absence of a product was revealed on a gel . Allele-specific RNA fluorescence in situ hybridization works by first detecting the mRNA of interest ( regardless of the allele of origin ) using conventional single molecule RNA FISH probes labelled in one color ( guide probes ) , and then colocalizing this signal with probes that discriminate specific single-nucleotide differences based on a “toehold probe” strategy and which are labelled in colors unique to the two different alleles . In this way , mRNAs are essentially “tagged” as being either from one or the other parental chromosome . In cultured cells , this approach can successfully distinguish RNA variants that contain just one single nucleotide variant ( SNV ) and thus can only be targeted with a single variant-specific probe [50 , 51] , but the decreased signal-to-noise ratio makes reliable detection of single probes more difficult in tissue [81] . We therefore opted to work with C57BL/6J ( BL6 ) and JF1/Ms ( JF1 ) mice , which belong to two different Mus musculus subspecies ( domesticus and molossinus , respectively ) [80 , 82] . Due to their distant relationship , JF1 mice harbor a large number ( >50 , 000 ) of SNVs in genic regions compared to BL6 [80] , allowing us to target most genes with multiple SNV-specific probes . For each gene of interest we first prepared a probe mix , containing a guide probe set ( labelled with Cal fluor 610 ) , the two allele-specific probe sets ( labelled in Cy3 and Cy5 , respectively ) and a set of mask oligos ( unlabelled , in 1 . 5x excess of the allele-specific probes ) in hybridization buffer ( 10% dextran sulfate , 2× SSC , 10% formamide ) . For detection of nascent Aebp1 RNA we also included intronic probes labelled either with Atto488 or Atto700 , and to verify the integrity of RNA in male tissues stained for Xist we also included Gapdh probes ( labelled with Atto488 ) . To stain the samples , we first washed the slides with tissues sections in 2x SSC , then incubated them in 8% SDS for 2 minat room temperature , washed again in 2x SSC and finally added the hybridization buffer with probes . Slides were covered with coverslips and left to hybridize overnight in a humidified chamber ( ibidi ) at 37°C . The next morning we performed two 30 min washes in wash buffer ( 2× SSC , 10% formamide ) , the second one including DAPI to stain nuclei . To label cell membranes ( to clearly identify single cells ) the first wash was sometimes substituted with a 15 min incubation in wash buffer containing wheat germ agglutinin coupled with Alexa488 ( LifeTech ) and a 15 min regular wash . After the final wash , slides were rinsed twice with 2x SSC and once with antifade buffer ( 10 mM Tris ( pH 8 . 0 ) , 2× SSC , 1% w/v glucose ) . Finally , slides were mounted for imaging in antifade buffer with catalase and glucose oxidase [83] to prevent photobleaching . We imaged all samples on a Nikon Ti-E inverted fluorescence microscope using either a 60x or a 100× Plan-Apo objective and a cooled CCD camera ( Andor iKon 934 ) . For whole-tissue scans we imaged at 60x and used Metamorph imaging software ( Scan Slide application ) to acquire a tiled grid of images . We used the Nikon Perfect Focus System to ensure that the images remained in focus over the imaging area . For 100× imaging , we acquired z-stacks ( 0 . 3 μm spacing between stacks ) of stained cells in six different fluorescence channels using filter sets for DAPI , Atto 488 , Cy3 , Calfluor 610 , Cy5 , and Atto 700 . The filter sets we used were 31000v2 ( Chroma ) , 41028 ( Chroma ) , SP102v1 ( Chroma ) , 17 SP104v2 ( Chroma ) , and SP105 ( Chroma ) for DAPI , Atto 488 , Cy3 , Cy5 , and Atto 700 , respectively . A custom filter set was used for CalFluor610 ( Omega ) . For Xist image analysis we worked with whole tissue scans , where we had collected data for Cal fluor 610 ( Xist guide probes ) , Cy3 and Cy5 ( BL6 and JF1 probes , respectively ) and DAPI ( nuclei ) . To visualize scans , we used the “Grid/Collection stitching” feature available in Fiji [84] to assemble tiles . To identify Xist RNA and assign them an allelic identity we developed a custom pipeline in MATLAB . First , we reconstructed the scan taking into account the tile order provided in a supplementary file . Then , we used the data from the guide channel to detect Xist foci , regardless of allelic identity: we performed background subtraction , removed small objects and smoothened boundaries by border clearing and morphological opening , and then used LoG filtering to sharpen objects , binarized the observed signals and created connected components . Visual inspection of these connected components showed that they largely corresponded to Xist foci , but some areas with high background signal were also being detected as connected components . We therefore applied a number of filters ( minimum fluorescence intensities for all RNA FISH channels , minimum cutoff for solidity , maximum area for connected components ) to yield the final segmentation . Each obtained spot was then parametrized as the ratio of the signal intensity ( background subtracted and normalized to the mean intensity of the scan ) of the two SNV probe channels and we applied k-means clustering ( 2 means ) to yield a critical angle above which we assigned spots JF1 identity , and below which we assigned BL6 identity . To verify the quality of these assignments , we designed a graphic user interface to manually annotate Xist foci and their allelic identity . We typically annotated 10 or more randomly selected tiles and the results of this quality control step are shown in S3 Table . On average ~90% ( mean 90 . 9% , standard deviation 5% ) of Xist foci were correctly detected , while the remaining 10% of identified spots were areas of high background intensity that had been miscategorized as Xist foci . When Xist spots were correctly identified , typically more than 90% were assigned the correct allelic identity ( mean 94 . 4% , standard deviation 5% ) . To assess spatial patterns of Xist allelic choice we then used the positional and identity information from our automatic assignments , and developed a metric for spatial heterogeneity . First , we tiled images into regular rectangles of equal size ( i . e . 16 tiles all 1/16 of the full scan size ) . For each rectangle , we calculated the fraction of cells expressing Xist from the BL6 allele . Next , we obtained the variance of these BL6 cell fraction values across all rectangles of a given size . This protocol was repeated for different sizes of rectangles ranging from 16 to 256 rectangles spanning the entire tissue section . We also calculated a baseline for spatial heterogeneity of random allelic choice by repeating this analysis on 1000 random permutations of the data for each sample generated by MATLAB’s randperm . We performed a similar analysis to determine the minimal cluster size of Xist foci with identical allelic identity , but instead of random permutations we generated simulations , where kidney sections were randomly seeded with clusters of a fixed size ( ranging from 1 to 10 ) while keeping the allelic ratio the same as for the measured data . For each seed size we generated 500 simulations . To obtain a likely minimal cluster size for cells with identical X chromosome inactivation we selected the seed size whose variance deviated least from the variance observed for the real data . We repeated this process for each subdivision size and determined the mean across all subdivision sizes . For analysis of single molecule RNA spots we used a combination of 60x whole tissue scans in DAPI and Cal fluor 610 to determine the overall structure of the tissue and collecting z-stacks at 100x resolution of 5–10 individual positions within that tissue to identify individual mRNA molecules and characterize their allelic identity . To determine allelic identity we first segmented and thresholded images using a custom MATLAB software suite ( downloadable at https://bitbucket . org/arjunrajlaboratory/rajlabimagetools/wiki/Home , changeset: d278b7d0012282ecb318fde3bebbe3beaba62032 ) . To quantify colocalization rates we first determined the ideal colocalization radius for each gene . To do so , we segmented extended areas of the tissue ( typically containing 10–50 cells ) . To ascertain subpixel-resolution spot locations the software then fitted each spot to a two-dimensional Gaussian profile specifically on the z plane on which the spot occured . Next , colocalization between guide spots and allele-specific spots was determined in two stages . In the first stage , we searched for the nearest-neighbor allele-specific probe for each guide spots within a 2 . 5-pixel ( 360-nm ) window and ascertain the median displacement vector field , which was subsequently used to correct for chromatic aberrations . After this correction , we tested a range of different radii ( r = 0 . 1 to 2 . 5 pixel ) for each gene to calculate colocalization rates for the real data , as well for pixel-shifted data , where we took our images and shifted the guide channel by adding 2*r pixels to the X and Y coordinates . This pixelshifted data was used to test random colocalization due to spurious allele-specific spots . For each gene we then visually inspected colocalization rates for real and pixel-shifted data at the different radii and determined a radius where both the colocalization rate for the real data and the difference between the real and the pixel-shifted data was maximal . The selected colocalization radii for each gene are included in S4 Table . To obtain allele-specific data for single cells we then repeated the colocalization analysis , but segmentation of cells was done by drawing a boundary around nonoverlapping individual cells using brightfield or wheat germ agglutinin signal , and colocalization was determined using only the previously determined ideal colocalization rate . Transcription site analysis was performed using a custom MATLAB software suite ( downloadable at https://bitbucket . org/arjunrajlaboratory/rajlabimagetools/wiki/Home ) . For this , we segmented cells , thresholded RNA FISH signal and identified transcription sites for Aebp1 by co-localization of spots in the intron and exon channel . Relative fluorescence intensities of transcription sites vs cytoplasmic RNAs were determined based on the fluorescence intensity of the guide probes using custom scripts written with R packages dplyr and ggplot2 . To determine whether any biophysical properties could differentiate between allele-specific probes that had high vs low colocalization rates , we compiled a table containing the the following parameters ( S8 Table ) : probe name , probe sequence , colocalization rate ( the colocalization rate determined for an entire probeset was applied to each individual probe ) , number of predicted secondary structures and folding energies . The latter two parameters were extracted by running sequences on the mfold web server [85] for DNA probes , with Na concentration set to 0 . 3M . Frequency of individual nucleotides , dAT , dGC , purines and pyrimidines was determined through analysis of the probe sequences . Since different fluorescent dyes have different detection sensitivity , we tested how this impacted our findings by performing “dye-swap” experiments in which we use the same probes but swap the fluorophore moieties used to label the probes for the BL6 and JF1 alleles . This enables us to measure the extent to which the labels on the probes affected probe binding efficiency and what the consequences were for our results and interpretations . In these experiments we probed Aebp1 , Lyplal1 and Mpp5 expression in BL6 and JF1 homozygous tissues with allele-specific probes labelled either with Cy3 for BL6 and Cy5 for JF1 , or the reverse ( S6A Fig ) . The unique detection rate ( i . e . the rate at which we could assign spots either a BL6 or a JF1 identity ) differed by 7% or less between the dye combinations for all three genes , with the exception of Mpp5 in BL6 homozygous tissue . Similarly , the false detection rate , which includes both the error from incorrect probe binding and the differential detection sensitivity , deviated by less than 4% between the dye combinations for all three genes , with the exception of Mpp5 in BL6 homozygous tissue . For Mpp5 in BL6 homozygous tissue we observed that the BL6 allele was detectable at higher efficiency when labelled with Cy5 ( 66% ) than when labelled with Cy3 ( 50% ) . We therefore also collected dye-swapped data for Mpp5 in heterozygous tissues ( S6B Fig ) and performed the “all-or-none” and “coin flip” simulations on both dye combinations ( S6C Fig and Fig 3B–3D ) . In addition , we used the empirically determined false detection rate for each dye combination and gene for all of our analysis and modelling of allelic imbalance in heterozygous tissue to avoid artefacts due to variable sensitivity of detection for different dyes . Given that for all three genes that we studied in more detail we could determine BL6 or JF1 identity for only approximately 50% of RNAs , we measured how colocalization rate impacted our ability to measure single-cell allelic imbalance . This was difficult to do in tissue , because the low number of RNAs per cell provided a limited dynamic range to probe this effect ( i . e . if 1 out of 2 RNAs can not be assigned an identity , this results in 50% detection efficiency ) . We therefore collected extensive data for Aebp1 in primary fibroblasts , where Aebp1 is expressed at higher levels ( 10s to 100s of RNAs per cell ) to determine the general effect of the colocalization rate on allelic ratios . In homozygous fibroblasts we found that there was an inverse relationship between colocalization rate and false detection rate , so that higher colocalization rate corresponded with a lower false detection rate and a higher rate of calling the correct allele ( S5A Fig ) . Most likely this was caused by low colocalization rates being indicative of other technical issues that complicate determining colocalization rate ( such as high background fluorescence ) . We observed this effect regardless of the dye combination used , but also noted that above a colocalization rate of ~0 . 5 ( the overall colocalization rate in tissue ) our ability to detect the correct allele was consistently high ( above 75% ) . Next , we used 10 heterozygous fibroblast cells with the highest colocalization rates . The cells all had colocalization rates >70% and randomly downsampling RNAs to 65 , 60 , 55 , 50 or 45% colocalization rate ( 1000 random sampling per condition ) showed the the mean allelic ratio for each downsampling was similar to the original measured ratio , but the variability around that mean increased with lower colocalization rates . The biggest change in the allelic ratio that we observed was ~0 . 2 ( S5B Fig ) . Next , we also tested how detection efficiency influenced our conclusions about the “all-or-none” or “coin flip” scenario in tissue . For this , we repeated both simulations using the total RNA count for each cell ( i . e . including unclassified RNAs and RNAs that colocalized with both allele-specific probes ) . We then randomly downsampled the RNA in each cell to the number of RNAs that had been assigned a unique BL6 or JF1 identity in the original measurement . Each simulation was performed 5000 times . We found that the log likelihoods and correlations from these downsampled simulations were essentially indistinguishable from the original simulations performed without downsampling ( S5C Fig ) . To quantify cell-to-cell variability of allelic state in single tissue cells , we extracted colocalization data from our image analysis pipeline , and used this for further analysis . Using this data , we first compiled a quality control table for each experiment ( S5 Table ) and excluded those where colocalization rates were <40% ( 4 out of 35 experiments ) . For all remaining data we combined replicates from the same genotype , and in the case of heterozygous data , combined results from C57BL/6J x JF1/Ms and JF1/Ms x C57BL/6J tissues . We then processed and visualised single-cell results using custom scripts written with R packages dplyr and ggplot2 . To determine how the observed data compared to random monoallelic expression ( all-or-nothing scenario ) or binomially distributed ( coin-flip scenario ) allelic calls we simulated those scenarios through modelling . For the binomial distribution we considered a null model wherein all heterozygous cells share the same allelic ratio , which was determined to be the overall allelic ratio observed at the population level . Then , for an experiment with overall estimated C57BL/6J allelic ratio equal to pBL6 ( above ) , we let nBL6j be the number of transcripts with C57BL/6J identity detected in cell j and nJF1j be the number the number transcripts with JF1/Ms identity detected in cell j . Under the null model , nBL6j was drawn from a binomial with ( nBL6j + nJF1j ) draws and probability pBL6 . We simulated single-cell label counts for cells by drawing from these conditional null distributions for each cell 10 , 000 times . We then compared the negative log-likelihood of the observed data with the distribution of negative log-likelihoods of each simulation iteration . To simulate random monoallelic expression each cell was assigned either a BL6 or a JF1 identity , based either on the majority of RNAs in a cell , or based on random assignment if both alleles had the same count . We then designated all RNAs in a given cell to the same allelic identity ( eg a cell that originally contained 4 BL6 RNAs and 2 JF1 RNAs would be assigned a BL6 identity with 6 BL6 RNAs ) . Next , we randomly added “technical noise” 10 , 000 times , by changing some RNAs in the population to the opposite identity , based on the false positive rates measured in the original BL6 and JF1 homozygous populations . These steps were performed while keeping the final overall RNA assignments in the population the same as the original heterozygous population . For the 10 , 000 simulations we then calculated negative log likelihoods similarly as we did for the binomial distributions , assuming two separate null models for the BL6 and JF1 populations , whose parameters were determined by the original homozygous population . To assess if the measured mRNA distributions were compatible with a transcriptional bursting scenario we used the negative binomial distribution to simulate expected mRNA counts [86] . First , we determined the burst size and frequency of the BL6 and JF1 alleles separately , by using the moments method to determine r and p parameters of the negative binomial distribution based on the mean and variance of our measurements ( where p = mean/variance and r = mean^2/ ( variance-mean ) ) , from which we obtained the burst size and frequency using: burst_size = ( 1-p ) /p and burst_frequency = r . We then generated 10 , 000 RNA counts for the two alleles separately by drawing from a distribution with the r and p parameters we had calculated . We visualized the obtained mRNA counts for both alleles individually using a randomly selected simulation , and also calculated the negative log-likelihood distribution of the 10 , 000 simulated datasets . Next , we randomly paired the data for the two alleles to generate “cells” with RNA counts from both alleles and calculated the correlation between the BL6 and JF1 counts in each of these modeled cells . In addition to using the negative binomial parameters that we had calculated from our data , we also tested a series of additional burst sizes ( from 0 . 5 to 5 RNAs per burst ) and repeated the entire analysis , which showed that our findings were consistent across a range of burst values ( see S10 Fig ) . Finally , to generate a model which included BL6-JF1 correlations that arise due to false assignments , we used the randomly paired data and changed some RNAs in the population to the opposite identity based on the false positive rates measured in the original BL6 and JF1 homozygous populations ( one round of reassignments for each simulation ) . Following reassignment we again calculated the correlation between the BL6 and JF1 counts . Raw and processed data , as well as scripts for all analyses presented in this paper , including all data extraction , processing , and graphing steps are freely accessible from the Open Science Framework ( URL https://osf . io/sbjcw/ ? view_only=09393298e00e4b8c9d0dd1b24b0318d9 ) . Our image analysis software ( changeset: d278b7d0012282ecb318fde3bebbe3beaba62032 ) is available here: https://bitbucket . org/arjunrajlaboratory/rajlabimagetools/wiki/Home All animal studies were approved by the Institutional Animal Care and Use Committee at the University of Pennsylvania ( IACUC protocol 805433 ) . The animals used in this study were treated humanely and with regard for alleviation of suffering .
|
In mammals , most cells of the body contain two genetic datasets: one from the mother and one from the father , and—in theory—these two sets of information could contribute equally to produce the molecules in a given cell . In practice , however , this is not always the case , which can have dramatic implications for many traits , including visible features ( such as fur color ) and even disease outcomes . However , it remains difficult to measure the parental origin of individual molecules in a given cell and thus to assess what processes lead to an imbalance of the maternal and paternal contribution . We adapted a microscopy technique—called allele-specific single molecule RNA FISH—that uses a combination of fluorescent tags to specifically label one type of molecule , RNA , depending on its origin , for use in mouse kidney sections . Focusing on RNAs that were previously reported to show imbalance , we performed measurements and combined these with mathematical modeling to quantify imbalance in tissues and explain how these can arise . We found that we could recapitulate the observed imbalances using models that only take into account the random processes that produce RNA , without the need to invoke special regulatory mechanisms to create unequal contributions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"x",
"chromosome",
"inactivation",
"gene",
"regulation",
"messenger",
"rna",
"dna",
"transcription",
"simulation",
"and",
"modeling",
"molecular",
"biology",
"techniques",
"epigenetics",
"rna",
"sequencing",
"kidneys",
"research",
"and",
"analysis",
"methods",
"gene",
"expression",
"molecular",
"biology",
"biochemistry",
"rna",
"biochemical",
"simulations",
"anatomy",
"nucleic",
"acids",
"genetics",
"biology",
"and",
"life",
"sciences",
"renal",
"system",
"computational",
"biology",
"dosage",
"compensation"
] |
2019
|
Allele-specific RNA imaging shows that allelic imbalances can arise in tissues through transcriptional bursting
|
Long non-coding ( lnc ) RNAs are numerous and found throughout the mammalian genome , and many are thought to be involved in the regulation of gene expression . However , the majority remain relatively uncharacterised and of uncertain function making the use of model systems to uncover their mode of action valuable . Imprinted lncRNAs target and recruit epigenetic silencing factors to a cluster of imprinted genes on the same chromosome , making them one of the best characterized lncRNAs for silencing distant genes in cis . In this study we examined silencing of the distant imprinted gene Slc22a3 by the lncRNA Airn in the Igf2r imprinted cluster in mouse . Previously we proposed that imprinted lncRNAs may silence distant imprinted genes by disrupting promoter-enhancer interactions by being transcribed through the enhancer , which we called the enhancer interference hypothesis . Here we tested this hypothesis by first using allele-specific chromosome conformation capture ( 3C ) to detect interactions between the Slc22a3 promoter and the locus of the Airn lncRNA that silences it on the paternal chromosome . In agreement with the model , we found interactions enriched on the maternal allele across the entire Airn gene consistent with multiple enhancer-promoter interactions . Therefore , to test the enhancer interference hypothesis we devised an approach to delete the entire Airn gene . However , the deletion showed that there are no essential enhancers for Slc22a2 , Pde10a and Slc22a3 within the Airn gene , strongly indicating that the Airn RNA rather than its transcription is responsible for silencing distant imprinted genes . Furthermore , we found that silent imprinted genes were covered with large blocks of H3K27me3 on the repressed paternal allele . Therefore we propose an alternative hypothesis whereby the chromosome interactions may initially guide the lncRNA to target imprinted promoters and recruit repressive chromatin , and that these interactions are lost once silencing is established .
Long non-coding ( lnc ) RNAs are a diverse and numerous group of non-protein-coding RNA species longer than 200 nucleotides , some of which have been shown to be involved in gene regulation [1 , 2] . A growing number of lncRNAs have been implicated in development and disease , sparking interest in how they may regulate gene expression [3 , 4] . However , the majority of lncRNAs remain relatively uncharacterized and of uncertain function , highlighting the value of model systems to identify modes of lncRNA action . One of the most studied functional lncRNAs in mammals are imprinted lncRNAs , which are expressed exclusively from either the maternally or paternally inherited chromosome . Mechanisms of lncRNA action identified in imprinted lncRNAs , such as the targeting of histone modifying complexes to genomic loci and the role of lncRNA transcription in gene regulation [5–8] , have later been shown for other non-imprinted lncRNAs [2 , 9] , emphasizing their value as model systems . Genomic imprinting is an epigenetic mechanism that restricts gene expression to one of the two parental alleles . Imprinted genes are often clustered in domains with a differentially DNA methylated genetic region called the imprint control element ( ICE , also called the imprinting control region ( ICR ) ) controlling allele specific expression of all genes in the cluster [10] . Although differential methylation of the ICE is established during gametogenesis and maintained through somatic cell division , the extent of imprinted silencing is dynamic throughout development , with imprinted clusters tending to show their maximum size in extra-embryonic tissues like the visceral yolk sac ( VYS ) and the placenta [11] . For example , the Igf2r cluster expands from 120kb in most embryonic and adult tissues to almost 10Mb in placenta , while the Kcnq1 cluster expands from 250kb in embryonic tissues to 690kb in VYS [11] . The number of imprinted genes in mammals appears to be limited to approximately one hundred [11] , a number of which have been shown to be key regulators of development and disease [12 , 13] . Mechanistically ICEs often act as promoters for a lncRNA , with the imprinted lncRNA being expressed from the non-methylated allele initiating silencing in cis of all genes in the cluster [14] . This has been shown in mouse by truncating the lncRNA to a non-functional length for Airn , Kcnq1ot1 , Nespas and Ube3a-ATS in the Igf2r , Kcnq1 , Gnas clusters and the orthologous cluster to the human Prader-Willi/Angleman region respectively [5 , 6 , 15 , 16] One of the best-characterized clusters is the Igf2r cluster where the Airn lncRNA causes imprinted silencing of Igf2r in most tissues , and a larger cluster of genes in extra-embryonic tissues [5 , 11 , 17] . The function of the Airn lncRNA was previously tested using two mouse models that ablate imprinted silencing in the Igf2r cluster: a deletion of the Airn promoter and ICE ( R2Δ ) , and the truncation of Airn by the insertion of a polyadenylation signal ( AirnT ) [5 , 18] . Using the AirnT model we showed that imprinted silencing in VYS extends over 450kb to Slc22a2 and Slc22a3 [17] , while more recently we used the R2Δ model to show that in placenta the domain of genes showing imprinted silencing by Airn extends over 10Mb , making it the largest imprinted cluster known [11] . Airn overlaps Igf2r in antisense and silences it by transcriptional interference [7] , but how non-overlapped imprinted genes in the cluster are silenced is disputed . In trophoblast stem ( TS ) cells the silenced paternal Igf2r cluster expressing Airn is contracted and associated with a so-called repressive domain that includes the polycomb repressive complex ( PRC ) modifications H3K27me3 ( PRC2 ) and H2AK119u1 ( PRC1 ) together with the PRC1 protein Rnf2 [19] . There is also some evidence that Airn may bind PRC2 [20] . In placenta Airn binds the H3K9 dimethylase EHMT2 ( also known as G9a ) , which is enriched on the Slc22a3 promoter , and required for Slc22a3 imprinted silencing [21] . The Airn RNA is closely associated with the Slc22a3 promoter in placenta , indicating that Airn may target EHMT2 to the Slc22a3 promoter to cause silencing [21] . These data indicate that the Airn RNA product may silence non-overlapped imprinted genes like Slc22a3 by targeting EHMT2 and perhaps PRC2/PRC1 to their promoters . However , given that this contrasts with the mechanism of Igf2r silencing , where Airn transcription and not its RNA product mediate silencing , we have proposed an alternative hypothesis to explain these data . Enhancers form specific chromosome interactions with promoters to activate them [22] , therefore we hypothesized that Airn transcription may prevent upregulation of non-overlapped imprinted genes like Slc22a3 by interfering with enhancer access to their promoters , and that as a secondary step EHMT2 and PRC2/PRC1 may deposit repressive chromatin modifications to maintain silencing [23] . Consistent with this we found enrichment of the active enhancer marker H3K27ac within the Airn gene in VYS endoderm and placenta , and open chromatin within the Airn gene in multiple tissues [11 , 24] . Enhancers often lie in the introns of actively transcribed genes and are not disturbed by transcription through them . We hypothesize that the RNA polymerase transcribing Airn has unique properties , as Airn lncRNA has unusual RNA biology features like a lack of splicing , nuclear retention and a short half-life [25 , 26] . It is therefore possible that this specific RNA polymerase complex enables not only transcriptional interference with the Igf2r promoter , but also transcriptional interference with enhancers [7 , 23] . In this paper we aimed to test the enhancer interference hypothesis . First we determined if the predicted chromosome interactions could be detected , and second we performed a genetic test to determine if disrupting the predicted enhancers affected expression of imprinted genes in the Igf2r cluster .
The enhancer interference model hypothesizes that transcription of Airn through enhancers for non-overlapped imprinted genes may disrupt enhancer activity preventing upregulation of these genes [23] . This predicts that regions within the Airn gene should interact with non-overlapped imprinted genes on the active maternal allele , and not on the paternal allele where expression of Airn causes imprinted silencing . To test this we conducted chromosome conformation capture ( 3C ) to compare interactions on the maternal and paternal alleles between the promoter of the non-overlapped imprinted gene Slc22a3 , lying 234 kb upstream of Airn , and the Airn gene body . We chose to examine Slc22a3 because it is the only non-overlapped imprinted gene in the Igf2r cluster to show imprinted expression in multiple tissue types [11] , and in order to compare to other studies where regulation of Slc22a3 imprinted expression was examined [19 , 21] . Slc22a3 shows imprinted expression in both the placenta and VYS , but we chose to use VYS for the 3C analysis , as it is a simpler tissue that contains no maternal cells [17] . To enable parental allele specific analysis , we collected VYS from reciprocal crosses of the spontaneous T-hairpin mutant mouse ( Thp ) [27] , which has a large deletion ( minimum 5 . 56 Mb ) that includes the Igf2r cluster [28] . We found that interactions between the Slc22a3 promoter and Airn are higher on the maternal allele across the entire Airn gene ( Fig 1A ) . This indicates that Airn blocks these interactions on the paternal allele consistent with the enhancer interference model . To test this , we conducted a second 3C experiment to determine if loss of Airn would restore interactions between Slc22a3 and the Airn gene on the paternal allele . We collected VYS from a cross between Thp mice and mice with a truncation of Airn ( AirnT ) that leads to a loss of imprinted silencing [5] . This enabled us to compare interactions between the Slc22a3 promoter and Airn gene in the presence and absence of a functional Airn . We found that truncation of Airn led to an increase in interactions with the whole Airn gene , both for the biallelic comparison ( +/+ vs +/AirnT ) and for the comparison where only the paternal allele was present ( Thp/+ vs Thp/AirnT ) ( Fig 1B ) . This indicates that Airn interferes with interactions between its gene body and the Slc22a3 promoter , as predicted by the enhancer interference model . The enhancer interference model predicts that essential enhancers for Slc22a3 should lie within the Airn gene , and that transcription of Airn through these enhancers should prevent upregulation of Slc22a3 on the paternal allele ( Fig 2A ) . This is supported by an enrichment in maternal interactions between the Slc22a3 promoter and the Airn gene in VYS ( Fig 1 ) , along with a broad enrichment of the active enhancer mark H3K27ac across the Airn gene in VYS endoderm and placenta [11] , and multiple regions of open chromatin within the Airn gene other tissues [24] . Therefore , to test the enhancer interference model we devised an approach to delete the entire Airn gene in a mouse . We chose to take advantage of existing mouse strains to engineer a deletion of Airn by targeted recombination during male meiosis [29] . We bred together the Airn promoter deletion mouse ( R2Δ ) with a Sod2 exon 3 deletion mouse ( Sod2Δ ) and the Hprt-Cre mouse [18 , 30 , 31] . Both R2Δ and Sod2Δ contained a single loxP site in the same orientation , which enables Cre mediated trans recombination during male meiosis to generate either a deletion or duplication of the 270kb intervening region , including the entire 118kb Airn gene ( Fig 2B ) [29] . By mating males containing all 3 alleles with wildtype females , and screening 72 offspring we were able to identify 1 male founder that contained the deletion , which we then used to establish the RSDel strain ( R2Δ to Sod2Δ deletion ) . If the hypothesis that essential enhancers are present within the RSDel region is correct , deletion of these enhancers on the maternal allele where Airn is not expressed should prevent expression of Slc22a3 on this chromosome . Slc22a3 silencing should also be maintained on the paternal allele when these enhancers are deleted ( Fig 2C left ) . This would be in contrast to all other mutations of the Airn gene that disrupt Airn expression , but do not delete potential enhancers , and that lead to a loss of imprinted silencing [5 , 7 , 18 , 32] . Alternatively , if the hypothesis is false , we would expect that deletion of candidate regions on the maternal allele would not affect Slc22a3 expression , whereas deletion of the paternal allele would lead to a loss of imprinted silencing , similar to other Airn mutants ( Fig 2C right ) . To assess the effect of the RSDel deletion on Slc22a3 expression we collected embryonic tissue from reciprocal crosses to wildtype FVB mice . We isolated the VYS endoderm layer to focus on the most relevant cell type where Slc22a3 shows imprinted expression [17] . We found that when the deletion was maternally inherited there was no effect on Slc22a3 expression , whereas when the deletion was paternally inherited Slc22a3 expression doubled ( Fig 2D left ) . This correlated with a loss of Airn expression , indicating this increase in expression was due to a loss of imprinted expression , as with other Airn mutants [5 , 7 , 18 , 32] . The non-imprinted gene Tcp1 that lies within the RSDel deletion showed a similar expression level whether the deletion was inherited maternally or paternally ( Fig 2D left ) . Similarly , in placenta where Slc22a3 also shows imprinted expression , the maternal deletion did not affect Slc22a3 imprinted expression , but the paternal deletion and loss of Airn expression led to a doubling of Slc22a3 expression , whereas Tcp1 showed a similar level of expression in both deletions ( Fig 2D right ) . To directly test the effect of the RSDel deletion on imprinted expression , and to extend our analysis to other genes in the Igf2r imprinted cluster , we performed allele-specific expression analysis on RNA-seq of embryonic tissue collected from reciprocal crosses between RSDel and the genetically distinct CAST mice ( Fig 3A ) . We used the Allelome . PRO pipeline that we previously developed to analyze expression over SNPs between these strains [11 , 33] . For the maternal deletion , in VYS endoderm we found that Slc22a3 , and also Slc22a2 , maintained maternal imprinted expression , while Airn within the deletion maintained paternal imprinted expression as expected . The non-imprinted Tcp1 gene within the deletion switched to paternal expression due to loss of the maternal copy , whereas the non-imprinted Mllt4 gene lying 750kb outside of the deletion was unaffected ( Fig 3B ) . Similarly in placenta Slc22a3 , Pde10a and Airn maintained imprinted expression , while Tcp1 showed paternal only expression and Mllt4 biallelic expression as in VYS endoderm ( Fig 3C ) . For the paternal deletion , in VYS endoderm we observed a loss of imprinted expression for Slc22a3 and Slc22a2 , while Airn expression was completely lost as it is expressed exclusively from the paternal allele . As expected , Tcp1 within the deletion showed maternal only expression and Mllt4 expression was unaffected by the deletion ( Fig 3D ) . The results in placenta were similar , with Slc22a3 and Pde10a showing a loss of imprinted expression , Airn expression being completely lost , and Tcp1 and Mllt4 showing the expected expression pattern ( Fig 3E ) . In summary , the maternal RSDel deletion did not affect imprinted expression of Slc22a2 , Slc22a3 , and Pde10a in VYS endoderm and placenta ( Fig 2D , Fig 3B and 3C ) , whereas the paternal deletion led to a loss of imprinted expression ( Fig 2D , Fig 3D and 3E ) . These results support the alternative hypothesis ( Fig 2C right ) , and indicate that there are no essential enhancers for Slc22a3 expression within the Airn gene or the downstream region to Sod2 . Given that the RSDel deletion disproves the enhancer interference hypothesis , we sought to further investigate the predictions of alternative models of Airn-mediated imprinted silencing in extra-embryonic tissues . Airn has been proposed to recruit and target the histone modifying complexes EHMT2 and the polycomb repressive complexes 1 and 2 ( PRC1 and PRC2 ) to distant imprinted genes in extra-embryonic tissues [19 , 21] . However , the parental allele specific chromosome localization of H3K27me3 has not been investigated at the Igf2r cluster in extra-embryonic tissues . Therefore , we performed H3K27me3 chromatin immunoprecipitation sequencing ( ChIP-seq ) on VYS endoderm from FVB x CAST reciprocal crosses to determine the allele-specific distribution of this mark in the Igf2r cluster and throughout the genome . Using the Allelome . PRO pipeline to analyze the data [11 , 33] , we found paternal enrichment of H3K27me3 with matching H3K27ac maternal enrichment across the entire 10Mb Igf2r cluster , despite imprinted expression in VYS endoderm being limited to a 450kb region from Slc22a3 to Airn ( Fig 4A ) [11] . Within this region we found broad enrichment of H3K27me3 over the silenced paternal alleles of Slc22a3 and Slc22a2 ( Fig 4A ) , similar to the broad enrichment of H3K27me3 over biallelically silenced genes that we have previously reported [34] . In genome-wide analysis we found that 97% of H3K27me3 enriched windows mapped to imprinted regions , with the Igf2r imprinted cluster showing the greatest number of enriched windows , followed by the Kcnq1 cluster , which has been previously reported to show paternal allele enrichment of H3K27me3 over silenced imprinted genes in placenta ( Fig 4B ) [8 , 35] . Interestingly , the Sfmbt2 imprinted region reported to show H3K27me3 mediated DNA methylation independent imprinted expression [36] , showed the third highest level of H3K27me3 parental allele specific enrichment ( Fig 4B ) , with maternal allele enrichment over Sfmbt2 and the Blustr lncRNA shown to positively regulate its expression ( Fig 4C ) [37] . These results are consistent with H3K27me3 playing a role in the initiation and/ or maintenance of imprinted silencing for both the lncRNA-mediated silencing that occurs in the Igf2r and Kcnq1 clusters , as well as for lncRNA independent imprinted silencing , such as occurs in the Sfmbt2 imprinted cluster .
LncRNA mediated imprinted silencing of one copy of genes like Igf2r and Cdkn1c is required for development , but imprinted lncRNAs also provide a tractable model system for understanding gene regulation by lncRNAs in general [10] . In this study we used imprinted silencing of the upstream imprinted genes Slc22a2 , Slc22a3 and Pde10a by the lncRNA Airn , as a model for how lncRNAs may silence non-overlapped distant genes in cis . We found chromosome interactions on the active maternal allele between the Airn gene body and the Slc22a3 promoter supporting the previously proposed enhancer interference hypothesis [23] . However , a genetic test where we deleted the entire Airn gene demonstrated that Airn contains no essential enhancers for Slc22a3 disproving this model , and requiring the development of a new hypothesis to explain the data in this and previous studies . Previously it has been shown in placenta using a technique derived from RNA FISH called RNA TRAP ( Tagging and Recovery of Associated Proteins ) that Airn is associated with the Slc22a3 promoter [21] . Surprisingly in this study in VYS using 3C we found an association between the Slc22a3 promoter and the Airn gene on the maternal allele , and not on the paternal allele . However , these results are not contradictory , as an association between the Airn RNA and the Slc22a3 promoter on the paternal allele ( detectable by TRAP ) is not the same as an interaction between the Airn genomic locus and the Slc22a3 promoter on the maternal allele ( detectable by 3C ) . Here we show in VYS endoderm that the repressed alleles of Slc22a3 and Slc22a3 in the Igf2r cluster are covered by a broad enrichment of the PRC2 mark H3K27me3 , as are imprinted genes in the Kcnq1 cluster and in other imprinted clusters . Imprinted lncRNAs including Airn , Kcnq1ot1 and Meg3 have been reported to directly interact with PRC2 and EHMT2 [8 , 20 , 21] , although the Airn-PRC2 interaction was reported in embryonic stem ( ES ) cells where Airn is very lowly expressed and no genes in the Igf2r cluster show imprinted expression [11 , 20] . PRC1 , PRC2 and EHMT2 have been shown to be required to maintain imprinted silencing of members of the Kcnq1 cluster that show extra-embryonic specific imprinted expression [19 , 38] , and it has also been recently reported that imprinted silencing of Dlk1 by Meg3 requires PRC2 [39] . Igf2r imprinted silencing by Airn does not require PRC2 or EHMT2 [21 , 40] , but while the effect of loss of PRC1 and PRC2 on Slc22a3 and other members of the Igf2r cluster has not been tested , loss of EHMT2 has been shown to lead to loss of imprinted silencing of Slc22a3 [21] . Although it is technically difficult to exclude a role for transcription of the lncRNA versus the RNA product , together these results indicate that imprinted lncRNAs like Airn may silence distant imprinted genes like Slc22a2 , Slc22a3 and Pde10a by recruiting and targeted PRC1 , PRC2 and EHMT2 to these genes to deposit repressive chromatin modification and cause silencing . This indicates that Airn silences imprinted genes in the Igf2r cluster by two different mechanisms: Airn transcription silences Igf2r by transcription interference that does not require repressive chromatin modifying complexes [7] , and the Airn RNA product recruits repressive chromatin modifying complexes and targets them to distant , non-overlapped genes like Slc22a2 , Slc22a3 and Pde10a to cause silencing . Importantly , the mechanism for targeting silencing remains unknown . In this study we showed that chromosome interactions between the Airn gene body and the Slc22a3 promoter are enriched on the maternal allele because Airn expression represses these interactions on the paternal allele . We showed that these interactions are not required to upregulate Slc22a3 expression on the maternal allele , indicating that they are not essential promoter-enhancer interactions , but they may serve to place the Airn locus and Slc22a3 promoter in close proximity in the nuclear space . Chromosome interactions can exist in the ground state or be formed during development [41] . Therefore , we propose that in the ground state interactions between the Airn locus and the promoter of Slc22a3 ( and likely all other genes silenced by Airn , like Slc22a2 ) , are present on both alleles ( Fig 5 left ) . During development Airn is upregulated on the paternal allele , and these pre-existing interactions allow Airn to target gene promoters while recruiting PRC2 and EHMT2 to deposit repressive histone modifications ( Fig 5 middle ) . The establishment of repressive chromatin on the targeted promoters then leads to the loss of chromosome interactions with the Airn locus on the paternal allele ( Fig 5 right ) . On the paternal allele the Airn RNA and Slc22a3 promoter are also in close proximity [21] . This may be achieved by the formation of a compacted repressive chromatin domain [19] , which may allow Airn to continue to find repressed promoters to help maintain silencing despite the loss of the chromosome interactions . Imprinted genes show tissue-specific expression . In the Igf2r and Kcnq1 cluster , where imprinted silencing is initiated by a lncRNA , genes closer to or overlapped by the lncRNA locus show imprinted expression in multiple tissues , whereas the more distant genes show imprinted expression restricted to extra-embryonic tissues [5 , 11 , 17 , 42] . Our model seeks to explain silencing of these distant , extra-embryonic specific imprinted genes . In the Igf2r cluster there is a relatively clear distinction between Igf2r , which is overlapped in antisense by Airn and silenced by transcriptional interference [7] , and other genes in the cluster that are not overlapped and are silenced only in extra-embryonic tissues . However , it has been recently shown that Slc22a3 also shows imprinted expression in neonatal tongue and adult liver [11] , and Kcnq1ot1 does not overlap the promoter of any of the proximal imprinted genes in the Kcnq1 cluster that show imprinted expression in multiple tissues [42] . Interestingly , Kcnq1 itself has been reported to be subject to lncRNA independent imprinted silencing despite Kcnq1ot1 lying within one of its introns , and to lose imprinted expression during heart development [43] . Therefore , it remains to be tested if the model can explain lncRNA mediated imprinted silencing of non-overlapped genes in all tissues , or if it is restricted to the specific epigenetic environment present in extra-embryonic tissues , known to have unique features such as low levels of DNA methylation [44 , 45] . Our model has parallels with one proposed with to explain how the lncRNA Xist may find its targets during the initiation of X inactivation . Xist initially binds at discrete sites throughout the X chromosome , before spreading to cover the whole chromosome [46] . Similar to imprinted lncRNAs like Airn and Kcnq1ot1 , Xist recruits and targets repressive histone modifying complexes like PRC1 and PRC2 to chromatin as part of the X inactivation process [47] . These early binding sites correlate with the Hi-C interaction map in undifferentiated ES cells that have 2 active X chromosomes , indicating that pre-existing interactions in the ground state may guide Xist to initiate silencing at these sites [46] . Imprinting and X inactivation show allele-specific differences in gene silencing and chromosome interactions within the same cell , making them powerful model systems for uncovering the mechanism of lncRNA mediated silencing . LncRNAs that show biallelic silencing may act in a similar way , but without a picture of chromosome interactions in the ground state detecting distant target genes may be difficult . Future studies should focus on testing the predictions of the model in imprinted and non-imprinted systems .
Mice were housed and treated according to Austrian law under Laboratory Animal Facility Permit GZ: 311633/2014/9 that was approved by the Office of the Vienna provincial government . Mice were maintained in accordance with the procedures outlined in the Guide for the Care and Use of Laboratory Animals from the NIH , the opinion of the European Group on Ethics in Science , and the European Union ( EU ) Protocol on the Protection and Welfare of Animals . The Animal Research is covered by Federal Austrian legislation ( Law of Animal Experiments 2012 ( “TVG-Tierversuchsgesetz”; regulating the “Experimentation on living animals” BGBI . I Nr . 114/2012 ) and the overriding EU and international legislation and codes of conduct . No experimental procedures were performed on the animals so no extra permissions were required . FVB/NJ ( FVB ) mice were obtained from Charles River . CAST/EiJ ( CAST ) mice were obtained from the Jackson Laboratory . The FVB . AK-Del ( 17 ) T<hp> ( Thp ) mouse ( EM:09898 ) contains an minimum 5 . 56Mb deletion on chromosome 17 that includes the Igf2r imprinted cluster allowing parental allele-specific analysis [27 , 28] . The FVB . 129P2-Airn<tm1Dpb> ( AirnT ) mouse ( EM:09895 ) has a polyadenylation cassette inserted into the Airn gene , 3kb downstream from its start site causing it to be truncated and non-functional [5] . The FVB . 129P2-Airn-R2D ( R2Δ ) mouse ( EM:09897 ) has a deletion that includes the Airn promoter and the imprint control element ( ICE ) of the Igf2r imprinted cluster [18] . Note that the Thp , AirnT and R2Δ mice have been cryopreserved by the EMMA mouse repository ( EMMA ID indicated ) . The Sod2-flox mice contain loxP sites flanking the exon 3 of Sod2 and were made in a 129 ES cell line [30] . The Hprt-Cre mice express Cre during male meiosis and were a kind gift from Simon Hippenmeyer [31] . Note in mouse crosses the maternal allele is always written on the left . The RSDel mice were created by Hprt-Cre-mediated trans-recombination during male meiosis between the remaining loxP site in the R2Δ and Sod2Δ alleles [18 , 29–31] . In the first generation Sod2-flox mice recovered from frozen embryos were crossed to Hprt-Cre mice , while in parallel Hprt-Cre was crossed to R2Δ , and the offspring of both crosses were genotyped . In the second generation Hprt-Cre/R2Δ were crossed to Sod2Δ , and Hprt-Cre/ Sod2Δ was crossed to R2Δ , and the offspring were screened for triple mutant males . In the third generation Hprt-Cre/R2Δ/Sod2Δ triple mutant males were crossed to FVB females and the offspring screened for the RSDel deletion . One RSDel male was detected among 72 offspring , and this male was backcrossed to FVB to establish the RSDel strain . Note that this strain has been cryopreserved and is stored at IMBA . Placenta was isolated from E12 . 5 embryos under a dissection microscope , taking care to remove as much decidua as possible . Visceral yolk sac ( VYS ) was isolated from E9 . 5 and E12 . 5 embryos under a dissection microscope . The whole VYS was used for the chromosome conformation capture ( 3C experiments ) , while for RNA isolation and for chromatin immunoprecipitation ( ChIP ) the VYS endoderm was mechanically separated away from the rest of the VYS after 1–2 hours of DispaseII digestion at 4°C , as previously described [17] . Chromosome Conformation Capture ( 3C ) was performed following established protocols with minor modifications [48 , 49] . To allow the maternal and paternal chromosome to be examined separately at the Igf2r imprinted locus we used reciprocal crosses of Thp and FVB mice [28] . To determine the influence of Airn on interactions we used Thp x AirnT cross , where AirnT mice have a truncated and non-functional Airn [5] . We collected visceral yolk sac ( VYS ) samples from E12 . 5 embryos , and processed samples for 3C using a protocol adapted from a method designed for cell culture cells with minor modifications [48] . Briefly , single VYS were fixed for 10 minutes in 500μl 2% formaldehyde/PBS at room temperature , before quenching by adding 56μl 2 . 5M glycine and incubating for 5 minutes at room temperature and then for at least 20 minutes on ice . The liquid was then removed and the samples frozen on dry ice before being stored at -80°C . DNA isolated from the embryonic heads was used to genotype samples by a DNA methylation sensitive Southern blot assay using a EcoRI/MluI digest and a 1013bp probe ( chr17:12 , 741 , 515–12 , 742 , 527; GRCm38/mm10 ) , which detects a 6 . 3kb ( methylated ) and 5 . 0kb ( unmethylated ) band at the differentially methylated Igf2r imprint control element ( ICE ) . Around 28 VYS were then pooled per genotype and thawed on ice and then incubated with 8ml lysis buffer for 15 minutes on ice ( 1 tab Complete Protease Inhibitor ( Roche ) per 25ml lysis buffer ( 10mM Tris-HCl ph8 , 10mM NaCl , 0 . 2% NP-40 ) ) . The samples were then dounced in a 15ml glass dounce ( Wheaton ) with a loose pestle about 30 times , and then 30 times with a tight pestle , before centrifuging for 5 minutes at 2000g at 4°C . The supernatant was then removed leaving a nuclear pellet , which was then resuspended in 1 x EcoRI buffer ( Fermentas ) . Samples were then subject to EcoRI digestion and ligation as previously described [48] . The formaldehyde crosslinks were then reversed by proteinase K treatment ( 66μg/ml ) and heating at 65°C overnight , followed by another 2 hours at 65°C with fresh proteinase K . The samples were then subject to phenol/chloroform extraction , precipitated , then resuspended in TE buffer before being subject to dialysis overnight at 4°C . The 3C material was then again precipitated and the pellet washed x6 with 70% ethanol and x2 with 100% ethanol , before finally being resuspended in 500 μl TE buffer . We detected 3C interactions by Taqman quantitative PCR following a previously published protocol with minor modifications [49] , and by using the standard curve method to analyze qPCR data . Briefly , a primer and Taqman probe were designed near to an EcoRI site on the “bait” EcoRI fragment ( e . g . Slc22a3 promoter ) and a “prey” primer was designed near the EcoRI site for fragments in the target region ( e . g . Airn gene body ) . All 3C interactions detected in the Igf2r imprinted cluster ( Thp deletion region on chromosome 17 ) were normalized by dividing by the mean of 2 interactions with the H19/Igf2 ICE , an independent locus on chromosome 7 . To correct for technical and biological variation between experiments the highest interaction level was then set to 1 . The primer/probe combinations used are given in S1 Table . Tissue from VYS endoderm or placenta was collected and homogenized in TRI reagent , and total RNA isolated according to the manufacturers protocol ( Sigma-Aldrich ) . Total RNA from E9 . 5 VYS endoderm and E12 . 5 placenta collected from RSDel x FVB reciprocal crosses was DNase treated using the DNA-free kit ( ThermoFisher Scientific ) , and then converted to cDNA using the RevertAid First Strand cDNA Synthesis Kit ( Thermo Fisher Scientific ) . Reverse transcription quantitative polymerase chain reaction ( RT-qPCR ) was then conducted using either a Taqman or SYBR Green system using the standard curve method to analysis the data , and normalization to a house keeping gene ( cyclophillin A ) . The RT-qPCR results in Fig 2 are shown relative to the mean of the wildtype controls . The primers and probes used are listed in S2 Table . Strand-specific polyA enriched RNA-seq libraries were generated from E12 . 5 placenta and VYS endoderm from RSDel x CAST reciprocal crosses using the TruSeq RNA Sample Prep Kit v2 ( Illumina ) modified as previously described [50] . For each tissue , a total of 12 libraries were generated: 3x WT and 3x Del ( RSDel x CAST , maternal deletion cross ) and 3x WT and 3x Del ( CAST x RSDel , paternal deletion cross ) . Native ChIP was performed using an H3K27me3 antibody ( Jenuwein lab antibody 6523 , 5th bleed ) on E12 . 5 VYS endoderm from FVB x CAST reciprocal crosses ( 2x CAST x FVB , 2x FVB x CAST , tissues from multiple litters were pooled ) as previously described [51] . ChIP-seq libraries were prepared using the TruSeq ChIP Sample Prep Kit ( Illumina ) . Both , RNA-seq and ChIP-seq libraries were sequenced with a 50bp single end on an Illumina HiSeq 2000/2500 . Note , that the H3K27ac ChIP-seq data from VYS endoderm included in this study was described in a previous study [11] . Allele-specific expression and histone modification enrichment was detected from RNA-seq and ChIP-seq data using the Allelome . PRO program [33] . The SNP annotation file containing 20 , 601 , 830 high confidence SNPs between the CAST/EiJ and FVB/NJ strains was extracted from the Sanger database as described previously described [33 , 52] . For RNA-seq analysis , but not ChIP-seq , SNPs overlapping retroposed genes including pseudogenes were removed ( RetroGenes V6 from UCSC genome browser ) . The RSDel mouse was backcrossed to FVB , but the region around the Igf2r cluster is likely to have a 129 background as both the R2Δ and Sod2Δ alleles from which the RSDel mouse is derived were made in 129 ES cells [18 , 30] . Therefore , in our allele-specific RNA-seq analysis we used only CAST/FVB SNPs where the FVB allele was shared with all three sequenced 129 strains ( Final SNP number: 16 , 988 , 479 SNPs ) . We used the following Allelome . PRO parameters for our analysis: RNA-seq: minread 2 ( allelic ratios extracted from debug folder ) , RefSeq annotation . H3K27me3 ChIP-seq enrichment: FDR 1% , allelic ratio cutoff 0 . 7 , minread 1 , 20Kb sliding windows . Note: minread = minimum number of reads that must cover a SNP for it to be included in the analysis .
|
Long non-coding ( lnc ) RNAs are numerous in the mammalian genome and many have been implicated in gene regulation . However , the vast majority are uncharacterised and of uncertain function making known functional lncRNAs valuable models for understanding their mechanism of action . One mode of lncRNA action is to recruit epigenetic silencing to target distant genes on the same chromosome . A well-characterized group of lncRNAs that act in this way to silence genes are imprinted lncRNAs . In this study we examined how the imprinted lncRNA Airn silences genes in the Igf2r imprinted cluster , focusing primarily on silencing of the distant imprinted gene Slc22a3 . We found that Airn expression blocks chromosome interactions between the Slc22a3 promoter and the Airn gene locus . By making a large genomic deletion including the Airn gene we showed that these interactions are not essential enhancer/promoter interactions , but may help to guide the Airn RNA to target genes to recruit epigenetic silencing . Our study adds to the understanding of how lncRNAs may act to silence distant genes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"prader-willi",
"syndrome",
"long",
"non-coding",
"rnas",
"disorders",
"of",
"imprinting",
"developmental",
"biology",
"epigenetics",
"chromatin",
"embryology",
"genetic",
"interference",
"chromosome",
"biology",
"genomic",
"imprinting",
"gene",
"expression",
"genetic",
"loci",
"clinical",
"genetics",
"biochemistry",
"rna",
"cell",
"biology",
"nucleic",
"acids",
"endoderm",
"genetics",
"biology",
"and",
"life",
"sciences",
"non-coding",
"rna"
] |
2019
|
The Airn lncRNA does not require any DNA elements within its locus to silence distant imprinted genes
|
Chagas disease ( CD ) , caused by the protozoan Trypanosoma cruzi , is a prototypical neglected tropical disease . Specific immunity promotes acute phase survival . Nevertheless , one-third of CD patients develop chronic chagasic cardiomyopathy ( CCC ) associated with parasite persistence and immunological unbalance . Currently , the therapeutic management of patients only mitigates CCC symptoms . Therefore , a vaccine arises as an alternative to stimulate protective immunity and thereby prevent , delay progression and even reverse CCC . We examined this hypothesis by vaccinating mice with replication-defective human Type 5 recombinant adenoviruses ( rAd ) carrying sequences of amastigote surface protein-2 ( rAdASP2 ) and trans-sialidase ( rAdTS ) T . cruzi antigens . For prophylactic vaccination , naïve C57BL/6 mice were immunized with rAdASP2+rAdTS ( rAdVax ) using a homologous prime/boost protocol before challenge with the Colombian strain . For therapeutic vaccination , rAdVax administration was initiated at 120 days post-infection ( dpi ) , when mice were afflicted by CCC . Mice were analyzed for electrical abnormalities , immune response and cardiac parasitism and tissue damage . Prophylactic immunization with rAdVax induced antibodies and H-2Kb-restricted cytotoxic and interferon ( IFN ) γ-producing CD8+ T-cells , reduced acute heart parasitism and electrical abnormalities in the chronic phase . Therapeutic vaccination increased survival and reduced electrical abnormalities after the prime ( analysis at 160 dpi ) and the boost ( analysis at 180 and 230 dpi ) . Post-therapy mice exhibited less heart injury and electrical abnormalities compared with pre-therapy mice . rAdVax therapeutic vaccination preserved specific IFNγ-mediated immunity but reduced the response to polyclonal stimuli ( anti-CD3 plus anti-CD28 ) , CD107a+ CD8+ T-cell frequency and plasma nitric oxide ( NO ) levels . Moreover , therapeutic rAdVax reshaped immunity in the heart tissue as reduced the number of perforin+ cells , preserved the number of IFNγ+ cells , increased the expression of IFNγ mRNA but reduced inducible NO synthase mRNA . Vaccine-based immunostimulation with rAd might offer a rational alternative for re-programming the immune response to preserve and , moreover , recover tissue injury in Chagas’ heart disease .
Chagas disease ( CD ) is a neglected tropical illness caused by the protozoan parasite Trypanosoma cruzi , which is transmitted by blood-sucking triatomines . The disease afflicts 8 to 15 million people in Latin America; more than 40 , 000 new cases occur every year and the rate of congenital transmission is greater than 14 , 000 cases per year . Furthermore , approximately 1 million immigrants to the USA and Europe have CD [1] . Despite the successful control of the main vector , an overview of the current challenges reveals the need for ( i ) permanent vector surveillance and attention to domestic and peri-domestic reservoirs of the parasite , ( ii ) new strategies to prevent or abrogate infection and ( iii ) new therapies for patients with chronic forms of CD [2] . The most frequent and severe manifestation of CD is chronic chagasic cardiomyopathy ( CCC ) , which is associated with inflammation , myocytolysis and fibrosis and affects 20–40% of infected individuals at 10–30 years after infection [1] . Innate and adaptive immunity play pivotal roles in parasite growth control during the acute phase of infection , allowing the establishment of chronic phase [3] . However , in patients with Chagas’ heart disease the natural immune response is mostly inadequate as parasite persistence and parasite-induced deregulated immune response are consensual explanations for CCC pathogenesis [4 , 5] . Several studies have proposed veterinary vaccine as tools to prevent infection , particularly to decrease parasitemia in hosts and reservoirs , such as dogs , to control the domestic and peri-domestic cycle [6] . Furthermore , human vaccines would generate a positive return on investment , because such vaccines would prevent the onset of CCC and provide both cost savings and health benefits [7] . In chronic T . cruzi infections , vaccination should be considered as a therapeutic strategy to redirect immunity to a protective status to delay disease progression and reverse heart alterations in chronic patients . Many attempts to generate a prophylactic vaccine for CD have been conducted in the last three decades , including use of the attenuated parasite , purified protein , recombinant protein and DNA and , more recently , replication-deficient bacteria and recombinant viral vectors to reduce acute parasitism and heart inflammation [8–11] and chronic myocarditis [12] . The amastigote surface protein-2 ( ASP2 ) , a protein with unknown function [13] and trans-sialidase ( TS ) , a trypomastigote-restricted enzyme that catalyzes the transfer of sialic acid from host glycoproteins to acceptor molecules on the parasite membrane [14] , are two of the most promising candidates for vaccine development . Researchers have attempted to produce an immunotherapeutic vaccine; however , these preparations were unable to control disease progression [10] . Furthermore , no vaccines that can reverse chronic Chagas’ heart disease are available . Vaccines using replication-deficient human recombinant Type 5 adenoviruses ( AdHu5 ) carrying sequences of the ASP2 ( rAdASP2 ) and TS ( rAdTS ) proteins of the Y T . cruzi Type II strain [15] elicited Th1-biased immunity with a substantial CD8+ T-cell-mediated long-term protective immune response against challenge with the Y strain [16] . Furthermore , heterologous priming with plasmid DNA and boosting with rAdASP2 and rAdTS protected mice from challenges with the CL and Colombian T . cruzi strains , thereby demonstrating cross protective immunity [17] . Based on the results of previous studies , we tested the therapeutic properties of combined rAdASP2+rAdTS ( rAdVax ) in a homologous prime-boost protocol to skew the immune response to prevent , hamper progression and potentially reverse chronic Chagas’ heart disease . Therefore , we challenged the idea that unappropriated immune response contributes to cardiac abnormalities and once it has been reshaped heart damage progression may be delayed and , even , reversed .
Initially , we determined the ability of rAdVax to induce antibodies and specific CD8+ T-cells , which are considered protective in T . cruzi infection [16 , 17] . To this end , C57BL/6 mice were vaccinated twice with rAdVax or rAdCtrl as described in the Materials and Methods section . Three to four weeks after boosting , the sera were collected and analyzed using ELISA and the CD8+ T-cell response was analyzed by in vivo cytotoxic assay and ELISpot for IFNγ detection ( S1A Fig . ) . Specific total antibodies ( IgM+IgG ) against recombinant ASP2 and TS proteins were detected in rAdVax-immunized mice , whereas saline-injected or rAdCtrl-immunized mice presented negligible reactivity to these proteins ( S1B Fig . ) . Protection against T . cruzi depends on CD8+ T-cell effector activities [4] . Significantly , high frequency of H-2kb-restricted anti-VNHRFTLV CTL CD8+ T-cells were detected in the spleen of rAdVax vaccinated mice but not in saline-injected or in rAdCtrl-immunized mice ( S1C and S1D Fig . ) . Moreover , immunization with rAdVax induced a significant increase in the number of ASP2-specific IFNγ-producing CD8+ T-cells , which are of pivotal importance in T . cruzi growth control [3] . In contrast , specific IFNγ-producing CD8+ T-cells were absent in saline-injected mice and the number of these cells was reduced in rAdCtrl-immunized mice ( S1E Fig . ) . Therefore , vaccination with rAdVax in a homologous prime-boost protocol stimulated anti-T . cruzi IgM+IgG antibodies and parasite-specific CTL and IFNγ-producing CD8+ T-cells , corroborating our previous data [16 , 17] . Initially , we tested whether prophylactic rAdVax administration could reduce heart parasitism and tissue damage in a model of acute phase of infection [17] . rAdVAx reduced the acute heart parasitism and cardiomyocyte damage induced by the CL-Brener T . cruzi Type VI strain ( S2 Fig . ) . Next , we examined whether a homologous prime-boost vaccination with rAdVax could prevent T . cruzi-induced CCC . When C57BL/6 mice are infected with 100 blood trypomastigote ( bt ) forms of the Colombian strain , parasitemia peak occurs at 42–45 dpi , parasitemia control occurred from 60–70 dpi and chronic phase is established after 90 dpi , when none or rare blood trypomastigotes are detected [5] . Importantly , in this model of T . cruzi infection parasitemia and heart parasitism are directly associated and 70–85% of infected mice survived and developed a chronic disease with electrical abnormalities [5 , 18] . Therefore , vaccinated C57BL/6 mice were challenged with 100 bt forms of the Colombian strain and tested for heart tissue parasitism and electrical alterations in the acute and chronic infection ( Fig . 1A ) . The rAdVax vaccination did not alter parasitemia curve or the number of circulating parasites at the peak of parasitemia at 42 dpi ( 34 . 7 ± 17 × 103 trypomastigotes/mL in rAdCtrl vs . 30 . 2 ± 15 . 7 × 103 trypomastigotes/mL in rAdVax; P > 0 . 05 ) . Although the prophylactic administration of rAdVax did not affect T . cruzi-induced splenomegaly ( Fig . 1B ) , the number of parasite nests in the heart tissue was significantly reduced during the acute phase ( Fig . 1C ) . rAdVax did not alter the numbers of CD4+ cells and F4/80+ macrophages , but reduced the number of CD8+ cells , infiltrating the cardiac tissue , at 50 dpi ( S3 Fig . ) . Further , in rAdVax-vaccinated mice no significant alterations in myocarditis were detected at 150 dpi ( 2206 ± 719 inflammatory cell/100 microscopic fields in rAdCtrl vs . 1989 ± 934 inflammatory cell/100 microscopic fields in rAdVax; P > 0 . 05 ) . Electrical abnormalities , including low heart rate , arrhythmia ( ART ) and first- and second-degree atrioventricular block ( AVB1 and AVB2 ) , are important features of the chronic cardiomyopathy induced by infection with the Colombian T . cruzi strain in C57BL/6 mice [5] . Notably , immunization with rAdVax remarkably decreased the frequency of mice presenting ART , particularly sinus arrhythmia ( sART ) and AVB2 at 150 dpi ( Fig . 1D ) . Moreover , compared with rAdCtrl injection , rAdVax inoculation reduced the frequency of mice afflicted with ART , AVB1 ( 100% in saline-injected , 100% in rAdCtrl-treated and 20% in rAdVax ) , AVB2 and other electrical abnormalities ( Fig . 1E ) . These observations supported our hypothesis that rAdVax is a feasible tool to ameliorate the outcome of chronic Chagas’ heart disease . To test the hypothesis that a vaccine might delay progression and , even , reverse CCC , chronically infected mice ( at 120 dpi ) were subjected to the homologous prime-boost vaccination with rAdVax ( Fig . 2A ) . At 120 dpi , electrocardiogram ( ECG ) abnormalities , such as low heart rate ( Fig . 2B ) and prolonged P wave , were evident ( Fig . 2B and 2C ) . Indeed , an analysis of all T . cruzi-infected mice showed that at 120 dpi ( pre-therapy ) , 80% of the Colombian-infected C57BL/6 mice were afflicted with electrical abnormalities ( Fig . 2D ) . At 150 dpi , 100% of the not-treated or saline-injected Colombian-infected C57BL/6 mice presented ECG alterations ( as shown in Fig . 1E ) , corroborating previous data [18 , 19] . At 160 dpi ( 40 days post-therapy initiation ) , 100% of rAdCtrl-injected mice presented ART and 70% presented AVB2 in a manner that 100% showed ECG abnormalities . In contrast , only 40% of rAdVax-immunized mice presented electrical abnormalities , suggesting that immunotherapy with recombinant rAdVax decreased the progression of CCC . Moreover , whereas 80% of T . cruzi-infected mice presented ART and other ECG abnormalities before therapy ( at 120 dpi ) , only 40% of rAdVax-vaccinated mice presented ART and AVB2 at 230 dpi . Taken together , these data demonstrated that homologous prime-boost immunotherapy with rAdVax vaccine reversed the chronic electrical conduction abnormalities induced by infection with the Colombian strain of T . cruzi . The chronic infection of C57BL/6 mice with the Colombian strain of T . cruzi induces CCC , which is characterized by heart injury with connexin-43 ( Cx43 ) disorganization and fibronectin ( FN ) deposition in the cardiac tissue and increased CK-MB activity in the serum [5 , 18 , 20] . Therefore , we tested the capacity of the homologous prime-boost rAdVax immunotherapy to reverse heart injury . To this end , C57BL/6 mice were infected and all mice were analyzed at 120 dpi ( pre-therapy ) , when groups were formed , and the prime-boost rAdVax immunization protocol was initiated . A group of T . cruzi-infected mice was euthanized and the tissues were collected ( pre-therapy ) . At 160 dpi and 40 days post-therapy , all mice were analyzed for electrical alterations , boosted and analyzed at 230 dpi , 110 days post-therapy ( Fig . 3A ) . No difference in survival rate was observed in T . cruzi-infected mice that received saline or rAdCtrl and all mice in these groups were dead at 200 dpi ( Fig . 3B ) . At 230 dpi , 87% of rAdVax-immunized mice survived ( 13/15 ) , compared with 0% of rAdCtrl-inoculated ( 0/14 ) and saline-injected ( 0/14 ) mice ( Fig . 3B ) . The surviving mice were analyzed at 230 dpi ( 110 days post-therapy ) for heart electrical abnormalities , sacrificed and analyzed for cardiac tissue alterations compared with mice sacrificed at 120 dpi ( pre-therapy ) . In post-therapy rAdVax-inoculated mice ( at 230 dpi ) , there was a significant decrease in T . cruzi-induced splenomegaly ( P < 0 . 01 ) . Similarly to pre-therapy ( 120 dpi ) mice , low heart parasitism persisted in rAdVax-immunized mice ( Fig . 3C ) . In addition , pre-therapy and post-therapy parasites were rarely detected in the circulating blood ( 110 ± 70 . 2 × 103 trypomastigotes/mL in pre-therapy vs . 22 . 8 ± 9 . 5 × 103 trypomastigotes/mL in rAdVax; P > 0 . 05 ) . Nevertheless , immunotherapy with rAdVax significantly reduced FN deposition in the cardiac tissue ( Fig . 3D and 3E ) . Furthermore , Cx43 disorganization in the cardiac intercalary discs revealed as enhanced distance of Cx43-stained junctions , a marker of cardiomyocyte injury [21] , was significantly reversed in mice treated with rAdVax compared with the pre-therapy condition ( Fig . 3E ) . In addition , levels of CK-MB activity in the serum were lower in post-therapy mice compared with pre-therapy mice ( Fig . 3F ) . Thus , these data support that immunotherapy with rAdVax during chronic T . cruzi infection ameliorated electrical abnormalities and recovered heart tissue injury . Next , we investigated whether the beneficial effect of the prime-boost immunotherapy with rAdVax in chronically infected C57BL/6 mice was associated with reduction in the abnormal polyclonal activation observed in chronically infected mice and/or shift of the immune response to a protective profile . To this end , chronically infected mice received the homologous prime-boost vaccine rAdVax and were analyzed at 190 and 230 dpi , corresponding to 70 and 110 days post-therapy ( Fig . 4A ) . As depicted in Fig . 4B , the potent IFNγ recall response after stimulation of mononuclear spleen cells with anti-CD3 plus anti-CD28 , previously described as a hallmark of chronically T . cruzi-infected mice [22] , was reproduced in the present study in the Colombian-infected rAdCtrl-immunized mice . In contrast , this response was abrogated by rAdVax immunotherapy ( Fig . 4B ) . Moreover , the intense anti-CD3 plus anti-CD28-triggered lymphoproliferative response observed in total splenic T-cells from T . cruzi-infected mice injected with rAdCtrl was also inhibited by rAdVax immunotherapy ( Fig . 4C ) . Notably , the increased anti-CD3 plus anti-CD28-triggered CD8+ T-cell proliferation observed during chronic infection was also reversed by rAdVax inoculation ( Fig . 4D ) , whereas CD8+ T-cell recognition of the H-2Kb-restricted VNHRFTLV ASP2 peptide was preserved in rAdVax-immunized mice ( Fig . 4E ) . Then , we evaluated the frequency of CD8+ T-cells expressing CD107a , a marker for T-cell degranulation used to evaluate CTL activity [23] . Ex vivo , the frequencies of CD8+ T-cells in the spleen were similar in all studied groups ( Fig . 5A , box ) . Compared with age-matched NI control mice , there is an increase in the proportions of CD8+ T-cells expressing CD107a in T . cruzi-infected mice injected with rAdCtrl ( Fig . 5A ) . Importantly , this increase in the frequency of CD107a+ CD8+ T-cells was abrogated by the therapeutic rAdVax administration ( Fig . 5A ) . Further , when in vitro stimulated with VNHRFTLV ASP2 peptide there is a preferential response of CD8+IFNγ+CD107a+ cells and CD8+CD107a+ cells in mice injected with rAdCtrl ( Fig . 5B ) . Therapeutic rAdVax administration significantly reduced the frequency of CD8+IFNγ+CD107a+ cells and , moreover , diminished the frequency of CD8+CD107a+ cells recognizing the VNHRFTLV ASP2 peptide ( Fig . 5B ) . It was previously demonstrated that perforin+ and IFNγ+ CD8+ T-cells play antagonistic roles in the heart tissue of T . cruzi-infected mice [5]; therefore , we examined whether rAdVax immunization influenced the number of cytotoxic ( Pfn+ ) and inflammatory ( IFNγ+ ) cells composing the chronic T . cruzi-induced myocarditis . All groups of chronically T . cruzi-infected mice presented increased numbers of Pfn+ and IFNγ+ cells infiltrating the heart tissue compared with age-matched NI controls ( Fig . 6A ) . However , compared with rAdCtrl-injected mice rAdVax-immunized mice showed reduced number of Pfn+ cells but similar number of IFNγ+ cells infiltrating the cardiac tissue ( Fig . 6A ) . Moreover , therapy with rAdVax ( analysis at190 dpi; 70 days post vaccine therapy ) decreased the number of Pfn+ cells infiltrating the heart tissue compared with mice pre-therapy ( 120 dpi ) . Interestingly , the IFNγ/Pfn ratio was increased in rAdVax-immunized chronically infected mice ( 5 . 83 ) compared with rAdCtrl-injected mice ( 3 . 20 ) and pre-therapy mice ( 2 . 09 ) ( Fig . 6A , box ) . Compared with NI control mice , a significant increase in IFNγ mRNA expression was detected in the cardiac tissue obtained from infected mice before ( at 120 dpi ) and after ( at 190 dpi ) immunotherapy with rAdCtrl and rAdVax ( Fig . 6B ) . Remarkably , vaccination with rAdVax significantly increased the expression of IFNγ mRNA in comparison with rAdCtrl-immunized and pre-therapy mice ( Fig . 6B ) . Therefore , these data support that rAdVax shaped the IFNγ/Pfn balance in the chronic T . cruzi-induced myocarditis favoring the presence of IFNγ+ cells and the production of IFNγ . Compared with pre-therapy chronically infected mice , rAdVax-inoculated mice exhibited no alterations in the serum concentrations of the inflammatory cytokines IL-1α , IL-2 , IL-4 , IL-5 , IL-10 , IL-17 and TNF ( S4A and S4B Fig . ) . Interestingly , rAdVax immunization significantly increased IFNγ levels compared with pre-therapy T . cruzi-infected mice ( S4A Fig . and Fig . 6C ) . Lastly , the NO/ iNOS pathway has been associated with heart injury and CCC severity in chronic T . cruzi infection [18 , 24] . The kinetic study of serum NOx levels in T . cruzi-infected mice revealed that during acute infection , NOx concentrations in the serum ( S5A Fig . ) paralleled parasitemia ( at 42–45 dpi ) and higher levels of CK-MB activity in the serum ( S5B Fig . ) . After parasite control , a reduction in NOx levels was detected; however , during the chronic phase of infection , NOx levels were increased and paralleled the levels of CK-MB activity in the serum ( S5B Fig . ) . Therefore , we assessed the effect of rAdVax therapy in chronically infected mice on the concentration of serum NOx and the expression of iNOS/NOS2 in cardiac tissue . All chronically T . cruzi-infected mice shown increased NOx levels in the serum compared with age-matched NI controls ( Fig . 7A and 7B ) . Interestingly , in comparison with rAdCtrl-inoculated mice rAdVax-immunized mice ( at 190 dpi; 30 days after the boost immunization with rAdVax ) showed a significant decrease in the concentrations of NOx in the serum ( Fig . 7A ) . Furthermore , a kinetic study revealed a significant reduction in the levels of NOx in the serum of rAdVax-immunized mice ( 190 dpi; 30 days after the boost ) in comparison with rAdCtrl mice . Further , reduction of serum NOx level was more prominent after two doses of rAdVax therapy , ( 230 dpi; 70 days after the boost immunization with rAdVax ) ( Fig . 7B ) . Moreover , there was a significant decrease in the levels of NOx in the serum of mice that received two doses of rAdVax compared with pre-therapy mice ( Fig . 7B ) . Importantly , the expression of iNOS/NOS2 mRNA was significantly reduced in the cardiac tissue of chronically infected mice immunized with two doses of rAdVax , in comparison with pre-therapy mice ( Fig . 7C ) .
One of the greatest challenges in chronic Chagas’ heart disease is to develop therapies that improve prognosis and , even , reverse cardiac injury . Here we used the recombinant Type 5 adenovirus carrying sequences of the trans-sialidase family ASP2 and TS proteins in a homologous prophylactic or therapeutic prime-boost protocol aiming at inhibiting development and progression or reversing chronic T . cruzi-induced heart damage . The prophylactic rAdVax immunization successfully reduced acute heart parasitism and cardiomyocyte damage and decreased the frequency of mice afflicted by chronic electrical abnormalities due to challenge with the Colombian T . cruzi Type I strain . Moreover , the therapeutic administration of rAdASP2+rAdTS to chronically Colombian-infected mice afflicted by CCC decreased cardiopathy progression and , remarkably , reversed electrical abnormalities and heart tissue injury . Further investigation showed that the clinical beneficial effects were associated with reduction of the prototypical T . cruzi-induced polyclonal T-cell proliferation and , moreover , with reprogramed immune responses , both systemically and in the heart tissue , favoring IFNγ production and decreasing cytotoxic activity , NOx production and iNOS/NOS2 expression in T . cruzi infection . The prophylactic administration of rAdASP2 , rAdTS or rAdASP+rAdTS combined vaccines in prime-boost protocols elicited humoral and cellular immune response [16 , 17] . Vaccination of C57BL/6 ( H-2b ) with either rAdASP2 or rAdTS increased survival frequency , but only the combined rAdASP+rAdTS inoculum induced complete and long lasting protection against a challenge with the Y T . cruzi Type II strain parasites [16] . Further , the combined rAdASP+rAdTS vaccination of A/Sn ( H-2a ) mice was shown to be protective against a challenge with the Y and Colombia T . cruzi Type II strains [17] . In addition , a heterologous plasmid DNA prime-rAdHu5 boost vaccination carrying the ASP2 sequence generated a stable pool of protective long-lived effector memory CD8+ T-cells specific for T . cruzi [25] . Altogether , ASP2 and TS are shown to be suitable candidate antigens to trigger both humoral and cellular protective immunity , which are critical requisites for an immunoprophylactic protein vaccine that is presented as a pure polypeptide or within delivery vectors ( plasmid , adenovirus ) [13 , 14 , 16 , 17 , 25–28] . Based on the demonstration that homologous rAdVax prime-boost scheme induced antibodies and efficient IFNγ-producing and CTL CD8+ T-cell effectors , corroborating previous data adopting different vaccination strategies [16 , 17 , 27] , we hypothesized that rAdVax would also induce protective response to T . cruzi Type I strain-induced CCC . To test this assumption , we used a low-dose ( 100 bt ) inoculum of the Colombian strain , facilitating acute phase survival but inducing CCC [5 , 18 , 19] . Importantly , the prophylactic vaccination of C57BL/6 mice with rAdVax significantly reduced heart parasitism during the acute phase , which remained low during chronic infection . Moreover , prophylactic therapy with rAdVax significantly precluded heart abnormalities ( including ARTs and AVBs ) , major clinical signs of Chagas’ heart disease [1] . Further , prophylactic therapy with rAdVax reduced the acute heart parasitism and cardiomyocyte damage induced by the CL-Brener T . cruzi Type VI strain . Importantly , our data support that immune responses triggered to antigen of a T . cruzi strain may allow resistance in a broad manner . Notably , human populations exhibit a powerful innate and acquired immune response to T . cruzi during acute infection , independently of the parasite strain , facilitating the development of the chronic phase of infection [1 , 3 , 15] . Therefore , the results of the present study corroborate our previous data showing that an association of different parasite antigens delivered in an appropriated protocol might have a beneficial impact on Chagas infection prophylaxis . Particularly considering vaccines as a strategy for decreasing parasite load in reservoirs , such as dogs , rAdVax may be a powerful tool to inhibit the domestic and peri-domestic cycle of T . cruzi , regardless the parasite strain faced in the natural infection [6] . Moreover , the prophylactic administration of rAdVax precluded relevant aspects of chronic Chagas’ heart disease [1] , therefore surging as a strategy to improve prognosis . Millions of people are infected with T . cruzi . Among these , 20–40% will manifest the cardiac form of CD , with mild to severe signs and symptoms and premature death , within 10–30 years after infection [1 , 7] . Therefore , therapeutic vaccines may emerge as a tool to improve prognosis of chronic patients . The present study is the first to show that a vaccine preparation may abrogate , delay progression and , moreover , reverse T . cruzi-induced ECG alterations , including bradycardia , ARTs and AVB2 . These electrical abnormalities represent important features detected in cardiophatic chagasic patients [1] recapitulated in the model of chronically T . cruzi-infected mice used [5 , 18 , 19] . Increased levels of CK-MB activity in serum , a biomarker of cardiomyocyte lesions [29] , that is consistently detected in chronically infected mice [5 , 20] , was also reversed by rAdVax immunization of chronically infected mice . Furthermore , two main heart tissue injuries of chronically Colombian-infected mice , i . e . enhanced FN deposition and Cx43 disorganization and loss [18 , 20] , were reversed through therapeutic immunization with rAdVax . Chronic cardiac fibrosis , revealed as deposition of extracellular matrix components including FN , is associated with CD severity in patients [30 , 31] , non-human primates [24] and mice [18] . Here we describe that chronically infected mice show overdeposition of FN in the cardiac tissue , which is reversed by therapeutic administration of rAdVax . In benznidazole-treated mice , reduced heart parasitism is accompanied by decreased FN deposition and fibrosis [32] . Furthermore , therapeutic intervention in the inflammatory axis with the CCR1/CCR5 antagonist Met-RANTES [20] and the anti-tumor necrosis factor Infliximab antibody also reduced FN deposition in the cardiac tissue of chronically Colombian-infected mice [19] , supporting that this a reversible feature of CCC . The disorganization and loss of Cx43 , the most abundant ventricular gap junction protein , is associated with arrhythmogenic disease [21] . This is another important biomarker of heart tissue damage associated with severity of electrical abnormalities in T . cruzi-infected mice [18] . Moreover , cardiac Cx43 expression is down-regulated in patients with CCC [33] and in cardiopathic compared with indeterminate chronically infected rhesus monkeys [24] . The intensity of C43 loss was also associated with disease severity in models of severe and mild CCC [18] . Therefore , a major beneficial effect of the immunotherapy with rAdVax was the restoration of Cx43 expression in the heart of chronically infected mice . Taken together , these data support the idea that T . cruzi-induced chronic heart damage can be ameliorated after appropriated interference , which may consist of targeting the parasite [32] or other putative pathogenic factors , such as chemotactic [20] or inflammatory [19] mediators . Hence , these findings reinforce the idea that vaccination with rAdVax ( rAdTS+rAdASP2 ) is a promising tool for intervention in patients affected by CCC . Taken together , these data encouraged further studies on the mechanisms by which rAdVax administration exerts beneficial effects in chronically T . cruzi-infected mice . Prophylactic rAdVax administration efficiently reduced acute heart parasitism , corroborating previous attempts using prophylactic vaccines carrying different immunogenic constructions , formulations and vectors that induce effective vaccines that reduce parasitism [8 , 9 , 16 , 17 , 27] . Further , prophylactic rAdVax also reduced the number of CD8+ cells infiltrating the heart tissue . There is a relation between parasite load and CD8+ cell infiltrating the heart tissue [34] . Therapeutic rAdVax immunization of chronically infected mice had no significant effect on the already low parasite load in the heart tissue . There is no relation between parasite load and myocarditis intensity in the chronic phase of infection [35] , when parasites are mostly intracellular and , therefore , less accessible to effector immune response . Recently , we showed that both IFNγ+ and Pfn+ inflammatory cells present in the cardiac tissue contribute to parasite control; however , Pfn+ cells also contribute to tissue injury [5] . Importantly , the number of Pfn+ cells was reduced after rAdVax immunization of chronically infected mice , associated with improvement of heart ECG abnormalities and cardiac damage . Undoubtedly , one cannot rule out the possibility that factors of the immune response , as cytokines not modulated by rAdVax immunization as TNF , shown to promote parasite growth [36] , also contribute to maintain low parasite load in vaccinated mice . Nevertheless , more than parasite load and intensity of myocarditis , the total scenario , which also includes effector cells and cytokine milieu contributing to immunological unbalance , may play crucial role in chronic Chagas’ heart disease pathogenesis [19 , 20 , 37] . Splenomegaly , associated with increased cellularity , is a hallmark of chronic experimental CD [5 , 22] . Reversion of splenomegaly in rAdVax-treated chronically T . cruzi-infected mice was the first indication that this therapeutic tool interferes with parasite-triggered immunological abnormalities . Chronically T . cruzi-infected mice show an intense proliferative response of CD8+ T-cells and increased frequency of IFNγ-producing CD8+ T-cells after polyclonal stimulation with anti-CD3 plus anti-CD28 [22] . Here we reproduced this abnormality in rAdCtrl-injected chronically Colombian-infected mice . Importantly , CD8+ T-cell proliferation and IFNγ production induced by polyclonal activation with anti-CD3 plus anti-CD28 were significantly reduced in rAdVax-immunized mice . However , the T . cruzi-specific IFNγ-producing CD8+ T-cells present in chronically T . cruzi-infected mice were preserved in these mice . Therefore , the therapy of chronically infected mice with rAdVax in a prime-boost homologous protocol reduced the T . cruzi-induced chronic polyclonal activation of CD8+ T-cells , but preserved the potentially beneficial IFNγ-producing CD8+ T-cells [5] . Previous studies with vaccines have provided evidence that prophylactic vaccines using ASP2 and/or TS constructs in different delivery tools stimulate both IFNγ producers and cytotoxic CD8+ T-cells , which play a protective role after challenge with virulent T . cruzi strains [16 , 17 , 27 , 28 , 38 , 39] . Our results demonstrated that therapeutic homologous prime-boost vaccination with rAdVax significantly reduced the frequency of ex vivo freshly isolated and VNHRFTLV ASP2 peptide-specific CD8+CD107a+ T-cells . CD107a marks degranulation , paralleling the cytotoxic activity of T-cells [23] . Recent data have suggested that the induction of multifunctional CD8+CD107a+ T-cells co-expressing IFNγ might be beneficial against acute T . cruzi infection [40] . The data obtained in the present study , however , suggest that the persistence of CD107a+ T-cells during chronic infection might be detrimental . Indeed , the down-modulation of the frequency of CD8+CD107a+ T-cells paralleled the improvement of heart ECG abnormalities and tissue injury in rAdVax-immunized mice , thereby reinforcing the idea that cytotoxic CD8+ T-cells are detrimental in chronic T . cruzi infection [5] . These findings led us to inquire whether this effect of rAdVax therapy impacted the immunological status of the cardiac tissue . Indeed , rAdVax administration to chronically infected mice reduced the number of Pfn+ cells colonizing the cardiac tissue , whereas the numbers of IFNγ+ cells persisted elevated . Truly , the IFNγ+/Pfn+ cells ratio was significantly increased in rAdVax-inoculated mice . Furthermore , the expression of IFNγ mRNA in the cardiac tissue was significantly increased in rAdVax-immunized mice in comparison with rAdCtrl-injected and pre-therapy mice . Also , a remarkable increase in IFNγ levels in serum was detected in rAdVax-immunized chronically infected mice . Importantly , systemically and in heart tissue increased IFNγ expression paralleled the amelioration of ECG abnormalities and cardiac tissue damage . Again , these results are consistent with the idea that IFNγ plays a beneficial role in the heart tissue injury during chagasic infection [5] . This , immunization with vaccine constructs carrying the coding sequences of ASP2 and TS parasite antigens in an appropriate delivery system shifted the immune response to a less detrimental ( cytotoxic ) profile and/or to a more beneficial ( IFNγ ) status , which may contribute to improve the experimental Chagas’ heart disease . Altogether , these data support the idea that parasite-triggered immunological unbalance promotes Chagas’ heart disease [5 , 19 , 41 , 42] . Chronic chagasic cardiomyopathy is undoubtedly the most severe form of CD [1] . The pathogenic factors leading to CCC remain largely unknown; therefore , the comprehension of these factors and the identification of biomarkers of prognosis and severity might contribute to the development of vaccines and more efficient therapies . In this context , studies have shown that NO levels in serum parallels CCC severity in humans [43] and non-human primates [24] . Indeed , here we demonstrated that in chronic experimental CD the levels of NOx paralleled CK-MB activity in serum , reinforcing the idea that NOx levels is associated with cardiomyocyte lesion [18 , 24] . In T . cruzi infection , NO is produced via iNOS/NOS2 in a wide range of cells and tissues and acts as an important trypanocidal agent [44]; however , NO can be cytotoxic and destructive to tissues when produced at high concentrations and for long periods [43] . Chagas’ heart disease relies on a complex host-parasite interrelationship . In this sense , it has been shown that T . cruzi persistence can serve as a stimulus for continuous iNOS/NOS2 expression in cardiac tissue and , consequently , a large amount of NO might accumulate in this tissue [45] . Hence , an increased expression of iNOS/NOS2 in heart tissue and enhanced supply of NO could lead to cardiomyocyte lesion and heart injury [24] . In the present study , chronic T . cruzi infection ( at 120 dpi ) was accompanied by a significant increase in serum NOx levels and the enhanced expression of iNOS/NOS2 in heart tissue , both paralleling the heart tissue injury ( Cx43 loss and FN overdeposition ) and electrical alterations . Consistently , NOx levels in serum and iNOS/NOS2 expression in cardiac tissue were successfully reduced after prime-boost therapy with rAdVax , paralleling amelioration of ECG abnormalities and heart tissue damage . Thus , delivery of ASP2 and TS genes in the form of rAd constructs emerges as a rational alternative tool to reprogram immune responses to a less detrimental and a more protective profile ( particularly in heart tissue ) , interrupt progression and recover tissue injury in chronic Chagas’ heart disease . Here we brought insights on the biological processes contributing to beneficial effects of the prime/boost with rAdVax on clinical signs of chronic experimental CD . However , the molecular mechanisms for the immunological reprograming mediating the effects of our vaccine constructions and regimen of administration were not addressed in the present study . Our data support that rAdVAx reverses several immunological abnormalities ( polyclonal T-cell activation , increased frequency of CD107a+CD8+ T-cells , accumulation of Pfn+ cells in the heart tissue and high NO levels in the serum ) , thus it is conceivable that distinct molecular mechanism may take part in the reprogramming of these biological alterations , opening a new avenue to be explored . Lastly , CD is a major public health problem in Latin America; therefore , a vaccine would be an important tool to improve the control of CD [46] and , mainly , to interfere with the outcome of CD . Here we propose the use of a recombinant AdHu5-based vaccine carrying ASP2 and TS T . cruzi sequences to decrease acute parasite load and prevent CCC and as an alternative to reprogram the immunological unbalance in chronically infected individuals to delay progression and , potentially , reverse prototypical pathological aspects of CCC . We consider that the main theoretical barriers ( safety and immunogenicity ) to use recombinant AdHu5-based ASP2/TS vaccine in chronic chagasic patients were recently overcame . Indeed , vaccine constructs for HIV and tuberculosis using recombinant AdHu5 as vector enlightened our knowledgement revealing that the vaccine regimen was safe and had an acceptable side-effect profile [47] . Moreover , the widely perceived negative effect of preexisting anti-AdHu5 immunity may not be universally applied to all AdHu5-based vaccines . Preexisting neutralizing antibodies did not affect the immunogenicity of an AdHu5-based malaria vaccine in humans [48] . Further , an AdHu5-vectored vaccine engineered to express the immune dominant M . tuberculosis antigen Ag85A was shown to be safe and robustly immunogenic , supporting a lack of correlation between preexisting anti-AdHu5 neutralizing antibody titers and the magnitude of vaccine-induced T-cell activation [49] . Therefore , the AdHu5-based ASP2/TS therapeutic vaccine administered either alone or combined with trypanocidal drugs as benznidazole [1] or target-based adjuvants as rapamycin [50] , might improve the prognosis of CCC , providing a new approach for the development of useful protocols to treat CD patients . Therefore , the results obtained here demonstrate that the development of a successful vaccine for CD is not beyond reach [2 , 7] . However , one cannot ignore that with success will come the economic barriers to produce and deliver a vaccine against a neglected disease . Further , the extemporaneous campaigns nurturing refractivity to vaccines may emerge as a new challenge to be surpassed .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br/ ) and the Federal Law 11 . 794 ( October 8 , 2008 ) . The Institutional Committee for Animal Ethics of Fiocruz ( CEUA-Fiocruz-L004/09 ) and the Brazilian Biosafety National Committee ( CQB/CTNBio-105/99 ) approved all experimental procedures used in the present study . The Experimental Animal Facility is fully accredited by the National Technical Commission on Biosafety ( CTNBio; last notification , October 27 , 2010 ) . All presented data were obtained from four ( INCTV1-4 ) independent experiments ( Experiment Register Books #3 and #4 , LBI/IOC-Fiocruz ) . Mice obtained from the animal facilities of the Oswaldo Cruz Foundation ( CECAL/Fiocruz , Rio de Janeiro , Brazil ) were housed under specific pathogen-free conditions in a 12-h light-dark cycle with access to food and water ad libitum . Five- to seven-week-old female C57BL/6 ( H-2b ) mice were intraperitoneally ( i . p . ) infected with 100 blood trypomastigotes ( bt ) of the Colombian T . cruzi Type I strain or with 1000 bt of the CL-Brener T . cruzi Type VI strain [15] that had been prepared by passage through C57BL/6 every 35 or 10 days , respectively . Parasitemia was used as a parameter to establish acute and chronic phases [5] and mortality was recorded weekly . Sex- and age-matched noninfected ( NI ) controls were analyzed in parallel . Accordingly to experimental designs , groups of mice were sacrificed under anesthesia ( 100mg/Kg ketamine associated with 5mg/Kg xylazine chloride ) . The construction of the replication-deficient human Type 5 recombinant adenoviruses carrying the Escherichia coli β-galactosidase ( rAdCtrl ) , the mouse Ig k chain SP fused to the ASP2 1–694 amino acid coding sequence ( rAdASP2 ) and the signal peptide and catalytic domain amino acid 34–678 of TS ( rAdTS ) coding sequences of the Y T . cruzi strain have been previously reported [16] . Mice were inoculated subcutaneously ( s . c . ) in the tail base with 100 μL of viral suspension comprising apyrogenic saline ( BioManguinhos , Brazil ) supplemented with 1% normal mouse serum ( Sigma , USA ) containing 2 × 108 plaque-forming units ( PFU ) of rAdLacZ or a mixture of 108 PFU of each adenovirus vaccine preparation ( rAdASP2+rAdTS ) . The mice were immunized twice at four- to six-week intervals , as shown in the scheme of the figures . As experimental groups were analyzed at different moments , we associated the colors of the arrows indicating the moments of analyses in the experimental schemes with the colors of the bars representing the results of the analyzed groups in the figures . Additionally to experiments to analyze the immune response , in two experiments mice were immunized as described above and death was weekly registered . The results of these experiments were combined to establish survival curve . For T-cell functional assays , we used the H-2Kb-restricted VNHRFTLV peptide from ASP2 [13] synthesized by GenScript USA Inc . ( USA ) . For in vivo cytotoxicity assays , target and control cells were tagged with the fluorogenic dye carboxyfluorescein diacetate succinimidyl ester ( CFSE , Molecular Probes , USA ) . For lymphoproliferation assays , we used a CFSE-based cell tracer for flow cytometry ( CellTrace Cell Proliferation kit , Invitrogen , USA ) . For immunohistochemical staining ( IHS ) , the polyclonal antibody against T . cruzi antigens and supernatants containing anti-mouse CD8a ( clone 53–6 . 7 ) and anti-mouse CD4 ( clone GK1 . 5 ) were produced in our laboratory ( LBI/IOC-Fiocruz , Brazil ) . Other antibodies included an anti-F4/80 polyclonal antibody ( Caltag , USA ) , polyclonal rabbit anti-connexin 43 ( Cx43 ) ( Sigma , USA ) , polyclonal rabbit anti-mouse fibronectin ( FN ) ( Gibco-BRL , USA ) , biotinylated anti-rat immunoglobulin ( Dako , Denmark ) and biotinylated anti-rabbit immunoglobulin and peroxidase-streptavidin complex ( Amersham , UK ) . The monoclonal antibodies anti-mouse Pfn ( CB5 . 4 , Alexis Biochemicals , USA ) and anti-IFNγ ( R4-6A2 , BD PharMingen , USA ) produced in rat were also used in IHS . For flow cytometry , PECy7-anti-CD3 ( clone 17A2 ) , APC-conjugated anti-mouse CD8a ( clone 53–6 . 7 ) , PerCP-anti-CD4 ( clone GK1 . 5 ) and PECy-7-conjugated anti-IFNγ ( clone XMG1 . 2 ) were purchased from BD Pharmingen ( USA ) . PE-conjugated anti-CD107a ( clone eBIO1D4B ) was obtained from eBioscience . Endotoxin-free purified anti-CD3 ( clone 145-2C11 ) and anti-CD28 ( clone 37 . 51 ) were purchased from Southern Biotech ( USA ) . Appropriate controls were prepared by replacing the primary antibodies with the corresponding serum , purified immunoglobulin or antibody isotype . All antibodies and reagents were used according to the manufacturers’ instructions . Recombinant TS or ASP2 proteins were produced in E . coli and total antibodies ( IgM+IgG ) against these proteins were detected using an enzyme-linked immunosorbent assay ( ELISA ) as previously described [51] . Each serum sample was serially diluted ( 1:200 , 1:400 and 1:800 ) for analysis . The optical density ( OD ) at 405 nm higher than twice the OD detected in serum of non-immunized mice ( in this case higher than 0 . 1 ) was considered positive for antibody detection . The results are presented as the mean OD of six to eight mice per group . The ELISpot assay for the enumeration of IFNγ-producing cells was performed in triplicate as previously described [39] . Plates were coated with anti-mouse IFNγ ( clone R4-6A2; BD PharMingen , USA ) antibody diluted in PBS ( 5 μg/mL ) . Antigen-presenting cells were primed with total T . cruzi antigens ( 10 μg/mL ) for 30 minutes at 37°C . Concanavalin A ( ConA , 5 μg/mL ) was used as a mitogenic stimulant . After incubation , the freshly isolated splenocytes were seeded at 5 × 105 cells/well and incubated with the ASP2 H-2Kb-restricted VNHRFTLV peptide [13] for 20 hours at 37°C and 5% CO2 . Biotin-conjugated anti-mouse IFNγ antibody ( clone XMG1 . 2; BD PharMingen , USA ) was used to detect the captured cytokines . Spots were revealed after incubation of the samples with a solution of alkaline phosphatase-labeled streptavidin ( BD PharMingen , USA ) and a solution of NBT ( Sigma , USA ) and BCIP ( Sigma , USA ) in Tris buffer ( 0 . 9% NaCl , 1% MgCl2 , 1 . 2% Tris in H2O ) . The mean number of spots in triplicate wells was determined for each experimental condition and the number of specific IFNγ-secreting T-cells was calculated by estimating the stimulated spot count/106 cells using a CTL OHImmunoSpot A3 Analyzer ( USA ) . The lymphoproliferative response was assessed as described previously [5] . Briefly , spleens were removed from NI or T . cruzi-infected vaccinated mice and single-cell suspensions of splenocytes were prepared . The red blood cells were lysed using ACK buffer ( Sigma , USA ) and mononuclear cells were labeled with CFSE at a final concentration of 7 μM ( CFSEhigh ) or 0 . 5 μM ( CFSElow ) . The cells were incubated in RPMI medium supplemented with 10% SBF in the presence of anti-CD3 and anti-CD28 ( 3 μg/mL ) or 2 . 5 μM of the VNHRFTLV ASP2 peptide for 72 hours at 37°C and 5% CO2 . CFSEhigh cells were washed and fixed with 1 . 0% paraformaldehyde . CFSElow cells were washed and labeled with APC-conjugated anti-CD8 antibody as described above and fixed using 1 . 0% paraformaldehyde . All samples were acquired using a Beckman Coulter CyAn 7 Color flow cytometer ( Fullerton , CA , USA ) and analyzed using the Summit v . 4 . 3 Build 2445 program ( Dako , Denmark ) . For the in vivo cytotoxicity assays , spleens collected from naïve C57BL/6 mice were treated with ACK buffer ( Sigma , USA ) to lyse the red blood cells . The cells were divided into two populations and labeled with the fluorogenic dye CFSE ( Molecular Probes , USA ) at a final concentration of 10 μM ( CFSEhigh ) or 0 . 1 μM ( CFSElow ) . CFSEhigh cells were coated with 2 . 5 μM of the VNHRFTLV ASP2 peptide [13] for 40 minutes at 37°C . The CFSElow cells remained uncoated . Subsequently , the CFSEhigh cells were washed and mixed with equal numbers of CFSElow cells before intravenous injection ( 1–2 × 107 cells per mouse ) into C57BL/6 recipients sedated with diazepam ( 20 mg/Kg ) . Spleen cells were collected from the recipient mice at 20 hours after adoptive cell transfer as indicated in the figure legends and fixed using 1 . 0% paraformaldehyde . All samples were acquired using a Beckman Coulter CyAn 7 Color flow cytometer ( Fullerton , CA , USA ) and analyzed using the Summit v . 4 . 3 Build 2445 program ( Dako , Denmark ) . The percentage of specific lysis was determined using the following formula: [1− ( %CFSEhigh infected/%CFSElow infected ) ( %CFSEhighNI/%CFSElowNI ) ] ×100% The mice were euthanized under anesthesia and their hearts were removed , embedded in tissue-freezing medium ( Tissue-Tek , Miles Laboratories , USA ) and stored in liquid nitrogen . The phenotypes of the inflammatory cells ( CD4+ , CD8+ , F4/80+ ) , IFNγ+ and Pfn+ cells colonizing the heart tissue and the T . cruzi parasitism were characterized and analyzed as previously described [5] . The FN- and Cx43-positive areas in 25 fields ( 12 . 5 mm2 ) per section ( three sections per heart ) were evaluated using a digital morphometric apparatus as previously described [20] . The resulting images were digitized using a color view XS digital video camera adapted to a Zeiss microscope and analyzed using AnalySIS AUTO software ( Soft Imaging System , USA ) . The data are presented as the percent positive area in the heart , the distance ( μm ) between stained gap junctions or the numbers of parasite nests or cells per 100 microscopic fields ( 400× ) . The activity of the creatine kinase cardiac MB isoenzyme ( CK-MB ) was measured as a cardiomyocyte lesion marker [29] using a commercial CK-MB Liquiform kit ( Labtest , Brazil ) according to the manufacturer’s recommendations , as previously adapted for mouse samples [5] . The spleens were minced and the red blood cells were removed using lysis buffer ( Sigma , USA ) . The splenocytes were labeled and events were acquired using a CyAn-ADP Analyzer ( Beckman Coulter , USA ) . The data were analyzed using the Summit v . 4 . 3 Build 2445 program ( Dako , USA ) as described elsewhere [5] . The VNHRFTLV ASP2 peptide-specific response was assessed as described previously [5] . Briefly , spleens were removed and processed as described above . The cells were incubated in RPMI medium supplemented with 10% SBF in the presence of 2 . 5 μM of the VNHRFTLV ASP2 peptide for 72 hours at 37°C and 5% CO2 . Cells were washed , processed and analyzed for flow cytometry as described above . The mice were sedated with diazepam ( 10 mg/kg ) and transducers were placed subcutaneously ( DII ) . The traces were recorded for 2 minutes using the digital Power Lab 2/20 System connected to a bio-amplifier at 2 mV for 1 second ( PanLab Instruments , Spain ) . The filters were standardized to 0 . 1–100 Hz and the traces were analyzed using Scope software for Windows V3 . 6 . 10 ( PanLab Instruments , Spain ) . ECG parameters were analyzed as previously described [5] . Cytokines ( IL-1α , IL-2 , IL-4 , IL-5 , IL-6 , IL-10 , IL-17 , GM-CSF , TNF , IFNγ ) were detected in serum using the commercial Th1/Th2 10plex FlowCytomix kit ( MNS820FF; Bender MedSystems Inc . , Austria ) according to the manufacturer’s instructions . The samples were assayed with suitable controls provided by manufacturer for the construction of standard curves . The fluorescence produced by the beads was measured on a FACSCalibur flow cytometer ( BD , Biosciences , USA ) and analyzed using the software contained in the kit . Nitrate and nitrite ( NOx ) were determined to estimate the nitric oxide ( NO ) levels in the serum samples using Griess reagent and vanadium chloride III; a standard curve of 0 . 8 to 100 mM NaNO2 and NaNO3 was prepared as described elsewhere [24] . For real-time quantitative RT-PCR ( RT-qPCR ) , the mice hearts were harvested , washed to remove blood clots , weighed and frozen in RNAlater ( #AM7021 , Life Technologies , USA ) . Total RNA ( for gene expression studies ) was extracted using TRI-Reagent ( Sigma , USA ) . All reverse transcriptase reactions were performed using a SuperScript III Kit ( # 18080-051 ) and RT-qPCR was performed using TaqMan gene expression assays for IFNγ ( #Mm01168134_m1 ) , inducible NO synthase ( iNOS/NOS2; #Mm00440502_m1 ) and the endogenous housekeeping control genes glyceraldehyde 3-phosphate dehydrogenase ( GAPDH; #Mm99999915-g1 ) and β actin ( #Mm00607939-s1 ) , which were purchased from Life Technologies ( USA ) . The reactions were performed and analyzed as previously described [52] . The data are expressed as arithmetic means ± standard deviation . Student t tests , ANOVA and other appropriate tests were used to analyze the statistical significance of the observed differences . The Kaplan-Meier method was used to compare the survival times of the studied groups . All statistical tests were performed using GraphPad Prism . Differences were considered statistically significant when P <0 . 05 .
|
The idea that Chagas disease ( CD ) has an important autoimmune involvement contributed to delay the development of therapies and vaccines . CD is a parasitic neglected disease which afflicts millions of people mostly in Latin America . The cardiac form is the main clinical manifestation of CD . Currently , patients with access to therapy receive medicaments that only mitigate symptoms . Because of the limited prospect of treatment , vaccine reemerged as a strategy to prevent infection , interfere with CD progression and , moreover , reverse heart abnormalities . Here we tested a recombinant adenovirus carrying sequences of ASP2 and TS T . cruzi antigens ( rAdVax ) as prophylactic and therapeutic tool using a model of chronic Chagas’ heart disease . We showed that prophylactic vaccination reduced heart parasite load , inflammation and electrical abnormalities . The rAdVax therapeutic vaccination also reduced heart injury and improved electrical function , preserved specific IFNγ-mediated immunity but reduced response to polyclonal stimuli , CD107a+ CD8+ T-cell frequency and plasma nitric oxide levels . Moreover , therapeutic rAdVax preserved the number IFNγ+ cells , but decreased perforin+ cells in the heart tissue . Therefore , our results support the hypothesis that vaccination can modify the immunological unbalance that concurs to Chagas’ heart disease to improve prognosis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
A Human Type 5 Adenovirus-Based Trypanosoma cruzi Therapeutic Vaccine Re-programs Immune Response and Reverses Chronic Cardiomyopathy
|
Cortical oscillations are likely candidates for segmentation and coding of continuous speech . Here , we monitored continuous speech processing with magnetoencephalography ( MEG ) to unravel the principles of speech segmentation and coding . We demonstrate that speech entrains the phase of low-frequency ( delta , theta ) and the amplitude of high-frequency ( gamma ) oscillations in the auditory cortex . Phase entrainment is stronger in the right and amplitude entrainment is stronger in the left auditory cortex . Furthermore , edges in the speech envelope phase reset auditory cortex oscillations thereby enhancing their entrainment to speech . This mechanism adapts to the changing physical features of the speech envelope and enables efficient , stimulus-specific speech sampling . Finally , we show that within the auditory cortex , coupling between delta , theta , and gamma oscillations increases following speech edges . Importantly , all couplings ( i . e . , brain-speech and also within the cortex ) attenuate for backward-presented speech , suggesting top-down control . We conclude that segmentation and coding of speech relies on a nested hierarchy of entrained cortical oscillations .
A large number of invasive and non-invasive neurophysiological studies provide converging evidence that cortical oscillations play an important role in gating information flow in the human brain , thereby supporting a variety of cognitive processes including attention , working memory , and decision-making [1]–[3] . These oscillations can be hierarchically organised . For example , the phase of ( 4–8 ) Hz theta oscillations can modulate the amplitude of ( 30–90 Hz ) gamma oscillations; the phase of ( 1–2 Hz ) delta oscillations can modulate the amplitude of theta oscillations [4]–[8] . Interestingly , speech comprises a remarkably similar hierarchy of rhythmic components representing prosody ( delta band ) , syllables ( theta band ) , and phonemes ( gamma band ) [9]–[12] . The similarity in the hierarchical organisation of cortical oscillations and the rhythmic components of speech suggests that cortical oscillations at different frequencies might sample auditory speech input at different rates . Cortical oscillations could therefore represent an ideal medium for multiplexed segmentation and coding of speech [9] , [12]–[17] . The hierarchical coupling of oscillations ( with fast oscillations nested in slow oscillations ) could be used to multiplex complementary information over multiple time scales [18] ( see also [19] ) for example by separately encoding fast ( e . g . , phonemic ) and slower ( e . g . , syllabic ) information and their temporal relationships . Previous studies have demonstrated amplitude and phase modulation in response to speech stimuli in the delta , theta , and gamma bands using electroencephalography ( EEG ) /magnetoencephalography ( MEG ) [13] , [15] , [20]–[25] and electrocorticography ( ECOG ) [26]–[29] . These findings support an emerging view that speech stimuli induce low-frequency phase patterns in auditory areas that code input information . Interestingly , these phase patterns seem to be under attentional control . For example , in the well known cocktail party situation , they code mainly for the attended stimulus [26] , [30] , [31] . Thus , brain oscillations have become obvious candidates for segmenting and parsing continuous speech because they reflect rhythmic changes in excitability [12] . This attractive model leaves three important points largely unresolved: First , a comprehensive account of how rhythmic components in speech interact with brain oscillations is still missing and it is uncertain if the previously reported hemispheric asymmetry during speech perception is also evident in a lateralized alignment of brain oscillations to continuous speech . Behavioural , electrophysiological , and neuroimaging studies [13] , [15] , [20] , [23] , [32] suggest that there is a relatively long integration window ( 100–300 ms , corresponding to the theta band ) in the right auditory cortex and a relatively short integration window ( 20–40 ms , corresponding to the gamma band ) in the left auditory cortex [14] . But it is unclear whether this differentiation is relevant for oscillatory tracking of speech . Second , it is unknown whether cortical brain oscillations are hierarchically coupled during perception of continuous speech . This is of particular interest because hierarchically coupled brain oscillations could represent hierarchically organised speech components ( prosody , syllables , phonemes ) at different temporal scales . Third , it is unclear how oscillatory speech tracking dynamically adapts to arrhythmic components in speech . If brain oscillations implement a universal mechanism for speech processing they should also account for variations or breaks in speech rhythmicity , so that the phase of low-frequency oscillations aligns to ( quasi-periodic ) salient speech events for optimal processing . Here , we addressed these three points using continuous speech and analysis based on information theory . Importantly , all three points were investigated for intelligible and unintelligible ( backward played ) speech . We analysed the frequency-specific dependencies between the speech envelope and brain activity . We also analysed the dependencies between cortical oscillations across different frequencies . We first hypothesised that a multi-scale hierarchy of oscillations in the listener's brain tracks the dynamics of the speaker's speech envelope—specifically , preferential theta band tracking in the right auditory cortex and gamma band tracking in the left auditory cortex . Second , we asked whether speech-entrained brain oscillations are hierarchically coupled and if so how that coupling is modulated by the stimulus . Third , we asked whether phase of low-frequency brain oscillations ( likely indicating rhythmic variations in neural excitability ) in the auditory cortex coincide with and adapt to salient events in speech stimuli . We presented a 7-min long continuous story binaurally to 22 participants while recording neural activity with MEG ( “story” condition ) . As a control condition the same story was played backwards ( “back” condition ) . We used mutual information ( MI ) to measure all dependencies ( linear and nonlinear ) between the speech signal and its encoding in brain oscillations [33] , [34] . We did so in all brain voxels for frequencies from 1 to 60 Hz and for important interactions ( phase-phase , amplitude-amplitude , cross-frequency phase-amplitude , and cross-frequency amplitude-phase , see Figure 1 and Materials and Methods ) . This resulted in frequency specific functional brain maps of dependencies between the speech envelope and brain activity . Similar analysis was performed to study dependencies between brain oscillations within cortical areas but across different frequency bands . Our results reveal hierarchically coupled oscillations in speech-related brain areas and their alignment to quasi-rhythmic components in continuous speech ( prosody , syllables , phonemes ) , with pronounced asymmetries between left and right hemispheres . Edges in the speech envelope reset oscillatory low-frequency phase in left and right auditory cortices . Phase resets in cortical oscillations code features of the speech edges and help to align temporal windows of high neural excitability to optimise processing of important speech events . Importantly , we demonstrate that oscillatory speech tracking and hierarchical couplings significantly reduce for backward-presented speech and so are not only stimulus driven .
We first asked whether there is phase-locking between rhythmic changes in the speech envelope and corresponding oscillatory brain activity . Whereas most previous studies quantify phase-locking to stimulus onset across repeated presentations of the same stimulus , here we studied phase-locking over time directly between speech envelope and brain oscillations . To do this , we compared the phase coupling between the speech and oscillatory brain activity ( in 1 Hz steps between 1 and 60 Hz ) in two conditions: story and back . Figure 2 summarizes the results . First , MI revealed a significantly stronger phase coupling between the speech envelope and brain oscillations in the story compared to back conditions in the left and right auditory cortex in delta ( 1–3 Hz ) and theta ( 3–7 Hz ) frequency bands ( group statistics , p<0 . 05 , false discovery rate [FDR] corrected , see Figure 2A and 2B ) . These results confirm that low-frequency rhythmic modulations in the speech envelope align with low-frequency cortical oscillations in auditory areas ( using phase-locking value ( PLV ) instead of MI and contrasting story with surrogate data lead to virtually identical results , see Figure S1 ) . To test for other couplings between the speech and cortical oscillations , we also computed MI between the amplitude of the speech and the amplitude of cortical oscillations and between the amplitude of the speech and the phase of cortical oscillations for each frequency between 1 and 60 Hz . These computations revealed no significant dependencies . Finally , we flipped the computations around , to test whether the phase of the speech envelope modulated the amplitude of cortical oscillations . Again , we carried out this computation across frequencies , for all combinations between 1 and 60 Hz and found one significant phase-amplitude coupling . Figure 2C illustrates that low-frequency changes in the speech envelope ( at 3–7 Hz ) modulate the amplitude of 35–45 Hz gamma activity in both auditory cortices significantly more strongly in the story compared to the back condition . In sum , this comprehensive analysis revealed two distinct speech tracking mechanisms in the brain . First , low-frequency speech modulations entrain ( that is , align the phase of ) delta and theta oscillations in the auditory cortex . Second , low-frequency speech modulations also entrain the amplitude dynamics of gamma oscillations . Both tracking mechanisms are especially sensitive to intelligible speech because the effects are stronger for the story than the back condition . Since the theta phase of the speech envelope is coupled to both , the theta phase ( Figure 2B ) and gamma amplitude ( Figure 2C ) of auditory brain oscillations , we investigated if both these signals represent the same or different information about the speech stimulus . Again , we performed the analysis within an information-theoretic framework based on that of Ince et al . [35] . Specifically , we investigated whether the information about speech in the theta phase of auditory oscillations is similar or complementary to that carried by gamma power . We computed whether gamma amplitude adds significant mutual information about the speech envelope over and above the information carried by the theta phase of brain activity ( see Materials and Methods section for details ) . The analysis revealed that gamma amplitude does add significant complementary information to theta phase . Gamma amplitude adds on average 23% ( ±7 standard error of the mean [SEM] ) to theta phase information . Figure 2D illustrates this complementarity and shows how it is particularly pronounced for the left auditory cortex . This suggests that each mechanism is partly independent of the other and thus can capture complementary information about the stimulus . Next we statistically tested for possible lateralisation of these different tracking mechanisms . The analysis was based on FDR-corrected dependent samples' t-tests of MI values for corresponding voxels in the left and the right hemisphere for the story condition . Interestingly , although present in both left and right hemisphere ( Figure 2A and 2B ) , delta and theta phase-locking to speech was significantly stronger in the right ( Figure 3A and 3B ) . Lateralisation maps also revealed a spatial dissociation whereby delta MI was right-lateralised in frontal and parietal areas whereas theta MI was only right-lateralised in superior temporal areas . In contrast , gamma amplitude tracking showed the opposite lateralisation with stronger coupling to speech in the left as compared to the right auditory cortex ( Figure 3C ) . Finally , we compared lateralisation of theta phase tracking to lateralisation of gamma-amplitude tracking for the story condition . The statistical map shows significantly higher lateralisation for theta phase tracking in the right auditory cortex but significantly higher lateralisation for gamma amplitude tracking in the left auditory cortex ( Figure 3D ) . We further confirmed these group results for single participants . A similar lateralisation pattern was seen in 17 out of 22 participants corroborating the group statistics ( Figure S2 ) . Mutual information values ( mean and SEM ) for the left and right auditory cortex are displayed as bar plots in Figure S3 for all conditions illustrating the lateralisation patterns . This analysis revealed differential hemispheric preference for the two coupling mechanisms . Whereas right hemisphere areas showed stronger low-frequency phase coupling to the speech envelope , left hemisphere areas showed stronger high-frequency amplitude coupling to the speech envelope . This delta and theta phase coupling together with gamma amplitude coupling suggests that the brain oscillations might be nested [4] . To test for this cross-frequency coupling we computed the mutual information between the theta phase and gamma amplitude of each voxel across the 7-min dataset . By contrast to the analysis shown in Figure 2C , both the theta phase and the gamma amplitude were derived from the same voxel . The resulting mutual information map for each participant quantifies cross-frequency coupling of theta phase and gamma amplitude in each voxel . As before , we performed group statistics on the individual mutual information maps to identify significant differences between the story and back condition . Figure 4A shows significantly increased cross-frequency coupling ( theta phase and gamma amplitude ) for the story condition compared to the back condition both in bilateral auditory areas and in language areas of the left hemisphere . Lateralisation analysis revealed that the modulation of gamma amplitude by theta phase is stronger in the left compared to right hemisphere ( Figure 4B ) . We performed the same analysis for cross-frequency coupling between delta phase and theta amplitude . The statistical difference map between the story and the back condition showed significant effects in bilateral temporal areas ( Figure S4A ) with lateralisation to left hemisphere ( Figure S4B ) but these effects were not as strong as those for the theta-gamma coupling . In summary , these results indicate that oscillatory speech tracking is supported by a nested hierarchy of oscillations at delta , theta , and gamma frequencies and that these cross-frequency interactions are stronger for intelligible than for unintelligible speech . At this juncture , it is important to note that speech , though rhythmic , is not strictly periodic: it comprises discontinuities and changes in syllable rate and duration . Any cortical speech tracking mechanism must be able to track these irregularities . We predicted that temporal edges in the speech envelope [36] should induce phase resets in the cortical oscillations tracking the speech thereby enhancing tracking . Here , we focussed on the theta band phase-locking because of its relation to the syllable rate . We used a thresholding algorithm to identify 254 separate temporal edges in the continuous stimulus ( see Materials and Methods for details ) . We then computed theta-band phase-locking between auditory theta activity and the theta phase of speech envelope time-locked to these edges . This quantifies the alignment between both signals as in Figure 2B but now time-locked to temporal edges . Figure 5 shows increased alignment between brain oscillations and speech envelope in the left ( blue solid line ) and the right ( red solid line ) auditory cortex following edges . t-Tests revealed significant ( p<0 . 05 ) increase of phase-locking in an early ( 100–300 ms ) and late ( 400–600 ms ) time window compared to baseline ( −200 to 0 ms ) . To measure the extent to which this increase can be explained by a stereotypical edge-evoked response we computed phase-locking of auditory theta activity across trials time-locked to edge onset ( dashed lines ) . This measure captures the evoked response to edge onset . As expected , this evoked response ( dashed lines ) increased following edge onset with a similar dynamics as the phase-locking to speech ( solid lines ) . But importantly , phase-locking to speech ( solid lines ) is significantly stronger in the late time window than phase-locking to edge onset ( dashed lines ) ( t-test , p<0 . 05 ) . This demonstrates that speech continuously entrains brain rhythms beyond a stereotypical short-lived phase reset evoked by edges . Finally , we computed the phase-locking between left and right auditory theta activity ( Figure 5 , black line ) . This measure quantifies the temporal coordination between both auditory cortices in the theta band . Interestingly , the increased phase alignment to speech coincided with a significant reduction of phase-locking between both auditory cortices in the early window . One interesting possibility is that this reduction in phase-locking reflects the more sensitive tracking of speech theta rhythms in the right auditory cortex compared to the left . Indeed , phase-locking to speech is significantly stronger in right than in the left auditory cortex from 50–100 ms ( t-test , p<0 . 05 ) . This could indicate that phase resetting in the left hemisphere is partly driven by the right auditory cortex . Overall , the results confirmed our prediction . Edges in speech increased the alignment of auditory theta oscillations to the speech envelope and this increase outlasted the standard evoked response to edge onset . In addition , speech edges caused a significant transient decoupling of both auditory cortices . Since oscillations represent rhythmic fluctuations in the excitability of neural populations we hypothesised that phase-locking ( assisted by phase resetting ) between the speech envelope and low-frequency oscillations in the auditory cortex implements a mechanism for efficient sampling and segmentation of speech [12] , [31] . To directly test this sampling hypothesis , we measured the correlation between each cortical oscillatory band between 1 and 60 Hz and the speech envelope for the 254 trials identified in the previous analysis . Figure 6A illustrates this analysis for a sample taken from one individual . The black line shows the speech envelope for a given trial and the dashed line shows the cosine of theta phase in the right auditory cortex for this participant . In the full analysis we computed the cross-correlation for each brain voxel and for each of the 254 trials ( defined as the 500 ms following an onset ) and then averaged the absolute correlation across trials , for each oscillatory band independently . To account for the different tracking mechanisms identified above ( phase tracking and amplitude tracking ) , we computed two correlations . First , we correlated the cosine of the phase of cortical oscillations with the speech envelope . Second , we correlated the amplitude of cortical oscillations with the speech envelope . For comparison , we also computed these correlations after randomly shuffling the trial order of the speech envelope . Figure 6B shows significantly higher correlations in left and right auditory areas for low-frequency phase oscillations compared with the shuffled condition . Figure 6C presents the spectral profile of correlation for the left and right auditory cortex . At frequencies below 10 Hz the phase of auditory oscillations shows higher correlations with the speech envelope than does amplitude . Above 10 Hz this pattern is reversed . Interestingly the correlation based on amplitude ( blue lines ) shows a peak at 40–50 Hz in agreement with Figure 2C . An additional peak is evident at about 20 Hz . Speech sampling by phase in the delta and theta band in the left and right auditory cortex is significantly higher for the story compared to the back condition ( and also compared to trial-shuffled data , paired t-tests , all p<0 . 05 ) . Speech sampling by amplitude in the gamma band is significantly higher for the story compared to the back condition in the left auditory cortex ( and compared to trial-shuffled data in both auditory cortices ) . Although the pattern of lateralisation was overall consistent with Figure 3 , the difference in lateralisation did not reach significance . This is probably because this correlation measure is less sensitive than the mutual information analysis on the band-pass filtered speech envelope reported in Figure 3 . These results indicate that temporal edges in speech amplitude induce modulations in low-frequency phase and high-frequency amplitude dynamics of brain oscillations that align windows of high neural excitability to salient speech events . Importantly , this alignment is not caused by an identical phase resetting for all edges because shuffling the speech trials reduces the correlation . We predicted that edge-specific phase resets coding stimulus features ( e . g . , edge amplitude ) cause this trial-specific alignment . We tested this hypothesis by sorting our previously identified 254 trials by maximum amplitude of speech envelope in the 200 ms window after onset . For each participant we computed in the left and right auditory cortex the theta phase at 100 ms after onset and correlated both quantities using circular correlation [37] . Significant correlation was observed in the left and right auditory cortex ( Figure S5 ) . Together , these results demonstrate that the phase of low-frequency cortical oscillations and the amplitude of high-frequency oscillations align to trial-specific speech dynamics , adapting to variations of speech over time . This trial-specific alignment suggests that oscillatory windows of high excitability sample salient speech components . Our analysis on the continuous data ( Figures 4 and S4 ) has demonstrated a nested hierarchy of oscillations in the auditory cortex with stronger cross-frequency coupling for intelligible speech compared to unintelligible speech . Since edges enhance oscillatory speech tracking we hypothesised that edges also increase this cross-frequency coupling . We tested this hypothesis in our final analysis . We first characterised the spatial distribution of edge-induced changes in cross-frequency coupling by computing coupling of gamma amplitude to theta phase in all brain voxels . We then computed the full cross-frequency coupling matrix separately for the left and the right auditory cortex . As before , we used MI to analyze cross-frequency oscillatory coupling ( as in Figure 4A ) but now time-locked to edges . For each brain voxel , across all 254 trials we computed a t-statistic of MI between theta phase and gamma amplitude for the two 500 ms windows preceding and following speech onset . Since this computation is based on the difference between post-stimulus and pre-stimulus data it captures the edge-induced changes of cross-frequency coupling . We performed the computation for both the story and back condition . As in Figure 2 we submitted individual maps to dependent samples t-test ( story versus back condition ) with randomisation-based FDR correction . Group t-maps are displayed with thresholds corresponding to p<0 . 05 ( FDR-corrected ) . Figure 7A shows the spatial distribution of theta phase to gamma-amplitude coupling . Left and right auditory areas show a significant difference of edge-induced changes in cross-frequency coupling between the story and back condition . The second analysis used the left and right auditory cortex as regions of interest to compute the full cross-frequency coupling matrix . Here , we computed MI as before but now for all combinations of phase ( 1–10 Hz ) and amplitude ( 4–80 Hz ) . We computed group t-statistics for the difference between the story condition and surrogate data ( significant pixels are opaque , see Materials and Methods ) . Both left and right auditory cortices show a frequency-specific coupling of theta phase to gamma amplitude and in addition a frequency-specific coupling of delta phase and theta amplitude ( Figure 7B ) . Both effects are significantly stronger ( t-test , p<0 . 05 ) in the story condition compared to the back condition , demonstrating a more precise hierarchical nesting of cortical oscillations for intelligible than unintelligible speech . Finally , we studied lateralisation of the cross-frequency coupling shown in Figure 7B . The results in Figure 7C demonstrate a significant lateralisation of theta-gamma coupling to the left auditory cortex .
We observed phase alignment between low-frequency components of the speech envelope and brain activity in the delta and theta band . No consistent phase-phase coupling was observed for frequencies higher than 10 Hz . Previous studies have shown that speech envelope frequencies below 10 Hz are important for intelligibility [38] . Indeed , delta and theta frequencies match the rhythmicity of important temporal structures in continuous speech . Slow speech envelope variations ( 0 . 3–1 s , delta band ) represent prosody whereas syllables tend to occur at a rate of about 3–7 Hz in normal speech [9] , [10] . These components are known to modulate oscillatory phase and amplitude dynamics in the auditory cortex [12] . Our study investigated the underlying mechanisms by using information theory to comprehensively quantify how the phase and amplitude of different frequency components of the speech envelope affect the phase and amplitude of different cortical brain oscillations . We reported two different mechanisms . First , the low-frequency phase in the speech envelope entrains the low-frequency phase of brain oscillations in delta and theta frequency bands . The specific entrainment patterns support the idea that delta and theta bands are qualitatively different [25] . Phase coupling in the delta band extends more towards right frontal areas compared to theta phase coupling and both frequencies show different spatial lateralisation patterns ( Figure 3 ) . This indicates selective engagement of different areas for processing the different quasi-rhythmic components of the stimulus . Interestingly , significant right-lateralisation was evident in the delta band in frontal , posterior temporal , and parietal areas but not in primary auditory areas ( in contrast to the theta band ) . These results are consistent with previous findings that right temporal and frontal brain areas are involved in prosodic processing [24] , [39] . Bilateral auditory areas show significant theta phase entrainment to the speech envelope . This effect is significantly lateralised to the right hemisphere and confirms previous findings [20] , [23] . The second mechanism revealed in our analysis is the alignment of gamma-amplitude modulations to the theta phase of the speech envelope in bilateral temporal , frontal , and parietal areas with lateralisation to the left hemisphere . Taken together , the auditory cortex showed right-lateralisation for theta phase entrainment and left-lateralisation for gamma amplitude entrainment . These results support the asymmetric sampling in time ( AST ) model [12] , [14] , [40] ( but see [41] ) that suggests a right-hemispheric preference for long temporal integration windows of 100–300 ms ( corresponding to theta band ) and a left-hemispheric preference for short temporal integration windows of about 20–40 ms ( corresponding to gamma frequencies ) . Indeed , this view is supported by studies of phase consistency in the theta band [20] , [23] and of oscillatory power in the gamma band [13] , [42] , [43] . Our results demonstrate a direct effect of specific speech components ( low-frequency phase of speech envelope ) on oscillatory brain activity and show significant lateralisation consistent with the AST-model . Interestingly , this coupling of brain oscillations to speech rhythms is supported by a hierarchical coupling of brain oscillations across frequencies . Delta phase modulates theta amplitude and theta phase modulates gamma amplitude and this modulation is stronger for intelligible compared to unintelligible speech . The hierarchically coupled oscillations could represent speech components ( prosody , syllables , phonemes ) in parallel at different timescales while preserving their mutual relationships . All entrainment effects were identified in a statistical contrast between the story and the back condition . This is important because it demonstrates that these entrainments are not just unspecific stimulus-driven effects but that they are modulated by intelligibility of the stimulus . A previous study [44] did not find entrainment differences between the two conditions . This might be explained by the fact that their stimulus material consisted only of three sentences across the whole study leading to learning effects even for the reversed speech . Also , the specific task used in that paper did not require comprehension and therefore might have masked differences between the speech and reversed speech condition . Reverse speech is often used as a control condition in speech experiments [44]–[46] since the physical properties of the stimulus are preserved . Especially , rhythmic components in the speech stimuli are still present in reversed speech ( although the quasi-periodicity of rhythmic components in speech will lead to some changes in the oscillatory dynamics of reversed speech ) . The enhanced entrainment observed in the story condition is therefore likely due to top-down mechanisms that have been previously shown to modulate activity in the auditory cortex during processing of degraded speech [47] , [48] or speech in noise [49] . These mechanisms could lead to changes in oscillatory phase dynamics [26] , [50] , [51] . We expect that within sentences , paragraphs , and over the entire course of the story participants will predict upcoming words and salient auditory events . This content-based prediction in the story condition seems to affect phase entrainment in early sensory areas [22] , [52]–[54] . Our study supports emerging models of speech perception that emphasise the role of brain oscillations [9] , [12] . Hierarchically organised brain oscillations may sample continuous speech input at rates of prominent speech rhythms ( prosody , syllables , phonemes ) and represent a first step in converting a continuous auditory stream to meaningful internal representations . Our data suggest that this step of sparsening the sensory representation occurs in parallel computations both in frequency ( as multiplexed oscillations ) and in the left and right hemisphere [40] albeit with lateralised preference for different time scales . Our results indicate that sharp large-amplitude transients ( edges ) in speech reset oscillations in the auditory cortex with important consequences . First , these resets increase the alignment between auditory oscillations and the speech envelope ( Figure 6 ) . This is important to re-align brain oscillations to speech after breaks . Second , this increase in alignment accounts for variations in continuous speech because randomly shuffling the speech signal across trials reduces the alignment . Since each trial represented a different segment of the continuous story this finding shows that brain oscillations are dynamically aligned to the time-varying dynamics of speech . Third , cross-frequency coupling between auditory oscillations increases following edges thereby enhancing precision of multi-scale nested dependencies . Fourth , temporal edges lead to a transient decoupling of the left and right auditory cortex that could be caused by a differential phase reset in both cortices and could indicate sensitivity to different acoustic properties of the stimulus . In the rat auditory cortex , increases in sound power in the frequency band matching the tonotopy of the considered location lead to large depolarizing currents in the input layers that reset intrinsic oscillations to an “excitable” phase [55] ( see also [56] , [57] ) . It is therefore conceivable that our observed phase resets to edges realigns the internal temporal reference frame to the sensory input to optimally sample relevant information at oscillatory phases of high excitability . This phase reset is stimulus dependent because correlation with speech is reduced for trial-shuffled data ( Figure 6 ) and because phase after edge-onset codes the amplitude of this edge ( Figure S5 ) . This coding of peak stimulus amplitude ( and possible other features ) in low-frequency phase could explain the previously reported classification of stimulus identity from low-frequency phase dynamics [58] , [59] . The stimulus-specific phase resetting could be an important mechanism for aligning time windows of high neural excitability to salient stimulus events because of similar time constants in speech and brain dynamics . The importance of edges for speech entrainment was very recently shown by Doelling et al . [60] . By manipulating the speech envelope they demonstrated that edges enhance speech entrainment and intelligibility . In summary , we report a nested hierarchy of auditory oscillations at multiple frequencies that match the frequency of relevant linguistic components in continuous speech . These oscillations entrain to speech with differential hemispheric preference for high ( left ) and low ( right ) frequencies . Our results indicate that temporal edges in speech increase first the coupling between auditory oscillations across frequency bands and , second , their coupling to the speech envelope . We can only speculate about the nature of the observed phase/amplitude alignments . Most likely the alignments are caused by a combination of modulatory and evoked effects [55] , [56] where stimulus-driven activity is top-down modulated via ongoing oscillatory activity [30] , [61] . In this framework oscillatory activity is a mechanism for attentional selection and flexible gating of information from primary sensory areas . Finally , going beyond speech perception , the entrainment of hierarchically organized oscillations between speaker and listener may well have a more general role in interpersonal communication [62] , [63] .
22 healthy , right-handed volunteers participated in the study ( 11 males; age range 19–44 years , mean 27 years ) . All participants provided informed written consent and received monetary compensation for their participation . The study was approved by the local ethics committee ( University of Glasgow Faculty of Information and Mathematical Sciences ) and conducted in conformity with the Declaration of Helsinki . MEG recordings were obtained with a 248-magnetometers whole-head MEG system ( MAGNES 3600 WH , 4-D Neuroimaging ) at 1 , 017 Hz sampling rate . The analysis of the MEG signal was performed using the FieldTrip toolbox [64] , the Information-Theory Toolbox [33] , and in-house MATLAB code according to recently published guidelines [65] . Stimuli have been previously used in an fMRI study [66] . The main stimulus consisted of a recording of a 7-min real-life story ( “Pie-man , ” told by Jim O'Grady at “The Moth” storytelling event , New York ) . The story was presented binaurally via a sound pressure transducer through two 5 m long plastic tubes terminating in plastic insert earpieces . Presentation was controlled with Psychtoolbox [67] under MATLAB . In addition to one standard presentation of the story ( story ) , individuals also listened to the backward played story ( back ) . Eye fixation was maintained throughout the experiment . Experimental conditions were recorded in randomised order .
|
Continuous speech is organized into a nested hierarchy of quasi-rhythmic components ( prosody , syllables , phonemes ) with different time scales . Interestingly , neural activity in the human auditory cortex shows rhythmic modulations with frequencies that match these speech rhythms . Here , we use magnetoencephalography and information theory to study brain oscillations in participants as they process continuous speech . We show that auditory brain oscillations at different frequencies align with the rhythmic structure of speech . This alignment is more precise when participants listen to intelligible rather than unintelligible speech . The onset of speech resets brain oscillations and improves their alignment to speech rhythms; it also improves the alignment between the different frequencies of nested brain oscillations in the auditory cortex . Since these brain oscillations reflect rhythmic changes in neural excitability , they are strong candidates for mediating the segmentation of continuous speech at different time scales corresponding to key speech components such as syllables and phonemes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"auditory",
"system",
"natural",
"language",
"processing",
"cognitive",
"neuroscience",
"computational",
"neuroscience",
"sensory",
"systems",
"biology",
"computational",
"biology",
"neuroscience",
"neuroimaging"
] |
2013
|
Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain
|
The objectives of this study were to 1 ) evaluate the influence of treatment with praziquantel on the inflammatory milieu in maternal , placental , and cord blood , 2 ) assess the extent to which proinflammatory signatures in placental and cord blood impacts birth outcomes , and 3 ) evaluate the impact of other helminths on the inflammatory micro environment . This was a secondary analysis of samples from 369 mother-infant pairs participating in a randomized controlled trial of praziquantel given at 12–16 weeks’ gestation . We performed regression analysis to address our study objectives . In maternal peripheral blood , the concentrations of CXCL8 , and TNF receptor I and II decreased from 12 to 32 weeks’ gestation , while IL-13 increased . Praziquantel treatment did not significantly alter the trajectory of the concentration of any of the cytokines examined . Hookworm infection was associated with elevated placental IL-1 , CXCL8 and IFN-γ . The risk of small-for-gestational age increased with elevated IL-6 , IL-10 , and CXCL8 in cord blood . The risk of prematurity was increased when cord blood sTNFRI and placental IL-5 were elevated . Our study suggests that fetal cytokines , which may be related to infectious disease exposures , contribute to poor intrauterine growth . Additionally , hookworm infection influences cytokine concentrations at the maternal-fetal interface . ClinicalTrials . gov ( NCT00486863 ) .
Adverse perinatal outcomes account for a substantial proportion of the global burden of disease [1] and lay the foundation for health in later childhood , adolescence , and adulthood [2–5] . Low birthweight ( LBW ) , fetal growth restriction ( FGR ) and preterm births together account for more than 80% of all neonatal deaths globally [6] . These conditions are more common in developing countries and a considerable part of this difference is attributable to poor nutrition and infections [6 , 7] . Specifically , infections such as malaria are known to predispose to preterm births , FGR and fetal loss among offspring of affected pregnant women [8 , 9] . With respect to helminthiasis , less is known with regard to treatment strategies for pregnant women . In a non-interventional study conducted in a Schistosoma japonicum endemic area , Kurtis and colleagues found increased concentrations of pro-inflammatory cytokines including interleukin-1 ( IL-1β ) and tumor necrosis factor ( TNF ) in placental and cord blood among women with S . japonicum infection [10] . Further , among infected women , that study found an increased risk for placental histopathologic evidence of an inflammatory response including acute subchorionitis . In a recent randomized controlled trial ( RCT ) however , Olveda and colleagues found that treatment with praziquantel at 12–16 weeks gestation had no impact on birthweight , or risk for LBW , small-for-gestational age ( SGA ) , or prematurity [11] . This raised the concern that treatment during pregnancy may be too late to modify a pro-inflammatory response at the maternal-fetal interface ( MFI ) . Healthy pregnancies are characterized by a placental microenvironment that is biased toward a T-helper 2 ( Th2 ) cytokine milieu [12 , 13] , and increased expression of pro-inflammatory cytokines in the placenta have been associated with poor pregnancy outcomes in both human and animal models [14–21] . Of particular relevance to pregnant women in low and middle-income countries ( LMICs ) , studies have demonstrated that malaria alters the placental Th2 bias toward a pro-inflammatory microenvironment and is associated with poor pregnancy outcomes , particularly FGR [18 , 19 , 22] . Specifically , in human studies , increased placental TNF staining has been associated with increased risk of FGR in the context of malaria and lower birthweight in the context of schistosomiasis [10 , 18] . Though alterations in placental cytokines likely contribute to both FGR and prematurity in the context of malaria and other infectious diseases of pregnancy , little is known about how helminth infections influence this environment and no studies have addressed whether treatment during pregnancy modifies this . A better understanding of these mechanisms could inform the timing of treatment for helminthiasis as well as its prioritization in the pre-natal period . As part of the aforementioned RCT conducted in Leyte , The Philippines , we investigated whether treatment for schistosomiasis at 12–16 weeks’ gestation and the presence of other helminth infections would influence the cytokine micro-environment . Specifically , the objectives of this study were to 1 ) examine the impact of treatment with praziquantel on the inflammatory milieu in maternal , placental , and cord blood , 2 ) assess the extent to which proinflammatory signatures in placental and cord blood impacts the risk for LBW , SGA , and prematurity , and 3 ) evaluate the impact of other helminths on the inflammatory micro environment .
This was a secondary analysis of data from a double blind placebo-controlled RCT examining the effects of praziquantel given at 12–16 weeks’ gestation for the treatment of schistosomiasis on pregnancy outcomes [11] . The RCT aimed to address the gaps in evidence concerning the safety and efficacy of praziquantel treatment , and thereby provision of praziquantel treatment to pregnant women infected with Schistosomiasis , in line with recommendations from the World Health Organization ( WHO ) . Briefly , pregnant women presenting for prenatal care at six Municipal Health Centers servicing approximately 50 baranguays ( villages ) in a schistosomiasis endemic region of Leyte , The Philippines , were approached by midwives for screening . Initial eligibility screening included a urine pregnancy test and three stool samples collected on different days for the quantification of S . japonicum and soil transmitted helminths ( STHs ) eggs using the Kato-Katz method [23 , 24] . The second phase of screening and enrollment was conducted at Remedios Trinidad Romualdez ( RTR ) Hospital in Tacloban , Leyte . The study physician performed a trans-abdominal ultrasound to assess fetal viability and estimate gestational age . Women were eligible if they provided informed consent and were infected with S . japonicum , age 18 or older , otherwise healthy as determined by physician history , physical examination and laboratory studies , and pregnant at 12–16 weeks’ gestation with a live , singleton , intrauterine fetus . Women who met eligibility criteria ( n = 370 ) were randomly assigned ( 1:1 ) to receive either over-encapsulated praziquantel ( 30 mg/kg × 2 ) or over-encapsulated placebo ( dextrose ) , as a split dose over three hours in a double-blind fashion . At 12–16 weeks’ gestation , a detailed demographic and medical history was collected and physical examination ( including anthropometric measures ) conducted . Weight , height and other anthropometric measures were made as described [25 , 26] . Anthropometric measures were repeated at 32 weeks’ gestation . Venous blood samples were collected at 12-weeks and at 32-weeks gestation for assessment of inflammatory and hematologic biomarkers . Women were scheduled for additional visits as needed based on obstetrician-identified diagnoses . All women received prenatal vitamins with iron , as per standard of prenatal care in The Philippines . Stool samples were collected and intensity of helminth infection was determined as the mean of the three samples , and categorized using WHO criteria as follows: S . japonicum , low , moderate and heavy intensity infections were defined as 1–99 , 100–399 and ≥400 eggs per gram ( epg ) , respectively; Ascaris lumbricoides , low , moderate and heavy intensity infections were defined as 1–4 , 999 , 5 , 000–49 , 999 and ≥50 , 000 epg , respectively; Trichuris trichuria , low , moderate and heavy intensity infections were defined as 1–999 , 1 , 000–9 , 999 and ≥10 , 000 epg , respectively; hookworm , low , moderate , and heavy intensity were defined as 1–1 , 999 , 2 , 000–3 , 999 and ≥4 , 000 epg , respectively [23 , 24] . Following initial stabilization of the newborn and mother , placental samples ( wedge biopsy and pooled blood ) and cord blood were collected . Newborns were examined and weighed within 48 hours of delivery on a Tanita model BD 585 portable scale ( Arlington Heights , MD ) . LBW was defined as birthweight below 2500g , and SGA as birthweight below the 10th percentile for gestational age based on the INTERGROWTH standard [27] . Preterm birth was defined as a birth before 37 weeks’ gestational age . Maternal 12-week , 32-week , placental , and cord blood serum samples were aliquoted and stored at -80°C prior to testing . All available samples at each timepoint were used for comprehensive biomarker testing–only 238 cord blood samples were available . Assessment of biomarkers in the blood samples was conducted at the Center for International Health Research Laboratory in Providence , RI , USA . Biomarkers measured include IL-1 , 2 , 4 , 5 , 6 , 8 , 10 , 12 and 13 , interferon gamma ( IFN-γ ) , TNF , chemokine ligand-9 ( CXCL9 ) and soluble TNF receptors I and II ( sTNFRI and sTNFRII ) . Analytes were quantified using a multiplex bead-based platform ( Bio-Rad , Hercules , CA ) as described previously [28] . The lower limit of detection was 2 . 44ng/L for most cytokines and 4 . 88ng/L for TNF receptors . Participants with undetectable concentrations of biomarkers were assigned the lowest detectable concentrations . In analyses examining the impact of praziquantel treatment and helminth infections on cytokine production in maternal , placental and cord blood , these biomarkers were outcome measures . These biomarkers were separately evaluated as predictors of adverse pregnancy outcomes . Cytokine production was considered as exposure or outcome in this analysis . Three different measures of cytokine production were also employed: ( i ) cytokine concentration in ng/L , ( ii ) the proportion of those with an 'elevated' cytokine concentration , and ( iii ) the proportion with cytokine present at a level above the assay detection limit . The means ( ±SE ) of maternal cytokine concentrations at 12- and 32-weeks’ gestation were also estimated and the mean difference and 95% confidence interval ( CI ) estimated . To investigate the effect of praziquantel treatment on cytokine production , the proportions of participants with cytokine concentrations above detection limits in maternal 32 weeks’ , placental , and cord blood samples were compared across treatment groups , and P-values obtained from Fisher’s exact tests . Further , the means ( ±SE ) of cytokine concentrations at 32-weeks’ gestation ( with 95% CI ) were estimated within treatment subgroups and compared using linear regression . The extent to which the ratios of placental blood cytokines to maternal 12-week cytokines , and placental blood cytokines to maternal 32-week cytokines differed by treatment was also evaluated using Wilcoxon rank-sum tests . Generalized estimating equation regression models were used to assess the impact of each helminth infection at 12 weeks’ gestation on the proportion of participants with cytokines at a level above the assay detection limits in maternal 12- and 32-weeks’ gestation , placental and cord blood samples . Log-binomial models were used to evaluate the relationship between elevated maternal 32-week peripheral cytokines and placental and cord blood cytokines , and risk ratios ( RRs ) with 95% CI obtained . Log-binomial models were also used to examine the influence of elevated placental and cord blood cytokines on the risk of LBW , SGA , and prematurity . Log-binomial models provide RR estimates , which are intuitive and more appropriate for non-case control studies . The log-binomial model is however numerically unstable , and often fails to converge , and in those instances , log-Poisson models , which provide consistent but not fully efficient estimates of the RR and its CIs were employed [29] . Potential confounders known to be related to cytokines and/or perinatal outcomes were considered for inclusion in multivariable models . In addition , potential confounders were identified through stepwise regression techniques , significant at P-value <0 . 15 , with no variables forced into the model . Regression models were adjusted for predictors as specified in the footnotes of the respective tables and figures . Variables included in the models were praziquantel treatment , maternal age ( <30 y , ≥30 y ) , newborn sex ( boy , girl ) , maternal height ( cm ) , maternal weight at 12 weeks ( kg ) , maternal underweight ( body mass index <18 . 5kg/m2 ) , parity ( number ) , socioeconomic status ( quartiles ) , reported smoking status ( yes , no ) , alcohol use ( yes , no ) , and detection of S . japonicum , A . lumbricoides , T . trichuria , and hookworm , at 12 and 32 weeks’ gestation ( yes , no ) . P-values for effect modification were obtained by introducing an interaction term to the log-binomial regression model , in which praziquantel treatment status was multiplied by the biomarker category , and the model compared to the model without the interaction term using the likelihood ratio test . Possible effect modification by hookworm infection at 12 weeks’ gestation was also explored . P-values were 2-sided and statistical significance was defined as P-value <0 . 001 , based on the Bonferroni correction for the familywise error rate ( α /N , where α is 0 . 05 and N is the number of tests conducted in most of the analysis sets–N = 50 ) , to account for multiple comparisons [30] . CIs were constructed at the 1-α level . All data in our study were de-identified . Analyses were conducted using SAS 9 . 4 ( SAS Institute , Cary , NC ) . The study was approved by both the Rhode Island Hospital Institutional Review Board in Providence , RI , USA and the Ethics Review Board of the Research Institute of Tropical Medicine in Manila , The Philippines . This trial was registered with ClinicalTrials . gov , number NCT00486863 .
Participants included in this analysis were 369 . Detailed information on the cohort’s participant characteristics have been previously presented [11] . Most of the infants in this cohort were born at term ( median gestational age– 39 weeks ( IQR: 38 , 39 ) , by vaginal delivery ( 341 , 95% ) and mean ( ±SD ) birthweight was 2 . 85kg ( ±0 . 42 ) . The prevalence of LBW , prematurity and SGA were 14% ( n = 50 ) , 9% ( n = 32 ) and 23% ( n = 83 ) , respectively . Fig 1 details the selection of samples for cytokine quantification . Maternal cytokine concentrations significantly decreased from 12 to 32 weeks’ gestation ( S2 Supporting Information ) for sTNFRI ( Mean difference = -71 . 9; 95% CI: -104 , -39 . 6 , P-value<0 . 0001 ) . The concentration of sTNFRII ( Mean difference = -26 . 2; 95% CI: -44 . 2 , -8 . 1 , P-value = 0 . 005 ) , IL-6 ( Mean difference = -13 . 4; 95% CI: -23 . 6 , -3 . 19 , P-value = 0 . 01 ) and CXCL8 ( Mean difference = -6 . 32; 95% CI: -11 . 6 , -1 . 10 , P-value = 0 . 02 ) decreased while the concentration of IL-13 ( Mean difference = 0 . 33; 95% CI: 0 . 11 , 0 . 55 , P-value = 0 . 003 ) increased from 12 to 32 weeks’ gestation but the Bonferroni corrected P-values were not significant . The proportion of participants with detectable cytokines varied widely from 1–100% but tended to be highest in cord blood . To examine the impact of praziquantel treatment on cytokine concentrations , the concentration of cytokines in maternal blood at 32 weeks’ gestation was compared by treatment group ( Table 1 and Fig 2 ) . Praziquantel treatment lowered the concentration of anti-inflammatory IL-10 by 32-weeks’ gestation ( Difference: -0 . 48 ( -0 . 84 , -0 . 13 ) ) , though the difference was not significant after Bonferroni’s correction ( P-value = 0 . 008 ) . Praziquantel treatment did not alter the concentration of other cytokines considerably . There was also no evidence that praziquantel significantly altered the likelihood of detecting cytokines in maternal serum at 12 and 32 weeks , or in placental or cord blood ( Table 2 ) . Although helminth infections were common at 12 weeks’ gestation ( hookworm– 36% , T . trichuria– 81% , and A . lumbricoides– 62% ) , most were of light intensity ( hookworm– 36% , T . trichuria– 73% , and A . lumbricoides– 28% ) . Hookworm infection was associated with a 1 . 42 to 2 . 58-fold increased risk of elevated placental levels above detection limits for some cytokines ( Fig 3 ) : IL-1 ( RR = 2 . 41; 95% CI: 1 . 38 , 4 . 23 ) , IL-5 ( RR = 2 . 63; 95% CI: 1 . 19 , 5 . 79 ) , CXCL8 ( RR = 1 . 42 , 95% CI: 1 . 09 , 1 . 87 ) and IFN-γ ( RR = 2 . 58; 95% CI: 1 . 09 , 6 . 07 ) in multivariable models . Hookworm infection was not associated with an increased risk of detectable cytokines in maternal peripheral or cord blood ( S3 Supporting Information ) . Infection with T . trichuria and A . lumbricoides were also not associated with detectable levels in any of the cytokines ( S4 and S5 Supporting Informations ) . Hookworm infection at 12 weeks’ gestation did not modify the change in concentration from 12 to 32 weeks’ gestation . We investigated the extent to which cytokine levels in maternal peripheral blood was related to cytokine levels in placental and cord blood in multivariable log-binomial regression models ( S6 Supporting Information ) . S7 Supporting Information reports the concentration of each cytokine at which the 90th percentile level was reached . Participants with elevated maternal 32-week IL-4 ( RR = 17 . 3; 6 . 43 , 46 . 4 ) , IL-12 ( RR = 14 . 2; 95% CI: 3 . 51 , 57 . 1 ) , and IFN-γ ( RR = 5 . 35; 95% CI: 2 . 05 , 14 . 0 ) were more likely to have elevated placental levels of the same cytokines . There were no significant associations in the level of maternal cytokines with the levels of the same cytokines in the cord blood . The prevalence of LBW , prematurity and SGA were 14% ( n = 52 ) , 9% ( n = 33 ) and 23% ( n = 84 ) , respectively . Elevated levels of certain cytokines in the cord blood ( Tables 3 and 4 ) were associated with 2-fold increased risk of SGA: IL-10 ( Th2 ) –RR = 1 . 80 ( 1 . 09 , 2 . 97 ) , IL-6 –RR = 1 . 84 ( 1 . 13 , 3 . 00 ) , and CXCL8 –RR = 1 . 84 ( 1 . 10 , 3 . 10 ) ( Fig 4 ) . Elevated sTNFRI ( RR = 2 . 56; 95% CI: 1 . 20 , 4 . 80 ) and IL-5 in placenta ( RR = 2 . 85; 95% CI: 1 . 27 , 6 . 42 ) were associated with increased risk of prematurity ( Fig 5 ) . These associations were not significant following Bonferroni correction . There was no evidence that levels of other placental and cord blood cytokines were related to the occurrence of prematurity , LBW and SGA . We investigated effect modification of the association of cord and placental cytokines with the risk of perinatal outcomes by praziquantel treatment and hookworm infection at 12 weeks’ gestation and found no significant effect modification . We also examined the baseline characteristics of included and excluded participants and observed no significant differences in the characteristics of both groups with respect to the 12 weeks’ , 32 weeks’ and placental analyses . Included mothers that contributed to the cord blood analyses were of slightly higher BMI and heavier hookworm egg burden at 12 weeks’ gestation compared to excluded participants ( S8 Supporting Information ) .
In a cohort of pregnant women in The Philippines infected with S . japonicum and enrolled in a placebo-controlled RCT of praziquantel treatment , we examined the extent to which helminth coinfection and praziquantel treatment modified the cytokine milieu in the maternal , placental , and fetal compartments . We further investigated the relationship between the cytokine micro-environments and risk of adverse pregnancy outcomes . While praziquantel treatment did not alter the concentrations of the cytokines , hookworm infection was associated with higher levels of some placental cytokine . We also found that the concentrations of specific pro-inflammatory and anti-inflammatory cytokines in the placenta and cord blood were related to the risk of SGA and prematurity . Evidence from animal and human studies suggests that maternal infections alter the placental and fetal inflammatory milieu , with important implications for health during the neonatal period and childhood [31–33] . For instance , Kurtis and colleagues have previously shown that maternal schistosomiasis is associated with a pro-inflammatory cytokine response in maternal , placental , and fetal compartments [10] . McDonald and colleagues have also demonstrated that schistosome egg antigens elicit pro-inflammatory immune responses from trophoblast cells in vitro , such that direct infection of the placenta may not be necessary to drive these responses [34] . In addition , McDonald and colleagues have found that infection with S . japonicum was associated with elevated endotoxin levels in placental blood and this was , in turn , associated with a pro-inflammatory signature [35] . It is thought that endotoxin is elevated in the context of schistosomiasis due to microbial translocation as eggs traverse the gut wall from the normally sterile systemic circulation into the gut lumen . In the context of malaria , altered placental cytokine concentrations have been demonstrated in the presence of infection , with increased expression of both pro-inflammatory cytokine ( IL-1β and TNF ) and chemokines ( CXCL8 ) , and decreased expression of IL-6 [18] . In this analysis , we also found that hookworm infection among pregnant women was associated with elevated Th1 ( IL-1β and IFN-γ ) cytokines , as well as IL-5 and CXCL8 in blood collected from the maternal-fetal interface . Cytokine production at the maternal-fetal interface is crucial for many aspects of healthy pregnancy , including protection of the fetus from invading pathogens and the initiation of labor [36 , 37] . Infiltration of leucocytes into the myometrium has been demonstrated in both term and preterm labor , with the type of cells and cytokine elevation patterns being dependent on the presence or type of specific immune triggers [38 , 39] . We observed a 2-fold increased risk in preterm births in the presence of elevated cord blood sTNFRI , the soluble component of TNF receptor 2 through which TNF facilitates prostaglandin production to initiate uterine contractions [40 , 41] . Altered cytokine production by the placenta may contribute to the risk of FGR , a process through which an adverse intrauterine environment places the newborn at risk for SGA birth [18 , 42 , 43] . In a previous study , Kurtis and colleagues had shown that placental blood IL-1β and TNFα were related to birthweight in a Filipino pregnancy cohort [10] . In the present study , pregnancies with elevated cord blood IL-10 , IL-6 and CXCL8 each had about 2-fold greater risk of SGA after adjusting for multiple potential confounders . IL-1 and IL-6 are proinflammatory . IL-10 is anti-inflammatory and belongs to the Th2 subset [44] . CXCL8 is a neutrophil chemotactic and activating factor produced by monocytes , and trophoblasts in normal human pregnancy [45] . CXCL8 production increases during infections and in response to LPS and pro-inflammatory cytokines ( TNF and IL-1 ) [46] . As part of the Th2 response , IL-13 inhibits the production of multiple cytokines including TNF , IL-10 , and IL-1β [47] . Costimulation of Th2-associated cytokines to counteract the effects of pro-inflammatory Th1 cytokines during an infection likely explains the associations with SGA observed . A similar pattern has been previously reported among Tanzanian pregnant women with placental malaria where both pro-inflammatory CXCL9 and anti-inflammatory IL-10 were observed to be related risk for LBW [48] . Praziquantel treatment leads to a substantial and prolonged immune response due to the release of immunogenic antigens from dying eggs and worms , a decrease in T regulatory cells , and increased production of both Th1 and Th2 cytokines [49] . In this study , praziquantel treatment did not significantly alter the trajectory of the concentration of any of the cytokines examined . Our results differ from previous studies among non-pregnant individuals infected with S . mansoni that have reported increases in Th2 cytokines following praziquantel treatment [50–52] . It , however , remains possible that schistosomiasis infection alters the inflammatory milieu at the MFI , but the prolonged immune response to treatment does not allow modification of this milieu during gestation , suggesting active treatment of all women of reproductive age as recently recommended [53] . There are limitations to this study that should be addressed . First , all women had S . japonicum infection at study inception , somewhat limiting generalizability . Although placental blood using wedge biopsy leads to substantial contamination with maternal blood , our interest in understanding the broader cytokine milieu at the MFI and its impact on birth outcomes support this approach [54] . Further cytokine biology is complex , and phenomena such as co-stimulation , redundancy and synergy complicate the interpretation of findings , particularly the attribution of causality to specific cytokines in mediating adverse birth outcomes . Our study was conducted in a setting of multiple , often comorbid parasitic infections , limiting our ability to definitively attribute variations in cytokine concentrations to the presence or intensity of individual infections . Finally , cytokine profiles appear to differ by complex constructs linked to race [55] , and this further limits the generalizability of our findings . We examined the associations of placental cytokines above the 90th percentile with the risk of clinical outcomes , though we are unable to rule out the possibility that thresholds differ for each cytokine . Limited statistical power and measurement error may also some of the insignificant findings from our analysis . We also cannot rule out potential unmeasured cofounding in some of the analysis . Finally , we adjusted P-values for multiple testing due to the large number of statistical tests performed to reduce the possibility that our findings may be due to chance; however , the consistency of our results and how these are related to the extant literature further support their veracity . We analyzed data from an RCT to examine the influence of alterations in the balance of cytokines during gestation on the risk of perinatal and neonatal outcomes . Our analysis examined intermediate steps in the causal pathway from praziquantel treatment to adverse pregnancy outcomes including FGR . Our finding of a lack of effect of praziquantel on cytokines is consistent with the main RCT’s null findings [11] with respect to FGR , in spite of significant associations of elevated cytokines and pregnancy outcomes . We found that hookworm coinfection among pregnant women with schistosomiasis was associated with elevated cytokine concentrations at the MFI , which is in turn associated with increased risk of FGR and preterm births . Our findings strengthen the evidence in favor of prenatal treatment of women of reproductive age group for both schistosomiasis and STHs .
|
Schistosomiasis is one of the most prevalent parasitic tropical diseases , and it is primarily treated with the drug praziquantel . This study examined the effects of praziquantel treatment for schistosomiasis and the presence of geohelminth infections during pregnancy on cytokines in maternal , placental , and cord blood , and examined the effects of pro-inflammatory signatures at the maternal-fetal interface on perinatal outcomes . We analyzed the data of 369 mother-infant pairs obtained from a randomized controlled trial of praziquantel given at 12–16 weeks’ gestation . Praziquantel treatment did not significantly alter the trajectory of the concentration of any of the cytokines examined . Elevated levels of both Th1 and Th2 cytokines were associated with the risk of adverse perinatal outcomes ( small-for-gestational age and prematurity ) . Hookworm coinfection at 12 weeks’ gestation was , however , related to elevated levels of certain cytokines in the placenta ( IL-1 , IL-5 , CXCL8 and IFN-γ ) .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"invertebrates",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"reproductive",
"system",
"immune",
"physiology",
"cytokines",
"body",
"fluids",
"maternal",
"health",
"obstetrics",
"and",
"gynecology",
"helminths",
"immunology",
"tropical",
"diseases",
"hookworms",
"parasitic",
"diseases",
"animals",
"developmental",
"biology",
"women's",
"health",
"pregnancy",
"molecular",
"development",
"neglected",
"tropical",
"diseases",
"embryology",
"placenta",
"immune",
"system",
"helminth",
"infections",
"schistosomiasis",
"eukaryota",
"blood",
"anatomy",
"trichuriasis",
"physiology",
"biology",
"and",
"life",
"sciences",
"soil-transmitted",
"helminthiases",
"organisms"
] |
2019
|
Maternal, placental and cord blood cytokines and the risk of adverse birth outcomes among pregnant women infected with Schistosoma japonicum in the Philippines
|
In HPV–related cancers , the “high-risk” human papillomaviruses ( HPVs ) are frequently found integrated into the cellular genome . The integrated subgenomic HPV fragments express viral oncoproteins and carry an origin of DNA replication that is capable of initiating bidirectional DNA re-replication in the presence of HPV replication proteins E1 and E2 , which ultimately leads to rearrangements within the locus of the integrated viral DNA . The current study indicates that the E1- and E2-dependent DNA replication from the integrated HPV origin follows the “onion skin”–type replication mode and generates a heterogeneous population of replication intermediates . These include linear , branched , open circular , and supercoiled plasmids , as identified by two-dimensional neutral-neutral gel-electrophoresis . We used immunofluorescence analysis to show that the DNA repair/recombination centers are assembled at the sites of the integrated HPV replication . These centers recruit viral and cellular replication proteins , the MRE complex , Ku70/80 , ATM , Chk2 , and , to some extent , ATRIP and Chk1 ( S317 ) . In addition , the synthesis of histone γH2AX , which is a hallmark of DNA double strand breaks , is induced , and Chk2 is activated by phosphorylation in the HPV–replicating cells . These changes suggest that the integrated HPV replication intermediates are processed by the activated cellular DNA repair/recombination machinery , which results in cross-chromosomal translocations as detected by metaphase FISH . We also confirmed that the replicating HPV episomes that expressed the physiological levels of viral replication proteins could induce genomic instability in the cells with integrated HPV . We conclude that the HPV replication origin within the host chromosome is one of the key factors that triggers the development of HPV–associated cancers . It could be used as a starting point for the “onion skin”–type of DNA replication whenever the HPV plasmid exists in the same cell , which endangers the host genomic integrity during the initial integration and after the de novo infection .
Papillomaviruses are small dsDNA viruses that infect the basal cells of differentiating epithelium in variety of animals , including humans [1] . Initial infection is followed by the transient nuclear amplification of the HPV circular genomes via the viral pre-replication complex ( pre-RC ) , which is assembled by the E1 and E2 proteins during the S-phase of the cell cycle [2]–[5] . E1 acts as the replication origin recognition factor and DNA helicase [6] , [7] . In cooperation with E2 , it licenses the papillomavirus origin within the upstream regulatory region ( URR ) and initiates DNA replication by loading the host cell replication complexes at the origin [8]–[12] . Unlike cellular DNA replication , the E1- and E2-dependent HPV DNA replication does not follow the once-per-cell cycle initiation mode [13] , [14] . During their normal life cycle , HPVs must maintain their genomes as multicopy nuclear plasmids . However , it is generally known that the DNA of “high risk” human papillomaviruses ( HR-HPV ) , most commonly HPV16 and HPV18 , are frequently integrated into the host cell chromosome in non-invasive squamous intraepithelial lesions ( SIL ) and squamous cell carcinomas ( SCC ) [15]–[23] . The integration of HR-HPV DNA is considered to be an accidental but crucial step in the development of invasive cervical cancers that drives the clonal selection of the HPV transformed cells due to the increased expression levels of viral oncoproteins E6 and E7 [24] . Characterization of the early events during the integration of HPV16 before the clonal selection has been studied thoroughly in the W12 model [23] , [25] , [26] . Limiting dilution cloning of the cells shows that viral genome integrants arise in the presence of the HPV16 episomes and exist under a non-competitive environment , while the expression of integrated E6 and E7 are transcriptionally repressed by the episome derived E2 protein [25] . Integration can occur at any time during episome maintenance , which results in the eventual loss of the HPV plasmids through the transient phase when the episomal and integrated HPV DNA coexist in the same cells [23] , [25] . By the time that the episome is lost and the expression of integrated E6 and E7 oncoproteins are derepressed , transformed cells that carry the integrated HPVs have acquired a growth advantage for clonal selection [27]–[29] . We recently reported that the HR-HPV E1 and E2 proteins that were expressed from expression vectors effectively initiate DNA replication from the integrated HPV origin in HeLa and SiHa cell lines . This results in the amplification of the integrated HPV origin and flanking sequences , as well as the induction of local rearrangements , such as duplications of the cellular genome [30] . We concluded that the DNA re-replication initiated from the integrated HPV origin can lead to the development of chromosomal abnormalities , which could drive the malignant progression and serve as a major trigger for the formation of HPV-related cancers . Recently published data obtained with W12 cells confirmed our findings under physiological conditions by showing that the integration of HPV16 in these cells is accompanied by frequent and local DNA rearrangements within the integration locus [25] . This provides evidence that episome-derived E1 and E2 proteins may actively interfere with the integration process at physiological level and modify the integration locus during the S-phase of the cell cycle by multiple initiations of DNA replication , which generates the replication intermediates that are targets for the DNA repair machinery . Earlier studies have shown that genomic instability of the HPV infected cells is also increased by the up-regulation of the expression of the viral oncogenes [31]–[36] . The activities of HR-HPV oncoproteins are instrumental in the later phases of integrated HPV transformation during the clonal selection of the cells in their progression to cancer . During the early events of integration , however , the episome derived E2 protein effectively represses the transcription of E6 and E7 proteins [27]–[29] , [37] . In the current study , we further characterize the molecular mechanisms of the early events during the integration of HR-HPV and the involvement of the cellular DNA repair recombination machinery in this process . We demonstrate that the E1- and E2-dependent initiation of the integrated HPV replication follows the “onion skin”-type replication mode , which leads to the formation of different by-products , including supercoiled plasmids . We demonstrate that these processes take place in the DNA repair/recombination centers , which incorporate viral and cellular replication proteins , the MRE complex , Ku70/80 , ATM , Chk2 , ATRIP and Chk1 ( phosphorylated at S317 ) . All these proteins are visualized in the repair/recombination centers in the S-phase cells by indirect immunofluorescence assays . In addition , activation of the ATM-Chk2 pathway is confirmed by IP-western blot analyses . This suggests that the replication of integrated HPV activates the DNA damage checkpoints , which results in repair of the damage by homologous recombination ( HR ) and non-homologous end-joining ( NHEJ ) . However , not all of the damaged sites are repaired properly in the surviving cells , as the replication of the integrated HPV can result in a variety of rearrangements , including cross-chromosomal translocations . In order to emphasize the role of the replication of the integrated HPV origin in the induction of genomic rearrangements , we also show that transfected HPV circular genomes are not only replicating in HeLa and SiHa cells but also mobilize the integrated HPV origin for DNA replication , which leads to the rearrangements within the integration locus . Based upon this , we hypothesize that the infection of immortalized cells of SIL with homologous and heterologous HPVs might lead to the replication of the integrated HPV origin followed by rearrangements within the integrated locus , which is reminiscent of the “hit-and-run” mechanism .
We have previously demonstrated that HPV replication proteins E1 and E2 , which are expressed from the heterologous expression vectors , can induce over-amplification of the integrated HPV origin , which leads to the chromosomal instability of the HPV positive cancer cells [30] . In the initial studies , we used regular one-dimensional agarose gels for the separation and detection of the integrated HPV replication products , followed by hybridization of the Southern blots with sequence-specific probes . In the current study , we opted to use two-dimensional agarose gels to further characterize the different molecular species generated at the integrated HPV locus as a result of the replication or the action of the cellular repair-recombination machinery . First , we enriched the samples for the replication intermediates of the integrated HPV by using the Hirt extraction method [38] . A considerable part of the replicated HPV DNA appears in the low molecular weight ( LMW ) fraction of the Hirt extracts ( Figure 1A and 1B , lanes 8 ) , while there is essentially no signal for unreplicated HPV ( Figure 1A and 1B , lanes 5–7 ) . In this experiment , HeLa cells ( 1A ) and SiHa cells ( 1B ) were transfected either with carrier DNA ( lanes 1 and 5 ) , HPV18 E1 expression vector alone ( lanes 2 and 6 ) , HPV18 E2 expression vector alone ( lanes 3 and 7 ) , or with HPV18 E1 and E2 expression vectors together ( lanes 4 and 8 ) . Low molecular weight ( LMW ) and high molecular weight ( HMW ) DNA fractions were separated on one-dimensional agarose gel and analyzed by Southern blot with HPV18 ( 1A ) or HPV16 ( 1B ) URR-specific probes . Quantification showed that over 50% of the replication signal of integrated HPV ( Figure 1A and 1B , compare lanes 4 and 8 ) can be found in the Hirt LMW extract compared to 5% of the unreplicated DNA of the integrated virus ( Figure 1A and 1B , compare lanes 1–3 with 5–7 ) . In order to identify the topology of the replication intermediates and replication products , the Hirt LMW DNA from the E1/E2-transfected HeLa cells was further fractionated by conventional CsCl density gradient centrifugation in the presence of ethidium bromide . This procedure separates the supercoiled and non-supercoiled DNA molecules based upon the differences in buoyant density . Both fractions were separately subjected to two-dimensional neutral-neutral gel-electrophoresis , and the HPV18 replication products were examined by hybridization of the Southern blots with an HPV18 genome probe . In the first dimension of 2D gels , using low voltage and low agarose concentration , the DNA molecules are separated exclusively based upon molecular weight . In the second dimension , the molecules are separated based upon their topology due to increased voltage , higher agarose concentration , the presence of EtBr , and the low temperature . As a result , the supercoiled ( sc ) , open circular ( oc ) , and branched molecules have an altered mobility when compared with the linear molecules with the same mass , which appear as distinct arcs on 2D gels . The theoretical scheme for this type of analysis of the replication products is presented in Figure 1C . Analysis of the fraction containing non-supercoiled DNA molecules from the CsCl gradient ( Figure 1D ) shows three arcs of the replicated molecules , which were arcs of the linear fragments , branched molecules , and open circular plasmids of different sizes . Linear DNA fragments and branched molecules can be generated by displacement synthesis , replication fork collisions , and by mechanical shearing of the DNA during cell lysis , even if the lysates are handled gently . The appearance of the arc of open circular molecules ( Figure 1D ) suggests that active processing of the replication products is ongoing in these cells and that this leads to the excision of the HPV DNA from the cellular genome . A fraction of the supercoiled DNA ( scDNA ) molecules from the CsCl gradient was analyzed in a similar manner on the 2D gel ( Figure 1E ) . No linear HPV fragments in this fraction were detected , but the arc of the covalently closed circular plasmids of heterogeneous size is clearly visible . The local shift in the migration of the arc of the supercoiled molecules ( black arrowhead , Figure 1E ) is caused by the large quantity of mtDNA that serves as an additional internal control for the arc of circular molecules [39] . Since the arc of scDNA continues beyond that shift further into the higher molecular weight region , we can conclude that covalently closed circular molecules larger than 16 kb are generated as a result of the replication of the integrated HPV . Several stronger signals can be detected on the arc of supercoiled plasmids , which indicates either that certain excised HPV molecules are replicating more efficiently than the others , or that specific replication products are accumulating as a result of the stalling of replication forks . Therefore , we conclude that excision and active processing of the replicated HPV sequences are ongoing in the cells where E1- and E2-dependent replication of the integrated HPV takes place . A heterogeneous set of the supercoiled and relaxed plasmid circles that was detected in such cells could comprise actively replicating molecules , since they do contain the HPV18 replication origin . The detection of the open circles and supercoiled molecules also suggests that the cellular repair and recombination machinery is actively involved in this process . The bidirectional nature of DNA replication that is initiated from the integrated HPV origin has been previously shown by us and was referred to as the “onion skin”-type of replication mode [30] . To confirm this , we analyzed the replication intermediates of the integrated HPV16 in SiHa cells carrying a single integration site of HPV16 on chromosome 13 ( Figure 1F ) . SiHa cells were co-transfected with HPV16 E1 and E2 expression vectors and the LMW DNA was extracted 24 h post-transfection by Hirt lysis . DNA samples were digested with Acc65I-BshTI or Eco91I-BcuI , separated by neutral-neutral 2D gel-electrophoresis , blotted , and hybridized with the corresponding HPV16 genome fragment ( Figure 1H , 1I , and 1J , respectively ) . The scheme in Figure 1G demonstrates the idealized autoradiographic pattern of typical replication intermediates of the bidirectional DNA replication , which include the arc of replication forks and the arc of bubbles/puffs . The specific pattern depends on the location of the origin within the restriction fragment . In the blot of Acc65I/BshTI digested DNA ( Figure 1H ) , the hallmarks of the bidirectional replication can be clearly detected . The full-size arc of the replication forks from 1 N to 2 N size of the cleaved fragment indicates that the HPV16 origin is actively used for initiation of DNA synthesis . In addition , the arc of linear DNA fragments along with the larger structures , which presumably represent various branched onion skin-type replication intermediates , can be detected . The symmetrical cleavage of replication products with Eco91I/BcuI ( Figure 1I and 1J ) confirms that the initiation of replication takes place at the HPV16 replication origin , because the tip of the arc representing the replication forks could be identified in addition to the linear fragments , branched DNA structures , and bubbles . Interestingly , the detected bubble arc was more upright than is typically seen in case of cellular eukaryotic origins [40] , although we have previously proven that replication starts from the HPV origin and extends bidirectionally [30] . The only explanation for the discrepancy is that the new replication forks from the integrated HPV origin start before the previous ones have been extended outside of the restriction fragment , and , as a result , DNA fragments with multiple bubbles ( DNA puffs ) are generated . Clear , upright arcs that could represent the replication puffs were even detected at lower E1 levels ( Figure 1J ) . Therefore , we conclude that the true “onion skin” type molecules were detected as replication bubbles/puffs . A similar upright arc of bubbles has been previously shown in the case of BPV-1 DNA replication , which also follows the “onion skin”-type replication mode [41] . DNA replication of eukaryotic cells occurs within defined sites throughout the nucleus , as identified by co-localization of replication factors and nascent bromodeoxyuridine ( BrdU ) labeled DNA into distinct foci [42]–[44] . It has also been demonstrated that papillomaviruses , which are similar to many other DNA viruses , replicate their genome at specific nuclear extrachromosomal foci in infected cells [45] . To identify the replication sites of integrated HPV , we transfected SiHa cells with HPV16 E1 and E2 expression vectors and HeLa cells with HPV18 E1 and E2 expression vectors . Twenty hours post-transfection , the cells were labeled with BrdU for 2 hours , followed by double immunostaining for BrdU and HA-tagged HPV E1 protein . The results show that BrdU was incorporated throughout the nucleus in the cells without the E1 protein . However , in cells that were positive for E1 and E2 , the BrdU signal was mostly co-localized within the E1 foci ( Figure 2A ) . In such cases , we always identified 2 foci in the SiHa cells and 3 foci in the HeLa cells , although they were heterogeneous in size and there was a tendency for satellite foci later in the time course ( Figure 2A ) . These foci likely represent the integrated HPV sites that are capable of active replication in these cell lines . The SiHa cells contain two chromosome 13's that carry the single HPV16 integration site [46]–[48] , which suggests that both copies of the HPV16 are active for replication . In HeLa cells , at least five HPV18 integration sites have been mapped . Three of them are located on normal chromosomes 8 at 8q24 and two on derivative chromosomes , which have been shown to contain material from 8q24 [49] . We conclude that there are three replication competent loci of the HPV18 in HeLa cells . BrdU incorporation within the E1 foci indicates that the replication of the integrated HPV origin can be visualized by E1 immunofluorescence analysis and that such compartmentalization of E1 to these foci occurs only during S-phase of the cell cycle . To confirm that the visualized foci represent the HPV replication centers , the immunostaining for E1 was combined with FISH analysis for the integrated HPV-specific DNA ( Figure 2B ) . The amplified DNA of HPV16 ( in SiHa cells ) and HPV18 ( in HeLa cells ) are detected at the same foci as the E1 protein ( Figure 2B ) . These data again suggest that the E1 foci are bona fide amplification sites of the integrated HPV . To further elaborate on the potential functionality of these detected foci , we performed co-immunostaining for E1 and E2 , PCNA , the RPA p70 subunit or the polymerase/α-primase ( polα ) in HeLa cells that were transfected with the HPV18 E1 and E2 expression vectors ( Figure 3 ) . We detected the same pattern of co-localization of E1 and the other replication proteins as earlier , which once more demonstrates that these foci are active DNA replication initiation centers of the integrated HPV . This is supported by the fact that the E2 protein is not required for the unwinding or the helicase activity of E1 during DNA replication elongation and , likewise , the fact that polα is not required for DNA repair processes . The percentage of the E1/E2 positive cells with the characteristic pattern of viral DNA replication was estimated to be 5% to 10% , which represents a fairly large fraction of cells in S-phase within the transfected cell population . In the case of the HPV18 replication proteins , we also detected some satellite foci in addition to the main ones in both HeLa cells ( Figure 3 ) and SiHa cells ( data not shown ) . The relative amount of the cells with satellite foci increased over time from 26% at 12 h post-transfection to 74% at 48 h , as evaluated by two independent experiments in the HeLa cells . Co-localization of HPV18 E2 , PCNA , the RPA p70 subunit , and polα with HPV E1 indicates that these additional foci may represent the extrachromosomal fraction of the replicating , HPV origin containing , plasmids , which originated from the integrated HPV locus due to its over-amplification . These molecules were clearly detected by the two-dimensional gel-electrophoresis analyses ( Figure 1E ) . Promyelocytic leukemia ( PML ) nuclear bodies are nuclear structures that serve the role of storage site for numerous proteins that are associated with almost every nuclear function , including transcription , DNA repair , viral defense , stress , cell cycle regulation , proteolysis , and apoptosis [50] . It has been suggested that the replication of the HPV genomes might take place in the PML bodies as well [45] . To investigate the location of the integrated HPV replication in relation to the PML bodies , we performed co-immunofluorescence analyses for the HPV18 E1 and Daxx proteins ( Figure 3 ) . The Daxx protein has been shown to be localized to PML oncogenic domains [51] . The analysis shows that there is no co-localization between the Daxx and HPV18 E1 foci , which suggests , first , that replication of the integrated HPV does not take place in PML bodies and , second , that the E1 foci are not the artificial accumulation centers for the replication proteins due to the overexpression of E1 and E2 . Double immunostaining was also performed on mock-transfected cells and on cells transfected with E1 expression plasmids alone . However , we failed to detect any such foci in these SiHa or HeLa cells . Taken together , these results demonstrate that the replication of integrated HPV takes place at specific foci in the cell nucleus , which can be visualized by indirect immunofluorescence analysis or by FISH . Our data show that the replication of integrated HPV gives rise to the linear , branched , “onion skin”-type replication intermediates , as well as open circular and supercoiled circular DNA molecules ( Figure 1 ) in the nuclear replication centers ( Figure 2 and Figure 3 ) . Clearly , the generation of such irregularities in the cellular genome should trigger the cellular responses to repair it . We assume that DNA double-strand brakes ( DSBs ) are generated at some stage of the HPV “onion skin” type of replication . There are two major mechanisms for the repair of DNA DSBs , which are homologous recombination repair ( HR ) and non-homologous end joining ( NHEJ ) [52]–[54] . Primary sensors that detect DNA DSBs are the Mre11-Nbs1-Rad50 ( MRN ) complex , in the case of HR , and the Ku70/80 heterodimer , in the case of NHEJ . To investigate the potential linkage of cellular DNA repair mechanisms to the integrated HPV DNA replication , we assessed the localization of the MRN complex and Ku70/80 heterodimer in relation to the E1 protein in HeLa cells that were transfected with the HPV18 E1 and E2 expression vectors . Indirect immunostaining demonstrates that all components of the MRN complex as well as the Ku70/80 heterodimer are co-localized within the foci of the E1 protein , which represents the integrated HPV amplification sites ( Figure 4 ) . These data suggest that the cell senses the irregularities in the genome that are generated by the viral replication proteins and attempts to resolve the over-amplification of the HPV region by the recruitment of the MRN complexes and the Ku70/80 heterodimers . We conclude that both pathways of DNA repair/recombination are activated at the site of the integrated HPV DNA replication . We believe that most of the irregularities are fixed , but , in some cases , these repairs can lead to rearrangements that involve genomic sequences [30] , which lays the ground for genomic instability . Eukaryotic cells respond to DNA damage with a rapid activation of signaling cascades that are initiated by the ataxia telangiectasia mutated ( ATM ) kinase and the ATM and Rad3-related ( ATR ) kinase . Response to the DSBs , which can be caused by ionizing radiation or radiomimetic drugs , occurs primarily through the ATM-Chk2 signaling pathway . In response to replication fork stalling and other forms of DNA damage that are caused by ultraviolet light , cells activate replication checkpoints , where the central players are the ATR kinase , ATRIP ( ATR-interacting protein ) , and their downstream effector kinase , Chk1 . To identify which pathways are activated and recruited to the foci due to the replication of integrated HPV18 , we performed co-immunostaining analyses for the HPV18 E1 protein along with ATM , Chk2 , ATRIP , or Chk1 proteins in HeLa cells that were transfected with the HPV18 E1 and E2 expression vectors ( Figure 5 ) . The results show clear co-localization of ATM and Chk2 at the HPV replication foci , which indicates that a response to DSBs is generated during the HPV replication process . ATRIP also localizes within the HPV replication foci , which demonstrates the presence of RPA-coated ssDNA . In the case of the Chk1 protein , there was only poor co-localization of the Chk1 and E1 proteins when an antibody against phosphorylated Chk1 ( S317 ) was used ( Figure 5 ) . There was no co-localization with the E1 protein when the primary antibody against the unphosphorylated form of Chk1 was used ( data not shown ) . The data presented above clearly indicate that the DNA double-strand break repair machinery is recruited to the replication foci of integrated HPV . We further studied the activation status of the DNA DSB repair systems . HeLa cells were transfected with the HPV18 E1 and E2 expression vectors ( Figure 6A–6C , lane 1 ) , the HPV18 E1 expression vector ( Figure 6A–6C , lane 2 ) , the HPV18 E2 expression vector ( Figure 6A–6C , lane 3 ) , or carrier DNA ( Figure 6A–6C , lane 4 ) . Untransfected HeLa cells ( Figure 6A–6C , lane 5 ) and HeLa cells that were treated for 1 hour with etoposide ( Figure 6A–6C , lane 6 ) were used as controls . The transfected cells were first analyzed for E1 and E2 expression ( Figure 6A , panels a and b , respectively ) 24 hrs post-transfection using normalized western blot analysis . DNA double-strand break repair originating from diverse causes in eukaryotic cells are accompanied by the upregulation of phosphorylated γH2AX protein ( at serine 139 ) at the sites of DSBs in chromosomal DNA . This phosphorylated form of γH2AX is rapidly formed in cells that are treated with ionizing radiation ( IR ) and also during V ( D ) J and class-switch recombination and apoptosis . Since γH2AX appears within minutes after IR , the production of the phosphorylated form of γH2AX is considered to be a sensitive and selective signal for the existence of DNA double-strand breaks . Indeed , treatment of the HeLa cells with etoposide , which generates DNA DSBs , considerably elevates the formation of the phosphorylated form of the γH2AX in these cells ( Figure 6A , panel c , lane 6 ) . In addition , we detected a considerable increase of the phosphorylated form of γH2AX when the E1 and E2 proteins were co-transfected into HeLa cells ( Figure 6A , panel c , lane 1 ) . This indicates that the cellular response to the DNA DSBs that are generated by the replication of the integrated HPV DNA is clearly activated . We further analyzed the activation status of Chk2 at the same time point by using IP-western blot analysis with phosphorylation specific antibodies ( Figure 6B ) . HeLa cells , which were transfected in a manner similar to the procedure that was used in Figure 6A , were lysed and subjected to immunoprecipitation with the anti-Chk2 antibody . The immunoprecipitated protein samples were further analyzed with phosphorylation-specific antibodies targeted against the Chk2 phosphopeptides that contain Thr68 or Ser19 . These sites are part of a cluster of S/TQ phosphorylation sites that are recognized by PIKKs ( PI3 kinase-like kinases ) such as ATM and ATR [55] . It is known that all S/TQ sites in the N terminus of Chk2 are individually sufficient to activate the protein [56] . As expected , strong phosphorylation of Chk2 at Thr68 and Ser19 were detected in the case of etoposide–treated cells ( Figure 6B , lane 6 ) . In addition , a modest activation of Chk2 can be observed in the cells that were transfected with E1 expression vector alone ( Figure 6B , lane 2 ) . However , this effect was considerably enhanced when E1 and E2-dependent replication was initiated in HeLa cells ( Figure 6B , lane 1 ) . Interestingly , Ser19 is phosphorylated exclusively in response to DSBs in an ATM- and Nbs1-dependent but ATR-independent manner [57] . We conclude that E1 protein expression can , to some extent , activate the Chk2 kinase , which is further activated by the replication of the integrated HPV . Similar IP-western blot analysis of Chk1 activation in these cells showed a very weak elevation of the phosphorylation at Ser317 in the E1-transfected cells , which was not enhanced by the replication of integrated HPV and , by no means , was comparable to the effect of the etoposide-treatment of the cells ( Figure 6C , compare lanes 1 and 2 to lane 6 ) . We can only speculate why Chk1 and Chk2 are slightly activated in response the E1 expression . It could be either direct interactions with the components of the DNA repair pathways or an unspecific binding and unwinding of the cellular DNA . Finally , we examined the phosphorylation status of Chk2 kinase at Ser19 in HeLa cells , which simultaneously contain the integrated and episomal HPV18 genomes and express the E1 and E2 proteins at physiological levels . HeLa cells were co-transfected with the circular HPV18 genome and the pBabePuro plasmid , untransfected cells were removed with puromycin treatment , and Chk2 phosphorylation was analyzed at a 72 h time point by IP/western blot with the Ser19 phosphorylation-specific antibody ( Figure 6D , lane 1 ) . Mock-transfection with the carrier plasmid ( Figure 6D , lane 2 ) as well as untransfected HeLa cells ( Figure 6D , lane 3 ) were used to determine the background level of phosphorylation . Etoposide-treated HeLa cells were used as a positive control ( Figure 6D , lane 4 ) . Despite the slightly higher levels of phosphorylated Chk2 in the mock-transfected cells , the increased activation of Chk2 is clearly observed in HeLa cells that were transfected with the HPV18 genome . This can be caused either by the replication of the HPV plasmid or by the integrated HPV origin , and this refers to the active processing of DSBs , which might lead to host chromosomal instability . Cervical carcinogenesis is associated with the acquisition of structural and numerical chromosomal abnormalities after the integration of HPV into the host cell genome [26] , [34] . One potential reason is believed to be the increased levels of the HPV E6 and E7 proteins , which is caused by the disruption of E2 transcriptional repressor expression [23] , [31] . However , there is also an alternative mechanism that might cause the chromosomal rearrangements , which involves the over-amplification of the integrated HPV combined with the cellular attempt to fix the resulting DSBs by HR and NHEJ . We previously demonstrated , by restriction analysis , that the local region of the integrated HPV DNA changes dramatically upon in situ re-replication of the integrated HPV [30] . In this previous work , SiHa cells were transfected with the HPV16 E1 and E2 expression plasmids , the resulting transfected cells were single cell subcloned , and changes in the HPV16 restriction pattern , which could represent either an internal rearrangement or a reintegration at a novel site , were examined . The subclones with altered restriction pattern was further investigated in the current study by metaphase FISH in order to detect possible chromosome alterations that are associated with the replication of the integrated HPV . We used the tyramide-enhanced FISH method for the detection of HPV16 sequence in combination with a subtelomeric probe specific for chromosome 13 ( CytoCell ) . The results demonstrate that there are two chromosome 13's within the SiHa cells and that both carry the HPV sequence ( Figure 7A , one nucleus in interphase , and two metaphase chromosome spreads are presented ) . The HPV16 DNA was labeled with Alexa Fluor 488 and was visible as green dots , while the chromosome 13 subtelomeric regions were labeled with Texas Red and were visible as red dots . More importantly , the de novo cross-chromosomal translocation of the HPV16 genome along with the entire q-arm of chromosome 13 could be detected in one of the subclones , where the DNA replication of integrated HPV had been initiated ( Figure 7B , one nucleus in interphase and two metaphase chromosome spreads are presented ) . As a result , there is third 13q arm in the haploid genome of this subclone . Immunofluorescence analyses of the subclone showed three replication foci as compared to the two foci that we exclusively detected in SiHa cells ( data not shown ) . Over one hundred metaphase spreads of SiHa cells were analyzed and no type of heterogenity in our cell population was detected with regard to the HPV integration site . The data presented here and previously by us [30] raise an important question about the stability of the integrated HPV loci in the presence of viral replication proteins at physiologically relevant expression levels . Recent findings from the W12 cell line indicate that local rearrangements occur frequently and shortly after natural HPV16 integration , during the phase when episomal and integrated viral genomes are present in the same cell [25] . Similar translocations of the viral-host DNA have also been detected in several cell lines that were derived from invasive genital carcinomas [33] , [58] , [59] . We decided to address this question by applying the protocols used in human keratinocytes [60] , [61] to the HPV-positive cancer cell lines— HeLa and SiHa . The HPV16 and HPV18 genomes were excised from the vector backbone , purified and re-circularized at low concentrations . The circular viral genomes were then transfected into HeLa and SiHa cells along with the linearized plasmid that carries the selection marker for G418 . At first , the efficiency of the transient replication of the viral genomes was evaluated in these cell lines . The low-molecular-weight DNA was extracted 24 h and 48 h posttransfection and used in Southern blot analysis with HPV16- or HPV18-specific probes . A clear DpnI-resistant replication signal from the HPV16 and -18 plasmids was detected in both cell lines ( HPV16 in HeLa: Figure 8A , lanes 1–2; HPV18 in HeLa: Figure 8B , lanes 1–2; HPV16 in SiHa: Figure 8C , lanes 3–4; HPV18 in SiHa: Figure 8D , lanes 3–4 ) . This demonstrated that circular HPV genomes are capable of transient replication in cells that carry the integrated HPV subgenomic fragments . With that knowledge , we subsequently studied the stability of the integrated HPV locus in the presence of the replicating HPV episome . The HPV16- and HPV18-transfected SiHa cells were grown under the G418 selection to eliminate any untransfected cells . The total DNA was extracted five weeks post-transfection , and the HPV16-specific restriction patterns were analyzed by Southern blotting . Compared to the restriction pattern in untreated SiHa cells ( Figure 8E , lanes 1–3 ) , the results revealed a few unique , but faint bands in the HPV16-transfected cells ( Figure 8E , lanes 4–6 ) and an unchanged restriction pattern in the HPV18-transfected cells ( Figure 8E , lanes 13–15 ) . These observations could be explained either by the low abundance of the cells that had altered host genomic content or by the presence of the episomal HPV16 plasmid in the HPV16-transfected cells with an intact host genome . To clarify this issue , the transfected cells were further subcloned . Six of the HPV16- and six of the HPV18-transfected subclones were analyzed as before by Southern blotting with HPV16-specific probe . Two HPV16-transfected subclones with unique restriction fragments were identified , and they represent either integration of the transfected HPV16 plasmid into the host genome or the excision and re-integration of the initially integrated HPV16 ( Figure 8E , lanes 7–12 ) . More importantly , unique HPV16-specific fragments were also identified in one of the HPV18-transfected subclones ( Figure 8E , lane 16–18 ) . In this case , it could only indicate the re-arranged loci of the integrated HPV16 due to the presence of an episomal HPV18 . Parallel Southern blot analysis with an HPV18-specific probe revealed that , similarly to HPV16 , the HPV18 plasmid itself had most likely tandemly integrated into the host genome ( Figure 8F ) . This has been confirmed with 2D gels ( data not shown ) . In conclusion , these data suggest that the presence of the HPV plasmid in cells that have an integrated HPV origin can change the host genomic content .
Once per cell cycle DNA replication in eukaryotic cells is accomplished by temporal separation of the assembly of pre-replication complex ( pre-RC ) and the actual initiation of DNA synthesis [62] , [63] . However , the formation of HPV pre-RC that is orchestrated by the viral E1 and E2 proteins can occur simultaneously with the viral DNA synthesis , which allows the HPV origin to be licensed for multiple initiations of DNA replication during a single cell cycle [5] . These multiple initiations can effectively complete the DNA replication of the small HPV plasmid at physiological conditions and guarantee its extrachromosomal amplification and maintenance . At the same time , HR-HPV DNA can integrate into the host genome at any time during its episomal maintenance [25] , which generates the combination of two HPV origin entities in the same cell – integrated and episomal . Our recent work showed that the integrated HR-HPV origins are effectively mobilized for replication by E1 and E2 , which can lead to the generation of irregularity in the genomic DNA [30] . Based upon our previous demonstrations , these genomic irregularities are partially resolved in the clonally derived cells , where we found the rearranged tandem repeats of the HPV-host DNA junctions [30] . It should be emphasized that similar rearrangements at the loci of integrated HPV are described in W12 cells during the integration of episomal HPV16 [25] and in SiHa cell lines that are transfected with the HPV16 and HPV18 genomes ( Figure 8 ) . These data indicate that the mobilization of an integrated HPV origin for DNA replication and the subsequent actions of the cellular DNA repair/recombination machinery occur during episomal HPV replication at a physiological level of the replication proteins . Current analysis of the replication intermediates in SiHa cells show that integrated HPV follows the “onion skin”-type of DNA replication mode . In addition to linear and branched DNA molecules , heterogeneous populations of supercoiled and open circular plasmids were formed . These HPV-origin containing plasmids are most likely the templates for the E1 and E2-driven DNA replication and might be , therefore , one of the mechanisms for gene amplification . Similar chromosomal excision and formation of a heterogeneous pool of circular molecules has also been detected earlier in case of the DNA re-replication of integrated SV40 in the presence of large T antigen [64]–[66] . The structures of DNA breaks that are generated by HPV DNA re-replication should not differ from other types of double strand breaks ( DSB ) , and they should be recognized in eukaryotic cells by either non-homologous end-joining ( NHEJ ) or homologous recombination ( HR ) . We demonstrated by indirect immunofluorescence that the initiation factors of both NHEJ and HR are localized at the integrated HPV replication centers , which suggests that DSBs are generated by the re-replication of the HPV locus . It is possible that HR , which is the primary DNA repair mechanism in the S-phase , might get saturated by the abundant generation of DSBs during the integrated HPV replication , which would lead to some of the DSBs being repaired by NHEJ . Although the NHEJ machinery plays a significant role in maintaining genome stability and suppressing tumorigenesis [67]–[69] , it is also responsible for the vast majority of tumorigenic chromosomal translocations . Even the “correct” re-joining of broken ends by NHEJ often results in mutations at the junctions [70] . Therefore , NHEJ may primarily contribute to the development of genetic instability that is found in HPV-associated cancers . Forced assembly of the cellular pre-RC in the S-phase leads to the re-replication of the cellular DNA and the activation of various checkpoint pathways [71]–[73] . In mammalian cells , the ATR-mediated S-phase checkpoint is immediately activated after accumulation of the RPA-coated ssDNA and before the appearance of DSBs to prevent further DNA re-replication [71] , [74] . However , our data indicate that ATR is unable to prevent the DNA re-replication from the integrated HPV origin . This allows us to speculate that ATR pathway does not recognize the DNA re-replication that is initiated from the integrated HPV origin , which might be an intrinsic property of the PV replication machinery necessary for the amplification of the viral genome during the initial or late phases of the viral life . The weak localization of ATRIP and Chk1 ( S317 ) to the sites of integrated HPV replication as well as the poor phosphorylation of Chk1 is not sufficient to block the replication . It is possible that the activation of ATR is caused instead by the availability of the RPA-coated ssDNA at the sites of fork collisions and dissociation ( Figure 9 ) . However , if the ATR and ATRIP proteins recognize the sites of integrated HPV replication , the possibilities to inhibit the viral pre-RC might still be limited , since there are only few targets in the viral replication complex that are available for ATR , when compared with the complex initiation mechanisms of the cellular DNA replication . Phosphorylation of the HPV E1 has been extensively studied , but the ability of the ATR to phosphorylate HPV E1 protein has not been demonstrated [75] . Additionally , the indirect signaling pathways of ATR through p53 and pRB could be down regulated by the HPV E6 and E7 oncoproteins . It has been also shown that the replication of BPV1 URR reporter plasmid can overcome the inhibitory effect of p53 [76] . If the prevention of DNA re-replication by ATR fails , the accumulation of DSBs can activate the ATM pathway , as we have concluded from our data . We observed clear localization of ATM and Chk2 to the replication sites of the integrated HPV as well as the clear phosphorylation of Chk2 kinase in the cell population where the replication of the integrated HPV occurred . This indicates that the ATM-Chk2 pathway plays the major role in resolving the DNA damage that is caused by the replication of integrated HPV . Supported by the observation that Cdt1 is overexpressed in human cancer cells , it has been suggested that DNA re-replication can lead to the chromosomal instability and malignant transformation [71] , [74] , [77] , [78] . The current study provides , for the first time , the experimental proof by metaphase FISH that DNA re-replication can indeed lead to chromosomal instability ( Figure 7 ) . Although we used high expression levels of the E1 in this experiment , similar translocations of the co-localized viral-host DNA have been detected in cell lines that were derived from invasive genital carcinomas with native expression levels of the viral proteins [33] , [58] , [59] . In addition , a recent study indicated that local DNA rearrangements occur frequently and shortly after one of the several HPV16 plasmids integrates into W12 cells [25] . The simultaneous presence of episomal and integrated HPV DNA has been documented in HPV-infected cells , and our data indicate that this dangerous combination can lead to the genomic instability that is driven by the replication of the integrated HPV origin . Such a situation can happen during the primary infection , when one of the hundreds of HPV plasmids accidentally integrates into the host cell genome . Loss of the episomes ultimately generates the cells that carry only the integrated HPV DNA . Such cells can exist in the tissue for a long time and are prone towards the clonal progression to cancer . It is interesting to speculate whether or not these cells can be de novo infected by homologous or heterologous papillomaviruses . Taking into consideration that HPV infections are frequent and wide spread , such a de novo HPV infection could generate a similar situation with the dual status of the HPV genome . Our data allow us to speculate that , in either case , the unscheduled DNA re-replication at the HPV integration locus could be induced , which would provide grounds for the development of genomic instability leading to rearrangements and the formation of the cancer cell . The cellular repair/recombination system is actively involved in this process and is actually the enzymatic machinery that is responsible for introducing the changes into the cellular genome ( Figure 9 ) .
Circular HPV16 and HPV18 genomes were prepared as described previously [60] , [61] . Briefly , HPV16 and HPV18 genomes were excised from the pUC or pBR vectors , respectively , purified from the agarose gel , re-ligated , and concentrated prior to the transfections . Plasmids pMHE1-16 and -18 and pQMNE2-16 and -18 , which were used for the expression of HPV E1 and E2 proteins , respectively , were made as described previously [30] . The pauxoMCF plasmid ( FitBiotech , Finland ) , which does not encode any gene product in animal cells and has no significant homology with the expression plasmids , was used in the transfections as carrier DNA . The following antibodies were used in the immunofluorescence assays . The antibodies against BrdU ( ab7384 ) , PCNA ( ab18197 ) , Mre11 ( ab214 ) , Rad50 ( ab89 ) , Ku70/80 ( ab3108 ) , ATM ( ab32420 ) , ATRIP ( ab19351 ) , Chk2 ( ab47433 ) , Chk1 ( ab47488 ) and phospho-Chk1 ( S317; ab38518 ) were purchased from Abcam . The Daxx ( sc7152 ) antibody was purchased from Santa Cruz Biotechnology . The Nbs1 antibody ( NB100-143 ) was purchased from Novus Biologicals . The mouse monoclonal HA antibody ( H9658 ) , which was used for the detection of HA-tagged HPV E1 , was purchased from Sigma-Aldrich . The polymerase α and RPA antibodies were provided by Heinz-Peter Nashauer . A rabbit polyclonal HA antibody was raised against the HA epitope , and a HPV18 E2 antibody ( 2E7 . 1 ) was raised against a bacterially expressed HPV18 E2 protein . Secondary antibodies that were conjugated with Alexa Fluor 488 or Alexa Fluor 568 were purchased from Invitrogen . Immunoprecipitations were performed with mouse monoclonal antibodies that recognize human Chk1 ( c9358 ) and human Chk2 ( c9233 ) ( Sigma-Aldrich ) . Western blotting was performed with rabbit monoclonal antibodies to Chk1 ( 2345 ) , phospho-Chk1S317 ( 2344 ) , Chk2 ( 2662 ) , phospho-Chk2Thr68 ( 2661 ) , and phospho-Chk2Ser19 ( 2666 ) ( Cell Signaling Technology ) . Mouse monoclonal antibodies against phospho-γH2AX-S139 ( Abcam , ab18311 ) , β-Actin ( Sigma-Aldrich , A2228 ) and HPV18 E2 ( 2E7 . 1 ) were also used . The HPV18 E1 with an HA epitope was detected by a rat monoclonal antibody ( 3F10 ) that was conjugated with peroxidase ( Roche , 12013819001 ) . Secondary antibodies conjugated with peroxidase were purchased from LabAs Ltd ( Estonia ) . HeLa and SiHa cells were grown in Iscove's Modified Dulbecco's Medium ( IMDM ) that was supplemented with 10% fetal calf serum ( FCS ) . Electroporation experiments were carried out as described earlier [2] , using the Bio-Rad Gene Pulser II apparatus supplied with a capacitance extender ( Bio-Rad Laboratories ) . Capacitance was set to 975 µF and voltage to 220 V in all experiments . Low molecular weight DNA was extracted by alkaline lysis [2] and total DNA was extracted from cells as described previously [79] . High-molecular-weight DNA and Low-molecular-weight DNA were fractionated and purified by Hirt lysis [38] . Extracted DNA was digested with the appropriate enzymes , as indicated in Figure 1 and Figure 8 . In addition , DpnI was always used to fragmentize the input plasmids . For 1D replication analysis , digested DNA was resolved on a 0 . 5–0 . 8% agarose gel in 1× Tris-acetate-EDTA buffer . For 2D replication analysis , the first dimension was run on a 0 . 4% agarose gel in 0 . 5× Tris-borate-EDTA at 0 . 4 V/cm for 45 h , and the second dimension was run in 1% agarose gel on a 0 . 5× Tris-borate-EDTA at 5 . 5 V/cm for 5 h at 4°C . Ethidium bromide at a concentration of 0 . 3 µg/ml was added into the gel and buffer of the second dimension . The separated DNA fragments were transferred onto a membrane and hybridized with the appropriate 32P-labeled probes that are specified in the legends of Figure 1 and Figure 8 . In order to isolate the supercoiled circular molecules from the replication products , conventional CsCl density gradient centrifugation of the Hirt LMW extract was performed according to the usual procedure using vertical rotor VTI80 in a Beckman Coulter Optima™ L-90 K ultracentrifuge at 50 , 000 rpm at 20°C for 24 h [80] . Cells were washed twice with phosphate-buffered saline ( PBS ) , permeabilized with 0 . 5% Triton X-100 in CSK buffer ( 10 mM Hepes-KOH , pH 7 . 4; 300 mM sucrose; 100 mM NaCl; 3 mM KCl ) for 2 min on ice , and fixed with 4% paraformaldehyde . Fixed cells were treated with 0 . 5% Triton X-100 in PBS , followed by 3 PBS washes for 5 min each at RT . After blocking with 3% bovine serum albumin ( BSA ) in PBS at RT for 30 min , the cells were incubated with primary antibodies in antibody-binding solution ( 3% BSA in PBS ) at RT for 30 min . Cells were then washed with 3 times with PBS for 5 min each at RT and incubated with secondary antibodies in binding solution at RT for 30 min . Cells were washed as before , placed under coverslips with mounting medium that contained 0 . 1 mM 4 , 6-diamidino-2-phenylindole ( DAPI ) , and examined with the FV1000-IX81 confocal microscope from Olympus . For BrdU labeling , the cells were pulse-labeled for 2 h with 10 µM BrdU 18 h posttransfection . Cells were then paraformaldehyde-fixed and immunostained for E1 ( Alexa Fluor 568 ) as described , followed by paraformaldehyde-fixation , acid denaturation of the DNA , and staining with anti-BrdU antibody conjugated with FITC ( Abcam ) in antibody binding solution ( 3% BSA , 0 . 5% Triton X-100 , PBS ) . When FISH analysis followed IF , the cells were fixed with ice-cold methanol . Mouse monoclonal or rabbit polyclonal HA antibody was used , depending on the origin of the antibody to the cellular factor that was being studied in the co-localization assay . To perform FISH analysis on metaphase cells , the cells were exposed to colchicine that was added to a final concentration of 1 µg/ml for 3 h to enrich the mitotic fraction . Colchicine -treated cells were incubated in a 1∶1 mixture of 0 . 55% KCl and 0 . 95% sodium citrate at 37°C for 10 min . The mitotic cells were then harvested by a “shake-off” and incubated an additional 10 min at 37°C in the same buffer , followed by another incubation in a 0 . 55% KCl solution for 10 min at 37°C . Mitotic cells were fixed in ice-cold methanol-glacial acetic acid ( 3∶1 ) . Spread-out chromosomes were prepared by dropping the cell suspension onto wet slides , followed by quick drying on a hot metal plate . The cells labeled by immunofluorescence were treated with ice-cold methanol-glacial acetic acid ( 3∶1 ) for 10 min , 4% paraformaldehyde for 10 min and a 70% , 80% , 100% ethanol series for 2 min each . Fixed cells were treated in both cases with RNAse A ( 100 µg/ml , 1 h , 37°C ) and pepsin ( 50 µg/ml , 4 min , 37°C ) . FISH hybridization probes were generated by nick translation , using biotin-16-dUTP as a label and HPV16 genome as a template . The final size of the probe fragments was adjusted to 200 to 500 bp by DNase I digestion . Chromosome and cell preparations were denatured at 75°C in 70% formamide for 3 min , immediately dehydrated in a series of ethanol washes ( 70% , 80% , and 100% ) , and air dried . The hybridization mixture ( 10 µl per slide ) was composed of 50% formamide in 2× SSC , 10% dextran sulphate , 100 ng of a denaturated probe DNA , 3 µl of denaturated subtelomeric probe for chromosome 13 ( Cytocell Technologies ) and 5 µg of denaturated herring sperm carrier DNA . Hybridization was performed overnight at 37°C in a moist chamber . The following FISH procedures were performed according to the manufacturer's protocol ( Invitrogen Corporation , TSA™ Kit #22 ) . Chromosomes were counterstained with DAPI and mounted in PBS with 50% glycerol . The slides were analyzed with Olympus IX81 fluorescence microscope equipped with the appropriate filter set . The chromosomes from at least 20 cells at metaphase were analyzed on each slide . Cells on a 10 cm plate were washed twice with 1× PBS and harvested in 0 . 5 ml ice-cold lysis buffer ( in 50 mM Tris , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1% Triton X-100 , 10 mM sodium fluoride , 1 mM β-glycerophosphate , 1 mM Na3VO4 , 1 mM PMSF , 1 mM DTT , and 1× complete EDTA-free protease inhibitor mix ( Roche ) ) . Samples were sonicated on ice three times for 5 seconds each and centrifuged for 10 min at 14 , 000×g , at 4°C . Two micrograms of primary antibody were added to the supernatant , which was followed by incubation with gentle rocking overnight at 4°C . Subsequently , 5 µl of protein G agarose beads were added to the sample and incubated for an additional 3 hours at 4°C . The beads were washed three times with 1 ml of 1× cell lysis buffer and re-suspended in 1× SDS sample buffer . The volumes of the samples were normalized according to the initial protein concentration in the crude lysates . The samples were heated at 100°C for 5 min and loaded onto an SDS-PAGE gel . Thirty micrograms of total protein or 15 µl of IP samples were separated by electrophoresis on 10–15% polyacrylamide–SDS gels and transferred to Immobilon-P membrane ( Millipore , USA ) , with the proteins of interest detected with antibodies described above .
|
High-risk human papillomavirus infection can cause several types of cancers . During the normal virus life cycle , these viruses maintain their genomes as multicopy nuclear plasmids in infected cells . However , in cancer cells , the viral plasmids are lost , which leaves one of the HPV genomes to be integrated into the genome of the host cell . We suggest that the viral integration and the coexistence of episomal and integrated HPV genomes in the same cell play key roles in early events that lead to the formation of HPV–dependent cancer cells . We show that HPV replication proteins expressed at the physiological level from the viral extrachromosomal genome are capable of replicating episomal and integrated HPV simultaneously . Unscheduled replication of the integrated HPV induces a variety of changes in the host genome , such as excision , repair , recombination , and amplification , which also involve the flanking cellular DNA . As a result , genomic modifications occur , which could have a role in reprogramming the HPV–infected cells that leads to the development of cancer . We believe that the mechanism described in this study may reflect the underlying processes that take place in the genome of the HPV–infected cells and may also play a role in the formation of other types of cancers .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"virology/viruses",
"and",
"cancer",
"virology/host",
"antiviral",
"responses"
] |
2009
|
Mechanism of Genomic Instability in Cells Infected with the High-Risk Human Papillomaviruses
|
An algorithm is presented that returns the optimal pairwise gapped alignment of two sets of signed numerical sequence values . One distinguishing feature of this algorithm is a flexible comparison engine ( based on both relative shape and absolute similarity measures ) that does not rely on explicit gap penalties . Additionally , an empirical probability model is developed to estimate the significance of the returned alignment with respect to randomized data . The algorithm's utility for biological hypothesis formulation is demonstrated with test cases including database search and pairwise alignment of protein hydropathy . However , the algorithm and probability model could possibly be extended to accommodate other diverse types of protein or nucleic acid data , including positional thermodynamic stability and mRNA translation efficiency . The algorithm requires only numerical values as input and will readily compare data other than protein hydropathy . The tool is therefore expected to complement , rather than replace , existing sequence and structure based tools and may inform medical discovery , as exemplified by proposed similarity between a chlamydial ORFan protein and bacterial colicin pore-forming domain . The source code , documentation , and a basic web-server application are available .
Determining the evolutionary relatedness of two protein sequences is most successfully performed by amino acid sequence comparison [1]–[5] . However , it is well known that structure can be preserved even when sequence has diverged past the point of amino acid similarity recognition [6] , suggesting that sequences can bestow local , subglobal , and global properties to a protein that can be preserved in the absence of strict conservation of the side chain atoms . In other words , similar properties could exist horizontally in a sequence even when recognizable vertical conservation is lost [7] . Even if such similarities are due to analogy rather than homology [8] , approaches are needed that can augment sequence based analysis by matching patterns that may be independent of amino acid conservation at each position . Comparison of three-dimensional atomic structures [9]–[13] is one example of such pattern matching . However , protein function and evolution arise from a manifold of physical , chemical , and biological mechanisms , only partly accounted for by side chain identity or structural similarity [14]–[18] . It may be the case that proteins can also be meaningfully characterized by other attributes , such as the energetic contributions to stability [19] or the predicted codon translation efficiency along the mRNA [20]–[22] . Yet , such attributes are not easily accommodated by simple adaptation of current algorithms , largely because the scoring systems for such algorithms are based on positional sequence identity ( amino acid substitution matrices ) or absolute geometric structural similarity ( Euclidean distance ) . As a result , properties other than sequence and structure , and their additional potential biological insight into proteins , have not been as thoroughly explored . For example , the local thermodynamic stability of a protein , as experimentally measured by deuterium-hydrogen exchange [23] , [24] , is described by a one-dimensional sequence of numerical values ( i . e . amide protection factors ) . These values are well-known to be a combination of sequence , structure , and solvent effects [25] , but no substitution matrix or distance measure exists for the objective comparison of two sets of protection factors . As such , important relationships could be overlooked , or worse , erroneous knowledge could be inferred from comparisons that separate the effects ( e . g . comparing side chain identity in the absence of information about the thermodynamic stability at the same position ) . One-dimensional software tools have been developed for the special case of hydrophobicity analysis , such as identification and alignment of the membrane spanning regions of non-globular proteins [26]–[28] . Although useful , these tools have historically incorporated family-specific scoring matrices [29] and empirical gap penalties . Such heuristics hinder the algorithms' transferability to different proteins or applicability to data types other than transmembrane protein hydrophobicity . In addition , the scoring functions for hydrophobicity analysis are often based on template-based matching or absolute similarity [30] , and while this is effective at finding matches that are similar in both shape and magnitude , two sets of data that describe the same shape , but are offset by a scalar value , would be missed . For example , such a case can arise for experimentally measured local thermodynamic stabilities of proteins , where the relative stabilities of the same structural region of two homologs are observed to be strikingly similar , yet offset by a constant ΔΔG value [31] . Finally , some of these previous tools lack the capability for large database searches or do not include estimates of statistical significance , limiting their usefulness and effectiveness even for the appropriate input data . To address these shortcomings , we have developed a tool to compare the internal consistency of one-dimensional profiles defined by arbitrary sequences of numerical data . To maximize the flexibility of the tool , we have deliberately chosen in the design to include two metrics that match both the relative shapes of the two profiles as well as the absolute similarity of the numerical values . Thus , the scoring system is designed to be independent of the input data type ( as opposed to the tool's probability model which is very much dependent on the data type ) . Since this design emphasizes the closeness in shape of the two sets scanned over a horizontal range of positions , in contrast to the vertical position-by-position independent scoring of a standard amino acid substitution matrix , the algorithm is named Horizontal Protein Comparison Tool ( HePCaT ) .
The algorithm proceeds by creating internal signed distance matrices from each of two sets of input numerical data vectors v ( Figure 1 , Steps 1 and 2 ) . The vector is composed of M elements given a protein of length M residues . In the following development , vi denotes an arbitrary numerical value at residue i . For a protein of M residues , each element of its distance matrix D is defined as ( 1 ) The signed distance matrices , while not symmetric , are reflections across the diagonal ( Figure 1 , Step 2 ) . Thus , both shape and magnitude information about each data set are encoded in these matrices . For example , the Protein 2 matrix D2 ( Figure 1 , Step 2 ) clearly indicates the strong local maximum in the N-terminal half relative to the strong local minimum in the C-terminal half as prominent red or blue regions . Equation 1 demonstrates a key conceptual difference from structure comparison algorithms that are usually based on distance or contact matrices restricted to only positive values [32] , [33] . This difference reflects the nature of the information being compared . For structure comparison , the distance between two atoms is identical whether it is computed between the first and second atom or vice versa , while in the case of thermodynamic stability , for example , there may be a relative stabilization between the first and second atoms , which becomes a relative destabilization between second and first . The sign in Equation 1 thus represents this key conceptual difference: a “distance” in HePCaT has both sign and magnitude . ( It is noted that Equation 1 may be extended to an arbitrary number of mathematical dimensions , but the present work only considers the one-dimensional case . ) A shape similarity matrix , S , is then constructed from the two distance matrices ( Figure 1 , Step 3 ) . To speed the calculation , a heuristic window size , W , is introduced . ( In this work , W is always five residues , but we note that this is potentially an adjustable parameter and a completely exhaustive search may be performed with W = 1 . ) For each position i = M− ( W−1 ) in Protein 1 and each position j = N− ( W−1 ) in Protein 2 , the relative shape similarity is computed between the two five-residue blocks originating at positions i and j: ( 2 ) Equation 2 is simply the average absolute value of the difference of equivalenced internal distances between the two blocks . If the shape similarity is high this value will be small , if the shape similarity is very different this value will be large . Such dissimilarity can be readily viewed for the example proteins: the Figure 1 similarity matrix contains strong positive values ( darkest red ) where the large peak in the middle of the first protein coincides with the deep valley in the C-terminal region of the second ( or vice versa ) . In this implementation , the signed internal distances within each block of W = 5 residues are scaled such that the longest absolute value of the internal distance is one , ( 3 ) Although this normalization can be disabled , we believe that emphasizing comparison of relative shape improves detection of relative trends in biological data , which can exhibit wide variations in scale . Practically , normalization also intuitively simplifies the choice of the user-defined alignment shape similarity cutoff , as described below . The optimal alignment between Proteins 1 and 2 is found by exhaustive search of the shape similarity matrix ( Figure 1 , Steps 4 and 5 ) . “Optimal” is defined as the largest unique set of blocks of size W , subject to at most GapMax skipped positions of the similarity matrix between blocks , which exhibits the smallest RMSD of all such sets passing a user-defined shape similarity cutoff , C . If C = 0 , exact shape matches only are permitted in the alignment list . For this work , where Equation 3 applies , C was set to 0 . 40 , meaning that an alignment whose average normalized distance between two five residue blocks was at most 40% different was counted as a matching shape . If Equation 3 were disabled , C would have to be adjusted empirically based on the dynamic ranges of data compared . The algorithm starts at cell ( 1 , 1 ) of S ( i . e . the lower left corner of the matrix in Figure 1 , Step 3 ) , corresponding to the average difference between the scaled intraprotein distances of residues 1–5 in Protein 1 and residues 1–5 in Protein 2 . If S1 , 1< = C , this match is kept and position S6 , 6 is checked , until all cells of S are evaluated up to the position SM-W+1 , N-W+1 ( i . e . the upper right corner of the matrix in Figure 1 , Step 3 ) . If at any point Si , j>C , single cell gaps are inserted in one or both sequences up to a maximum of GapMax in an attempt to obtain the longest path through S subject to C . A list of the longest gapped paths is kept at this stage ( Figure 1 , Step 3 , colored arrows ) . Therefore , all paths in this list are comprised of equivalenced positions in the two proteins such that , on average , the intraprotein distances seen at every position match to at least degree C; this average value is named Average Path Distance ( APD , Figure 1 , Step 4 ) . GapMax was empirically set to 4 for this work . No penalty is applied to APD for insertion of a gap . Importantly , at this first stage only relative shape similarity is checked; any systematic offset between the two data sets is ignored because only the differences between intraprotein distances are evaluated . After S has been exhaustively searched , the list of longest alignments passing the shape cutoff is filtered by RMSD of the aligned positions ( Figure 1 , Step 5 ) . The smallest RMSD alignment is defined as the optimal ( thus , the RMSD is effectively a magnitude filter ) . If multiple alignments of identical longest length happen to exhibit identical RMSD , only the first such one encountered is returned . In HePCaT , the RMSD calculation is executed after translation of both sets to data to their respective centers-of-mass , thus effects of a global offset between each data set are again minimized . Following Jia , et al . [34] , we define an Optimal Path Score ( OPS ) for this optimal alignment according to the formula: ( 4 ) In Equation 4 , L is the alignment length and Gaps is the total number of cells skipped in S to obtain that alignment . Note that , as mentioned above , gaps are not explicitly penalized during alignment , but gaps will penalize the final score according to Equation 4 , under the reasonable and common assumption that a gapless match is a “better” match than a gapped one . Alternatively , the GapMax parameter could be set to zero if desired so that all gaps are forbidden . A probability model to estimate the significance of an OPS score s of an alignment of length L was derived from analysis of randomly generated alignments ( Figure 1 , Step 6 ) . It is important to realize that a probability model is specific to the type of data aligned and must also be recalibrated for a specific combination of W , C , and GapMax . The probability model for Kyte-Doolittle hydropathy [35] , averaged over a 15-residue window , is listed in Tables 1 and 2 and was built for the following HePCaT parameters: W = 5 residues , GapMax = 4 residues , C = 0 . 4 with the local scaling of Equation 3 . ( Other probability models have been constructed and tested by the authors , including models based on eScape predicted native state thermodynamic stability [19] , and predicted translation efficiency index tAI [20] , [21] , and are available upon request . ) Significance of the Equation 4 score of optimal HePCaT alignments was estimated with respect to the scores of optimal alignments of identical length between proteins of random amino acid sequence . Two random proteins of equal lengths between 10 and 500 residues were generated according to background amino acid frequencies as given by Robinson & Robinson . [36] Sets of at least 20 , 000 such pairs for each length were optimally aligned using HePCaT , and the distributions of Equation 4 scores for a given optimal alignment length from the entire pool were tabulated ( Figure 2A ) . It was observed that these skewed unimodal distributions exhibited a strong dependence on alignment length ( Figure 2B ) . Out of several possible two-variable formulae , it was empirically determined that these score distributions were statistically best fit by Scaled Inverse Chi-Squared probability density functions ( Figure 2 , Tables 1 and 2 ) [37] , ( 5 ) In Equation 5 , L is optimal alignment length , and Γ ( x ) is the Gamma function . [38] Parameters ν and σ2 were estimated by minimum chi-squared fits to the binned score data at each observed alignment length ( Figure 2A ) . Binning and parameter estimation were performed using custom Mathematica 8 . 0 scripts , such that each variable-width bin contained at least 20 points , additional details are provided in Table 1 . Ad-hoc analytical expressions were fitted to the collected best-fit parameters of Equation 5 as a function of optimal alignment length L ( Figure 2B ) : ( 6 ) ( 7 ) Determination of coefficients a , b , c , and m only employed reasonably well-fit Equation 5 values whose null hypotheses ( i . e . that the simulated data were drawn from Inverse Chi Square Distributions ) could not be rejected at p<0 . 05 . Equations 6 and 7 coefficients for protein hydropathy are given in Table 2 , all resulted from excellent fits of R2 = 0 . 99 or better using gnumeric spreadsheet software ( Figure 2B ) . Therefore , given an observed optimal HePCaT alignment of length L with Equation 4 score s , the probability p of observing that alignment of protein hydropathy by chance could be estimated from the corresponding Scaled Inverse Chi-Squared cumulative distribution function as: ( 8 ) In Equation 8 , Q ( a , x ) is the complement of the regularized Gamma function [38]; ν and σ2 were estimated from Equations 6 and 7 , using coefficients of Table 2 . All 1604 amino acid sequences corresponding to every membrane protein structure in SCOP 1 . 73 ( class f ) [39] were obtained from the ASTRAL domain database [40] and clustered at 70% sequence identity by the cd-hit server [41] , resulting in 214 representative sequences . The Kyte-Doolittle hydropathy values [35] for each sequence were averaged over a window size of 15 residues , with the average being assigned to the middle position of the window . These 214 hydropathy profiles were then compared using HePCaT in an all-vs-all manner , with the probability value for each optimal match computed using the model coefficients listed in Table 2 . For each protein , a vector of length 214 containing the probability values against all other proteins was constructed . These 214 vectors were then clustered by Manhattan Distance and Ward's minimum variance criterion as implemented in the Hierarchical Clustering Package of Mathematica 8 . 0 ( Wolfram Research ) to create a dendrogram . A similar tree was computed from FASTA [42] E-values of all pairwise sequence comparisons . Significance of each grouping was estimated using the bootstrap “Gap Test” option of the software . The human proteome was obtained from translation of the DNA sequences contained in the NCBI CDDS [43] build 36 . 3 ( April 30 , 2008 ) . Each amino acid in every protein was assigned a value according to the Kyte-Doolittle hydropathy scale . [35] The values for each protein were averaged using a 15 residue sliding window; averaged values for the first and last seven residues in each protein were subsequently ignored . The averaged values for the G-protein coupled receptor ( GPCR ) human adenosine receptor A2a ( CCDS 13826 . 1 , gi|5921992 ) were used as query against the human proteome , i . e . the averaged hydropathy values of each protein in the proteome were optimally pairwise aligned to A2a using HePCaT with the following parameters: W = 5 residues , C = 0 . 4 , GapMax = 4 residues . P-values for each alignment were computed using the probability model specific to these data as described above . GPCRs were checked and annotated in our local copy of the human proteome by FASTA-aligning [42] amino acid sequences of the proteome with amino acid sequences of known GPCRs obtained from the GPCRDB [44] . Modeling was performed with a local installation of I-TASSER software [45] using default parameters . Structural similarity between the first I-TASSER model and known proteins was assessed using the DALI server [46] . A dataset of 8812 ORFan protein sequences was obtained from Yomtovian , et al . [47] As described above , HePCaT was used to optimally align the Kyte-Doolittle averaged hydropathy profiles of each ORFan protein with the profile of each member of the non-redundant set of 214 membrane proteins of known structure described above Secondary structure prediction was performed using the PSIPRED server [48] [49] and Hidden Markov Model sequence profile comparison was performed using the HHpred server [50] , both with default parameters . Modeling was performed with a local installation of I-TASSER software [45] using default parameters . Structural similarity between the first I-TASSER model and known proteins was assessed using the DALI server [46] .
Unlike most globular proteins , most membrane protein structures can be classified , independent of evolutionary relationships , into two main groups , “all-alpha” and “all-beta” , based on structural characteristics alone [51] , [52] . One dominant characteristic is the requirement for stability within the nonpolar interior of the membrane , and this is reflected in recurring patterns of defined length hydrophobic segments , imposed by the physical constraints of alpha-helical or beta-strand secondary structure elements . Such patterns can be used for the effective prediction of transmembrane spanning segments and fold topology of the inserted protein [53]–[55] . Analysis and clustering of a set of diverse membrane protein structures , based on similarities in the proteins' average hydropathy patterns using HePCaT , reflects this major level of structural organization ( Figure 3A ) . In this dendrogram , the “all-beta” proteins clearly segregate into distinct and statistically significant sub-branches of the tree . Finer levels of overall fold similarity , including the G-protein coupled receptors ( f . 13 ) , toxins' membrane translocation domains ( f . 1 ) , and the transmembrane beta barrels ( f . 4 ) , can also largely be resolved only on the basis of hydropathy similarity ( labeled sub-branches in Figure 3A ) . Interestingly , proteins belonging to f . 13 , annotated as “single transmembrane helix” and thus “not a true SCOP fold” [56] , are spread among several dispersed sub-branches , consistent with this provisional expert curation . In contrast , clustering of the identical proteins based on pairwise amino acid sequence similarity alone appears less resolved at levels higher than pairs of highly similar sequences ( Figure 3B ) . In particular , the “all-beta” proteins , while also resolved to a particular statistically significant sub-branch , are not cleanly segregated from other “all-alpha” proteins . Few fold families are clustered at statistical significance , probably due to the overall low level of sequence similarity in this diverse set ( approximately 30% identity over 40 residues on average ) . Clearly , patterns of hydropathy , reflecting the well-known idea that protein structure similarity is more conserved than sequence similarity [57] , [58] , can be objectively recovered using pairwise HePCaT alignments in conjunction with the appropriate probability model described above . Given the ability of HePCaT to match expected hydropathy patterns , an exploratory search was initiated to discover unknown matches . The hydropathy profile of the human adenosine A2a 7Tm G-protein coupled receptor ( GPCR ) was used to search the human proteome for close unreported matches . As expected , hundreds of known 7Tm GPCRs were significantly matched by HePCaT ( p<0 . 01 , data not shown ) . The most significant ten matches are displayed in Figure 4 . These hits fell into two categories: those that matched the transmembrane region [59] of A2a ( Figure 4 , blue ) and those that mostly matched the tail region ( Figure 4 , red ) . The longest match to the transmembrane region was the A2b isoform , which is also 59% sequence identical to A2a ( Figure 5A ) . Unexpectedly , a Type 2 taste receptor also exhibited a significant match to this region ( Figure 4 ) . As this taste receptor has insignificant pairwise sequence identity to A2a ( Figure 5B ) and its structure has not been experimentally determined [60] , this observed similarity was consistent with an independently produced model of the taste receptor , constructed using no HePCaT information ( Figure 5C ) . Additionally , the original HePCaT match was demonstrated to be a useful template for a homology model [61] based on the A2a structure ( data not shown ) . The validity of the hydropathy similarity between A2a and the taste receptor was further demonstrated to be robust with respect to the particular hydrophobicity scale used ( Text S1; Figures S1 and S2 in Text S1 ) . We attempted to rationalize the best matches to the A2a tail region in terms of sequence , structure , or function . However , in contrast to the transmembrane region matches , biological explanations for these remain unknown . The shortest hit to the tail region was possibly a statistical artifact: this metallothionein is naturally short and contains a high frequency of cysteine residues; such low-complexity sequences are normally filtered out of amino acid sequence searches [62] , which was not done in the present study . Some of the proteins in this group are medically important , such as the hematological and neurological expressed-1 like protein , ephrin A4 isoforms , and the B and T-lymphocyte attenuator precursor . Structural information , where available about the matches , could not be confidently transferred to the putatively disordered tail region of A2a , which is thought to be involved in ligand specificity of the GPCR [63] . These tail matches may also result from the local scaling ( Equation 3 ) , which could potentially be disabled , illustrating the sensitivity vs . specificity tradeoffs inherent to relative shape matching . A third example of the utility of HePCaT concerns the possible discovery of remote similarity with medical importance . The C . muridarum protein TC0624 , classified as an “ORFan” due to the absence of significant sequence similarity between any other known proteins [47] , nonetheless exhibited a significant HePCaT hydropathy match to the pore forming domain of E . coli colicin A ( Figure 6A ) . This match spanned the entire chain length of the ORFan protein and the experimentally-determined minimal length region of functional importance of the pore-forming domain [64] . The validity of the hydropathy similarity between colicin and TC0624 was further demonstrated to be robust with respect to the particular hydrophobicity scale used ( Text S1; Figures S1 and S2 in Text S1 ) . Secondary structure prediction was consistent with the proposed tertiary structural similarity ( Figure 6A ) , and sensitive sequence profile search using hidden Markov models revealed marginal ( maximum HHPred P-Value 30% [50] ) , but repeated , similarity to the sequence of colicin implicated in the hydropathy match ( Figure 6B ) . Thus , a total of four lines of evidence ( hydropathy , secondary structure prediction , sensitive sequence similarity , and the regional correspondence between the sequence and structure matches ) all converged on similarity between TC0624 and the pore forming domain of colicin . Modeling [45] of TC0624 also resulted in a low-confidence fold prediction consistent with colicin ( data not shown ) . However , these conclusions would have not been possible without the original statistical significance of the HePCaT hydropathy match . Importantly , the hydrophobic region of colicin implicated in this match has long been thought to be functionally crucial for colicin's lethal ability to travel from a hydrophilic extracellular environment , insert into the hydrophobic membrane interior , and form toxic pores in its host [65] . TC0624 has independently been placed [66] in a class unique to Chlamydiae that is observed by experiment to also similarly partition into the membrane interior of the chlamydial inclusion [67] . These so-called “Inc” proteins , difficult or impossible to predict using existing computational tools [66] , are nonetheless important for chlamydial survival and maturation within its human or animal hosts . It appears that the extreme hydrophobicity exhibited by the Inc proteins [67] facilitates their computational prediction using HePCaT . Taken together , the results suggest a novel functional hypothesis for these medically important proteins: the Incs may form membrane-spanning pores that obtain nutrition from the host cytoplasm . This example also suggests that this particular ORFan may actually belong to a known protein family . Experiments are currently in progress to test these hypotheses .
Most protein and nucleic acid data contained within the avalanche of next-generation genome sequencing can be expressed as sequentially numeric “peaks” and “valleys” . These data include , but are not limited to , gene expression , ribosomal profiling , ChIPSeq , RNASeq , mRNA translation efficiency , thermodynamic stability of protein or mRNA , and physico-chemical properties such as hydropathy . A gap exists among software algorithms for analysis of such data , and the HePCaT algorithm described in this work is designed to help fill this gap . To facilitate such analysis and discovery , a webtool that allows execution of the algorithm , visualization of the result , and access to the raw and analyzed data is freely available at http://best . bio . jhu . edu/HePCaT . ( A detailed manuscript describing the use and capabilities of this web portal is in preparation . ) Due to patent and license restrictions , information about access to source code is available through The Johns Hopkins University Office of Technology Transfer from the corresponding author . There are at least three distinguishing features of the HePCaT algorithm . First , the input is completely arbitrary: if the data can be expressed in numeric form regardless of its source , patterns can potentially be detected . Second , its scoring system is sensitive to both shape and magnitude similarity , allowing some degree of pairwise alignment flexibility . Third , the W parameter emphasizes a horizontal matching of patterns , as contrasted with the vertical matching that commonly occurs with amino acid substitution matrices or profile PSSMs . In our view , vertical evolutionary conservation of amino acids has been thoroughly explored using tools such as BLAST [4] , [5] and FASTA [42] , while horizontal conservation of other protein properties has not . Thus , non-local properties of proteins , depending on correlations across residue positions , such as thermodynamic stability , can now be potentially explored with HePCaT . The case studies presented in Figures 5 and 6 suggest that substantial horizontal similarity can be detected in one pass through a database , minimizing the need for longer iterative searches when the vertical similarity may be weak or statistically impossible to detect . Importantly these anecdotal examples are not intended to demonstrate the superiority of the HePCaT algorithm , or the information contained in horizontal conservation , over current state-of-the-art methods for remote homology detection that are based on vertical conservation . To the contrary , HePCaT is intended as a complementary tool that would be most usefully applied to cases where vertical conservation is weak or absent . Furthermore , although the tool formally returns a pairwise positional alignment , it is not clear if such an alignment , could or should be quantitatively compared to existing amino acid sequence alignment tools . The HePCaT input is subject to possible averaging over one window size ( e . g . the hydropathy is averaged over 15 positions ) and the output is matched using quantized blocks of a second multi-residue window size ( e . g . 5 positions ) . Future work is necessary to determine whether HePCaT can substantially improve upon the accuracy of the best current pairwise alignment methods . Rigorous evaluation of the statistical significance of a result is an essential piece of scientific data that is often neglected in bioinformatics tools . The significances returned by HePCaT allow prioritization of matches and aid expert interpretation . As with other tools , the HePCaT statistical significances require calibration specific to the input data and algorithm parameters . Although recalibration for random simulation data not covered by Table 2 parameters is straightforward and has been achieved for other types of numerical data , an alternative estimate of statistical significance is available . Specifically , the non-parametric statistics of the MIC score reported by Reshef , et al . [68] could potentially be used to evaluate a match returned by HePCaT . In this way , the significances of arbitrary pattern associations reported by Reshef , et al . could be greatly leveraged by using HePCaT as a “front-end” for other types of numerical data . Although this idea has not yet been thoroughly studied , we believe that the applicability of the MIC statistics would be maximized with HePCaT parameters of GapMax = 0 and W = 1 .
|
Trend discovery is an important way to generate understanding from large amounts of data . We have developed a novel tool that discovers significantly similar trends shared between two numerical data sets . Since the tool's algorithmic method compares both the relative shapes of the “peaks” and “valleys” in the data , as well as the absolute magnitudes of the numerical values , we believe the tool is tolerant of imperfections and could be applicable to a wide range of scientific , engineering , social , or economic problems . In short , if measurements can be converted to a series of numbers , our tool may potentially be useful for trend discovery . Since we are a protein biophysics group , we are most naturally interested in discovering new similarities between proteins , and we have discovered a particularly interesting , statistically significant similarity between a protein unique to Chlamydia and a bacterial pore-forming protein , colicin . This previously unreported similarity may have medical relevance , and we are currently experimentally testing the properties of the chlamydial protein in the laboratory . In a second example , we demonstrate the tool's ability to easily recover a known , but difficult to detect , relationship between two other GPCR proteins .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
|
A Horizontal Alignment Tool for Numerical Trend Discovery in Sequence Data: Application to Protein Hydropathy
|
Despite many prior studies demonstrating offline behavioral gains in motor skills after sleep , the underlying neural mechanisms remain poorly understood . To investigate the neurophysiological basis for offline gains , we performed single-unit recordings in motor cortex as rats learned a skilled upper-limb task . We found that sleep improved movement speed with preservation of accuracy . These offline improvements were linked to both replay of task-related ensembles during non-rapid eye movement ( NREM ) sleep and temporal shifts that more tightly bound motor cortical ensembles to movements; such offline gains and temporal shifts were not evident with sleep restriction . Interestingly , replay was linked to the coincidence of slow-wave events and bursts of spindle activity . Neurons that experienced the most consistent replay also underwent the most significant temporal shift and binding to the motor task . Significantly , replay and the associated performance gains after sleep only occurred when animals first learned the skill; continued practice during later stages of learning ( i . e . , after motor kinematics had stabilized ) did not show evidence of replay . Our results highlight how replay of synchronous neural activity during sleep mediates large-scale neural plasticity and stabilizes kinematics during early motor learning .
The cardinal features of motor skill learning are enhanced speed and automaticity of motor execution with preserved accuracy [1–3] . Motor learning is known to progress through a series of stages: an early stage accompanied by rapid improvements in accuracy with continued variability of movement kinematics , followed by consolidation of these processes and transition to a later stage of learning , in which kinematics are largely stabilized but slow improvements in accuracy continue to occur [4–6] . The underlying neural basis by which kinematics become stabilized during early motor learning is not well understood . Human studies suggest that non-rapid eye movement ( NREM ) sleep is essential for this consolidation and , in addition , results in additional gains in skilled motor performance [7–14] . Even brief naps during the day can mediate these “offline” motor improvements [11 , 15 , 16] , including faster movements and reduced variability in timing . There is evidence that NREM sleep promotes offline gains . Prior studies have described a relationship between motor learning , local slow-wave oscillations [17] , the expression of immediate-early plasticity-related genes [18] and stabilization of dendritic spines [19] . However , how large-scale patterns of neural activity drive plasticity of specific motor circuits to result in enhanced motor performance is unknown . Based primarily on studies of single-unit activity conducted during hippocampal-dependent behaviors [9 , 13 , 16 , 17 , 20–25] , we hypothesized that reactivations of task-related emergent neural firing during NREM sleep may be related to subsequent neural plasticity and associated offline behavioral gains . This hypothesis is largely consistent with theoretical models for how NREM sleep promotes learning more generally [20–22 , 26–29] , but to our knowledge , there is little experimental support of this during procedural memory formation .
Microelectrodes were implanted ( tetrode and microwire arrays were used in different animals , see Materials and Methods ) into the lateral part of the caudal forelimb area of rats , the region most strongly associated with fine motor control of the distal forelimb , and the region directly involved in plasticity following skilled motor learning [30–32] ( S1 Fig ) . Five days after electrode placement , animals began skilled motor training ( Fig 1A and 1B ) . Skilled motor learning was conducted using the Whishaw forelimb reach-to-grasp task [33 , 34] . We chose this task both due to homology to skilled learning tasks in humans [35 , 36] and the extensive evidence that this task is associated with multiple levels of neural plasticity , including changes in Long-Term Potentiation ( LTP ) [37] , spine growth [38–40] and motor map plasticity [30 , 41 , 42] . Neural activity was monitored during the following sequence of blocks: a “baseline” sleep block ( Sleep1 ) , a skilled motor learning session ( Reach1 ) , a sleep block ( Sleep2 ) , and a subsequent learning block ( Reach2 ) ( Fig 1C ) . Thus , we were able to compare both how task-related neural activity was modulated after sleep ( i . e . , by comparing Reach1 and Reach2 ) as well as how motor learning affects neural activity during sleep ( i . e . , by comparing Sleep1 and Sleep2 ) . Motor skill learning is typically assessed across two dimensions: speed and accuracy [1 , 13 , 43 , 44] . We examined both here , measuring accuracy as percent success in retrieving the pellet and speed as the overall time the animal took to perform the full reach-grasp-retract motor sequence ( Fig 2 ) . Online changes in skilled motor performance were quantified by comparing the first 20 trials ( hereafter “Reach1early” ) to the last 20 trials ( hereafter “Reach1late” ) of the first learning session; offline gains were measured by comparing the last 20 trials from the initial learning session ( i . e . , “Reach1late” ) with the first 20 trials from the subsequent reach block ( hereafter “Reach2early” ) . Across all animals , we found significant online improvements in accuracy ( Fig 2D , p < 0 . 001 , Wilcox rank-sum test ) in Reach1early versus Reach1late; see Materials and Methods ) but without improvements in speed ( Fig 2C , overall ANOVA F ( 2 , 98 ) = 6 . 6 , p < 0 . 01; post-hoc t tests comparing Reach1early with Reach1late , p = 0 . 3 ) during the initial training session ( Reach1 ) . Following sleep , however , animals executed the entire movement sequence considerably faster ( Fig 2C , post-hoc t test comparing between Reach1Late and Reach2early , p < 0 . 001 ) , with no decrements in accuracy ( Fig 2D , p = 0 . 3 , Wilcox rank-sum test between Reach1Late and Reach2early ) . Thus , sleep appeared to increase movement efficiency and was associated with no decrement in accuracy . To further probe the effects of sleep on motor performance , we also analyzed online and offline changes in the trajectory of forelimb movements . To perform this analysis , we calculated whether there was a change in movement trajectories either during online learning or after sleep ( S2 Fig ) . Trajectories were calculated both using an “external frame of reference” ( i . e . , relative to the end-point position of the pellet ) and an “internal frame of reference” ( i . e . , relative evolution of the trajectory after the start of movement ) . During online training , we found that animals changed their external frame trajectory , suggesting greater orienting towards the pellet at the start of the movement , without significantly altering the kinematics of the trajectory itself . In contrast , after sleep , while the externally referenced trajectory remained unchanged , the internally referenced trajectory kinematics appeared to be significantly changed . This suggests that different aspects of kinematics are modified during online versus offline processing . To investigate how changes in neural activity underlie the offline gains in motor efficiency described above , we compared the task-related activity during Reach1early , Reach1late , and Reach2early . For each neuron , we calculated the peri-event time histogram ( PETH , smoothed using a Poisson-based Bayesian-adaptive regression model [45] ) time-locked to reach-onset ( Fig 3A ) . Reach onset was defined by the start of physical movements ( i . e . , identical to those used to calculate the behavioral metrics above ) and not based on external cues . During each of the early , late , and post-sleep blocks , we estimated the degree of modulation ( i . e . , the peak firing divided by the pre-reach baseline ) and the time to peak firing . Even in the very earliest learning block ( Reach1 ) , 92/102 neurons showed some evidence of task modulation , defined as at least a 2-fold increase in firing rate compared to the baseline ( S3 Fig ) . As with other studies that have examined neural recordings during forelimb reach tasks in rodents [46 , 47] , we found that single units demonstrated time-locked activity across many phases of reach ( S3 Fig; See figure legend for further discussion on the distribution of neural activity observed ) . Three units were excluded from further analyses because of very sparse firing , which made it difficult to properly estimate PETH and/or timing information . We next analyzed online and offline changes in the task-related modulation of neural activity . Interestingly , we found a strong and specific effect of sleep in changing both the timing and magnitude of task-related activity at a single-unit level ( example shown in Fig 3A ) . During online learning , the relative timing to reach-onset ( Fig 3B , Kolmogorov-Smirnoff , p = 0 . 9 ) and task-related modulation ( Fig 3C , overall repeated-measures ANOVA F ( 2 , 98 ) = 5 . 5 , p < 0 . 01; p = 0 . 9 , paired t test between Reach1 and Reach2 ) did not change significantly . However , after sleep , there was a strong and highly significant change in the time to peak of the PETH ( i . e . , “temporal coupling shift , ” Fig 3B , Kolmogorov-Smirnoff p < 0 . 001 ) and a smaller but significant change in task-related modulation ( Fig 3C , p < 0 . 05 , paired t test comparing modulation for Reach2early to both Reach1 blocks ) . Across all neurons , we also analyzed the mean shift following sleep . We found that there was a 200 ± 65 ms shift in the temporal coupling of neural activity to reach onset after sleep ( repeated-measures ANOVA F ( 2 , 98 ) = 7 . 7 , p < 0 . 001; p < 0 . 001 paired t test comparing modulation for Reach2early to both Reach1 blocks ) . S4 Fig shows the entire distribution of shift in neural firing for neurons during both the online and offline periods , demonstrating a widespread effect across most neurons we recorded from . Prior reports in humans have demonstrated that sleep is required for offline performance gains [9 , 13] . To study whether sleep is essential for the behavioral and neural changes described above in our experimental paradigm , we performed a follow-up experiment in which a new set of animals were given 2 h of sleep-restriction between Reach1 and Reach2 ( see Materials and Methods ) . To assess how this modified offline changes in behavior and neural firing , we compared the last 20 trials in Reach1 with the first 20 trials in Reach2 ( Fig 4A ) . We found significant differences in offline changes in movement speed ( Fig 4B “motor speed , ” p < 0 . 001 , 2-sided t test comparing the sleep and sleep restriction groups ) . We also found significant differences in the offline changes in the temporal shifts of peak task-related activity when compared to animals allowed to sleep ( Fig 4B “neural speed , ” p < 0 . 01 , 2-sided t test ) . Fig 4C also shows the cumulative distributions of the individual timings of neural PETHs both before and after the sleep restriction ( Kolmogorov-Smirnoff p = 0 . 7 between Reach1late and Reach2early ) . Thus , our data suggests that in the absence of sleep there were no changes in movement or neural speed , i . e . , no offline gains at either a behavioral or neural level . It is also important to note that the sleep-restriction paradigm did not impair performance at either a behavioral or neural level; these animals were neither significantly slower ( p = 0 . 7 , one-sample t test comparing last 20 trials pre versus first 20 trials post sleep-restriction , n = 5 animals ) nor significantly less accurate ( p = 0 . 15 , one-sample t test comparing accuracy in the last 20 trials before versus first 20 trials after sleep-restriction , n = 5 animals ) . Moreover , the population-tuning curve was not significantly different ( Fig 4C ) . Thus , we did not observe any significant changes in performance ( i . e . , either a decrement or offline gains ) after the period of sleep-restriction , but likewise , no offline gains in neural or motor speed . The experiments described above were conducted with animals that were allowed to sleep twice ( i . e . , Sleep1 and Sleep2 ) , with offline behavioral changes occurring after the second sleep block . The sleep-restriction control described above indicates that such offline gains do not occur simply as a result of the passage of an equivalent period of time awake . However , it is possible that the effects observed ( e . g . , the changes in movement speed after Sleep2 ) occur simply as a result of the animals having more overall sleep before performing the task . In other words , because animals in our experimental group are able to sleep for two sessions , it is possible that what we interpret as offline gains are simply a product of normal practice-related improvements ( i . e . , during online training ) that occur when animals are well-rested . To address this issue , we allowed more sleep prior to the initial training block . Thus , animals were allowed to undergo both Sleep1 and Sleep2 blocks before any training occurred ( S5A Fig ) . Animals were awoken for 20 min between those two blocks and were given 50 pellets to generally mimic the task structure of the first set of experiments . Animals were then given 150 trials of reach training to assess whether having extended sleep prior to training would result in improvements in motor speed during the training sessions itself . We found that extra sleep prior to training was not sufficient to induce significant changes in speed during training ( S5B–S5D Fig , p = 0 . 36 , paired t test , comparing the first 20 and last 20 trials , n = 5 animals ) . Animals were also tested the next day ( i . e . , after 24 h ) , to observe whether they showed similar offline gains as described in our early experiments . Indeed , these animals showed offline improvements in speed ( p < 0 . 0001 , paired t test comparing last 20 trials from Day 1 and the first 20 trials on Day 2 , n = 5 animals ) with maintenance of accuracy ( p = 0 . 1 , paired t test comparing last 20 trials from Day 1 and the first 20 trials on Day 2 , n = 5 animals ) . This control experiment further confirms the role of offline processes in mediating speed improvements during skill acquisition . Having demonstrated large-scale , sleep-dependent changes in neural activity and related motor behavior , we next examined what neural processes may be mediating these effects during the sleep block . We hypothesized that replay of task-related neural ensembles during NREM sleep drives the offline behavioral gains and neural modulation previously described . To investigate this , we used principle components analysis to identify task-related patterns of neural activity ( i . e . , neural ensembles ) and then probed replay of these ensembles during sleep using methods described previously [29 , 48 , 49] . Across animals , mean time spent in NREM sleep during the Sleep1 block was 28 . 6 ± 11 . 3 min , and the mean time spent in NREM sleep during Sleep2 block was 24 ± 4 . 1 min ( paired t test = 0 . 6 ) . Ensemble reactivation during sleep was quantified by applying a template created using principle components analysis ( PCA ) of the task-related neural activity . In other words , PCA was used to create templates that captured patterns of synchronized neural activity during task performance ( S6 Fig ) . PCA resulted in a number of principle components ( hereafter termed “ensembles” ) that reflected patterns of common variance across the recorded single-units , with each component comprised of weights that reflected the contribution of each neuron to that particular ensemble . To represent the activity of a particular ensemble the traditional method is to multiply the weights from each neuron in a particular ensemble with the Z-scored activity matrix [48] . This same method can be used during sleep to assess the degree to which this ensemble is being reactivated ( S6 Fig ) [29 , 50] . Specifically , the ensemble defined from the task was multiplied by the Z-scored neural activity recorded during sleep blocks , resulting in a one-dimensional vector that represented the “activity” of that ensemble during the sleep blocks before and after ( Fig 5A ) . Reactivation was thus defined as increased “activity” of the ensemble during the sleep-block after learning compared to the sleep-block prior to learning ( Fig 5A–5C ) . In this relatively rapid motor task ( ~1 s ) , the first ensemble captured more variance than any other component and we therefore focused our analysis on it . Prior reports using this method have found that weaker ensembles ( i . e . , those with lower eigenvalues ) show limited evidence of reactivation [50] . When we did examine the second ensemble , we found considerably more variability in terms of reactivation across animals ( i . e . , only some animals showed evidence of reactivation of this ensemble ) . After learning , activation strength was significantly stronger during NREM sleep blocks for these task-related ensembles in comparison to the sleep block that occurred before learning ( Fig 5B , p < 0 . 001 , Wilcox non-parametric sign-rank test ) . We originally hypothesized a significant relationship would occur between reactivation of neural ensembles and subsequent temporal shifts of cortical neurons . To evaluate whether such a relationship existed , we calculated the ensemble reactivation for each unit . Reactivation was defined by calculating the difference in activation strength between Sleep1 and Sleep2 for that ensemble ( i . e . , Sleep1Activation−Sleep2Activation ) , multiplied by the principle component ( PC ) score for each neuron in that ensemble , resulting in that neuron’s “weighted” reactivation score . Interestingly , we found that the degree of reactivation at a single neuron level during sleep strongly and significantly predicted the increase in temporal coupling to reach onset that occurred upon awakening ( Fig 5C , r = -0 . 41 , pearson correlation , p < 0 . 001 , analysis conducted across all neurons , n = 4 animals ) . This indicates that neurons that experienced the strongest reactivation during the NREM sleep block also experienced the greatest shift in temporal coupling to reach onset during the skilled reach task upon awakening . To better understand the relationship between reactivation events and task-related neural activity , we performed two additional analyses ( S7 Fig ) . First we performed an analysis to demonstrate that the reactivations truly reflect a specific pattern of task-activity—in other words , that the same neurons active during the task are highly active during the replay event . To perform this analysis , we calculated PETHs for each neuron during the ensemble replay ( binned at 25 ms , and including data 250 ms before and 250 ms after each replay event , using only the top 10% of activation strengths ) and compared this with the PETHs and PC ensemble created from activity during the reach task ( S7A and S7B Fig ) . Importantly , this binning allows us to estimate variability in firing rates across neurons during reactivation events , but not temporal variability across neurons . Each PETH was then sorted according to the PC weight extracted during the task block . As predicted , there was good correspondence between the PC weight and the degree to which these neurons were firing during the reactivation ( S7C Fig ) . This provides evidence that reactivations represent synchronous co-activation specifically of task-related neurons; in other words , variation in firing rates during the task are observed as variations in synchronous firing during reactivation events during sleep . Prior studies have distinguished “reactivation , ” observed as synchronous activity of task-related ensembles during subsequent sleep periods [50] , and “replay” which involves a recurrence of sequential activity during subsequent sleep epochs . To assess the degree to which there is replay , we next examined the microstructure of reactivation events at a single millisecond resolution . To perform this analysis , we used a previously described template matching technique [51] . To create the task-template , we extracted the PETH both 250 ms prior to and 1 , 000 ms after the reach onset in each animal . This activity pattern was binned at different resolutions ( 50 ms , 125 ms , 250 ms , and 1 , 250 ms ) , to create bin templates ranging in size from 25 bins down to 1 bin ( S7D Fig ) . Importantly , each binning resolution contained less temporal structure ( i . e . , the 1 , 250 ms bin does not retain any temporal information ) . The template matrix ( neurons x bin ) was then correlated with single-unit activity patterns that occurred during reactivation events identified above . Reactivation events were kept at a 1 ms resolution ( i . e . , no binning ) for the template matching procedure . We found a general increase in the degree of template correlation after learning ( p < 0 . 0001 across every template studied , S7D Fig ) . However , the highest degree of correlation and the greatest change post occurred with the largest bin size ( i . e . , with the least temporal information ) . This suggests that even while there is a small but significant change in the temporal structure of reactivation events , they are more linked to synchronous activation of neurons that fired during task performance ( another example shown in S7E Fig ) . Prior studies have suggested that both spindles and slow-wave oscillations may mediate offline gains in motor performance [9–11 , 13] . To further probe the relationship between ensemble reactivations and these phenomena , we calculated the event related local field potential ( LFP ) locked to the top tenth percentile of reactivation events after sleep ( Fig 6 ) . We first calculated the event-triggered average ( ETA ) of LFP filtered at slow/delta-oscillations ( i . e . , 0 . 5–4 Hz ) . We next subdivided spindles into slow and fast frequencies because of prior evidence suggesting that fast-spindles in particular are specific to offline gains [10 , 51] . We thus examined the ETA of slow-spindles ( filtered at 9–12 Hz ) and fast spindles ( filtered at 13–16 Hz ) . Interestingly , we found an increase in the locking of fast but not slow spindles with reactivation events after learning ( Fig 6A and 6B , * above indicates significant post-hoc differences using a paired t test ) . Likewise , we saw a change in the association of these events with slow oscillations ( Fig 6C ) . We probed phase-locking in two different ways . First , we calculated the coefficient of variance across events for those time points that were significantly different in Sleep1 versus Sleep2 . As expected , we found a significant reduction in the coefficient of variation ( CV ) across these time points for fast-spindles and slow-oscillations ( Fig 6D , p < 0 . 0001 , ranksum test ) . Because there were no significant differences in the slow-spindle frequencies , to assess CV we used the same time points as used for fast-frequencies . We found a slight increase in the CV ( Fig 6D , p < 0 . 05 ranksum test ) , suggesting that the increased locking is specific to fast-spindle oscillations and not a general phenomenon . We also calculated the instantaneous phase at the slow and fast spindle oscillation , and the slow-wave oscillations at t = 0 ( i . e . , when the reactivation event occurred ) , using circular statistics comparisons [52] . Prior to learning , there was no significant phase-relationship between fast-spindles and reactivation events . However , after learning , the mean phase ( in radians ) of the fast-spindle oscillation at the time of these reactivation events -2 . 06 , ( 95% confidence interval [CI] of . 5542 ) . There was not a significant phase-relationship between reactivation events and slow spindle oscillations . Finally , there was evidence for a slight but significant phase-shift after learning in coupling with slow-oscillations . Prior to learning , the mean phase of the slow oscillation at the time of reactivation was -2 . 395 ( 95% CI . 0953 , using circstats toolbox ) ; after learning , the mean phase at the time of reactivation was -2 . 07 ( 95% CI . 077 , using circstats toolbox ) , a highly significant difference ( p < 0 . 0001 ANOVA using circstats toolbox ) . These analyses demonstrate that , independent of changes in the mean amplitude , after learning there was significant phase-coupling to the spindle-oscillations , and a significant phase shift relative to the slow-wave oscillation , and these changes partly explain the results observed in Fig 6A–6D . These results suggest that following learning , high reactivation events are more strongly coupled to both fast spindle oscillations and slow oscillations ( Fig 6E ) . To probe this further , we calculated the instantaneous analytic amplitude of the local field potential filtered in the fast-spindle frequency at the trough of the reactivation-triggered slow-oscillation for reactivation event . Across events , we found a strong and significant increase in the analytic amplitude at this time-point ( Fig 6F , p < 0 . 001 , Wilcox ranksum ) . Finally , using an automated detection algorithm , we assessed whether there was a significant change in the proportion of distinct spindle events associated with these high strength reactivation events . Specifically , we analyzed the LFP filtered in the fast-spindle frequency time-locked to high-reactivation events pre/post learning , to assess whether there was an increase in the proportion of spindles associated with the highest reactivation events after learning . We found that after motor learning , spindles were 60% more likely to occur in association with these high reactivation events compared to before learning . ( Fig 6G , p < 0 . 001 , Wilcox ranksum ) . We next analyzed patterns of activity during “later-stages” of learning , specifically defined here as continued practice on the skilled motor task on subsequent days . Prior research has divided motor learning into an early phase associated with the establishment of gross kinematic patterns and rapid gains in motor performance , and a later phase associated with overall kinematic stability and slower incremental gains in skill acquisition [5] . We have so far demonstrated that reactivation of task-related ensembles occurred after the initial motor learning session , when animals appeared to first form and then consolidate a novel kinematic trajectory . To explore whether changes in reactivation continues to occur through later stages of motor practice , we investigated whether task-related neural activity continued to experience an increase in the reactivation strength on subsequent days of motor learning ( Fig 7A ) . For this analysis , data was gathered from three animals across two additional days of motor training . As there were no significant differences between day 2 and 3 on any of the parameters assessed below , data was pooled across these two days for the purposes of this analysis . On subsequent sessions of motor learning ( i . e . , conducted on days 2–3 ) , there were no further offline changes in movement speed/efficiency ( example of one animal Fig 7B , p > 0 . 8 comparing Reach1Late and Reach2Early across all animals ) , indicating overall stabilization of the kinematic pattern . Despite this , there was continued evidence of learning , i . e . , accuracy improved by 33% during the reach training in these subsequent sessions p < 0 . 05 Wilcox rank-sum test . The stable kinematics and slow improvements in accuracy are largely consistent with these subsequent training days being part of the late/slow-period of motor learning . During this later phase of learning , we found no evidence of increased reactivation strength of task-related ensembles during sleep ( Fig 7C , sign-rank p = 0 . 2 ) . To assess whether this lack of an effect was significantly different compared to changes observed on day 1 of motor learning , we next calculated the overall reactivation ( rank-ordered Sleep2–Sleep1 activations for each animal ) on day 1 and compared this with reactivation on subsequent days of training . In addition , because prior analyses [29 , 50] have suggested that there is a highly skewed distribution of ensemble activations during sleep ( i . e . , many low-value activations ) , we further subdivided reactivation differences according to the overall percentile strength and compared across deciles ( Fig 7D ) . This analysis demonstrates the highly skewed nature of these re-activations while also demonstrating the lack of an effect in later motor learning periods . Finally , across behavioral sessions in these animals over the first 3 d , we found a significant correlation between the degree of reactivation observed ( using the top 10% reactivations pre versus post learning ) versus changes in the motor speed before and after sleep ( Spearman correlation , Rho = -0 . 76 , p < 0 . 05 , Fig 7E ) .
Many previous studies have examined the role of sleep in promoting offline gains in motor skill performance , with NREM sleep in particular promoting various types of motor learning [9 , 13 , 14 , 16 , 17 , 53 , 54] . However , little was known about the neurophysiological processes by which NREM sleep mediated motor memory consolidation . Here we show that reactivation of task-related neural patterns during NREM sleep is explicitly related to both performance improvements and plasticity of neural responses . We specifically found that these high reactivation events were closely linked to an increase in fast spindle oscillations and became slightly phase-shifted relative to the slow oscillations . Finally , we found that neural reactivation is very specific to early motor learning , and not simply a reflection of motor practice; NREM-sleep reactivation was not evident during later stages of motor learning once kinematic patterns had been stabilized . These results suggest that task-related neural reactivation during NREM sleep plays a key role in stabilizing the basic motor pattern during motor learning , with subsequent improvements not dependent on large-scale reactivation during sleep . We show here that sleep-dependent reactivation of neural ensembles occurs in the context of procedural learning . Prior demonstrations of reactivation in cortex have been described in visual [21] and prefrontal [20 , 50 , 55 , 56] cortex . Importantly , these studies occurred in the context of hippocampal-dependent behavior and primarily in coordination with replay events from the hippocampus [20 , 21 , 50 , 55 , 56] . Another demonstration of sleep-dependent reactivation , by our group , occurred in the context of neuroprosthetic learning [29] . While neuroprosthetic learning is beginning to be explored in more detail [57–59] , it is unclear whether this learning represents declarative , procedural , or some more abstract form of learning that is fundamentally different than either of the above . We thus identify the role of neural reactivation measured at single-neuron resolution for motor memory consolidation . Prior research has divided motor learning into an early phase , associated with the establishment of gross kinematic patterns and rapid gains in motor performance , and a later phase , associated more strongly with subtle refinements of kinematic patterns and more incremental changes in skill acquisition [1 , 2 , 5] . We show here that large-scale reactivation of neural ensembles is associated with kinematic improvements and related changes in neural activity patterns . Indeed , our study suggests that offline processing during sleep may play a key role in the consolidation of motor memories , thus allowing animals to transition to a later phase of learning that is more strongly associated with more subtle motor refinements and increased automation [60] . We also found that after sleep there was greater temporal binding of single-unit activity in motor cortex to the initiation of the reach movement . We hypothesize that the faster activation of neurons after sleep are related to the binding of separate motor programs encoding specific parts of the complex movement ( i . e . , “reach , grasp , retract” ) , into one integrated motor program . While our evidence suggests that motor cortex is the final output of this program , it may not be where this program is ultimately “stored;” in other words , the ensembles controlling the different motor actions may be bound together by intrinsic connectivity within motor cortex or may be activated through distributed circuits that occur across cortico-striatal or prefrontal circuits [61] . It is important to point out that in hippocampal-dependent replay , systems consolidation theory would suggest that memories are being transferred from subcortical to cortical representations . In motor learning , it may well be that memories are being transferred from cortical to subcortical representations , perhaps representing a fundamentally different role for reactivation . Further research will be required to assess these ideas . Studies conducted in both rodents and humans have demonstrated that early motor learning and motor cortical activity itself involves a distributed set of circuits including attentional/prefrontal regions , as well as cerebellum and dorso-medial striatum ( i . e . , the associative , prefrontal-connected portions ) [3 , 12 , 60 , 62–68] . By contrast , later phases of skill learning seem to be more strongly associated with changes within motor cortex [31 , 41 , 64 , 64] and between dorso-lateral striatum ( i . e . , the “motor” striatial circuit ) [62 , 67–68] . This suggests that ensemble reactivation during sleep , which seems to occur most strongly in association with this early learning phase , may occur through distributed reactivation across cortical and subcortical regions involved in various aspects of these motor actions . This theory that motor ensembles are being driven by large-scale distributed networks during early motor learning processes , with stabilization associated with transfer of procedural memory into motor cortex , is consistent with what is known about how hippocampal associative circuits drive cortical replay in NREM sleep following declarative memory paradigms [22–24] . Moreover , this theory is also consistent with a recently proposed “active systems consolidation theory” [26 , 28] that suggests that spindle-dense Stage-2 sleep [28] serves to synchronize global cortical processes , thus mediating long-range consolidation of synaptic plasticity [18 , 19] and memory transfer to distant cortical regions [26] . Further studies involving dual recordings from disparate cortical regions during early/late motor learning sessions and during sleep will be required to demonstrate that ensemble reactivation truly is associated with active consolidation across a distributed set of cortical/subcortical brain regions . Interestingly , we found a specific involvement of fast spindles occurring during slow-wave oscillations in the reactivation of task-related neural ensembles . While largely consistent with a body of work demonstrating the involvement of fast spindles in motor learning [10 , 11 , 51] , this was quite distinct from what had been observed across the two previous studies demonstrating cortical reactivation in rodents [29 , 50] . In those studies , cortical reactivations seemed to be coupled most strongly to the peak negativity of the slow-oscillation [29 , 50] and in the case of prefrontal cortex , also to hippocampal sharp-wave ripples [50] , which themselves occurred near the peak negativity of slow-wave oscillations and prior to spindle oscillations . By contrast , here we find that after motor learning , reactivation events seemed to be particularly time-locked to fast spindles that occur a short time after the trough of slow delta waves . This difference may represent an important and fundamental difference between motor and other forms of learning . Indeed , one recent study has demonstrated that during the trough of slow-wave oscillations , cortical neurons are driven strongly by hippocampal circuits whereas during spindle events hippocampal circuits are suppressed and processing is driven strongly by thalamo-cortical circuits [69] . Given that the bulk of non-cortical motor processing ( i . e . , from the cerebellum and striatum ) are transmitted back to the cortex through the ventral thalamus [69] , it is certainly plausible that emergent task-related ensembles during NREM sleep may be locked to spindle events , particularly if this reactivation is being driven by distributed mechanisms as previously postulated . Together , our results shed light on the neural processes associated with offline gains in skilled motor performance . We have identified a specific neural correlate of the widely observed sleep-dependent improvement in movement efficiency and linked them to sleep dependent reactivation of activity patterns established during online earning . Our results particularly emphasize the importance of sleep during early motor learning when motor sequences are initially established . This phenomenon might be most relevant to early skill building during musical or sports training , or during early child development , when essentially all skills across both sensory and motor domains are new [54 , 70] . In addition , these results suggest a potential mechanism by which NREM sleep may serve to enhance plasticity and functional recovery following brain injury [71] .
This study was performed in strict accordance with guidelines from the USDA Animal Welfare Act Regulations and United States Public Health Science ( PHS ) Policy . The protocol was approved by the San Francisco VA Medical Center Institutional Animal Care and Use Committee ( IACUC , Protocol Number 13–006 ) . We used 15 adult Long–Evans male rats ( approximately 8 wk old; see S1 Table for complete details of animals , units , etc . ) . Animals were kept under controlled temperature and a 12–h light , 12–h dark cycle with lights on at 06:00 A . M . Probes were implanted during a recovery surgery performed under isofluorane ( 1%–3% ) anesthesia . The post–operative recovery regimen included administration of buprenorphine at 0 . 02 mg/kg b . w . and meloxicam at 0 . 2 mg/kg b . w . Dexamethasone at 0 . 5 mg/kg b . w . and Trimethoprim sulfadiazine at 15 mg/kg b . w . were also administered post–operatively for 5 d . All animals were allowed to recover for 5 d prior to start of experiments . We recorded extracellular neural activity using both tungsten microwire electrode arrays ( MEAs , n = 3 rats , Tucker–Davis Technologies or TDT , FL ) and tetrodes ( n = 4 rats , Neuronexus , Michigan ) . Arrays were implanted in the caudal forelimb area of primary motor cortex ( M1 ) , centered at 3–4 mm lateral to bregma , 0 . 5 mm anterior to bregma to target upper limb primary motor cortex ( M1 ) ( S1 Fig ) . Final localization of depth ( 1 , 000–1 , 500 μm ) was based on quality of recordings across the array at the time of implantation . We recorded spike and LFP activity using a 128–channel TDT–RZ2 system ( Tucker–Davies Technologies ) . Spike data was sampled at 24 , 414 Hz and LFP data at 1 , 018 Hz . ZIF–clip based analog headstages with a unity gain and high impedance ( ~1 GΩ ) were used . Only clearly identifiable units with good waveforms and high signal-to-noise were used . MEA recordings were sorted offline using PCA-based algorithms followed by manual cluster-cutting using TDT’s OpenSorter software . Tetrodes were sorted using “UltraMegaSort” toolbox ( available online at https://physics . ucsd . edu/neurophysics/software . php ) , a set of MATLAB based scripts for tetrode sorting described in detail previously [29 , 72] . Specifically , a voltage-based threshold was set based on visual inspection for each channel that allowed for best separation between putative spikes and noise ( typically this threshold was 4 . 5–5 standard deviation [SD] away from the mean ) . Snippets of data that crossed threshold were time-stamped as events , and waveforms for each event were peak aligned . K-means clustering was then performed across the entire data matrix of waveforms ( 30 samples/ch x 4 chs x # of waveforms ) . Automated sorting was performed by: ( 1 ) first over clustering waveforms using a K-means algorithm ( i . e . , split into many mini-clusters ) , ( 2 ) followed by a calculation of interface energy ( a nonlinear similarity metric that allows for an automated decision of whether mini-clusters are actually part of the same cluster ) , and ( 3 ) followed by aggregation of similar clusters . Such aggregation allows for a reduction in the total numbers of clusters that need to be manually inspected . Automated sorting was followed by manual inspection and sorting of spikes ( including further merging or splitting of automatically identified clusters and removing significant outliers based on Gaussian distribution of PC space ) , using feature space , auto-correlations , cross-correlations and linear discriminant analysis to determine which clusters represent single units and to prevent over-sorting ( S8 Fig ) . Trial-related timestamps ( i . e . , trial onset , trial completion and timing of when animals reach the pellet ) were sent to the RZ2 analog input channel using an Arduino digital board and synchronized to neural data . Prior to surgery , animals were handled and acclimated to behavioral boxes and oriented to the pellet tray for 1 wk , at the end of which they were evaluated on 10 trials of the Whishaw forelimb reach to grasp single pellet task to determine handedness . This was followed by electrode implantation on the contralateral motor cortical hemisphere as described above . Five days after electrode implantation , animals were food-restricted for 2 d , followed by feeding animals a fixed amount during the course of training ( 2 average sized food pellets/day ) . Whishaw forelimb-reach was conducted using a clear plexiglass chamber , with a 1 . 5 cm slit for animals to place their forelimb through in order to reach a 45 gm pellet on a shallow dish 1 . 5 cm away from the front of behavioral chamber , using an automated chamber described in more detail in [34] ( Fig 1A and 1B ) . Animals typically performed from 100–150 reaches in the first reach block ( Reach1 ) and 50–75 in the re-test block ( Reach2 ) . All reaches were videotaped for post-hoc analysis of accuracy , kinematics , and dynamics . All behavioral sessions began in the morning and consisted of 2 h of spontaneous recording ( to record a “baseline” sleep period , Sleep1 ) ; motor skill learning ( Reach1 ) ; a second 2-h block of spontaneous recording ( Sleep2 ) ; and finally a “re-test” motor skill block to assess for changes in behavior/neural activity after sleep ( Reach2 ) . Sleep-restriction experiments were conducted similarly to the experiments described above , with the exception that during the second 2-h block of spontaneous recording ( termed Sleep2 above ) , animals were kept awake . Specifically , sleep-restriction sessions began in the morning and consisted of 2 h of spontaneous recording ( to record a “baseline” sleep period , Sleep1 ) , motor skill learning ( Reach1 ) , a second 2-h block of sleep-restriction ( Sleep Restriction ) , and immediately after this a re-test motor skill block to assess for changes in behavior/neural activity after sleep ( Reach2 ) . For the sleep restriction experiments , animals were kept in the behavioral box in which they conducted initial training sessions . The animals were closely observed for any behavioral evidence of sleep . In addition , the LFP was monitored in real-time to detect any evidence of sleep signatures . If detected , we gently tapped the box to keep awake . Tapping was typically required less than 1x/min early during the restriction period; and by the end of the restriction period had escalated to around 3 taps/min to keep animals awake . The entire paradigm was carried out identical to the training paradigm . Specifically , neural data was recorded during a 2-h block of spontaneous activity , during which time animals were allowed to sleep . Subsequently , animals performed the skilled motor learning task . After this , we performed sleep restriction for a 2-h period , after which animals were re-tested . Data analysis was performed using a combination of custom written scripts in MATLAB and toolboxes developed for neural analysis . We compared changes in task performance between and across sessions . Specifically , we compared the performance change between early and late trials by comparing changes in behavior between the first 20 and last 20 trials in block 1 , and the effects of sleep by comparing the last 20 trials in block 1 with the first 20 trials in block 2 . Three different aspects of learning were measured: accuracy ( here defined as successful retrieval of the pellet into the chamber ) , speed ( defined as time from the beginning of the reach to the pellet , and through to the execution of retract movement ) , and finally similarity of movements , assessed by calculating the Pearson correlations between movement trajectories in X-Y space . For this analysis , within each block ( i . e . , Reach1early , Reach1late , and Reach2early , we correlated movements in both the X-direction and Y-directions and averaged this together to get a trial × trial correlation matrix . We then calculated the mean correlation across all trials within the different blocks . Across all animals , statistical changes in accuracy were assessed by assigning , for each trial , a “1” for correct trials in which animals successfully retrieved the pellet and a “0” for incorrect trials , followed by logistic regression analysis of the overall distribution of 1’s and 0’s across groups . Changes in speed ( measured as changes in time for the overall reach trajectory ) were assessed using ANOVA/post-hoc Fisher’s test; and changes in Pearson correlations were calculated using ANOVA/post-hoc Fisher’s test after first Z-transforming correlation coefficients . In addition to the above analyses , we also conducted trajectory analyses in “state-space” using two different procedures . First , trajectories were performed without any processing , termed here an “external frame of reference . ” For this analysis , we analyzed the distribution of Y-coordinates relative to X-coordinates , as a way of determining overall differences in the state-space of the trajectory . Statistical analysis of these distributions across the different groups of trials for each X-coordinate ( binned into units of 4; Reach1Early Reach1Late and Reach2 ) was performed using bootstrap techniques ( confidence intervals were estimated by performing random sampling with replacement 2 , 000 times for each X-coordinate being estimated ) . We also performed an analysis in which we specifically looked at displacement over time by referencing each trajectory to its initial starting point . In this way , we measured changes in movement relative to an “internal” frame of reference . Identification of NREM sleep epochs was performed by visual assessment of LFP during spontaneous recordings in 10-s increments ( S6 Fig ) . During any period denoting sleep , if there was a sustained reduction >2 s in the amplitude of the slow-wave activity below threshold during a continuous epoch we excluded these segments . Neural activity from sleep epochs during spontaneous recordings was concatenated together , in order to analyze ensemble activations specifically during NREM sleep . All sleep-related analyses were constrained to the minimum amount of sleep achieved in either sleep epoch , in order to ensure that analyses were not biased by different amounts of sleep post-learning vs pre-learning . Spindle detection was used using automated algorithms to detect such oscillations , adapting methods previously described [76] . Spindles were then detected using a threshold of 2 . 5 SD of the signal , with start and finish times calculated as the time points 1 . 5 SD of the signal . Events were identified as spindles only if they were longer than 400 ms and shorter than 3 s . Automatically detected spindles/delta-oscillations were visually inspected to ensure the algorithm was correctly detecting these events . We also tested more stringent criteria ( 3 SD of the signal for example ) ; results reported here ( an increase in spindle oscillations after learning ) do not depend on the specific parameters chosen . We performed either non-parametric tests ( Wilcox signrank/ranksum ) or one–way ANOVA with post-hoc Fisher’s for most comparisons , as noted in the text . Logistic regression was used to identify changes in binary measures of success rate during learning or after sleep .
|
Sleep has been shown to help in consolidating learned motor tasks . In other words , sleep can induce “offline” gains in a new motor skill even in the absence of further training . However , how sleep induces this change has not been clearly identified . One hypothesis is that consolidation of memories during sleep occurs by “reactivation” of neurons engaged during learning . In this study , we tested this hypothesis by recording populations of neurons in the motor cortex of rats while they learned a new motor skill and during sleep both before and after the training session . We found that subsets of task-relevant neurons formed highly synchronized ensembles during learning . Interestingly , these same neural ensembles were reactivated during subsequent sleep blocks , and the degree of reactivation was correlated with several metrics of motor memory consolidation . Specifically , after sleep , the speed at which animals performed the task while maintaining accuracy was increased , and the activity of the neuronal assembles were more tightly bound to motor action . Further analyses showed that reactivation events occurred episodically and in conjunction with spindle-oscillations—common bursts of brain activity seen during sleep . This observation is consistent with previous findings in humans that spindle-oscillations correlate with consolidation of learned tasks . Our study thus provides insight into the neuronal network mechanism supporting consolidation of motor memory during sleep and may lead to novel interventions that can enhance skill learning in both healthy and injured nervous systems .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation
|
During infection the host imposes manganese and zinc starvation on invading pathogens . Despite this , Staphylococcus aureus and other successful pathogens remain capable of causing devastating disease . However , how these invaders adapt to host-imposed metal starvation and overcome nutritional immunity remains unknown . We report that ArlRS , a global staphylococcal virulence regulator , enhances the ability of S . aureus to grow in the presence of the manganese-and zinc-binding innate immune effector calprotectin . Utilization of calprotectin variants with altered metal binding properties revealed that strains lacking ArlRS are specifically more sensitive to manganese starvation . Loss of ArlRS did not alter the expression of manganese importers or prevent S . aureus from acquiring metals . It did , however , alter staphylococcal metabolism and impair the ability of S . aureus to grow on amino acids . Further studies suggested that relative to consuming glucose , the preferred carbon source of S . aureus , utilizing amino acids reduced the cellular demand for manganese . When forced to use glucose as the sole carbon source S . aureus became more sensitive to calprotectin compared to when amino acids are provided . Infection experiments utilizing wild type and calprotectin-deficient mice , which have defects in manganese sequestration , revealed that ArlRS is important for disease when manganese availability is restricted but not when this essential nutrient is freely available . In total , these results indicate that altering cellular metabolism contributes to the ability of pathogens to resist manganese starvation and that ArlRS enables S . aureus to overcome nutritional immunity by facilitating this adaptation .
Staphylococcus aureus is a ubiquitous pathogen that colonizes 30% of the population at any given time and can infect virtually every human tissue [1] . These facts and the continued spread of antibiotic resistance have led both the Centers for Disease Control and the World Health Organization to state that S . aureus poses a serious threat to human health [2 , 3] . Both organizations have highlighted the need to identify new strategies for treating S . aureus and bacterial infections in general . Elucidating how pathogens overcome nutritional immunity , a critical component of the immune response in which the host restricts essential nutrients from the invading pathogen , has the potential to address this need . Transition metals such as iron ( Fe ) , manganese ( Mn ) and zinc ( Zn ) are essential for virtually all forms of life . Their importance is emphasized by the prediction that 30% of all enzymes interact with a metal cofactor [4 , 5] . During infection , invading microorganisms must acquire all of their nutrients from the host . Vertebrates take advantage of this fact and combat invading pathogens by restricting the availability of essential metals [6 , 7] . While the most well characterized aspect of nutritional immunity is the Fe-withholding response , it has recently become apparent that the host also restricts access to Mn and Zn during infection [7–12] . The prototypic example of Mn and Zn restriction is the staphylococcal abscess , which is rendered devoid of these two essential metals [8 , 13] . This depletion starves S . aureus for these metals resulting in the inactivation of metal-dependent enzymes , such as the staphylococcal superoxide dismutases [8 , 9] . A critical component of this withholding response is the Mn- and Zn-binding protein calprotectin ( CP ) . CP comprises ~50% of the cytosolic protein in neutrophils and at sites of infection it can reach concentrations in excess of 1 mg/ml [14 , 15] . Mice lacking CP have defects in metal sequestration and are more susceptible to a range of bacterial and fungal pathogens , including S . aureus , Acinetobacter baumannii , Klebsiella pneumoniae , and Candida albicans [8 , 9 , 16–19] . CP , a member of the S100 family of proteins , is a heterodimer comprised of S100A8 and S100A9 ( also known as calgranulin A/calgranulin B and MRP8/MRP14 ) , and has two transition metal-binding sites [9 , 20] . The first site , known as the Mn/Zn site , is capable of binding either Mn or Zn with nanomolar and picomolar affinities ( Kd ) , respectively [9 , 20–22] . The second site , known as the Zn site , binds Zn with picomolar affinity [9 , 21–23] . CP exerts antimicrobial activity against a variety of bacterial and fungal pathogens in vitro , including S . aureus , by starving them for metals [8 , 9 , 16–19] . While the sequestration of both Mn and Zn contributes to the antimicrobial activity of CP , the Mn/Zn site is necessary for maximal antimicrobial activity by CP against a wide range of Gram-positive and Gram-negative pathogens including S . aureus [21] . The ability of S . aureus and other successful pathogens to cause disease indicates that they possess adaptations that allow them to minimize and overcome host-imposed Mn and Zn starvation . One mechanism employed by pathogens to cope with nutrient limitation is the expression of dedicated high-affinity acquisition systems . Mn and Zn import systems that contribute to pathogenesis are found in numerous pathogens including: S . aureus , Brucella abortus , Campylobacter jejuni , Salmonella enterica , Yersinia pestis , Streptococcus pneumoniae , Streptococcus pyogenes , Streptococcus suis and A . baumannii [6 , 24–34] . S . aureus expresses two dedicated Mn import systems: MntH , an NRAMP family member , and MntABC , an ABC-type transporter [13 , 31 , 35] . In S . aureus , MntH is constitutively expressed , while MntABC is induced by Mn limitation [13 , 31] . High-affinity Mn acquisition systems play a critical role in resisting Mn starvation during infection , and staphylococcal mutants lacking these systems are attenuated in several models of infection [13 , 31 , 36] . The virulence defect of a staphylococcal mutant lacking both Mn importers is reversed in CP-deficient mice , indicating that these systems specifically contribute to resisting host-imposed Mn starvation [13] . The ability of a mutant lacking dedicated Mn importers to cause comparable disease to wild type bacteria when Mn is not limited also highlights the critical importance of Mn sequestration to host defense . While high-affinity metal acquisition systems contribute to infection , they do not prevent the host from imposing Mn starvation . This is evident by the increased bacterial burdens observed in CP-deficient mice and by inhibition of staphylococcal SOD activity during infection [8 , 9] . S . aureus and other pathogens are able to successfully cause infection despite experiencing Mn and Zn starvation , thus they must possess additional adaptations that allow them to resist nutritional immunity . In this study , we identified the S . aureus two-component signal transduction system ArlRS as an important factor in resisting CP-imposed Mn starvation . Infection studies using wild type and CP-deficient mice revealed that ArlRS is necessary for establishing invasive S . aureus infection and resisting Mn starvation in vivo . Additionally , we discovered that S . aureus is more sensitive to Mn starvation when using glucose as a carbon source as compared to when amino acids are provided . Furthermore , ArlRS appears to play a critical role in facilitating the use of amino acids as a carbon source . These results indicate that altering core metabolic pathways is critical to overcoming host-imposed metal starvation .
S . aureus experiences Mn and Zn starvation during infection , yet it is still able to successfully cause infection . This fact indicates that S . aureus possesses adaptations that allow it to overcome this host defense . To identify the factors that allow S . aureus to resist Mn and Zn starvation during infection , a transposon library was screened for mutants with enhanced sensitivity to CP . This screen identified a mutant that has an insertion in arlRS ( arlRS:erm ) , which is more sensitive to CP than wild type S . aureus ( Fig 1A ) . ArlRS is a two-component system and global virulence regulator that influences many staphylococcal processes , including autolysis , toxin expression , surface protein expression and biofilm formation [37–40] . As with most two-component systems , the signal sensed by ArlS is currently unknown . Subsequent assays using an ArlRS deletion mutant ( ΔarlRS ) and an arlR insertion mutant ( arlR:erm ) created in S . aureus Newman produced similar results to those obtained with the transposon mutant ( Figs 1B and S1A ) . Similar to what was observed by Walker and colleagues , loss of ArlRS did not impact hla expression or hemolysis on blood agar plates [40] ( S1B Fig ) . Expressing ArlRS from a plasmid reversed the increased sensitivity of ΔarlRS to CP ( Fig 1C ) . Increased sensitivity to CP was also observed in arlR:erm derivatives of the community-acquired MRSA strain USA300 ( JE2 ) as well as the methicillin-sensitive strain SH1000 ( Fig 1D and 1E ) . In total , these results indicate that ArlRS promotes resistance to host-imposed metal starvation in both methicillin-sensitive and methicillin-resistant strains of S . aureus . CP sequesters both Mn and Zn preventing the individual impact of withholding either metal from being evaluated with wild type protein . To circumvent this issue , the sensitivity of ΔarlRS to a series of engineered CP variants with altered metal-binding properties was assessed [9 , 21] . Specifically , CP variants lacking the Mn/Zn site ( ΔMn/Zn ) , the Zn site ( ΔZn ) , or both sites ( ΔMn/ZnΔZn ) were utilized . When incubated with the ΔMn/ZnΔZn double site mutant , which cannot bind Mn or Zn , ΔarlRS grew as well as wild type ( Figs 1F and S1C ) . This result confirms that the increased sensitivity of ΔarlRS to CP is due to an inability to cope with either Mn or Zn starvation . Similar to WT CP , ΔarlRS was more sensitive than wild type bacteria to the ΔZn site mutant , which can bind either Mn or Zn . However , the increased sensitivity of ΔarlRS was almost completely abrogated when grown in the presence of the ΔMn/Zn mutant , which can only bind Zn ( Figs 1F and S1C ) . These results indicate that loss of ArlRS impairs the ability of S . aureus to cope with host-imposed Mn starvation . ArlRS has been shown to repress expression of the staphylococcal autolysins LytM , LytN and Atl . As a result , loss of ArlRS can result in increased autolysis of methicillin-sensitive strains of S . aureus [38 , 39 , 41] . As such , cell lysis could potentially explain the enhanced sensitivity of ΔarlRS to CP . Previous studies revealed that the increased autolysis of strains lacking ArlRS can be reversed by individually deleting Atl or LytM in the ΔarlRS mutant background [41] . To determine if the diminished ability of ΔarlRS to resist Mn limitation is the result of increased autolysis , ΔarlRS lytM:erm , ΔarlRS atl:erm and ΔarlRS lytN:erm double mutants were assessed for CP sensitivity . Loss of LytM , LytN or Atl did not diminish the sensitivity of ΔarlRS to CP ( Fig 2A ) . Control experiments revealed that loss of Atl , LytM , or LytN alone did not alter the sensitivity of S . aureus to CP ( Fig 2B ) . These results indicate that increased sensitivity to CP of strains lacking ArlRS is not a result of increased autolysis . This idea is further supported by the increased sensitivity of the USA300 ( JE2 ) arlR:erm mutant to CP ( Fig 1D ) , as loss of ArlRS does not result in increased autolysis of methicillin-resistant isolates [41] . Cumulatively , these results indicate that increased autolysis does not contribute to the diminished ability of strains lacking ArlRS to resist Mn starvation . Previous work demonstrated that loss of MntABC and MntH renders S . aureus twice as sensitive to CP as wild type bacteria [13] . Initially , to determine if the increased sensitivity of strains lacking ArlRS is due to decreased expression of Mn importers , the transcript levels of mntA and mntH were assessed by qRT-PCR . Following growth in metal-replete medium , wild type and ΔarlRS expressed comparable levels of both mntA and mntH ( Fig 3A ) . Consistent with previous studies , CP treatment significantly increased mntA transcript levels in wild type bacteria ( Fig 3A ) [13] . CP also increased mntA expression in the strain lacking ArlRS , suggesting that the increased sensitivity of ΔarlRS is not due to reduced expression of Mn importers . We also assessed the impact that loss of ArlR had on a strain lacking the Mn importers ( ΔmntCΔmntH arlR:erm ) to grow in the presence of CP . As before , the arlR:erm mutant was more sensitive to CP treatment than wild type bacteria ( Figs 3B and S2 ) . However , loss of ArlR in the ΔmntCΔmntH mutant background further increased sensitivity of the transporter double mutant to CP , suggesting that ArlRS and the Mn transporters function independently to promote resistance to Mn starvation . To further evaluate if loss of ArlRS impacts the ability of S . aureus to acquire Mn or Zn , intracellular metal levels in wild type and ΔarlRS were directly assessed using inductively coupled plasma optical emission spectrometry ( ICP-OES ) . This analysis revealed that loss of ArlRS does not impair Mn or Zn acquisition in the absence of CP ( Fig 3C ) , as intracellular metal levels were the same in wild type bacteria and in ΔarlRS . In the presence of CP both WT and ΔarlRS had reduced levels of intracellular Mn ( Fig 3C ) . No reduction in intracellular Zn or Fe were observed in the presence of CP . This observation is consistent with prior studies , which indicated that Mn binding is necessary for maximal antimicrobial activity [21] . Combined , these results indicate that a defect in metal acquisition is not responsible for the increased sensitivity of ΔarlRS to host-imposed metal starvation , suggesting that loss of ArlRS prevents S . aureus from adapting to limited Mn availability . ArlRS contributes to hematogenous pyelonephritis and endocarditis in mouse and rabbit models of infection [39 , 40 , 42] . To determine whether ArlRS also contributes to systemic infection , wild type ( C57BL/6 ) mice were retro-orbitally infected with wild type S . aureus Newman or ΔarlRS . During the course of the infection mice infected with ΔarlRS lost significantly less weight than mice infected with wild type S . aureus ( Figs 4A and S3A ) . Consistent with the weight loss , the ΔarlRS mutant had significantly diminished bacterial burdens in the liver , heart , and kidneys when compared to wild type bacteria ( Fig 4B and 4C ) indicating that ArlRS plays an important role in establishing systemic disease . To evaluate the contribution of ArlRS to resisting Mn starvation during infection , CP-deficient ( C57BL/6 S100A9-/- ) mice , which do not remove Mn from liver abscesses [8 , 13] , were infected with wild type bacteria and ΔarlRS . Compared to C57BL/6 mice , the CP-deficient mice infected with ΔarlRS lost significantly more weight ( Figs 4A , S3B and S3C ) . The CP-deficient mice infected with ΔarlRS also had increased bacterial burdens in the liver when compared to wild type C57BL/6 mice . Notably , despite the substantial virulence defect of the mutant in wild type mice , there was only a minimal difference between wild type S . aureus and ΔarlRS in the livers of CP-deficient mice ( less than half a log difference vs . a 4 log difference ) . These results indicate that ArlRS contributes to systemic disease and that this two-component system is critical for resisting host-imposed Mn starvation during infection . While the results so far demonstrate that ArlRS contributes to resisting host-imposed Mn starvation both in culture and during infection , the underlying mechanism is not apparent . ArlRS is a global regulator that is involved in many cellular processes including virulence factor gene regulation [37–39] . It is unlikely that the regulation of toxins and other virulence factors whose targets are absent in media would have an effect on resisting metal limitation in culture . In addition to controlling virulence factor expression , ArlRS negatively regulates the expression of genes encoding for several phosphotransferase systems ( PTS ) and positively regulates the expression of enzymes potentially involved in amino acid utilization [39] . This includes a locus that encodes for a putative alanine dehydrogenase , threonine/serine deaminase , and amino acid importer . This locus is induced upon exposure to CP and this induction is dependent on ArlRS ( Fig 5 ) . Glucose and other sugars are the preferred carbon source utilized by S . aureus and many other bacteria to generate energy [43 , 44]; however , energy can also be generated using amino acids . While the metal dependency of glycolytic enzymes in S . aureus is unknown , Mn is a critical cofactor involved in sugar utilization by both Bradyrhizobium and S . pneumoniae [45 , 46] . In contrast to sugars , amino acids can bypass the potentially Mn-dependent steps of glycolysis by being directly converted to pyruvate or TCA cycle intermediates [44 , 47 , 48] . Cumulatively , these observations lead to the hypothesis that Mn and Zn starvation may impair glycolysis . Furthermore , they suggest that the carbon source utilized could impact staphylococcal CP sensitivity and that ArlRS contributes to resisting Mn limitation by shifting S . aureus away from using sugars as an energy source to amino acids . If this hypothesis is correct , S . aureus would be expected to be more resistant to CP-induced Mn sequestration when using amino acids as opposed to glucose as a carbon source . Furthermore , loss of ArlRS would be expected to reduce the ability of S . aureus to grow when amino acids are provided as the sole carbon source and alter staphylococcal metabolism when both nutrient types are present . To test this hypothesis , a defined medium compatible with CP growth assays , which allows the carbon source to be altered , was developed ( Fig 6A ) . This medium was then used to assess the sensitivity of S . aureus to CP when glucose or casamino acids were provided as the sole energy source . These assays revealed that S . aureus Newman is more sensitive to CP when glucose was provided as the sole carbon source compared to bacteria using casamino acids ( Figs 6B and S4A ) . Similar results were also observed when a defined medium containing purified amino acids as an energy source was used ( S4B Fig ) . USA300 ( JE2 ) was also more sensitive to CP when only glucose was available as a carbon source . ( Figs 6C and S4C ) . To determine whether the increased sensitivity of S . aureus to CP when glucose is used as a carbon source is dependent on Mn or Zn sequestration , the experiment was also performed with the ΔMn/Zn and ΔZn site mutants . While decreased growth in glucose-containing medium was observed when bacteria were growing in the presence of the ΔZn mutant ( binds both Mn and Zn ) , growth in the presence of the ΔMn/Zn mutant ( binds only Zn ) was comparable to that of growth in medium containing amino acids , meaning that no growth defect was observed ( Fig 6D ) . Additionally , the addition of excess Mn to glucose-containing medium reversed the phenotype ( Fig 6E ) . Combined , these results suggest that Mn sequestration is responsible for the reduced ability of S . aureus to grow in glucose-containing medium in the presence of CP . If using glucose as a carbon source requires more Mn than amino acids , mntA expression would be expected to be higher in medium containing only glucose as an energy source relative to medium containing amino acids when Mn availability is restricted . Consistent with our hypothesis , mntA levels increased in the presence of intermediate concentrations of CP , but not in Mn-replete medium , when bacteria were grown in the presence of glucose but not in amino acids ( Fig 6F ) . Cumulatively , these results indicate that utilizing glucose as carbon source increases the cellular demand for Mn when compared to when amino acids are used . Vitko et al . have previously shown that lactate is produced when bacteria are grown in glucose-containing medium but not when they are grown in amino acid-containing medium [49] . When Newman and USA300 ( JE2 ) were grown in medium containing both glucose and amino acids in the presence of CP , lactate production decreased with increasing CP concentrations ( Fig 6G and 6H ) . This observation is consistent with the idea that S . aureus shifts away from utilizing glucose as a carbon source when Mn is limiting and that utilizing amino acids as a carbon source minimizes the cellular demand for this metal . Next , the ability of ArlRS mutants to utilize amino acids as an energy source was assessed . Analysis of the ΔarlRS derivative of Newman revealed that this strain was severely delayed in growth when utilizing amino acids as a sole carbon source ( Figs 6A and S4D ) , suggesting a role for ArlRS in amino acid utilization . More modest but still significant reductions were also observed with arlR:erm derivatives of S . aureus USA300 ( JE2 ) and SH1000 when only amino acids were provided as an energy source ( S4E and S4F Fig ) . To evaluate if loss of ArlRS alters staphylococcal metabolism , the production of lactate was assessed in the ΔarlRS and arlR:erm derivatives of Newman and USA300 ( JE2 ) following growth in the presence and absence of CP ( S4G–S4J Fig ) . Both of the mutants had decreased lactate production relative to the parent strain regardless of CP treatment . Notably , differing from wild type , the strains lacking ArlRS did not reduce their production of lactate at inhibitory concentrations of CP ( Fig 6G and 6H ) . In conjunction with the growth phenotypes , these results suggest that loss of ArlRS disrupts staphylococcal metabolism and results in reduced growth in the presence of amino acids as a carbon source . Combined , these results support the idea that switching from utilizing sugars to amino acids as an energy source reduces the staphylococcal demand for Mn enhancing the ability of S . aureus to resist host-imposed metal starvation . They also suggest that ArlRS critically contributes to this process .
Transition metals such as Fe , Mn and Zn are important for virtually all forms of life , as they are involved in numerous biological processes ranging from metabolism to regulation of virulence factor expression [4–6 , 50] . To combat invaders , the host takes advantage of this essentiality by starving invaders for these metals . Recent work has revealed that in addition to restricting Fe availability , the essential transition metals Mn and Zn are also withheld by the host . Despite expressing high-affinity Mn acquisition systems , S . aureus , and presumably other successful pathogens , experience metal starvation during infection [8 , 9] . However , the adaptations that allow pathogens to overcome host-imposed Mn and Zn starvation are unknown . The investigations in this study revealed that to successfully cope with host-imposed Mn starvation , S . aureus must alter core metabolic pathways . Previous studies have emphasized the importance of sugar consumption and fermentation to staphylococcal virulence [49 , 51–54] . These obsevations include the finding that the catabolite control protein ( CcpA ) , which promotes the consumption of sugars , and an expanded repertoire of glucose importers enhances the ability of S . aureus to cause disease [53 , 54] . At the same time other studies sugest that uptake of amino acids facilitate the development of staphylococal disease [55] . In this study , we found that when bacteria encounter Mn starvation the presence of amino acids enhances the growth of the bacterium . These prior observations in conjunction with the current results emphasize the dynamic nature of sites of infection and further highlight the important contribution of metabolic plasticity to staphylococcal virulence and bacterial pathogenesis in general [49 , 51 , 52 , 56 , 57] . This work also revealed that the two-component system ArlRS enhances the ability of S . aureus to grow when amino acids are available as a sole carbon source and contributes to the ability of S . aureus to resist host-imposed Mn starvation during infection . This observation significantly expands the contribution of this two-component system to staphylococcal disease , which is canonically associated with regulation of toxin production and biofilm formation [37–40] . Recently , CP has been reported to bind Fe ( II ) with high affinity leading to the suggestion that the antimicrobial activity of the protein is derived from the ability to bind Fe , not Mn [58] . However , consistent with prior studies of A . baumannii [19] , analysis of metal levels in S . aureus revealed that CP does not reduce intracellular Fe levels ( Fig 3C ) . In contrast , in both S . aureus and A . baumannii CP reduced the accumulation of Mn [19] . These results suggest , at least for these two pathogens , that Fe sequestration is not a major contributor to the antimicrobial activity of CP . Additionally , the virulence defects in wild type mice of staphylococcal ΔmntCΔmntH and ΔarlRS mutants , which are more sensitive to Mn starvation in culture , are reversed in CP-deficient mice [13] . These results , in conjunction with the inhibition of Mn-dependent enzymes during infection [9] , further support the body of work indicating that Mn sequestration by CP contributes to host defense . Canonically , glycolysis is thought to be a magnesium-dependent process . However , many bacteria contain a Mn-dependent isoform of phosphoglycerate mutase and other glycolytic enzymes such as enolase and pyruvate kinase that are either dependent on Mn or are highly activated by small amounts of Mn [24] . The increased sensitivity of S . aureus to Mn starvation when only glucose is available as a carbon source suggests that at least one essential step in glycolysis is dependent on Mn . This observation adds S . aureus to the growing list of organisms , including S . pneumoniae and Bradyrhizobium japoincum , which are dependent on Mn in order to consume glucose [45 , 46] . The presence of Mn-utilizing glycolytic enzymes in a variety of microbes and the dependency of glycolysis in some pathogens on this metal suggests that host-imposed Mn starvation may also impede the ability of other pathogens to utilize sugars as an energy source . The Fe-sparing response , the repression of Fe-rich enzymes when the availability of this metal is limited , enhances the ability of bacteria to grow in Fe-poor environments . This response allows bacteria to prioritize the usage of Fe by reducing the production of non-essential Fe-dependent proteins thereby preserving the limited quantity of available Fe for essential functions [59 , 60] . The preferred carbon source of S . aureus and many other bacteria is glucose [43 , 44]; however , energy can also be generated by utilizing amino acids . As such , glucose utilization is not strictly essential in S . aureus . In contrast to glycolysis , which can require Mn , the degradation of amino acids ( e . g . , alanine , serine and threonine ) utilizes enzymes that do not employ this metal as a cofactor [44 , 47 , 48] . This observation suggests that relative to utilizing sugars , amino acid consumption should decrease the cellular demand for Mn , increasing the availability of this metal for essential Mn-dependent enzymes ( Fig 7 ) . Both the reduced expression of mntABC in Mn-limited medium when S . aureus is utilizing amino acids vs . glucose as a carbon source and the observation that growth on amino acids diminished staphylococcal sensitivity to Mn starvation support this idea . The latter observation suggests that consumption of amino acids instead of sugars is functionally a Mn-sparing response analogous to that of Fe . At higher concentrations of CP , S . aureus is equally sensitive to metal limitation regardless of whether the bacteria were grown in glucose- or amino acid-containing medium . Inhibition of Mn-dependent processes , which cannot be circumvented by switching carbon source utilization , could explain this observation . In response to Mn limitation , S . pneumoniae also downregulates glycolytic enzymes and increases the expression of amino acid utilization genes [46] . This observation and the current studies suggest that switching from utilizing sugars to amino acids is likely a conserved response to host-imposed Mn starvation . While the utilization of amino acids as an energy source reduces the cellular demand for Mn , in most bacteria , including S . aureus , catabolite repression prevents them from utilizing non-preferred carbon sources , such as amino acids , when a preferred carbon source is present [61 , 62] . ArlRS represses the expression of alternative sugar uptake systems and stimulates the expression of genes encoding for enzymes involved in amino acid utilization [39] . As such , ArlRS provides a mechanism by which S . aureus can override the normal carbon source preference of the cell . In response to CP , ArlRS positively regulates the expression of proteins predicted to be involved in the catabolism of alanine and serine . Differing from Liang et al , loss of ArlRS did not impact the expression of these proteins in the absence of CP . This difference can likely be explained by differences in growth conditions . These two amino acids can be converted directly to pyruvate , bypassing any metal-dependent enzymes in glycolysis [39 , 44 , 47 , 48] . Notably , a global screen for staphylococcal factors that contribute to abscess formation identified alanine and serine importers as contributing to the ability of S . aureus to cause disease [55] . As with most two-component systems , the signal sensed by ArlS is currently unknown . The necessity of ArlRS to resist Mn starvation suggests that Mn availability alters the activity of this system . ArlRS may respond directly to Mn availability or indirectly by sensing a disruption of glycolysis or other Mn-dependent processes induced by Mn limitation . However , additional experimentation is necessary to evaluate this possibility . ArlRS contributes to the ability of S . aureus to cause disease in several models of infection [42] . In addition to regulating metabolic processes , ArlRS increase the production of surface proteins and influences the expression of numerous virulence factors , biofilm formation , as well as autolysis and cell growth . It also directly and/or indirectly interacts with other regulators [37 , 39] . Thus , a reduced ability to grow on amino acids may not be the only factor that contributes to the diminished ability of strains lacking ArlRS to resist CP . As ArlRS regulates the expression of other staphylococcal regulators , including Agr , LytSR , MgrA and Rot , the factors that are directly vs . indirectly regulated by this system are unknown [37–39] . While the direct targets are unknown , it does link Mn availability to virulence factor expression . Even though the benefit to S . aureus of co-regulating presumably Mn-independent virulence factors is not immediately apparent , this does appear to be a common theme amongst bacterial pathogens . In Borrelia burgdoferi , Mn influences BosR expression , which in turn regulates expression of the alternative sigma factor , RpoS . This alternative sigma factor facilitates the adaptation of B . burgdoferi to the mammalian host [63] . S . pneumoniae uses the Mn-responsive regulator PsaR to regulate the expression of adhesins [64–67] . Due to the continued emergence of antibiotic resistance , bacterial pathogens remain a serious threat to human health . The current study provides new insight into the mechanisms utilized by pathogens to overcome nutritional immunity . It suggests that alterations in carbon source utilization and reducing the cellular demand for Mn is important for resisting host-imposed Mn starvation . These results significantly broaden our understanding of how bacteria overcome nutritional immunity . The continued study of this bacterial response and the associated metabolic changes has the potential to identify new opportunities for therapeutic intervention .
All experiments involving animals were reviewed and approved by the Institutional Animal Care and Use Committee of the University of Illinois Urbana-Champaign ( IACUC license number 15059 ) and performed according to NIH guidelines , the Animal Welfare Act , and US Federal law . For routine overnight cultures , bacteria were grown in 5 ml of tryptic soy broth ( TSB ) or Chelex-treated RPMI plus 1% Casamino acids ( NRPMI ) supplemented with 1 mM MgCl2 , 100 μM CaCl2 and 1 μM FeCl2 [13] in 15 ml conical tubes at 37°C on a roller drum . As needed , 10 μg/ml of chloramaphenicol was added for plasmid maintenance . S . aureus strain Newman and its derivatives were used for all of the experiments , unless otherwise indicated . For experiments using USA300 ( JE2 ) and derivatives ( USA300 ( JE2 ) arlR:erm , USA300 ( JE2 ) lytM:erm , USA300 ( JE2 ) lytN:erm and USA300 ( JE2 ) atl:erm ) , strains were obtained from the Nebraska library [68] . Newman ΔarlRS was generated by amplifying the 5’ and 3’ flanking regions ( ~1 Kb up- and downstream ) of arlRS using the indicated primers ( Table 1 ) . 5’ and 3’ fragments were cloned into the pKOR1 knockout vector via site-specific recombination . The deletions were created using allelic replacement , as described previously [69] . All constructs were verified by sequencing and all mutant strains were confirmed to be hemolytic by growth on TSA blood agar plates . To generate constructs for complementation studies , the arlRS coding sequence was amplified with the indicated primers ( Table 1 ) and cloned into the pOS1 vector under the control of the Plgt promoter . The lytM , atl and lytN mutants in Newman and Newman ΔarlRS and arlR mutants in SH1000 , Newman and Newman ΔmntCΔmntH were constructed by transducing the lytM:erm , atl:erm , lytN:erm and arlR:erm alleles via Φ85 phage from USA300 ( JE2 ) lytM:erm , USA300 ( JE2 ) atl:erm , USA300 ( JE2 ) lytN:erm and USA300 ( JE2 ) arlR:erm . CP growth assays were performed , as described previously [9 , 21] . Briefly , overnight cultures were back-diluted 1:50 into fresh TSB ( 5 ml in a 15 ml conical tube ) and grown for 1 h at 37°C on a roller drum . The cultures were then diluted 1:100 into 96-well round-bottom plates containing 100 μl of growth medium ( 38% TSB and 62% calprotectin buffer ( 20 mM Tris pH 7 . 5 , 100 mM NaCl , 3 mM CaCl2 , 10 mM β-mercaptoethanol ) ) in presence of varying concentrations of CP . The growth medium was supplemented with 1 μM MnCl2 and 1 μM ZnSO4 except for assays with the Newman arlRS:erm transposon mutant . For all assays , the bacteria were incubated with orbital shaking ( 180 RPM ) at 37°C and growth was measured by assessing optical density ( OD600 ) every 2 hours . Prior to measuring optical density , the 96-well plates were vortexed . For CP growth assays using defined medium , a medium based on the one previously reported by Richardson et al . [51] was used . For these assays , the preculture was the same as a growth assay using TSB in the growth medium . The growth medium for assays using defined medium consisted of 38% medium and 62% CP buffer ( 20 mM Tris pH 7 . 5 , 100 mM NaCl , 1 mM CaCl2 , 10 mM β-mercaptoethanol ) . The defined medium ( 2 . 6X ) consisted of 0 . 5 g/L NaCl , 1 . 0 g/L NH4Cl , 2 . 0 g/L KH2PO4 , 7 . 0 g/L Na2HPO4 , 0 . 228 g/L biotin , 0 . 228 mg/L nicotinic acid , 0 . 228 mg/L pyridoxine-HCl , 0 . 228 mg/L thiamine-HCl , 0 . 114 mg/L riboflavin , 0 . 684 mg/L calcium pantothenate , 0 . 104 g/L phenylalanine . 0 . 078 g/L lysine , 0 . 182 g/L methionine , 0 . 078 g/L histidine , 0 . 026 g/L tryptophan , 0 . 234 g/L leucine , 0 . 234 g/L aspartic acid , 0 . 182 g/L arginine , 0 . 078 g/L serine , 0 . 15 g/L alanine , 0 . 078 g/L threonine , 0 . 130 g/L glycine , 0 . 208 g/L valine and 0 . 026 g/L proline . The defined medium was then supplemented with 6 mM MgSO4 , 1 μM FeCl2 , 1 μM MnCl2 and 1 μM ZnSO4 . Casamino acids ( 6 . 5% ) , glucose ( 1 . 3% ) or glucose ( 1 . 3% ) and 18 amino acids ( 1 mM alanine , serine , threonine , lysine , asparagine , glutamic acid , isoleucine , arginine , histidine , methionine , valine , proline , cystidine , glycine , phenylalanine , tyrosine , leucine and tryptophan ) were provided as carbon sources as indicated . In the figures , “DM” refers to defined medium without a carbon source , “glc” refers to defined medium with glucose as a carbon source , “AA” refers to defined medium with casamino acids as a carbon source and “glc+18AA” refers to glucose and 18 amino acids as a carbon source . For complementation experiments , overnight cultures were back-diluted 1:50 into fresh TSB and grown for 2 h at 37°C [9 , 21 , 51] . When a metal starvation step was included the bacteria were grown overnight in NRPMI supplemented with 1 mM MgCl2 , 100 μM CaCl2 and 1 μM FeCl2 and directly inoculated 1:100 in to the assay medium . Calprotectin was purified , as previously described [9 , 21] . The initial ArlRS transposon mutant was identified during optimization experiments for screening a Tn917 mutant library . For these assays bacteria arrayed in 96-well plates were grown overnight in NRPMI supplemented with 1 mM MgCl2 , 100 μM CaCl2 and 1 μM FeCl2 . These cultures were then assayed for CP sensitivity , as described above . To assess the expression of mntA , mntH and NWMN_1348 , S . aureus was grown as for CP inhibition assays in complex medium in the presence and absence of 240 μg/ml of CP or in defined medium in the presence and absence of 120 μg/ml of CP . Bacteria were harvested during log phase growth ( OD600 = ~0 . 1 ) , samples were collected , an equal volume of ice-cold 1:1 acetone-ethanol was then added to the cultures , and they were frozen at -80°C until RNA extraction . RNA was extracted and cDNA was generated , as previously described [70–72] . Gene expression was assessed by quantitative reverse transcription-PCR ( qRT-PCR ) using the indicated primers ( Table 1 , [13] ) and 16S was used as a normalizing control . To assess intracellular metal levels in wild type and ΔarlRS , S . aureus strains were grown as for CP inhibition assays using complex medium in the presence and absence of 240 μg/ml of CP . Bacteria were harvested during log phase growth ( OD600 = ~0 . 1 ) , washed twice with 0 . 1 mM EDTA , washed twice with water , and digested with nitric acid . Prior to nitric acid digestion an aliquot was used to determine the total number of bacteria in the sample . After digestion , ICP-OES was performed by the Microanalysis facility at the University of Illinois Urbana-Champaign . Mouse infections were performed , as described previously [8 , 9] , with the exception that mice were anesthetized with isoflurane . Briefly , 9-week old mice were infected retro-orbitally with approximately 1 x 107 CFU in 100 μl of sterile phosphate-buffered saline . Following injection , the infection was allowed to proceed for 96 h before the mice were sacrificed . Livers , hearts and kidneys were removed , the organs were homogenized , and bacterial burden was determined by plating serial dilutions . L-lactate production was assayed , as described previously [49] . Briefly , bacteria were grown as for CP inhibition assays described above using NRPMI overnight cultures and back-diluted 1:100 into defined medium in the presence and absence of 60 , 120 , 240 and 480 μg/ml of CP . Samples were harvested every hour during log phase , heat inactivated ( 70°C for 5 min ) , and supernatants were collected . Samples were stored at -20°C . L-Lactic acid concentrations were measured using a Roche Yellow Line kit ( R-Biopharm ) .
|
The ubiquitous pathogen Staphylococcus aureus is a serious threat to human health due to the continued spread of antibiotic resistance . This spread has made it challenging to treat staphylococcal infections and led to the call for new approaches to treat this devastating pathogen . One approach is to disrupt the ability of S . aureus to adapt to nutrient availability during infection . During infection , the host imposes manganese and zinc starvation on invading pathogens . However , the mechanisms utilized by Staphylococcus aureus to overcome this host defense are unknown . We report that ArlRS , a global staphylococcal virulence regulator , is important for resisting manganese starvation during infection . Loss of ArlRS does not prevent S . aureus from acquiring metals but instead renders the bacterium incapable of adapting to limited manganese availability . ArlRS mutants also have metabolic defects and a reduced ability to grow on amino acids . When using glucose as a carbon source S . aureus is more sensitive to manganese starvation and increases the expression of manganese transporters relative to when amino acids are provided suggesting a higher demand for manganese . These observations indicate that ArlRS contributes to resisting nutritional immunity by altering metabolism to reduce the staphylococcal demand for manganese .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"chemical",
"compounds",
"manganese",
"engineering",
"and",
"technology",
"pathogens",
"microbiology",
"carbohydrates",
"staphylococcus",
"aureus",
"organic",
"compounds",
"glucose",
"organisms",
"bacterial",
"diseases",
"nutrition",
"carbon",
"sequestration",
"bacteria",
"malnutrition",
"bacterial",
"pathogens",
"infectious",
"diseases",
"staphylococcus",
"medical",
"microbiology",
"environmental",
"engineering",
"microbial",
"pathogens",
"chemistry",
"staphylococcal",
"infection",
"organic",
"chemistry",
"monosaccharides",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"starvation",
"chemical",
"elements"
] |
2016
|
The Two-Component System ArlRS and Alterations in Metabolism Enable Staphylococcus aureus to Resist Calprotectin-Induced Manganese Starvation
|
The mechanisms by which human immunodeficiency virus type 1 ( HIV-1 ) crosses mucosal surfaces to establish infection are unknown . Acidic genital secretions of HIV-1-infected women contain HIV-1 likely coated by antibody . We found that the combination of acidic pH and Env-specific IgG , including that from cervicovaginal and seminal fluids of HIV-1-infected individuals , augmented transcytosis across epithelial cells as much as 20-fold compared with Env-specific IgG at neutral pH or non-specific IgG at either pH . Enhanced transcytosis was observed with clinical HIV-1 isolates , including transmitted/founder strains , and was eliminated in Fc neonatal receptor ( FcRn ) -knockdown epithelial cells . Non-neutralizing antibodies allowed similar or less transcytosis than neutralizing antibodies . However , the ratio of total:infectious virus was higher for neutralizing antibodies , indicating that they allowed transcytosis while blocking infectivity of transcytosed virus . Immunocytochemistry revealed abundant FcRn expression in columnar epithelia lining the human endocervix and penile urethra . Acidity and Env-specific IgG enhance transcytosis of virus across epithelial cells via FcRn and could facilitate translocation of virus to susceptible target cells following sexual exposure .
Sexual transmission of HIV-1 requires that virus establish infection across genital tract or intestinal tissue . Sexually transmitted infections , other causes of inflammation , and localized trauma may allow susceptible CD4+ target cells at skin or mucosal surfaces to become directly exposed to secretions from infected sexual partners [1] , [2] . However , when skin and mucosa are intact , it remains unclear precisely how HIV-1 gains access to target cells . One possibility is that virus translocates between epithelial cells until susceptible cells are found either in or below the epithelium [3] . Alternatively , Langerhans cells may sample the surface , acquire virus , and move it to areas of abundant target cells [4] , [5] . Finally , transcytosis of HIV-1 ( i . e . , movement through cells ) has been studied as a potential mechanism to translocate virus from mucosal surfaces to deeper-lying CD4+ cells [6] , [7] , [8] . Transcytosis offers an explanation for movement of virus across epithelial cells forming tight junctions , which might normally exclude pathogens from moving beyond the surface . However , in vitro , only a very small amount of virus , usually less than 0 . 3% of a cell-free virus inoculum , finds its way through cells into the medium bathing basolateral surfaces ( [9] ) . Interactions between HIV-1 Env and several host cell surface molecules , including glycolipids , heparan sulfate proteoglycans and gp340 , have been proposed to play a role in transcytosis [10] , [11] , [12] , [13] , [14] . With the exception of the acute phase prior to development of anti-HIV-1 immune responses , semen , cervicovaginal , and rectal fluids from HIV-1-infected individuals contain antibodies against HIV-1 Env [15] , [16] , [17] . The concentration of Env-specific IgG present in such secretions varies considerably from person to person and is usually on the order of 100 to 1 , 000-fold less than concentrations found in plasma [18] . The presence of Env-specific IgG strongly suggests that some proportion of Env molecules on the surface of infectious virions in genital tract secretions is coated with IgG . Since HIV-1 is successfully transmitted sexually , the coating antibody is either of insufficient quantity or quality to neutralize virus infectivity upon contact with an uninfected partner . Antibody in genital tract secretions of HIV-1-infected individuals could play a role in facilitating the transport of virus across mucosal epithelia . Such a role is made particularly plausible by the reported expression of the Fc neonatal receptor ( FcRn ) in human genital mucosal tissue [19] . FcRn is a heterodimeric receptor belonging to the MHC class I family of proteins [20] , [21] . The expression of FcRn in endothelial cells is thought to be critical for IgG homeostasis in blood [22] , and its expression in placental syncytiotrophoblasts is a key factor in transporting maternal IgG to the fetal circulation [23] . A characteristic of FcRn is its ability to bind the Fc region of IgG at acidic pH and to release it at neutral pH [24] . This pH-dependent binding allows the transport of intact IgG or of IgG immune complexes from luminal surfaces bathed in acidic fluids , for example , cervicovaginal secretions , to basolateral surfaces exposed to a neutral intracellular milieu [19] . Cervicovaginal secretions are maintained at acidic pH by acid-producing bacteria that make up part of the normal vaginal microbiota [25] . Although perturbations of normal microbiota , such as occur with bacterial vaginosis , raise the pH , the secretions generally remain in the acidic range [26] , [27] . Semen rapidly neutralizes cervicovaginal secretions , but the extent of the pH change is variable . For example , a large amount of ejaculate may raise the pH to the neutral range , whereas a small amount may not [28] , [29] . The pH of rectal secretions ranges from about 6 . 8 to 7 . 2 [30] . Given that HIV-1 in genital tract secretions may be complexed with IgG antibody , that female genital tract secretions are acidic , and that FcRn has been demonstrated in genital tract tissues , we evaluated the role of pH and antibody on transcytosis of HIV-1 through polarized epithelial cells .
To investigate the effect of low pH and antibody on HIV-1 transcytosis across epithelial cells forming tight junctions , we exposed the apical surface of HEC-1A cells to HIV-1 at pH 6 . 0 or 7 . 4 with or without HIV-1-specific IgG ( HIVIG ) . Virus was quantified in the medium bathing the basolateral cell surface ( “subnatant fluid” ) by RT-PCR and , although detectable as early as six hours after exposure of virus to the apical cell surface , the quantity was greater at 12 hours ( Figure S1 ) [8] . Thus , in subsequent experiments , transcytosis was measured at 12 hours . Using HIV-1US712 , a clade B R5 clinical isolate , HIVIG enhanced transcytosis in a dose-dependent manner when virus and antibody were exposed to the apical surface at pH 6 . 0 ( Figure 1A ) . There was no increase in transcytosis with HIVIG at pH 7 . 4 or with HIV-negative IgG ( IVIG ) at pH 6 . 0 or 7 . 4 . We found similarly enhanced transcytosis using additional R5 as well as X4 and X4/R5 strains ( Figure 1B ) . Importantly , transcytosis of four of five clade C transmitted/founder Env-pseudotyped viruses was enhanced in a pH and antibody-dependent manner ( Figure 1C ) . Enhanced transcytosis with HIV-1-specific antibody at low pH also occurred with T84 colon carcinoma cells ( Figure S2 ) . Since sexual transmission may occur with small amounts of virus , we investigated if pH- and antibody-dependent enhancement of transcytosis could occur at very low HIV-1 inocula . Using HIVIG or the anti-gp41 monoclonal antibody ( mAb ) 2F5 , we found that transcytosis occurred with virus inocula as low as 2 pg of p24 ( about 60 , 000 RNA copies ) with HIVIG and 0 . 02 pg of p24 ( about 500 RNA copies ) with 2F5 , amounts too small to be detectable in subnatant fluid in the absence of low pH and HIV-1-specific antibody ( Table 1 ) . These quantities of virus are within the range observed in seminal and cervicovaginal fluids of HIV-infected individuals [31] , [32] , [33] . The impact of both antibody and low pH suggested FcRn involvement [34] , [35] . We knocked down FcRn in HEC-1A cells , verifying lower expression by flow cytometry and by Western blot ( Figure S3A ) . The knock-down HEC-1A cells attained the same level of electrical resistance as did the wild-type cells ( data not shown ) , indicating that FcRn knockdown did not affect the ability to form tight junctions . Unlike with wild-type HEC-1A cells , there was no enhanced transcytosis with FcRn-knockdown HEC-1A cells when either mAb 2F5 ( Figure 2A ) or polyclonal HIVIG ( data not shown ) were used . We also evaluated Fc mutants of the HIV-1 Env-specific mAb b12 . A mutant designed to abrogate FcRn binding ( I253A ) , markedly lowered transcytosis compared with wild-type b12 ( Figure 2B ) [36] . The second mutant ( M428L ) , designed to bind with higher affinity to FcRn , increased transcytosis compared with wild-type b12 ( Figure 2B ) [37] . Binding to HIV-1JRFL gp120 ( Figure S3B ) and neutralization of HIV-1JRFL ( Figure S3C ) were nearly equivalent for the wild-type and Fc mutant versions of b12 , indicating that the Fc mutations did not affect Fab-antigen binding . Blockade of FcRn with anti-FcRn antibody and inhibition of endosomal acidification by bafilomycin A1 also substantially reduced or eliminated enhanced transcytosis ( Figure S3D–F ) , as did competition between the non-HIV-1-specific mAb Den3 and the anti-HIV-1 Env mAb VRC01 ( Figure S3G ) . Consistent with other investigations of Fc-FcRn interactions , maximally enhanced transcytosis occurred at pH 5 . 5–6 . 0 , with some enhanced transcytosis apparent at pH 4 . 5 and 6 . 5 ( Figure S3H ) [38] , [39] . Since FcRn binds to IgG and not to IgA , we compared two different IgG1 mAbs , b12 and HGN194 , with their IgA class-switched versions . Both IgG1 mAbs enhanced transcytosis of HIV-1JRFL pseudoviruses and SHIV1157ipEL-p at pH 6 . 0 , whereas the IgA class-switched versions did not ( Figure 2C and 2D ) . In fact , as reported , dimeric IgA1 HGN194 inhibited transcytosis [40] . Thus , enhanced transcytosis at low pH in the presence of specific antibody is mediated by IgG and is dependent on FcRn . Using 50 µg/ml of VRC01 or Den3 , transcytosis of IgG alone increased approximately 3 fold from about 0 . 4% at pH 7 . 4 to about 1 . 3% at pH 6 . 0 ( Figure S4 ) . However , the effect of FcRn-mediated transcytosis on IgG alone does not appear as strong as the effect on IgG immune complexes , where , for example , with complexes made with 50 µg/ml of VRC01 or 2F5 , there was about an 8-fold increase in transcytosis under conditions allowing FcRn engagement . This difference may be due to the contribution of fluid phase uptake of IgG by the epithelial cells at both pH 6 . 0 and pH 7 . 4; IgG thus internalized can engage FcRn in acidic endosomes and be shuttled to the basolateral side of the cells [41] . The internalization of immune complexes , on the other hand , likely depends primarily on FcRn engagement at the surface of the cell at pH 6 . 0 . We evaluated the ability of HIVIG and a panel of mAbs with variable neutralizing activities to mediate pH-dependent transcytosis with fully infectious HIV-1JRFL; 50% inhibitory concentrations ( IC50s ) of the antibodies ranged from 0 . 06 to >50 µg/ml ( Fig . 3E ) . Both poorly neutralizing antibodies ( HIVIG and mAbs b6 and F240; Figure 3A ) and neutralizing mAbs ( 4E10 , 2F5 , 2G12 , VRC01 , and b12; Figure 3B ) enhanced transcytosis at pH 6 . 0 . At 50 µg/ml , transcytosis correlated directly with mAb binding to HIV-1JRFL ( Spearman rho = 0 . 75; p = 0 . 052 ) and inversely with the IC50 of the mAbs ( Spearman rho = −0 . 71; p = 0 . 050 ) ( Figure S5 ) . At pH 6 . 0 , all Env-specific mAbs and HIVIG mediated transcytosis of virus that infected TZM-bl cells ( Figure 3C and D ) . However , there was a strong correlation between the amount of transcytosed infectious virus and the neutralizing activity ( IC50 ) of the antibody that mediated the transcytosis ( Spearman's rho = 0 . 86; p = 0 . 001 ) . Virus whose transcytosis was mediated by poorly neutralizing antibodies HIVIG , F240 and b6 , at least at concentrations of 100 and 50 µg/ml , was more infectious than virus which crossed the epithelial cells in a non-FcRn-dependent manner ( i . e . , in the presence of Den3 control mAb ) ( Fig . 3C ) . Conversely , transcytosis mediated by antibodies with the lowest IC50s , such as VRC01 and b12 , resulted in less infectious virus than was observed with the Den3 control antibody ( Figure 3D ) . Thus , strong binding activity results in more FcRn-dependent transcytosis , whereas strong neutralizing activity renders the transcytosed virus less infectious . This point is further illustrated by the ratio of percent-transcytosed:percent-infectious virus ( Figure 3E ) . For example , for every infectious unit , about 30 times more virus is transcytosed with VRC01 than with HIVIG ( Figure 3E ) . Note that independently of transcytosis , HIV-1JRLF infectivity on TZM-bl cells increased about 3 . 5-fold after incubation of virus for 12 hours at pH 6 . 0 compared with pH 7 . 4; however , IC50s were very similar ( <15% difference ) at the two pH values ( pH comparisons done for 2F5 and VRC01 only; data not shown ) . Virus infectivity was essentially abrogated after a 12-hour incubation at pH 4 . 0 ( data not shown ) . We next determined whether IgG purified from cervicovaginal fluid and from seminal fluid could enhance transcytosis at low pH . Using IgG from cervicovaginal fluid of three HIV-infected women and from seminal fluid of three infected men , enhanced transcytosis occurred at IgG concentrations well within their expected range in genital tract secretions ( Figure 4A and 4B ) [18] . The ability of genital tract IgG to mediate transcytosis correlated strongly with infectious virus capture activity by the IgG ( Spearman's rho = 0 . 94 , p = 0 . 005; Figure 4C ) and less so with binding to monomeric Env glycoprotein from the same virus strain ( HIV-1US657 ) ( rho = 0 . 65 , p = 0 . 16; Figure S6 ) . None of the genital tract IgGs were able to neutralize HIVUS657 , the clinical R5 strain used in these experiments , at IgG concentrations as high as 50 µg/ml ( not shown ) . Consistent with HIVIG and the non-neutralizing mAbs , higher concentrations of genital tract IgGs generally resulted in greater infectivity of the transcytosed virus ( Figure 4D and 4E ) . FcRn expression was previously reported in human uterine and vaginal epithelial cells [19] . Using immunohistochemistry to survey FcRn protein expression at various sites in the human genital tract , we detected abundant FcRn expression in columnar epithelial cells lining the human penile urethra ( Figure 5A and 5D; Figure S7A and S7D ) and endocervix ( Figure 5B and 5E; Figure S7B and S7E ) . In contrast , little to no FcRn protein was observed in vaginal/ectocervical squamous epithelia , and expression occurred only in the basal epithelial layer ( Figure 5C and 5F; Figure S7C ) . A similar staining pattern was observed in foreskin tissue ( data not shown ) .
Female genital tract secretions are often acidic , and the secretions of HIV-infected individuals have antibody capable of coating virus contained in those secretions . These facts led us to explore the role of antibody and low pH on transcytosis of HIV-1 across epithelial cells . Our primary finding is that at acidic pH , IgG enhances transcytosis of HIV-1 clinical isolates , including transmitted/founder Env-pseudotyped strains . Moreover , antibody from both cervicovaginal and seminal fluid mediates enhanced transcytosis at low pH . The enhanced transcytosis is abrogated by blocking or knocking down FcRn , which is known to bind IgG and immune complexes at low pH and release them at neutral pH [24] , [42] . We also establish that virus translocated across epithelial cells after incubation with antibody at low pH remains infectious . Although neutralizing antibodies generally promote more transcytosis , the transcytosed virus is relatively less infectious than virus whose transcytosis is mediated by non-neutralizing antibodies . Finally , we demonstrate abundant FcRn protein expression in columnar epithelial cells of the human endocervix and penile urethra , suggesting that these sites could play a major role in FcRn-mediated immune complex transcytosis . Our results indicate that FcRn may be responsible for shuttling IgG-bound HIV-1 across epithelial cells in the genital tract . This is consistent with other studies that have highlighted a role for FcRn in immune complex shuttling across tissues [34] , [35] , [43] . Mice expressing human FcRn in intestinal epithelial cells were able to deliver IgG to the luminal intestinal surface , which could then bind to its cognate antigen and return the immune complex back to the lamina propria for presentation by dendritic cells to CD4+ T cells [34] . In addition , cytomegalovirus ( CMV ) applied to human placental explants from women with high anti-CMV neutralizing antibody activity was rapidly transcytosed across syncytiotrophoblasts and captured by villus macrophages [35] . Under these conditions , the virus did not replicate . However , in explants from CMV-seropositive women with low or undetectable neutralizing antibodies , virus replication readily occurred in cytotrophoblasts underlying an intact , uninfected syncytiotrophoblast layer . Thus , it appeared that neutralizing antibody inhibited infection after allowing virus to cross the syncytiotrophoblast layer . On the other hand , non-neutralizing antibody allowed or even promoted infection . Syncytiotrophoblasts express high levels of FcRn , and when FcRn on explants was blocked , IgG-virion complexes were not transported across the surface [35] . Just as we found with HIV-1 , FcRn-mediated transcytosis of CMV occurred with both neutralizing and poorly neutralizing antibody , but transcytosed virus remained infectious only when complexed with poorly neutralizing antibody . Finally , immunohistochemical staining of placentas from in utero infections were consistent with this model of FcRn-mediated transcytosis [35] . To our knowledge , ours is the first study to investigate transcytosis using virus coated with HIV-specific antibody in an acidic environment that mimics that of the female genital tract . Our in vitro observations are applicable to male-to-female transmission via vaginal intercourse , where enhanced transcytosis could facilitate infection . In this regard , Li et al . reported FcRn expression and bidirectional IgG transport in a human vaginal tissue model [19] . Although we did not detect FcRn in the apical layers of vaginal epithelium , we did detect abundant FcRn expression in columnar endocervical epithelial cells . These cells may be exposed to acidic vaginal secretions where they occur at the cervical os . Furthermore , cervical ectopy , a common condition characterized by the extension of endocervical columnar epithelium into the ectocervix and upper vagina , has been implicated as a risk factor for HIV-1 infection [44] , [45] . Prevalent in reproductive-age women , these cervical lesions are exposed to vaginal pH conditions and could provide portals for FcRn-mediated male-to-female HIV-1 transmission [46] . FcRn was also found , though not consistently , in basal epithelial cells of the vagina . These cells lie deep in the epithelium and are unlikely to come in contact with acidic secretions and HIV-1 immune complexes unless there were trauma or substantial thinning of the overlying squamous epithelium . It is important to note that seminal fluid can rapidly raise the pH of cervicovaginal secretions to levels which would not support immune complex-FcRn binding . However , the pH of cervicovaginal fluid following ejaculation is dependent on the quantity of the ejaculate and may stay within an acidic range [29] . Furthermore , HIV is present in preejaculate secretions and could be introduced into the female genital tract prior to ejaculation [47] . With respect to female-to-male transmission , the penis comes in contact with vaginal secretions that would remain at acidic pH at least until ejaculation , allowing time for exposure of penile tissues , including the foreskin and urethra , to IgG-coated virus at low pH [28] , [29] . Our demonstration of abundant FcRn on human penile urethral epithelium supports a model where exposure to antibody-bound HIV-1 might lead to enhanced female-to-male transmission . It should be noted that the pH of vaginal secretions is typically about 4 , which is below the pH required for Fc-FcRn binding [48] . However , there is substantial variability in normal vaginal pH [26] , [48] , and we did begin to observe enhanced transcytosis at pH 4 . 5 ( Figure S3H ) . Furthermore , it is possible that there is some buffering effect of foreskin and urethral secretions . The foreskin , whose presence increases HIV infection rate , could trap secretions containing HIV-1 immune complexes and thereby allow greater urethral exposure to infected material within the pH range of Fc-FcRn binding [49] . Additionally , bacterial vaginosis , a condition associated with an increased risk of female-to-male ( as well as male-female ) HIV transmission , results in vaginal secretions ideal for Fc-FcRn binding [26] , [27] , [50] . Exposure of penile tissues to the pH range of Fc-FcRn binding may also occur after ejaculation , since complete neutralization of vaginal acidity may not occur immediately or at all [29] . It is also possible , though less likely , that FcRn mediates HIV transmission via the penis during insertive anal intercourse , where the penis may come into contact with slightly acidic rectal secretions [30] . The finding that IgG from cervicovaginal and seminal fluids obtained from HIV-infected individuals mediate enhanced transcytosis of infectious virus further suggests the biological relevance of our results . Cervicovaginal and seminal fluids are reported to contain an average of ∼3 µg/ml and up to ∼15 µg/ml of Env-specific IgG [18] . Four of the six samples we evaluated bound to infectious HIV-1 at 5 µg/ml . Moreover , all of our samples mediated transcytosis at ≤12 . 5 µg/ml of total IgG , well below total IgG concentrations found in genital secretions of HIV-infected men and women [18] . Even during acute HIV infection , when the risk of transmission to an uninfected partner is highest , 23 of 23 subjects ( 100% ) were reported to have anti-gp41 IgG antibodies and 40% had anti-gp120 IgG antibodies in cervicovaginal and seminal fluids [17] . Anti-gp41 IgG levels were on average 11-fold higher than gp41-specific IgA levels; anti-gp41 IgM was found less frequently and in lower quantity . Thus , HIV-1 immune complexes are likely to occur in mucosal secretions , are likely to contain predominantly IgG , and under acidic conditions , would be subject to FcRn-mediated transcytosis in an exposed host . The relevance of our findings is also supported by our demonstration that transcytosis of transmitted/founder strains of HIV-1 Env pseudotyped virus is enhanced by antibody . We are currently evaluating whether transmitted/founder strains , in comparison with chronic strains , are preferentially transcytosed , which would be consistent with a report showing a higher sensitivity of clade B transmitted/founder strains to anti-Env antibody binding [51] . Our findings represent a new model of antibody-dependent enhancement ( ADE ) of HIV-1 infection . Previous studies have demonstrated ADE in vitro due to FcγR- or complement-mediated mechanisms or to modulation of the interaction of gp120 with CCR5 [52] , [53] , [54] . Here we demonstrate that enhancement in vitro occurs at the level of transcytosis across epithelial cells and involves FcRn . In vivo , Ig isotype , as well as neutralizing activity , are likely to play a determining role in whether an antibody might protect from or enhance infection . As demonstrated recently , intrarectally applied dIgA1 HGN194 mAb , but less so the IgG1 version , prevented SHIV infection following intrarectal challenge [40] . In vitro , the dIgA1 inhibited transcytosis , whereas we now show that the IgG1 version enhances transcytosis at pH 6 . 0 . Another study showed that , compared to irrelevant- and no-antibody controls , there was an increase in the number of transmitted/founder SHIV variants when vaginal challenge followed systemic or local infusion of a non-neutralizing IgG1 mAb [55] . Clearly , other studies have found that IgG with neutralizing activity can prevent lentivirus infection after vaginal challenge [56] , [57] . Thus , whereas a strong vaccine-induced neutralizing IgG response may protect , non-neutralizing IgG or waning titers of neutralizing IgG present in an acidic lumen might enhance transcytosis across mucosal barriers while allowing infection of susceptible target cells . However , whether an antibody protects , enhances or has no effect is likely to depend on the potency and breadth of antiviral activity , the viral strain , the inflammatory state of the exposed individual , and genetic factors—such as FcγR polymorphisms—that might influence antibody function [58] . Finally , if FcRn-mediated transcytosis applies in vivo , our results would strengthen the argument for a mucosal IgA response to vaccination—though not at the exclusion of a strong IgG neutralizing or other anti-viral response—since IgA can inhibit transcytosis , would not engage FcRn , and mediates only uni-directional translocation of immune complexes from the subepithelial space into external secretions [40] , [59] . Some studies have reported that anti-HIV-1 Env IgG antibodies can inhibit transcytosis [9] , [60] , [61] . One of these studies found that polyclonal anti-HIV Env IgG inhibited transcytosis of cell-free virus on HEC-1 cells , whereas none of 13 mAbs did; in fact , some of the mAbs might have increased transcytosis , although by no more than about 50% [61] . To our knowledge , none of these studies was carried out under the acidic conditions that characterize female genital tract secretions . Our results suggest that FcRn might facilitate infection in hosts without pre-existing antibody or with a non-neutralizing IgG response to prior infection ( which would result in secondary infection ) or to vaccination . However , FcRn could also play a beneficial role in preventing infection after exposure . FcRn mediates the bidirectional transcytosis of IgG , and in immunized individuals , could provide a conduit for antibodies to neutralize virus as shown for herpes simplex virus type 2 [19] . In addition , IgG immune complexes can prime CD4+ and CD8+ T cells in an FcRn-dependent manner , and FcRn targeting may be a useful mucosal immunization strategy [62] , [63] , [64] . In summary , we have demonstrated that FcRn mediates enhanced transcytosis of HIV-1 in the presence of low pH and HIV-1-specific antibody . We have also shown that FcRn is present on epithelial cells in areas of the genital tract that are potentially exposed to HIV-1 during sexual intercourse . Our findings point toward a novel mechanism by which the sexual transmission of HIV-1 may be facilitated .
This research was approved by the Institutional Review Boards at the University of California , Irvine , Boston University , and the University of Alabama , Birmingham . Subjects from whom specimens were collected for study purposes provided written informed consent . Human Endometrial Carcinoma ( HEC-1A ) cells ( ATCC ) were propagated in Modified McCoy's 5a Medium , and Human Colon Carcinoma ( T84 ) cells ( ATCC ) in Dulbecco's modified Eagle's medium; media were supplemented with 2 . 5 mM L-glutamine ( Gibco , Invitrogen Technologies ) , 1% Penstrep ( Cellgro Mediatech Inc . ) and 10% FBS ( Atlas Biologicals ) and maintained at 37°C with 5% CO2 . TZM-bl cells ( NIH AIDS Reagent Program ) for infectivity assays were propagated in RPMI 1640 supplemented with L-glutamine , Penstrep and 10% FBS as above . Five primary clinical HIV-1 strains , HIV-1US657 , HIV-1US712 , HIV-1JRFL , HIV-1HT593 , and HIV-1HT599 were obtained from the NIH AIDS Reagent Program . SHIV1157ipEL-p , provided by Ruth Ruprecht , was grown in rhesus peripheral blood mononuclear cells [65] . HIVIG ( IgG derived from pooled plasma of HIV-infected individuals ) and IgG1 monoclonal antibodies ( mAbs ) 2F5 , 4E10 , 2G12 , F240 , b6 , and VRC01 were obtained from the NIH AIDS Reagents Program . IVIG ( Gamunex , Taleris Biotherapeutics ) was commercially acquired . mAb b12 and control mAb Den3 were provided by Dennis Burton and Brian Moldt , and control mAb Fm-6 was a gift of Wayne Marasco ( Dana-Farber Cancer Institute ) ; b12 and the control mAbs are IgG1 . Generation and purification of dimeric and monomeric IgA2 versions of b12 ( dIgA2 b12 and mIgA2 b12 ) are described elsewhere [66] . Briefly , the IgG constant region in pDR . 12 ( IgG b12 ) was replaced with the constant region of IgA2 . IgA2 b12 was expressed in CHO-K1 cells with human J chain and purified by Protein L affinity matrix ( Pierce ) . mIgA b12 and dIgA b12 were isolated by size exclusion chromatography . IgG1 HGN194 ( a human mAb against HIV-1 Env V3 ) , dIgA1 HGN194 , and dIgA2HGN194 were provided by Davide Corti and Antonio Lanzavecchia [67] . HGN194 variants were constructed as follows: human J chain precursor ( accession number NP_653247 ) , IgA1 ( allele IGHA1*01 , accession number J00220 ) and IgA2 ( allele IGHA2*01 , accession number J00221 ) constant region nucleotide sequences were codon optimized and synthesized by Genscript . Constant regions were cloned into a mammalian expression vector used for subcloning of the HGN194 VH region . The HGN194 VH and VL chain were codon optimized and synthesized by Genscript and cloned into an IgG1 and Ig-lambda expression vector . MAbs HGN194 dIgA1 , dIgA2 , and IgG1 were produced by transient transfection of 293 freestyle cells with polyethylenimine and expression plasmids encoding corresponding heavy and light chains ( in the case of dIgA1 and dIgA2 , the J chain expression plasmid was included ) . Supernatant fluid from transfected cells was collected after 7–10 days of culture . HGN194 dIgA1 , dIgA2 , and IgG1 were affinity purified by Peptide M ( dIgA1 and dIgA2 ) or Protein A ( IgG1 ) chromatography . Purified Abs were quantified by ELISA using dIgA1 and dIgA2 or IgG1-specific Abs ( Southern Biotech ) . Purity and polymeric state of dIgA1 and dIgA2 were confirmed by native-PAGE analysis and gel filtration chromatography . The presence of dIgA1 and dIgA2 associated J-chain was confirmed by Western blot from native and SDS-PAGE gels . Sera from 20 Zambian clade C-infected subjects ( obtained from Zdenek Hel , University of Alabama , Birmingham ) were pooled for IgG isolation using the Pierce Melon Gel IgG Spin Purification Kit ( Thermo Scientific ) according to the manufacturer's instructions . Env-specific IgG , determined as for CVL and seminal fluid ( see below ) , was 0 . 98% of total IgG . Sera from five uninfected individuals were pooled and processed for IgG isolation in the same manner . Fc mutants designed to enhance ( M428L ) or reduce ( I253A ) mAb b12 binding to FcRn were constructed as follows: briefly , the b12 variable regions were PCR-amplified from pDR12 and cloned into the pγ1HC and pκLC vectors [68] , [69] . Amino acid substitutions were introduced by QuikChange site-directed mutagenesis ( Stratagene , La Jolla , CA ) . Constructs were verified by sequence analysis before transiently expressed in FreeStyle 293 cells ( Invitrogen , Carlsbad , CA ) and purified by protein A affinity chromatography ( GE Healthcare , United Kingdom ) . Antibodies were tested for neutralizing activity against indicated HIV-1 strains using TZM-bl cells . Half-area 96-well plates ( Corning ) were coated with 5 µg/ml ( 250 ng/well ) of goat anti-human Fc antibody and incubated over night at 4°C . Plates were then washed with PBS and blocked with 4% non-fat dry milk for 1 hour at room temperature ( RT ) . After washing , capture antibodies were added at 5 µg/ml ( 250 ng/well ) , and plates were incubated an additional hour at RT . Next , virus was added to washed plates ( 20 ng p24/well ) and incubated for 3 hours at 37°C . Unbound virus was removed by washing with PBS . Subsequently , 1×104 TZM-bl cells/well were added in the presence of 10 µg/ml DEAE dextran and incubated for 48 hours at 37°C . Cells were then washed , lysed , and developed with luciferase assay reagent according to the manufacturer's instructions ( Promega ) . Luminescence ( relative light units ) was measured using a Synergy 2 microplate luminometer ( BioTek ) . We measured binding of antibodies either to virus directly coated on ELISA-plate wells or to solubilized Env . For the direct virus binding assay , plates were coated with HIV-1JR-FL ( 20 ng p24/well ) for 2 hours at 37°C , washed with PBS and blocked with 4% non-fat dry milk in PBS . After 1 hour at 37°C , plates were washed , antibodies were added in serial dilutions and incubated for 1 hour at 37°C . Detector antibody ( horse radish peroxidase-labeled goat anti-human Fc ) was added to the washed plate and incubated for 45 min at 37°C . Finally , plates were washed , developed ( TMB solution , Life Technologies ) , and read at 450 nm using a plate reader ( BioTek ) . The soluble Env binding assay was performed as previously described with some modifications [70] . Briefly , wells were coated with 250 ng of a gp120 Env specific anti-C5-antibody ( D7324 [Aalto Bioscience] ) , washed and blocked with 4% non-fat dry milk . Serial dilutions of detergent-solubilized HIV-1JR-FL ( starting at 150 ng p24 ) was added and incubated for 2 hours at 37°C . Plates were then incubated with a constant concentration of antibodies ( 1 µg/mL ) for 1 hour at 37°C followed by detection and development steps as described above . Five R5 clade C transmitted/founder Env pseudotyped strains were constructed as described [71] , [72] . Briefly , rev-vpu-env cassettes from the transmitted founder strains were cloned into pcDNA 3 . 1D/V5-HIS TOPO® expression vector . The pseudotyped viruses were then produced by co-transfecting 293T cells with pcDNA 3 . 1 ( rev-vpu-env ) , pNL4-3 . lucR-E- , and fugene 6 ( Roche ) . Cervicovaginal lavage ( CVL ) and seminal fluid were collected from HIV-1-infected patients and healthy volunteers at the University of Alabama , Birmingham . All subjects gave written consent in accordance with an IRB-approved protocol . CVL was collected from one 34 year-old uninfected women and from three infected women ( age 29 to 46 years ) with CD4+ lymphocyte counts of 458/mm3 , 181/mm3 and 498/mm3 and plasma viral loads of 14100 copies/ml , 824 copies/ml and 88 copies/ml , respectively . Viral loads were not measured in the CVL fluid specimens . Two of the women ( with the lower plasma viral loads ) were receiving anti-retroviral therapy . Briefly , CVL fluid was obtained by flushing the cervix and vagina with 5 ml sterile saline , and the wash was collected into tubes with protease inhibitors ( [73] ) . Seminal fluid was obtained from two uninfected men ( ages 25 and 40 years ) , and from three infected men ( age 43 to 53 years ) by masturbation ( 58 ) . CD4 counts in the infected men were 404/mm3 , 336/mm3 and 407/mm3 and plasma viral loads were <100 copies/ml , 8092 copies/ml and 6750 copies/ml , respectively; only one of these subjects ( with viral load of 11 copies/ml ) was receiving anti-retroviral therapy . Seminal fluid was assayed for HIV-1 RNA by PCR , but none was detected . The cervicovaginal and seminal fluids were centrifuged and supernatant fluids aliquoted and frozen at −80°C until assayed . Total IgG was determined by ELISA [74] . IgG isolation was accomplished by incubating samples with Protein G-Sepharose ( GE Healthcare Bio-Sciences Corp . ) followed by elution of bound IgG according to manufacturer's instructions . The IgG preparations were concentrated and dialyzed against DPBS using Amicon Centrifugal Filter Units ( Millipore Corp . ) . The IgG preparations from the two uninfected men were pooled to obtain sufficient quantity for experiments; all other IgG preparations were tested individually . Env-specific IgG binding levels in seminal and CVL fluids were quantified by ELISA . Detergent-solubilized Env from HIV-1US657 was captured by a polyclonal sheep anti-gp120 antibody ( D7324 , Aalto Bio Reagents Ltd ) . Sample IgG and an anti-gp120 mAb standard ( b6 ) were serially diluted , added to wells , washed , and detected by anti-human IgG ( gamma ) -HRP ( Sigma-Aldrich , A6029 ) antibody . Plates were subsequently developed , stopped , and read at OD450 nm . The concentrations of Env-specific IgG in the seminal and CVL IgG samples were calculated using the mAb b6 standard and are reported as a percent of total IgG in each sample . Anti-Env IgG ranged from 0 . 9 to 2 . 6% of total IgG in the seminal fluid specimens and from 0 . 1 to 0 . 6% of total IgG in the CVL fluids specimens ( Figure S6 ) . Transcytosis assays were conducted using reproductive tract-derived ( human endometrial carcinoma [HEC-1A] ) or intestinal tract-derived ( human colonic carcinoma [T84] ) cells . HEC1-1A or T84 cell monolayers were created on 0 . 4 µm polyethylene terephthalate membrane hanging transwell inserts ( Millipore ) . Cell viability was >95% at the time of plating . Electrical resistance across the membrane , which ranged from 400–450 mOhms/cm2 at the start of the transcytosis assay , confirmed monolayer integrity . Resistance was re-measured after the transcytosis assay in more than 50% of wells and ranged from 450–480 mOhms/cm2 . HIV-1 alone or with antibody was added to monolayers in media buffered to pH 6 . 0 or 7 . 4 . After 12 hours , fluid in the lower chamber ( “subnatant fluid” ) , maintained at pH 7 . 4 , was collected and used to measure viral RNA copy number and infectivity . In the absence of cell monolayers , about 69% of the virus inoculum was present in the lower chamber of the wells after 12 hours . Viral RNA was extracted from cell-free subnatant fluid using PureLink Viral RNA Mini Kits ( Invitrogen ) or NucleoSpin RNA Virus extraction kits ( Macherey Nagel Inc . ) , according to the manufacturers' instructions . Quantitative one-step real-time RT-PCR of extracted HIV-1 viral RNA was done using Quantitect SYBR Green RT-PCR kits ( Qiagen GmbH ) and that of SHIV1157ipEL-p with Rotor Gene Probe RT-PCR kits according to the manufacturers' instructions . HIV-1gag primers: SK462 d ( AGTTGGAGGA-CATCAAGCAGCCATGCAAAT ) and SK431 d ( TGCTATGTCAGTTCCCCTTGGTTCTCT ) ( AnaSpec Inc . ) . SIV-1 gag primers: d ( GGG AGA TGG GCG TGA GAA A ) and d ( CGT TGG GTC GTA GCC TAA TTT T ) . SIV-1 gag probe: d ( TCA TCT GCT TTC TTC CCT GAC AAG ACG GA ) ( Integrated DNA Technologies , Inc . ) . 150 µl of subnatant fluid was used to infect 1×104 TZM-bl cells . TZM-bl cells were lysed 2 days post-infection with 1X Cell Culture Lysis Reagent ( Promega Corp . ) , and luciferase activity was determined by chemiluminescence using Luciferase Substrate ( Promega Corp . ) . HEC-1A cells were transduced with FcRn shRNA Lentiviral Particles ( Santa Cruz Biotechnology Inc . ) following manufacturer's protocol . Cells were selected in medium containing 5 µg/ml Puromycin dihydrochloride ( Sigma-Aldrich Inc . ) , and FcRn expression was verified by flow cytometry using rabbit polyclonal anti-FcRn antibody ( Santa Cruz Biotechnology Inc . ) , normal rabbit IgG ( negative control ) and FITC-goat anti-rabbit IgG F ( ab′ ) 2 secondary antibody ( Jackson ImmunoResearch Laboratories Inc . ) ( Figure S3A ) . Cytofix/Cytoperm Plus Kits ( BD Biosciences ) were used to fix , permeabilize and stain cells . Knockdown of FcRn was also confirmed by western blot using rabbit anti-FcRn antibody ( Novus Biologicals ) ( Figure S3A ) . Wild-type and knockdown cells had similar viability . Neither wild-type nor knockdown HEC-1A cells stained for FcγRIIa or FcγRIIIa ( not shown ) . HEC-1A cells were incubated with 50 µg/ml rabbit polyclonal anti-FcRn IgG ( H-274; Santa Cruz Biotechnology Inc . ) or normal rabbit polyclonal IgG for 1 hour at pH 7 . 4 before exposing the apical surface to HIV-1US712 and HIVIG , b12 , IVIG or Synagis . Similarly , HEC-1A cells were incubated with 0 . 1 µM bafilomycin A1 ( Santa Cruz Biotechnology Inc . ) for 1 hour prior to HIV-1 and antibody exposure . Transcytosis was then carried out as above . Cervical tissue , which included portions of endocervix and upper vagina ( ectocervix ) , was obtained from 10 women aged 31–50 undergoing hysterectomy for nonmalignant conditions . Vaginal tissue was also obtained from women undergoing vaginal repair ( n = 6 , aged 44–78 years ) . Penile tissue , including urethra ( n = 16 ) and foreskin ( n = 2 ) , was harvested at autopsy from 16 men aged 34–73 with no history of hormonal or immunosuppressive medications . Tissues were processed within 60 minutes of surgical removal . Samples were either embedded in Tissue-Tek Optimal Cutting Temperature Compound ( Sakura Finetek U . S . A . , Inc . ) and rapidly frozen and stored at −70°C ( frozen sections ) or were fixed in formaldehyde and processed for paraffin embedding . The alkaline phosphatase immunohistology technique was described previously ( [75] ) . Two anti-FcRn antibodies were used: 1 ) Anti-FcRn antibody purified from rabbit serum raised against α2 ( 88–177aa ) and α3 ( 1782-247aa ) domains of human FcRn ( provided by Neil Simister , Brandeis University ) for use on frozen sections ( Figure 5 ) , and 2 ) rabbit anti-FcRn antibody obtained from Novus Biologicals for use on paraffin sections following citrate buffer ( pH 6 . 0 ) antigen retrieval ( Figure S7 ) . Sections were blocked with serum-free protein solution , and optimally diluted primary FcRn antibodies or rabbit IgG ( negative control ) were added and incubated for 1–2 hours at RT . Binding of antibodies to FcRn in tissues was visualized using an alkaline phosphatase detection system that stains positive cells bright red . Sections were counterstained with hematoxylin and cover-slipped using aqueous mounting medium . Differences in amounts of transcytosed or infectious virus between conditions were compared using Kruskal-Wallis or repeated-measures ANCOVA . For repeated-measures ANCOVA , the percentage of transcytosed or infectious virus was logit-transformed and normality evaluated using the Shapiro–Wilk test . Correlations between continuous variables were evaluated by Spearman's rho . Two-tailed p-values are reported .
|
HIV-1 causes a sexually transmitted disease . However , the mechanisms employed by the virus to cross genital tract tissue and establish infection are uncertain . Since cervicovaginal fluid is acidic and HIV-1 in cervicovaginal fluid is likely coated with antibodies , we explored the effect of low pH and HIV-1-specific antibodies on transcytosis , the movement of HIV-1 across tight-junctioned epithelial cells . We found that the combination of HIV-1-specific antibodies and low pH enhanced transcytosis as much as 20-fold . Virus that underwent transcytosis under these conditions was infectious , and infectivity was highly influenced by whether or not the antibody neutralized the virus . We observed enhanced transcytosis using antibody from cervicovaginal and seminal fluids and using transmitted/founder strains of HIV-1 . We also found that the enhanced transcytosis was due to the Fc neonatal receptor ( FcRn ) , which binds immune complexes at acidic pH and releases them at neutral pH . Finally , staining of human tissue revealed abundant FcRn expression on columnar epithelial cells of penile urethra and endocervix . Our findings reveal a novel mechanism wherein HIV-1 may facilitate its own transmission by usurping the antibody response directed against itself . These results have important implications for HIV vaccine development and for understanding the earliest events in HIV transmission .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
|
The Neonatal Fc Receptor (FcRn) Enhances Human Immunodeficiency Virus Type 1 (HIV-1) Transcytosis across Epithelial Cells
|
The mutation rate is known to vary between adjacent sites within the human genome as a consequence of context , the most well-studied example being the influence of CpG dinucelotides . We investigated whether there is additional variation by testing whether there is an excess of sites at which both humans and chimpanzees have a single-nucleotide polymorphism ( SNP ) . We found a highly significant excess of such sites , and we demonstrated that this excess is not due to neighbouring nucleotide effects , ancestral polymorphism , or natural selection . We therefore infer that there is cryptic variation in the mutation rate . However , although this variation in the mutation rate is not associated with the adjacent nucleotides , we show that there are highly nonrandom patterns of nucleotides that extend ∼80 base pairs on either side of sites with coincident SNPs , suggesting that there are extensive and complex context effects . Finally , we estimate the level of variation needed to produce the excess of coincident SNPs and show that there is a similar , or higher , level of variation in the mutation rate associated with this cryptic process than there is associated with adjacent nucleotides , including the CpG effect . We conclude that there is substantial variation in the mutation that has , until now , been hidden from view .
The mutation rate is thought to vary across the human genome on several different scales . At the chromosomal level , the Y chromosome evolves faster than the autosomes , which evolve faster than the X chromosome [1 , 2] . This is thought to be due to males having a higher mutation rate than females . The autosomes also appear to differ in their rates of mutation for reasons that are unclear [3 , 4] . At the next level down , there appears to be variation in the mutation rate over a scale of several hundred kilobases [4 , 5] , another pattern that remains unexplained . However , the most dramatic variation in the mutation rate is observed over fine scales in which adjacent sites can have very different mutation rates . In the nuclear genome , this variation has been shown to be associated with context , the best-known example being the CpG dinucleotide in mammals . CpG dinucleotides are generally methylated in mammals and since methyl-cytosine is unstable , this leads to a high rate of C→T and G→A transitions at these sites , which is about 10- to 20-fold higher than at other sites [6 , 7] . However , the CpG effect is not the only source of fine-scale variation in the mutation rate; the rate of mutation appears to vary by about 2- or 3-fold as a function of other adjacent nucleotides [8–11] . Although variation in the mutation rate has been well-characterised in terms of adjacent nucleotides [8 , 9 , 11] , it is possible that there is other variation in the mutation that is associated with either distant or complex context effects , which has hitherto escaped detection . We investigated this question by testing whether human and chimpanzee single nucleotide polymorphisms ( SNPs ) occur at orthologous sites in the genome . If there is variation in the mutation rate , we expect to see an excess of sites at which both humans and chimpanzees have a SNP .
To investigate whether human and chimpanzee SNPs tend to occur at the same sites in the genome , we BLASTed all chimpanzee SNPs against a dataset of human SNPs . This yielded a dataset of 309 , 158 alignments of 81 base pairs ( bp ) with the chimpanzee SNP in the central position and a human SNP elsewhere within the alignment . Of these alignments , 11 , 571 have the human and chimpanzee SNP at the same position ( Figure 1 ) ; we refer to these as coincident SNPs . This number of coincident SNPs is much greater than the 3 , 817 we would expect if the human SNPs were distributed at random across the alignment , and also much greater than the 6 , 592 we would expect taking into account the influence of the adjacent nucleotides on the mutation rate , henceforth known as “simple” context effects . The observed excess of coincident SNPs is significantly greater than the expected number ( ratio of observed over expected with simple context effects = 1 . 76 , with a standard error of 0 . 02 , p < 0 . 0001 under the null hypothesis that the ratio is 1 ) . This excess is not due to our inability to correct for CpG effects; if we remove CpG dinucleotides from the analysis , we observe 5 , 028 coincident SNPs but would only expect 2 , 533 taking into account simple context effects ( ratio = 1 . 98 ( 0 . 03 ) ; p < 0 . 0001 ) . If we look at the pattern of coincident SNPs , it is evident that almost all the excess is due to the same SNP being present in both humans and chimpanzees , with A-T/A-T SNPs being dramatically over-represented ( Table 1; see Table S1 for the analysis with CpG sites removed ) . Although the excess of coincident SNPs is consistent with variation in the mutation rate that is not associated with simple context , there are several other explanations that warrant consideration . In correcting for simple context effects , we have also made two assumptions; we have assumed that the pattern of mutation is the same on the two strands of the DNA duplex , and we have assumed that context effects are the same across the genome . As a consequence of these assumptions , we could be underestimating the expected number of coincident SNPs . For example , let us imagine that the triplet AAA has a high mutation rate on one strand , say the transcribed strand , and a low mutation rate on the other strand , but that the pattern is the opposite for the triplet CCC ( note that when we refer to the mutation of a triplet , we are referring to the mutation rate of the central nucleotide ) . Because the relative mutation rates of AAA and CCC depend on which strand we are considering , we would tend to underestimate the expected number of coincident SNPs . The pattern of mutation is known to differ between the two DNA strands in a manner that depends on transcription [12 , 13] . However , what is important for our analysis is whether the relative mutation rates of the triplets differ between strands; it is the relative , rather than the absolute rate , that matters , because for each alignment we calculate the chance of a coincident SNP relative to the chance that the human SNP occurs at one of the other triplets in the sequence . To investigate this , we estimated the mutation rate of the central nucleotide in each triplet for a set of human genes for which we knew the direction of transcription; we also considered a subset of these genes known to be expressed in the testis . In agreement with Green et al . [12] , we observe a 25% excess of A→G transitions over T→C transitions; however , we did not observe an excess of G→A transitions over C→T transitions , even in our testis-expressed genes . Crucially for our analysis , the mutation rate of each triplet is highly correlated to its reverse-compliment triplet for all genes ( Pearson correlation coefficient r = 1 . 00 for all triplets , r = 0 . 85 without triplets containing CpGs; Figure S2A ) and for genes expressed in the testes ( r = 0 . 99 for all triplets , r = 0 . 75 without triplets containing CpGs; Figure S2B ) ; genes expressed in the testes are expressed in the male germ-line , where any strand asymmetry in the pattern of mutation will have an evolutionary effect . It therefore seems unlikely that strand asymmetry in the pattern of mutation is leading to an underestimate of the expected number of coincident SNPs . The excess of coincident SNPs could also be due to variation in the pattern of mutation across the genome for reasons similar to those given for strand asymmetry; if the relative rate at which each triplet mutates differs between genomic regions , then we will underestimate the expected number of coincident SNPs . Since such variation in the pattern of mutation might be expected to generate differences in base composition , we divided our dataset of alignments according to their GC content and estimated the mutation rate of the central nucleotide in each triplet in the chimpanzee sequence using the human sequence to infer the ancestral sequence . The relative rates of mutation inferred from the sequences in the upper and low GC content quartiles are highly correlated to each other ( r = 0 . 99 using all triplets; r = 0 . 88 excluding triplets involving CpGs; Figure S3 ) , which suggests that triplets that are highly mutable in high–GC content sequences also tend to be highly mutable in the low–GC content sequences . It therefore seems unlikely that we are underestimating the expected number of coincident SNPs because of variation in the pattern of mutation . As expected , we find a significant excess of coincident SNPs in both the upper and lower GC quartile datasets , although the excess of coincident SNPs appears to be slightly stronger in GC-poor DNA ( Table S2 ) . The excess of coincident SNPs could be due to inheritance , in humans and chimpanzees , of polymorphisms that were present in their last common ancestor . Two lines of evidence suggest that this is not the case . First , we repeated the analysis using human and macaque SNPs . Since these two species diverged more than 23–34 million years ago ( Mya ) [14] , as opposed to the 6–10 My that separates human and chimp [14] , one would expect very few polymorphisms to be shared between human and macaque . However , in this dataset we also see a significant excess of coincident SNPs whether we consider all sites ( ratio = 1 . 64 ( 0 . 19 ) ; p < 0 . 001 ) or non-CpG sites ( 1 . 51 ( 0 . 26 ) ; and p < 0 . 05 ) . Second , the pattern of coincident SNPs ( Table 1 ) is inconsistent with ancestral polymorphism . All four of the possible transversion SNPs are approximately equally common amongst SNPs in general ( proportion of transversions amongst human SNPs: G/T = 0 . 092 , C/A = 0 . 091 , C/G = 0 . 088 , A/T = 0 . 075; transitions: C/T = 0 . 33 , G/A = 0 . 33 ) . We would therefore expect a G-C SNP in chimps to be coincident with a G-C SNP in humans approximately equally often as an A-T SNP in humans is coincident with an A-T SNP in chimps . However , we see distinct biases , with coincident A-T/A-T SNPs being much more common than the other transversions . It is also possible for the apparent excess of coincident SNPs to be due to selection; if some regions of the genome are under selection , then we expect them to have a low density of SNPs , because many SNPs will be removed as they are deleterious . As a consequence , SNPs will be clustered between these regions , causing an apparent excess of coincident SNPs . This seems an unlikely explanation , since the vast majority of our data is intergenic and intronic ( 98% and 99% of the human and chimpanzee SNPs in our BLAST databases , respectively ) , and although selection is known to act within these regions , it is thought to only affect a small percentage of sites [15–17] . Furthermore , if selection was causing an excess of coincident SNPs , we would expect SNPs to be clustered generally , but this is not observed ( Figure 1 and Figure S1 ) . There is a small excess of human SNPs adjacent to the chimpanzee SNP , but this is a consequence of CpG effects—the chimpanzee SNP is disproportionately likely to occur within a CpG , which means that a human SNP is also likely to occur at the same site , or at an adjacent site . If we remove CpGs , this slight excess of adjacent SNPs disappears ( Figure S1 ) . Otherwise there is no tendency for SNPs to cluster . It therefore seems that the excess of coincident SNPs is a consequence of variation in the mutation rate that is not associated with simple context effects , variation in these context effects between strands or regions of the genome , or natural selection . The question therefore arises whether the variation in the mutation rate is associated with other contexts that are distant from the target site , degenerate in nature , or sufficiently complex to be difficult to discern . It should be noted that simple context effects beyond the adjacent nucleotides ( e . g . , 1 bp removed from the target site ) are not responsible for the excess . Although these effects exist [11] , they are much smaller than those of adjacent nucleotides , which themselves have a relatively modest effect if we remove CpGs; e . g . , the expected number of non–CpG coincident SNPs is 2 , 115 if we ignore adjacent nucleotide effects , and it is 2 , 533 if we include these effects . To investigate whether there are other , more complex context effects , we tabulated the frequency of each triplet at each site in the alignments containing coincident SNPs , and a similar-sized dataset of alignments with noncoincident SNPs . Surprisingly , we found significant heterogeneity in triplet frequencies that extends to about 80 bp on either side of the coincident SNP ( Figure 2A ) ; i . e . , the relative frequencies of the triplets at sites close to the coincident SNP are different from the average across the alignments . In contrast , if we consider alignments without a coincident SNP , but with a chimpanzee SNP , we only see significant heterogeneity in triplet frequencies within 10 bp of either side of the SNP ( Figure 2B ) . Despite the heterogeneity in triplet frequencies surrounding a coincident SNP , we could discern very few patterns in the triplets that are over- or under-represented . The only conspicuous pattern is an excess of TTT triplets upstream and AAA triplets downstream of coincident SNPs . However this seems to explain little of the overall excess of coincident SNPs . If we repeat the analysis but remove all cases in which there is a run of three or more nucleotides , of any type , with or without SNPs within them , then from our alignments we find 8 , 536 alignments with a coincident SNP versus an expected number of 4 , 434 , taking into account simple context effects ( ratio = 1 . 93 ( 0 . 02 ) ; p < 0 . 0001 ) . Considering pentamers , rather than triplets , also fails to reveal any context that is associated with coincident SNPs , except for the α-polymerase pause site motif , TG ( A/G ) ( A/G ) ( G/T ) ( A/C ) , which has been suggested as a hypermutable motif [18 , 19] . However , we only observe an excess of α-polymerase pause sites immediately downstream of coincident SNPs , and the total number of coincident SNPs explained by this motif is trivial ( 2 . 2% ) . To quantify the level of cryptic variation in the mutation rate , we fit two models to the ratio of the observed number of coincident SNPs over the number expected with simple context effects . In the first model , we assumed that the variation in the mutation rate was log-normally distributed; in the second , we assumed that there were two types of sites—normal and hypermutable . These models give qualitatively similar estimates of the variation , so we only discuss the log-normal model in detail , because this is a model with a single parameter ( details of the two-rate model are given in Text S1 ) . Because our method for controlling for simple context effects tends to underestimate the expected number of coincident SNPs when we have CpG sites , we concentrate on non-CpG sites . We fit two sub-models to our data . In the first , we assume that the mutation rate of a site is invariant in both humans and chimpanzees . Under this “static” model , we estimate the shape parameter of the log-normal to be 0 . 83 ( 95% confidence intervals ( CIs ) of 0 . 81 , 0 . 84 ) for non-CpG sites . However , this model may not be realistic , since we might expect sites with high mutation rates to destroy themselves; e . g . , if a site has a high rate of C→T mutation , then it will rapidly become fixed for T and therefore become nonhypermutable . We therefore also fit a model in which the time a site remains at a certain mutation rate depends upon that mutation rate , assuming an average divergence between humans and chimpanzees of 0 . 92% for non-CpG sites [20] . Under this model , we estimate slightly higher levels of cryptic variation: we estimated the shape parameter to be 0 . 85 ( 0 . 83 , 0 . 87 ) —higher shape parameters mean more variation . The level of variation that these distributions represent is considerable; with a shape parameter of 0 . 85 the fastest 5% of sites mutate at least 16 . 4-fold faster than the slowest 5% of sites . This level of variation in the mutation rate is greater than the variation associated with simple context: the variance due to simple context , including CpGs , is 0 . 59 , whereas the variance due to cryptic variation at non-CpG sites is 1 . 05 . However , this large difference in variance might be due to the model . If we consider a simple two-rate model in which sites are either hypermutable or normal , and constrain the proportion of hypermutable sites to be 2% , which is the proportion of sites that are involved in CpGs in the human genome [21] , then we estimate that hypermutable sites would have to mutate 9 . 3-fold faster than normal sites to explain the excess of coincident SNPs . This is similar to 10–20-fold higher rate that CpGs mutate [9 , 20] .
We have shown that there is an excess of sites that have a SNP in both the human and chimpanzee genomes . We demonstrated that this is not due to neighbouring nucleotide effects , shared ancestral polymorphism , or natural selection . It therefore seems that this excess is due to variation in the mutation rate that is not associated with simple context effects and is cryptic in nature . We also show that triplet frequencies surrounding sites with coincident SNPs are highly nonrandom , but we have been unable to discern any specific motifs in these regions . This suggests that there are probably complex context effects that extend some distance from the site they effect . Furthermore , we show that there has to be considerable variation in the mutation rate to explain the observed excess of coincident SNPs . The presence of such cryptic variation in the mutation rate is perhaps not surprising given the evidence that some sites in the human mitochondrial genome are hypermutable . Hypermutation had long been suspected based on the excess of homoplasies in human mitochondrial DNA ( mtDNA ) phylogenies ( e . g . , see [22] ) and although such an excess could be due to hypermutation or recombination [23] , two recent analyses have provided convincing evidence that the excess is due to hypermutation . Stoneking [24] showed that mitochondrial mutations in human pedigrees tend to occur at sites that have high levels of homoplasy , and Galtier et al . [25] have recently shown that synonymous mitochondrial SNPs tend to occur at the same positions in different species . However , although many of the hot spots in mtDNA appear to be due to strand slippage–type mutational mechanisms [26 , 27] , this does not appear to be case for the cryptic variation in the mutation rate in nuclear DNA that we describe here . There are two slippage mechanisms that can operate: template strand and primer strand dislocation . Template strand dislocation is controlled for in our simple context analysis , and primer strand dislocation is controlled for in the analysis of homonucleotide runs . It has also been shown recently that the mutation rate is elevated close to insertion and deletion mutations in the nuclear genomes of several eukaryotes , including humans [28] . However , it seems unlikely that this process is generating the excess of coincident SNPs . Indels appear to increase the rate of mutation but not at specific sites; rather the mutation rate is elevated close to an indel and this elevation in the mutation rate declines over several hundred nucleotides . This would manifest itself as general tendency for SNPs to cluster , which we do not observe ( Figure 1 and Figure S1 ) ; we only observe a large excess of coincident SNPs and a small excess of adjacent SNPs . Furthermore , humans and chimpanzees would both have to have segregating indels in the same locality to generate an excess of coincident SNPs . Over the last few years , DNA sequence analysis has revealed that the mutation process is highly complex , varying between different parts of the genome and between different sites . Unfortunately we do not yet understand many of these patterns .
We downloaded human and chimpanzee SNPs from dbSNP build 126 . Dividing the data into chromosomes , we BLASTed each chimpanzee SNP , along with 50 bp of flanking DNA on either side of the SNP , against a database of human SNPs . We set the BLAST parameters as follows; e-value = 1 × 10−30 , mismatch score = −1 , and simple sequence filter off . We retained those alignments , which were 101 bp in length , and in which the human or chimpanzee sequence showed identity at 96 sites if the SNPs were coincident , or 94 sites if they were not coincident . We adjusted the number of matches required to control for the fact that if the SNPs are not coincident , then there must be two extra mismatches . We randomly chose one alignment if a chimpanzee SNP matched more than one human SNP at the levels of identity we set; we obtained very similar results removing these cases from the analysis . The alignments were trimmed to 40 bp on either side of the central chimpanzee SNP because there is a slight bias away from finding human SNPs at the edges of the chimpanzee query sequence . This bias occurs because SNPs , being classed as mismatches , tend to cause BLAST to prematurely terminate the alignment . To perform the analysis of triplet frequencies , we downloaded an extended flanking sequence for the chimpanzee SNPs analysed . The macaque SNPs were kindly provided by Dr . Ripan Malhi [29] . We repeated the analysis as we did for chimpanzee but we relaxed the criteria used to identify orthologous human sequences containing SNPs to 86 matches if there was a coincident SNP , and 84 if there was not , with the e-value adjusted to allow this level of similarity to be found . Sites were designated as CpG if the site , or any of the SNPs at the site , would yield a CpG dinucleotide . We estimated the expected number of coincident SNPs , taking into account the effects of adjacent nucleotides on the rate of mutation , what we term “simple” context effects , as follows . Our data consist of a set of alignments in which we have both a human and a chimpanzee SNP . We start by tabulating the numbers of each triplet , nxyz , where x , y , and z can be T , C , A , or G , in the chimpanzee sequence in the alignments , along with the number of chimp triplets that have a human SNP opposite the central nucleotide , nxyz . Hsnp . From these , we can estimate the probability of observing a human SNP opposite a chimpanzee triplet in our alignments: pxyz = nxyz . Hsnp / nxyz . We can also calculate the frequency of each triplet in the chimpanzee sequences: fxyz = nxyz/Σnxyz To calculate the probability that the human and chimpanzee SNPs are coincident , we need to take into account that there are two alleles in the chimpanzee SNPs , and the triplets they are a part of will have different probabilities of having a human SNP opposite them . If we knew the relative frequencies of the chimpanzee alleles , we could calculate the chance of a coincident SNP as gy pxyz + ( 1– gy ) pxy'z where y and y' are the two chimpanzee alleles and gy is the frequency of the y allele . However , we do not have allele frequency information , so we estimated the relative probabilities of each of the two ancestral states for the chimpanzee SNP , since the ancestral allele is likely to be at a higher frequency in the population . For example , let us imagine we have a CYC SNP—i . e . , a Y SNP surrounded by C on both sides . The ancestral triplet could have been CCC or CTC . The probability that the SNP was generated from a CCC can be estimated as mCCC = fCCC rCCC/ ( fCCCrCCC + fCTCrCTC ) where rxyz is the rate at which triplet XYZ generates a SNP in the central position of the triplet . We estimate rxyz by orienting the chimp SNPs using the human sequence , excluding coincident SNPs and SNPs for which the human nucleotide is different to both chimp alleles; let sxyz . Csnp be the number of chimp triplets that are inferred to have generated a SNP , then rxyz = sxyz , Csnp/nxyz . The expected number of coincident SNPs in each alignment is then , using the above example , ( mCCCpCCC + mCTC pCTC ) /Σpxyz , where the summation is across all the triplets in the alignment . The total number of expected coincident SNPs was simply the sum across alignments . We used two methods to calculate the standard error for the ratio of the observed number of coincident SNPs over the expected number: we bootstrapped the data by alignment and then summed the observed and expected values across the bootstrapped datasets . However , it turned out that this was very closely approximated by assuming that the observed number of coincident SNPs was Poisson distributed and the expected value was known with no error; these are the standard errors we present . We performed a number of simulations to check that the BLAST analysis was not biased and that our method to estimate the number of coincident SNPs under simple context effects worked well . In each simulation , we evolved human genomic sequences under a mutation pattern , in which the mutation rate depended on the adjacent nucleotides , to generate a simulated human and chimpanzee sequence . Into these we introduced SNPs according to the same mutation pattern at the density found in dbSNP—one SNP every 266 bp in humans and every 2 , 128 bp in chimp . We then constructed a BLAST database of ∼140 , 000 human SNPs with 100 bp of flanking DNA sequence , and a query dataset of ∼18 , 000 chimpanzee SNPs with 50 bp of flanking DNA . We ran the BLAST analysis and analysed the output exactly as we had with the real data . We ran simulations in which we had no mutation bias and datasets in which the mutation rate of all triplets was the same except for triplets containing CpGs , which had a mutation rate 10 , 15 , or 20 times the background rate . We ran a set of simulations in which we had 0% , 1% , and 2% divergence . Our method works well at all divergences and under all mutation patterns , except when the CpG rate is very high , where the method tends to underestimate the expected number of coincident SNPs ( Table S3 ) . Surprisingly , the method tends to slightly overestimate the expected number of coincident SNPs when CpG sites are removed for reasons that are not clear . To investigate strand asymmetry , we estimated the mutation rate of the central nucleotide in each triplet by tabulating the number of times each triplet contained a SNP . The direction of mutation was inferred from the frequency; i . e . , the minority allele was judged to be the new mutation . We inferred mutation rates across 964 human genes from the Seattle SNPs [30] and Environmental Genome Projects [31] . To investigate which of these genes are expressed in the male germ line , we downloaded gene expression data from the human testis from the study of Ge et al . [32] . We obtained raw CEL files of gene expression levels from the NCBI Gene Expression Omnibus database ( http://www . ncbi . nlm . nih . gov/projects/geo/ ) . We normalized the results from the mouse and rat arrays separately using the RMA algorithm [33] as implemented in Bioconductor [34] . We judged a gene to be expressed within the testis if its expression was above 200 [35] . We estimated the variation in the mutation rate as follows . We start by assuming there is no divergence between humans and chimpanzees so a hypermutable site in humans will also be hypermutable in chimpanzees . Let the average probability of detecting a SNP at a site in humans and chimpanzees be μh and μc , respectively; if μh and μc are small , the probability at a particular site will be γμh and γμc , where γ is the relative rate of mutation . Let us assume that γ takes some distribution D ( γ ) which has a mean of one . The expected number of coincident SNPs is If there is no variation in the mutation rate then this reduces to such that the ratio of the number of coincident SNPs , over the number expected with no variation , is an equation which only depends upon the distribution of γ . We assume that γ is either log-normally distributed , or that it has a two state distribution in which sites can either be hypermutable or normal ( see Protocol S1 ) . We estimate the parameters of the distribution of γ by considering the ratio of the observed number of SNPs over the number expected with simple context effects ( i . e . , the number expected without cryptic variation in the mutation rate ) . This model is unrealistic , because we assume that a site does not change its mutation rate; however , hypermutable sites are more likely to change , and this may lead them to become nonhypermutable . Under the log-normal model , we assume that once a site changes , its mutation rate is drawn randomly from the log-normal distribution . Let v be the average rate of mutation per unit time in both humans and chimpanzees . Consider a site , in the ancestor of humans and chimpanzees , that currently has a mutation rate vγ . The probability that the site will remain unchanged along both the human and chimpanzee lineage is where t is the time since humans and chimpanzees diverged . The probability that such a site will produce a coincident SNP is If the site changes in one of the lineages , then the mutation rates in the two lineages become independent of one another; since the mean of a product is the product of the means , when two random variables are independent , the probability of a coincident SNP at a site which has undergone at least one substitution is The expected number of SNPs with no variation in the mutation rate is still P0 , as given by Equation 2 , so we can write the ratio of the expected number of coincident SNPs with variation over the expected number without variation in the mutation rate as This equation depends on the compound parameter 2vt , which is the average divergence between humans and chimpanzees and the distribution of γ . Since we set the average of the log-normal distribution to one , we need only find the shape parameter of the log-normal distribution . To estimate the variance associated with simple context effects , we calculated the mutation rate of each triplet as above , when correcting simple context effects . We then scaled the mutation rates so the mean across triplets , taking into account their frequencies in the genome , had a mean of one . We then calculated the variance . This can be compared directly to the variance of the log-normal distribution which we had also constrained to have a mean of one . We weighted the variance estimates from the CpG and non-CpG sites by the relative frequency of the sites .
|
Understanding the process of mutation is important , not only mechanistically , but also because it has implications for the analysis of sequence evolution and population genetic inference . The mutation rate is known to differ between sites within the human genome . The most dramatic example of this is when a C is followed by G; both the C and G nucleotides have a rate of mutation that is between 10- and 20-fold higher than the rate at other sites . In addition , is it known that the mutation rate may be influenced by the nucleotides flanking the site . Here we show that there is also very substantial variation in the mutation rate that is not associated with the flanking nucleotides , or the CpG effect . Although this variation does not depend upon the adjacent nucleotides , there are nonrandom patterns of nucleotides surrounding sites that appear to be hypermutable , suggesting there are complex context effects that influence the mutation rate .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics",
"evolutionary",
"biology"
] |
2009
|
Cryptic Variation in the Human Mutation Rate
|
The positioning of the DNA replication machinery ( replisome ) has been the subject of several studies . Two conflicting models for replisome localization have been proposed: In the Factory Model , sister replisomes remain spatially co-localized as the replicating DNA is translocated through a stationary replication factory . In the Track Model , sister replisomes translocate independently along a stationary DNA track and the replisomes are spatially separated for the majority of the cell cycle . Here , we used time-lapse imaging to observe and quantify the position of fluorescently labeled processivity-clamp ( DnaN ) complexes throughout the cell cycle in two highly-divergent bacterial model organisms: Bacillus subtilis and Escherichia coli . Because DnaN is a core component of the replication machinery , its localization patterns should be an appropriate proxy for replisome positioning in general . We present automated statistical analysis of DnaN positioning in large populations , which is essential due to the high degree of cell-to-cell variation . We find that both bacteria show remarkably similar DnaN positioning , where any potential separation of the two replication forks remains below the diffraction limit throughout the majority of the replication cycle . Additionally , the localization pattern of several other core replisome components is consistent with that of DnaN . These data altogether indicate that the two replication forks remain spatially co-localized and mostly function in close proximity throughout the replication cycle . The conservation of the observed localization patterns in these highly divergent species suggests that the subcellular positioning of the replisome is a functionally critical feature of DNA replication .
Rapid and faithful replication of the chromosome is essential for the proliferation of all living cells . Many bacteria possess a single circular chromosome . Replication is initiated at a single origin and two multicomponent protein complexes ( replisomes ) replicate bidirectionally around the chromosome , meeting at the terminus . Previous investigations of replisome localization have advocated two distinct models: In the Factory Model , the replication machineries at sister forks remain relatively stationary and spatially proximal forming a factory through which the replicating DNA is pulled [1–7] . Alternatively , in the Track Model , the replication machineries translocate along a stationary DNA track , resulting in significant separations of the forks as replication progresses to sequences genomically distal from the origin of replication ( oriC ) , but re-localize as the forks converge at the terminus [8 , 9] . Although previous fluorescence microscopy studies have already reported on replisome localization , a significant weakness of these studies is that the statistical analysis relied heavily on still images ( snapshot data ) . As we will describe in this paper , without direct knowledge of the replication dynamics , it is difficult to differentiate between two pairs of co-localized replisomes that form as the result of re-initiation of replication and spatially-separated sister replisomes . In fact , both structures are observed in both E . coli and B . subtilis . Furthermore , the lengths of the replication and division cycles are highly variable in individual cells [10] , creating the need for large-scale automated analysis which produces consistent results between time-lapse and snapshot data . Our method of analysis is based on the localization of the beta-clamp ( DnaN ) in both E . coli and B . subtilis . DnaN is a particularly informative proxy for the replisome complex because it is localized to the replisome [11] at sufficiently high copy-number that its position can be observed clearly throughout the cell cycle without significant photobleaching of the fluorescent label . Using time-lapse data , we tracked the progress of the replisome in individual cells over multiple cell cycles . Under slow growth conditions , the initiation and termination of replication can be observed explicitly by the assembly and disassembly of the DnaN foci . Analysis of the time-lapse data reveals a significant confounding feature in the analysis of snapshot images . For most of the cycle , sister replication forks maintain a sufficiently small separation such that the two foci cannot be resolved , but two sister forks are transiently resolvable in some cells , consistent with previous reports [12 , 13] . Irrespective of whether forks are co-localized or spatially resolved , they typically remain localized around midcell . This story is complicated by the phenomenon of re-initiation of replication before the end of the cell cycle [5] . Even under slow growth conditions , re-initiation pre-division is observed in some cells . This uncoupling between the replication and division cycles leads to the appearance of DnaN foci localized to the quarter-cell positions . Although this phenomenon is clearly observable in the time-lapse analysis , these foci can easily be misinterpreted as sister replication forks in snapshot analysis . However , careful statistical analysis of the snap-shot data clearly resolves two distinct subpopulations ( resolved-sisters and colocolized replisome pairs ) . Our analysis of snapshot images produces results consistent with the replisome dynamics observed in time-lapse imaging , and can be applied to lower stoichiometry replisome components , for which we find similar results . Therefore the analysis of both time-lapse and snapshot images supports a model for replisome positioning in E . coli and B . subtilis where the sister replisomes localize with diffraction-limited separation for the majority of the cell cycle .
Replisome positioning was observed using time-lapse fluorescence microscopy by imaging fluorescent fusions to DnaN in both E . coli and B . subtilis . Cells were elongating exponentially with a doubling time of roughly 3 hours ( Fig 1 ) , and multiple complete cell cycles were tracked under these relatively slow growth conditions . It is widely accepted that the DnaN focus is localized to the replisome [11] and a good proxy for replication since: the beta-clamp is essential for replication , the focus is observed with midcell positioning ( consistent with other replisome components ) , and the timing of assembly and disassembly is consistent with the known duration of replication . A qualitative summary of the observed dynamics ( see Fig 2A ) is as follows: In the absence of replication , there is no DnaN focus whereas actively replicating cells generally have between zero and four DnaN foci . DnaN foci tend to be localized either to midcell or the quarter-cell positions , as has been reported previously [11 , 14] and is consistent with the localization of other replisome components . There is typically no focus or one focus at the midcell position . The typical temporal history of the position of a DnaN focus under slow growth conditions is concisely summarized by the kymograph in Panel B of Fig 2: A focus appears . The position of the focus executes confined random motion . In some cells , the focus fissions into two dim foci which then fuse to re-form a single focus ( of the initial intensity ) . The focus is then observed to disassemble . Typically there is a short period between the disappearance of the focus at midcell and either the roughly synchronous appearance of new foci at the quarter cell positions or cell division . DnaN foci were not observed to be intermittent: Once DnaN foci assembled they did not disappear and re-appear at the same cellular location . It is important to note the following qualifications about the number of DnaN foci: When the separation of foci are below the diffraction limit of our system ( <250 nm ) , only one focus will be resolved . Even when forks transiently separate enough to be resolvable , they remain well within the quarter cell positions: The average separation ( when two foci are observed ) is 0 . 2 cell lengths versus 0 . 45 cell lengths for foci near the quarter-cell positions . Single DnaN foci positioned at midcell are seen to fission and fuse ( e . g . Fig 2A , arrow 1 ) . These midcell-positioned foci are always seen to disappear before cell division . On-the-other-hand , foci observed at the quarter-cell positions can persist through a cell division , consistent with these foci representing re-initiation of replication . If instead these foci were separated pairs of sister forks , they would be expected to co-localize at the terminus and disassemble before the end of the cell cycle . Therefore qualitative analysis of the kymographs strongly supports a model where quarter-cell-localized foci each include pairs of re-initiated sister replisomes . In fact , these quarter-cell foci are also seen to fission and fuse , occasionally allowing resolution of the individual sister replisomes ( e . g . Fig 2A , arrow 2 ) . To further test this model , we tracked DnaN foci in E . coli cells blocked for restart via a temperature sensitive version of the helicase loader protein , DnaC ( dnaC2 allele ) [9] . Under the non-permissive conditions for the temperature sensitive mutant , the wild type cells were able to form quarter-cell-localized foci , however , the cells blocked for initiation were not ( compare Fig 3 panels A and C ) . To extend this analysis to many cells , we show conditional probability distributions of focus position given cell length in both the wild type and dnaC2 mutants . The absence of localizations near the quarter-cell positions is clearly seen by comparison of Fig 3 , panels B and D . These data support our model that quarter-cell foci represent re-initiated replication fork pairs . In the event that re-initiation of the sister chromosomes happens before cell division ( about 45% of the time under our conditions ) , we can only observe complete replication cycles if we analyze overlapping cell cycles . We visualize entire replication cycles using kymographs , where we project the cell images onto the long axis of the cell , and align the projections in sequence ( See Fig 2 , Panel B ) . This representation confirms that for the majority of the replication cycle , the sister forks remain near mid-cell and usually cannot be resolved separately . Since the timing of division is inferred from the analysis of the phase-contrast image of the cell , some of the observed asynchrony could be accounted for by a failure to correctly segment the septum . However two lines of evidence refute this hypothesis: ( i ) Our previous work analyzing the cell-cycle dependent localization of FtsZ suggests that the timing of division is determined to a precision better than ±10% of the cell cycle in E . coli [15] . ( ii ) In this study , we never observed the midcell DnaN focus persist through cell division , consistent with accurate determination of cell division . Due to significant cell-to-cell variation , it is essential to present statistical evidence for the classification of each focus as an individual replisome or co-localized replisome pair . We apply a fully automated analysis to characterize localization patterns for over 10 , 000 time points ( detailed description included in the materials and methods section ) . For this analysis , foci are identified in the fluorescence image ( S2 Fig ) and precisely located within the cell . It is important to note that there was no hand selection of data . The dataset consists of all cells observed that were elongating and segmented without errors and therefore contains no investigator-based cell-selection bias . We first investigate the focus positioning relative to cell length . In this analysis , cell length was used as a proxy for cell age because of B . subtilis chaining which makes detection of septum formation in phase images difficult . Cell length provided a consistent and reliable proxy for cell age in our analyses across both species and cell-length based analysis can be applied in snapshot analysis where the cell age is unknown . The conditional probability density of focus position given cell length is shown in Fig 4 . The Factory Model predicts that foci are localized at midcell throughout the replication cycle and that re-initiation occurs at the quarter cell positions ( if it occurs ) . In terms of the conditional probability , this model would predict a density blob at midcell which persists from early until late in the cell cycle . A second pair of blobs are expected to form at the quarter-cell positions corresponding to re-initiation in some cells . This second pair of blobs is expected to persist into the next cell cycle , each ending up in a new cell without the other . In contrast , in the Track Model , a blob is expected to begin the cell cycle at midcell , before splitting into two blobs ( if the separation is high enough ) before merging into a single blob again at midcell . The conditional probability data alone favor a factory-like model , but do not exclude the possibility of a Track Model , provided the separation of the forks remains very small as the replisomes translocate along the DNA . Further analysis is informed by the typical cellular focus localization patterns shown in Fig 5A . The relative frequencies of these localization patterns were quantified both overall , and by cell length . We find that short/young cells are generally observed to have a single focus . As the cells grow , the probability of observing two foci monotonically increases . In the Track Model , we expect the total number of two-foci cells to grow significantly and then shrink as the forks first diverge from the origin and then reconverge at the terminus . This trend is not observed . Furthermore , before cells grow to a length where two foci is most probable , there is an increase in the probability of zero-foci cells , consistent with the Factory Model where two-foci cells have re-initiated but inconsistent with the Track Model where cells should transition from one to two foci without foci disassembling . Again , a Factory-like Model best summarizes the observed data . To automatically distinguish between the observed cellular localization patterns , it is essential to make a distinction between co-localized replication fork pairs and resolvable sister replication forks . Motivated by the qualitative observation that resolved sister forks rarely separate more than 0 . 2 cell lengths , we examine the joint probability distribution between focus separation ( relative to cell length ) and cell length shown in Fig 5 , Panel B . The joint distribution reveals that the population consists of two distinct sub-populations: In short/young cells , resolved foci are on average 0 . 2 cell-lengths in separation whereas in long/old cells , resolved foci are typically 0 . 45 cell-lengths in separation , consistent with quarter-cell positioning . The subpopulation model facilitates the identification of resolvable sister replication fork pairs . All pairs of foci whose separation is consistent with the lower-separation population are counted as resolvable sister replication forks , whereas all other foci ( including foci in single-focus cells ) are inferred to represent a pair of co-localized replication forks ( See S3 Fig and the associated methods section for more detail ) . Considering cells of all lengths , we find that replication forks co-localize about 82% of the time for DnaN in E . coli ( Fig 5C ) . Statistical analysis was performed similarly in B . subtilis , but because B . subtilis has a tendency to chain , division events are not always observable , at least not at the time of their occurrence . Since the division is not detected , more long cells that have re-initiated are observed . The exaggerated size of the high-separation population ( S4 Fig ) relative to the corresponding E . coli data is consistent with this known artifact . We also find that there is a longer time between termination of replication and re-initiation of replication on the sister chromosomes ( D phase/G1 and G2 phase ) , increasing the fraction of zero focus cells . Strikingly , we find that the forks co-localize with 79% probability , roughly the same as observed in E . coli ( Fig 5C ) . We applied the same statistical analysis used to quantify DnaN dynamics to snapshot images of three independent markers for the replisome ( Fig 6 and S5 Fig ) . Although DnaN is the only replisome component present at sufficiently high copy number to be imaged throughout entire cell cycles , the analysis we developed can be used to infer the dynamics of lower stoichiometry proteins based on snapshot images . We use SSB ( single-stranded binding protein ) and DnaQ ( PolIII subunit ) in E . coli and DnaX ( clamp loader ) in B . subtilis as additional markers for the replisome . We again find that separated foci can be classified into two populations , one representing separated sister replisomes , and the other representing pairs of co-localized forks ( S5 Fig , panel A ) . We count the number of foci in each cell ( S5 Fig , panel B ) and classify each focus as an individual replisome or replisome pair . It is important to note that the number of zero-focus cells is not particularly meaningful in this context as it depends on the growth rate of individual cells , and in particular , non-replicating cells cannot be excluded based on snapshot images . We find that sister replisomes co-localize with 74-85% probability ( Fig 6 ) , consistent with our observations based on DnaN .
Both time-lapse and snapshot analysis strongly support a model where the two replication forks remain proximal throughout the cell cycle . At the beginning of the replication cycle , we always observe a single focus . We associate this event with co-localization of the forks at or near oriC . After initiation , the replisomes remain well within the quarter cell positions , usually co-localizing , but separating enough to be individually resolvable approximately 20% of the time . The observed midcell positioning of the replisome is consistent with previous reports from our lab suggesting that chromosomal loci in E . coli are localized near midcell immediately prior to duplicating [6] . Together this study and the aforementioned previous report suggest movement of the DNA through a relatively stationary replisome . The occasional separation of sister replisomes suggests that , while the Factory Model correctly predicts the cellular-scale positioning of forks in both E . coli and B . subtilis , some elements traditionally associated with the Track Model are also correct in the sense that the sister forks need not be continuously co-localized . However , this must be qualified by noting that the vast majority of sister replisomes do appear to be co-localized by conventional fluorescence microscopy , and it is unlikely that this would be the case if the replisome translocating along the DNA were the complete model . Under our conditions , at least 80% of observed foci are in fact pairs rather than individual replisomes . This quantitative picture will be essential in interpreting short-time scale experiments which do not capture the cell cycle dynamics . Our data strongly support a factory-like model , but do not address whether there is indeed a “factory” . In the strictest definition of a factory , sister replisomes are coupled by a physical linker . While we do not exclude the possibility of a physical linker , such a linker would need to allow the replisomes to occasionally uncouple , or be sufficiently long to account for the observed separation events . The physical mechanism by which sister replisomes remain spatially proximal is an intriguing problem for future studies . We see two distinct types of separation and dynamics . In the first class of separation , the relative distance between the foci is under a third of the cell length , and is about a fifth of the cell length on average . Foci with these small separations always merge . In contrast , if re-initiation precedes division , we observe half-cell-length focus separation . In addition to having a larger separation , these foci are never observed to merge and ultimately end up in different cells . Furthermore , widely-separated foci are unable to form in conditional mutants where replication restart is prevented . This localization behavior indicates that the half-cell-length foci we observe pre-division are new pairs rather than individual replisomes . These observations are summarized schematically in the model cell tower and kymograph shown in Fig 7 . Factory-like models where the replisomes remain relatively confined ( but are not necessarily constantly co-localized or physically coupled ) have been previously suggested in both B . subtilis [1 , 2 , 13 , 16] and C . crescentus [7] , while the Track Model was formulated based predominantly on data from E . coli [8 , 9] . The reason for the existence of the outlying model in E . coli was often attributed to differences between the species . However , our comparison of replisome dynamics in E . coli and B . subtilis suggests that the conflicting models likely originated from differences in analysis and interpretation of data rather than species-specific replisome dynamics . The evidence presented here unifies the models for DNA replication in all three of these bacterial organisms . In every stage of our analysis , the replisome positioning data reveals striking similarities between E . coli and B . subtilis . Gram-negative and Gram-positive bacteria are highly divergent . Although the basic principles of replication are conserved , many differences exist between E . coli and B . subtilis replication , including regulation , the leading and lagging strand polymerases , and the loading of the replicative helicase . The data presented here reveal that replisome positioning is one of the conserved features , suggesting that there may be fundamental mechanistic reasons for precise subcellular localization of this complex . Future studies should reveal the mechanism by which the two forks remain proximal throughout the replication cycle and identify the underlying reasons ( if any ) for this conserved localization pattern .
See Table 1 for strain list . PAW1181 was produced by P1 transduction of the mutation from PAW542 into PAW914 . Cells were cultured overnight at 30°C in minimal medium with shaking . Prior to imaging , cells were set back to OD600 0 . 1 and allowed to grow to OD600 0 . 3 . To ensure sufficiently slow growth that initiation would happen only once per-division cycle , we use minimal medium supplemented with only the essential nutrients . For E . coli , cells were cultured in M9-minimal medium ( 1X M9 salts , 2 mM MgSO4 , 0 . 1 mM CaCl2 , 0 . 2% Glycerol , 100 μg/ml each Arginine , Histidine , Leucine , Threonine and Proline and 10 μg/ml thiamine hydrochloride ) . B . subtilis was cultured in Minimal Arabinose Medium ( 1x Spitzizen’s salts ( 3 mM ( NH4 ) 2SO4 , 17 mM K2HPO4 , 8 mM KH2PO4 , 1 . 2 mM Na3C6H5O7 , 0 . 16 mM MgSO4- ( 7H2O ) , pH 7 . 0 ) , 1x metals ( 2 mM MgCl2 , 0 . 7 mM CaCl2 , 0 . 05 mM MnCl2 , 1 μM ZnCl2 , 5 μM FeCl2 , 1 μg/ml thymine-HCl ) , 1% arabinose , 0 . 1% glutamic acid , 0 . 04 mg/ml phenylalanine , 0 . 04 mg/ml tryptophan , and as needed 0 . 12 mg/ml tryptophan ) . For imaging , we gently heat the appropriate growth medium with 2% by-weight low melt agarose ( Fisher: 16520050 ) . The agarose mixture is then molded into a thin rectangular strip ( 2mm X 4mm X 0 . 05 mm ) and allowed to dry . One micro-liter of OD600 0 . 3 liquid culture is spotted centrally on the pad . Once the spot has dried , a cover glass is placed over the pad and sealed around the edges with VaLP ( 1:1:1 Vaseline , Lanolin , and paraffin mixture ) . This leaves a small channel of air around the pad , particularly important for the growth of B . subtilis . Imaging was performed on our lab-built inverted fluorescence microscope . Cells were imaged through a Nikon CFI Plan Apo VC 100x 1 . 4 NA objective . A retractable external phase plate ( Ti-C CLWD Ph3 Annulus Module ) was inserted into the light path during phase-contrast imaging but removed for fluorescence imaging to avoid decreased signal due to the neutral density annulus on the phase plate . For fluorescence imaging , we excite GFP and YPet proteins using a Coherent Sapphire 50 mW 488 nm or 150 mW 514 nm CW laser ( see Table 2 ) . The beam diameter is expanded , providing uniform illumination over the field of view . An Acousto-Optic Tunable Filter ( AOTF , AA Opto-Electronic AOTFnC-400 . 650 ) controls the laser excitation intensity . Images were collected on an iXon Ultra 897 512x512 pixel EMCCD camera . The microscope system is controlled by Micro-Manager . Cells are imaged at about 25°C in both phase contrast ( to determine cell boundaries ) and fluorescence ( to measure replisome position ) at five minute intervals . The built-in software autofocus is used to ensure focus at each time point . Laser intensity is maintained as low as possible to avoid bleaching the fluorescent protein and damaging the cells . We are generally able to image cells for about four hours before photobleaching becomes too significant to reliably track the forks . For lower stoichiometry replisome components , snapshot imaging was used where one phase-contrast and one fluorescence image was taken at each field of view . Non-permissive conditions for the temperature sensitive initiation mutant were achieved using an objective heater ( Bioptechs , 38°C set point ) . Cells were placed on the heated objective about 10 minutes prior to the start of imaging . Imaging continued with frames at 5 minute intervals for several hours , long enough to capture the beginnings of subsequent replication cycles in wild-type cells . Using the cell boundaries determined from segmentation of the phase image , we track the long-axis length over time . Fig 1A and 1B show typical length vs . time curves for E . coli and B . subtilis . Importantly , the cells were elongating exponentially ( curves appear linear on a log scale ) and the growth rate ( slope ) appears constant over time , indicating cell growth was not affected by prolonged exposure to the laser . We determine the doubling time for each cell by least squares fitting of the length vs . time curve with an exponential of the form: L = L 0 exp - t / t 0 Where the parameter t0 was allowed to vary and L0 represents the length of the cell immediately following division . The doubling time was taken as: t D = t 0 log 2 The distribution of cell doubling times for individual cells is shown Fig 1C . Cells were imaged in both phase-contrast and fluorescence at five minute intervals . Higher time resolution led to significant photobleaching and/or slowed cell elongation . Imaging continued sufficiently long to capture at least one full cell cycle , about 3 . 5–4 hours . For snapshot imaging , only one phase-contrast and fluorescence image were taken at each field of view . The process used to analyze the phase and fluorescence images is outlined below .
|
Cell proliferation depends on efficient replication of the genome . Bacteria typically have a single origin of replication on a circular chromosome . After replication initiation , two replisomes assemble at the origin and each copy one of the two arms of the chromosome until they reach the terminus . There have been conflicting reports about the subcellular positioning and putative co-localization of the two replication forks during this process . It has remained controversial whether the two replisomes remain relatively close to each other with the DNA being pulled through , or separate as they translocate along the DNA like a track . Existing studies have relied heavily on snapshot images and these experiments cannot unambiguously distinguish between these two models: i . e . two resolvable forks versus two pairs of co-localized forks . The ability of replication to re-initiate before cell division in bacterial cells further complicates the interpretation of these types of imaging studies . In this paper , we use a combination of snapshot imaging , time-lapse imaging , and quantitative analysis to measure the fraction of time forks are co-localized during each cell cycle . We find that the forks are co-localized for the majority ( 80% ) of the replication cycle in two highly-divergent model organisms: B . subtilis and E . coli . Our observations are consistent with proximal localization of the two forks , but also some transient separations of sister forks during replication . The conserved behavior of sub-cellular positioning of the replisomes in these two highly divergent species implies a potential functional relevance of this feature .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"fluorescence",
"imaging",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"cell",
"division",
"analysis",
"cell",
"cycle",
"and",
"cell",
"division",
"pathogens",
"cell",
"processes",
"bacillus",
"microbiology",
"light",
"microscopy",
"parasitic",
"protozoans",
"balantidium",
"coli",
"prokaryotic",
"models",
"probability",
"distribution",
"protozoans",
"mathematics",
"dna",
"replication",
"microscopy",
"experimental",
"organism",
"systems",
"bioassays",
"and",
"physiological",
"analysis",
"dna",
"bacteria",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"imaging",
"techniques",
"cell",
"analysis",
"medical",
"microbiology",
"fluorescence",
"microscopy",
"microbial",
"pathogens",
"probability",
"theory",
"biochemistry",
"cell",
"biology",
"nucleic",
"acids",
"bacillus",
"subtilis",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"organisms"
] |
2017
|
The Replisomes Remain Spatially Proximal throughout the Cell Cycle in Bacteria
|
The gamma-herpesvirus Epstein-Barr virus ( EBV ) persists for life in infected individuals despite the presence of a strong immune response . During the lytic cycle of EBV many viral proteins are expressed , potentially allowing virally infected cells to be recognized and eliminated by CD8+ T cells . We have recently identified an immune evasion protein encoded by EBV , BNLF2a , which is expressed in early phase lytic replication and inhibits peptide- and ATP-binding functions of the transporter associated with antigen processing . Ectopic expression of BNLF2a causes decreased surface MHC class I expression and inhibits the presentation of indicator antigens to CD8+ T cells . Here we sought to examine the influence of BNLF2a when expressed naturally during EBV lytic replication . We generated a BNLF2a-deleted recombinant EBV ( ΔBNLF2a ) and compared the ability of ΔBNLF2a and wild-type EBV-transformed B cell lines to be recognized by CD8+ T cell clones specific for EBV-encoded immediate early , early and late lytic antigens . Epitopes derived from immediate early and early expressed proteins were better recognized when presented by ΔBNLF2a transformed cells compared to wild-type virus transformants . However , recognition of late antigens by CD8+ T cells remained equally poor when presented by both wild-type and ΔBNLF2a cell targets . Analysis of BNLF2a and target protein expression kinetics showed that although BNLF2a is expressed during early phase replication , it is expressed at a time when there is an upregulation of immediate early proteins and initiation of early protein synthesis . Interestingly , BNLF2a protein expression was found to be lost by late lytic cycle yet ΔBNLF2a-transformed cells in late stage replication downregulated surface MHC class I to a similar extent as wild-type EBV-transformed cells . These data show that BNLF2a-mediated expression is stage-specific , affecting presentation of immediate early and early proteins , and that other evasion mechanisms operate later in the lytic cycle .
The detection and elimination of virally infected cells by the host immune system relies heavily upon CD8+ T cells recognizing peptides endogenously processed and presented by HLA class I molecules . Proteasomal degradation of endogenously synthesized proteins provides a source of peptides which are delivered into the endoplasmic reticulum by the transporter associated with antigen processing ( TAP ) , where they are loaded onto nascent HLA-class I molecules . Peptide:HLA-class I complexes are then transported to the cell surface where CD8+ T cells examine these complexes with their T cell receptors . Recognition of these complexes leads to the killing of the infected cell by the CD8+ T cell ( reviewed in [1] , [2] ) . As such , many viruses have developed strategies to evade CD8+ T cell recognition in order to aid their transmission and persistence within hosts . This is particularly true for the herpesviruses; large double-stranded DNA viruses characterized by their ability to enter a latent state within specialized cells in their respective hosts , with this itself a form of immune evasion due to the transcriptional silencing of most if not all genes . However , herpesviruses occasionally undergo reactivation into their lytic cycle , where a large number of viral genes are expressed . Here there is a sequential cascade of gene expression beginning with the immediate early genes , followed by the early genes and finally the late genes . Potentially then many targets for CD8+ T cell recognition are generated during lytic cycle replication . The finding of immune evasion mechanisms in members of each of the three α- , β- and γ-herpesvirus subfamilies highlights the strong immunological pressure these viruses are under . These evasion strategies often subvert cellular processes involved in the generation and presentation of epitopes to T cells ( reviewed in [3] , [4] ) . The importance of these processes is highlighted by the convergent evolution seen in herpesviruses , where members of the different subfamilies target the same points involved in the generation of CD8+ T cell epitopes but use unrelated proteins to do this . Until recently , less evidence has been available on immune evasion by the lymphocryptoviruses ( LCV , γ1-herpesviruses ) during lytic cycle . The prototypic virus of this genus , Epstein-Barr virus ( EBV ) , infects epithelial cells and B lymphocytes , establishing latency in the latter cell type . Central to EBV's biology is its ability to expand the reservoir of latently infected B cells through growth-transforming gene expression , independent of lytic replication [5] . It was unclear then whether lytic immune evasion mechanisms would be required by EBV to amplify the viral reservoir within a host . However , during lytic cycle replication , presentation of EBV epitopes to cognate CD8+ T cells falls with the progression of the lytic cycle , while B cells replicating EBV have decreased levels of surface HLA-class I and decreased TAP function [6]–[8] . These observations suggested that EBV interferes with antigen processing during lytic cycle replication . Targeted screening of EBV genes for immune evasion function led to the identification of the early expressed lytic cycle gene BNLF2a which functions as a TAP inhibitor [9] . This novel immune evasion gene encodes for a 60 amino acid protein that disrupts TAP function by preventing both peptide- and ATP-binding to this complex . Consequently , cells expressing BNLF2a in vitro show decreased surface HLA-class I levels and are refractory to CD8+ T cell killing when co-expressed with target antigens [9] . In the current study we analyze the influence BNLF2a has on presentation of EBV-specific epitopes during lytic cycle replication , to determine whether BNLF2a acts alone or whether other immune evasion mechanisms are present in EBV and how BNLF2a affects antigen presentation during the different phases of gene expression . The impact of BNLF2a was isolated through the construction of a recombinant EBV lacking the gene and this virus used to infect cells for antigen processing and presentation studies . Cells replicating this BNLF2a-deleted virus were found to be better recognized by immediate early and early antigen-specific CD8+ T cells but not late antigen-specific T cells . Consistent with this finding , surface class I HLA expression was restored to normal levels in cells expressing immediate early but not late expressed EBV proteins . Our results suggest that immune evasion mechanisms in addition to BNLF2a are operational during EBV lytic cycle replication .
We initially disrupted the BNLF2a gene of the B95 . 8 strain of EBV contained within a BAC by insertional mutagenesis ( Figure 1A ) . A targeting plasmid was created in which the majority of the BNLF2a gene was replaced with a tetracycline resistance cassette which in turn was flanked by FLP recombinase target ( FRT ) sites . This vector was recombined with the EBV BAC and recombinants selected . Such clones , designated ΔBNLF2a , had the tetracycline gene removed by FLP recombinase and were screened for deletion of the BNLF2a gene by restriction endonuclease analysis and sequencing ( data not shown ) . ΔBNLF2a BACs were then stably transfected into 293 cells and virus replication induced by transfection of a plasmid encoding the EBV lytic switch protein BZLF1 . Virus was also produced from cells transduced with the wild-type B95 . 8 EBV BAC and a B95 . 8 EBV BZLF1-deleted BAC ( ΔBZLF1 ) [10] , encoding a virus unable to undergo lytic cycle replication unless BZLF1 is supplied in trans . The different recombinant EBVs derived from the 293 cells were used to transform primary B cells , to establish lymphoblastoid cell lines ( LCLs ) . To determine if expression of other viral proteins was affected by the deletion of BNLF2a , western blot analysis on lysates of LCLs generated from wild-type , ΔBNLF2a and ΔBZLF1 viruses was performed . As a subset of cells in the LCL culture will spontaneously enter lytic cycle replication , blots were probed with antibodies specific for representative proteins expressed during lytic cycle as well as latent cycle expressed proteins . Figure 1B shows typical blots of lysates probed for the immediate early proteins BZLF1 and BRLF1 , the early proteins BALF2 , BNLF2a and BMRF1 , the late protein BFRF3 and the latent protein EBNA2 . No difference in expression of these proteins was observed between the wild-type and ΔBNLF2a virus transformed LCLs , with the exception of BNLF2a protein which was not present as expected in ΔBNLF2a LCLs . No lytic cycle protein expression could be detected in ΔBZLF1 LCLs . A panel of different donor derived LCLs transformed with wild-type , ΔBNLF2a , and ΔBZLF1 viruses were employed to study lytic antigen recognition by EBV lytic phase-specific CD8+ T cells . Here we planned to incubate these LCLs with the different types of lytic antigen-specific CD8+ T cells and assay for T cell recognition by IFN-γ secretion . However , the percentage of LCLs that spontaneously enter lytic cycle is variable . Initially then we quantified the number of cells within the LCL cultures expressing the lytic cycle marker BZLF1 by flow cytometry . Figures 2A and 2B show representative flow plots of wild-type , ΔBNLF2a and ΔBZLF1 LCLs stained for BZLF1 expression using LCLs derived from two donors . Typically we found between 0 . 5–3% of wild-type and ΔBNLF2a LCLs expressed BZLF1 ( upper and middle panels ) , whilst none was observed in ΔBZLF1 LCLs ( lower panels ) . To ensure we used equivalent numbers of the different types of lytic antigen positive cells in our T cell recognition experiments , we developed a system to equalize the number of lytic antigen positive cells in each assay . Here the proportion of BZLF1 expressing cells in each culture were equalized by making a dilution series of the LCL with the highest percentage of BZLF1 expressing cells with the antigen negative ΔBZLF1 LCL derived from that donor . T cell recognition of the different LCL transformants was then measured by incubating these LCLs with CD8+ T cells specific for epitopes derived from proteins expressed in immediate early , early and late phases of the EBV lytic cycle and measuring IFNγ release by the T cells . We have previously shown that CD8+ T cells in these assays directly recognize lytically infected cells and not cells which have exogenously taken up antigen and re-presented it [6] . Figure 2C shows results of a T cell recognition experiment using LCL targets derived from donor 1 . In this case the more lytic wild-type LCL was diluted with the ΔBZLF1 LCL to give equivalent numbers of lytic targets in the assay . When CD8+ T cells specific for the immediate early HLA-B*0801 restricted BZLF1 RAK epitope were incubated with the different LCLs , a 6-fold increase in recognition of the ΔBNLF2a LCL was observed compared to the wild-type LCL as measured by secretion of IFNγ . Similar results were obtained using LCLs derived from donor 2 ( Figure 2D ) . In this case the more lytic ΔBNLF2a LCL was diluted with the antigen-negative ΔBZLF1 LCL . When the cultures were equalized for BZLF1 expression a 3-fold increase in recognition of the ΔBNLF2a LCL was seen when compared to recognition of the wild-type LCL . A similar trend was observed for recognition of epitopes derived from the other immediate early protein BRLF1 . Here CD8+ T cells specific for the HLA-C*0202 restricted epitope IACP ( Figure 2E ) and the HLA-B*4501 restricted epitope AEN ( Figure 2F ) were used to probe antigen presentation by the LCL sets derived from donors 3 and 4 respectively . As shown in Figures 2E and 2F , the ΔBNLF2a LCLs from both donors were recognized more efficiently than the wild-type LCL using both T cell specificities . The IACP clones showed a 50-fold increase and the AEN clones showed a 4–5-fold increase in IFNγ secretion upon challenge with the LCLs . We next measured recognition of the different LCL types using CD8+ T cells specific for two early antigens; the HLA-B*2705 restricted ARYA epitope from BALF2 and the HLA-A*0201 restricted TLD epitope from BMRF1 . Here we tested multiple T cell clones derived from three donors against three different donor derived sets of LCLs . Figure 3 shows representative results using ARYA- and TLD-specific T cell clones against LCLs derived from donor 3 . Similar to what was seen for the immediate early antigens , T cell recognition of the early antigens was increased upon challenge with the ΔBNLF2a LCL compared to the wild-type LCL , with the most potent increase in recognition observed using the BALF2-specific clones which showed a 20-fold increase in recognition ( Figure 3A ) . The TLD epitope from BMRF1 was found to be recognized the poorest in these assays , never the less a two-fold increase in recognition of the ΔBNLF2a LCL compared to the wild-type was consistently observed using independently derived T cell clones and LCLs derived from different donors ( Figure 3B ) . Multiple clones of a third early specificity , HLA-A*0201 BMLF1 , also showed increased recognition of the ΔBNLF2a LCL ( see below ) . We next turned to study recognition of late-expressed antigens using T cells specific for the HLA-A*0201 restricted FLD epitope from BALF4 and the HLA-B*2705 restricted RRRK epitope from BILF2 . We have found that these two epitopes are processed independently and dependently of the proteasome respectively , with the BALF4 epitope presented independently of TAP ( data not shown ) . We would predict from our previous studies of TAP dependence of peptide-epitopes that the hydrophilic BILF2 peptide RRRK would be processed in a TAP dependent manner [11] . Figures 4A and 4B show representative results of experiments using two FLD-specific clones and one RRRK-specific clone assayed against two different donor derived LCLs . T cell recognition of late-expressing ΔBNLF2a and wild-type LCLs was found to be low but of an equivalent level . This pattern of recognition was seen using LCL sets derived from three other donors ( data not shown ) . To confirm the above results and minimize any variability between assays , we tested the recognition of the different LCL types in parallel by CD8+ T cell clones specific for epitopes that were presented by the same HLA molecule but produced at different phases in the replication cycle . Initially we compared recognition of the donor 1 set of LCLs by the HLA-A*0201 restricted CD8+ T cells specific for the YVL epitope from the immediate early protein BRLF1 , the GLC epitope derived from the early expressed protein BMLF1 and the FLD epitope from the late expressed BALF4 protein . In LCLs made with the BNLF2a-deleted virus there was a clear increase in the ability of YVL- and GLC-specific CD8+ T cells to recognize these targets in comparison to the wild-type LCLs , with these specificities showing a 20- and 6-fold increase in IFN-γ secretion respectively ( Figure 5A left panels ) . We also checked recognition in parallel with the HLA-A*0201 restricted TLD-specific clones which showed an increase in recognition similar to what we observed above ( data not shown ) . By contrast , no apparent difference in recognition was observed using the CD8+ T cells specific for the late-derived FLD epitope . In parallel we also estimated the functional avidity of these T cell clones by IFNγ secretion in response to ΔBZLF1 LCLs loaded with 10-fold dilutions of epitope peptide ( Figure 5A right panels ) . The 50% optimal recognition of the late effector FLD c21 was similar to that of the immediate early effector YVL c10 , both being in the 10−8–10−9 M range of peptide avidity , whilst the early effector GLC c10 was less avid with a 50% optimal recognition of 10−6 M . In a second series of experiments we compared the ability of the donor 3 set of LCLs to be recognized by HLA-B*2705 restricted CD8+ T cells . Here we used clones specific for the ARYA epitope derived from the early protein BALF2 and the RRRK epitope derived from the late protein BILF2 . Again we found that the LCLs made using the BNLF2a-deleted virus were well recognized by the early antigen-specific effector compared to the wild-type transformed LCLs with a 14-fold increase in recognition ( Figure 5B left panels ) , but both LCL types were recognized at an equivalent low level by the late-specific cells . In peptide titration assays the 50% optimal CD8+ T cell recognition values for the ARYA and RRRK clones were similar , at 4×10−7 and 2×10−7 respectively ( Figure 5B right panels ) . To confirm that the increased recognition of the ΔBNLF2a LCLs by the immediate early and early T cells seen in these experiments was due to the absence of BNLF2a and not to a secondary mutation within the ΔBNLF2a virus , we re-expressed BNLF2a in the ΔBNLF2a LCLs and conducted recognition assays on these cells . ΔBNLF2a LCLs were transfected with a BNLF2a expression vector which co-expressed the truncated nerve growth factor receptor ( NGFR ) and cells expressing this receptor selected with magnetic beads . These BNLF2a expressing cells and were used as targets in standard recognition assays alongside NGFR negative BNLF2a negative cells from the transfection , wild-type LCLs , unmanipulated ΔBNLF2a LCLs and ΔBZLF1 LCLs . T cells specific for the immediate early epitope AEN and early epitope ARYA were used as effectors in parallel assays . Figure S1 shows representative results of two independent transfection experiments . For both CD8+ T cell clones , re-expression of BNLF2a in the ΔBNLF2a LCLs decreased recognition of these LCLs to low levels relative to the unmanipulated ΔBNLF2a LCL , suggesting the increased recognition of the ΔBNLF2a LCLs observed in the previous experiments is due to the absence of BNLF2a . An unexpected outcome of the recognition experiments was the increased detection of immediate early antigens in the ΔBNLF2a transformed LCLs by the cognate CD8+ T cells . Immediate early genes are expressed prior to when the early gene BNLF2a would be expected to be expressed and so epitopes derived from immediate early proteins would not likely be well protected from presentation to CD8+ T cells . To clarify when BNLF2a is transcribed and expressed relative to the other genes of interest , we studied the transcription and protein expression kinetics of this gene and others that were used in our T cell recognition assays by qRT-PCR and western blot analysis during lytic replication . Here we used the EBV-infected AKBM cell line in which lytic EBV replication can be induced by cross-linking surface IgG receptors with anti-IgG antibodies [8] as a source of RNA and protein for analysis . Following induction of EBV replication in the AKBM cells , RNA samples were harvested over 48 hours post-induction ( pi ) . qRT-PCR analysis was conducted on the two immediate early genes ( BZLF1 and BRLF1 ) , two representative early genes ( BMLF1 and BNLF2a ) and two representative late genes ( BLLF1 ( encoding gp350 ) and BALF4 ( encoding gp110 ) ) . Upon induction , immediate early gene expression ( BZLF1 and BRLF1 ) occurred very rapidly with an increase in transcripts observed 1 hr pi , followed by peak expression at 2–3 hours pi ( Figure 6A , upper panel ) . Transcripts for these two immediate early genes did not disappear completely after their peak expression , however BZLF1 decreased quickly to low levels consistent with previous findings [12] . There were still more than 40% of the maximal BRLF1 transcripts present 24 hours pi compared to only 5% of the maximal BZLF1 transcripts at the same time point . Early gene message was expressed rapidly after induction with both BMLF1 and BNLF2a reaching their peak expression at 4 hours pi ( Figure 6A , middle panel ) . However , BMLF1 message decreased quickly over the next 8 hours almost to its final levels , while high relative levels of BNLF2a message were maintained over the next 20 hours from peak expression dropping to 40% of the maximal level by 48 hours pi . As expected , induction of the late gene BALF4 and BLLF1 transcripts was slower , with peak expression at 12 hours and 24 hours , respectively ( Figure 6A , lower panel ) . We next turned to examine the protein expression kinetics in lytically induced AKBM cells by western blot analysis , employing antibodies specific to proteins used in our recognition assays where available ( Figure 6B ) . Protein from each of the genes that had been measured by qRT-PCR was detected shortly following the expression of the corresponding transcript . Thus BZLF1 , BRLF1 and BMLF1 protein were clearly detected at 2 hours pi as was another early protein BALF2 . BNLF2a protein was also weakly detected at this point and clearly detected at 3 hours pi . BMRF1 showed delayed protein expression kinetics , being detected at 3–4 hours pi . Expression of the protein levels remained mostly stable for the duration of the time course , with the exception of BNLF2a which was lost from the cells at 12–48 hours pi . The late protein BALF4 was expressed by 6 hours and increased with time , while a second representative late protein , BFRF3 , showed much delayed expression kinetics . The results from our recognition experiments indicated that the deletion of BNLF2a did not lead to any increase in recognition of late antigens by their cognate CD8+ T cells . Interestingly these late proteins were expressed when protein levels of BNLF2a were declining to low levels . Potentially other immune evasion proteins may be active at these later time points , preventing efficient presentation of epitopes to CD8+ T cells . To explore this possibility we performed flow cytometric analysis of surface HLA class I levels on wild-type and ΔBNLF2a LCLs from different donors , which had been co-stained for viral proteins expressed at different phases of lytic cycle . Wild-type LCLs stained for BZLF1 expression showed a decrease in surface HLA class I levels by around 1/3 of the level in latent ( lytic antigen negative ) cells , yet BZLF1 expressing ΔBNLF2a LCLs showed little to no decrease in surface HLA class I levels ( Figure 7A and B upper panels ) . However , when cells were stained for the late lytic cycle protein BALF4 , surface HLA class I levels in both the wild-type and ΔBNLF2a LCLs were decreased by around half of the level of that seen in latent cells ( Figure 7A and B lower panels ) .
In this study we have shown that CD8+ T cell recognition of immediate early and early lytic cycle antigens is dramatically increased in LCLs transformed with a mutant EBV lacking the immune evasion gene BNLF2a compared to the recognition of wild-type EBV transformed LCLs . This increase in recognition was conserved across different HLA-class I backgrounds and these effects were seen using multiple different CD8+ T cell specificities , reinforcing the role of BNLF2a in active immune evasion during EBV lytic cycle replication . No observable difference in recognition of late lytic cycle antigens was observed , and peptide titration analysis of the late-specific CD8+ T cell clones ruled out the possibility that these effectors were simply less avid than those specific for the immediate early and early phases . The observed increase in recognition of immediate early antigens was not anticipated when considered in the light of BNLF2a's previously described expression kinetics , where BNLF2a transcripts were not found to peak until at least 4 hours after immediate early gene expression [13] . By performing detailed analysis of the transcription and protein expression kinetics of BNLF2a and the immediate early genes in an EBV-infected B cell line in which lytic replication could be induced , we found that although immediate early protein expression was initiated prior to that of BNLF2a , there was a substantial increase in the expression immediate early proteins coincident with the expression of BNLF2a at 3 hours post induction . Epitopes derived from the first wave of immediate early protein synthesis will have no protection from being processed and presented to CD8+ T cells . However given that the major source of epitopes feeding the class I antigen processing pathway is now thought to be from de-novo synthesized proteins in the form of short-lived defective ribosomal products ( DRiPs ) rather than long lived protein ( reviewed in [14] ) , expression of BNLF2a during this second wave of expression of the immediate early proteins would restrict the supply of epitope peptides at this time . Analysis of the sequence of early protein expression using the inducible lytic replication system showed that BNLF2a was expressed with the first wave of early proteins , BALF2 and BMLF1 . Similar to what is seen with the immediate early proteins , BNLF2a's expression was upregulated coincident with the increasing expression of these early proteins , again at a time when epitope production from these proteins is likely to be maximal . T cell recognition experiments using effectors specific for these proteins showed that deletion of BNLF2a from the targets caused clear increases in recognition of epitopes derived from these proteins compared to those expressed in wild-type targets . This indicates that although BNLF2a is expressed coincidently with these proteins , it can afford a substantial degree of protection from T cell recognition at this stage . Consistent with this finding was the observation that BNLF2a-deficient cells expressing BZLF1 , and thus including those cells progressing through to early stages of the replicative cycle , showed an increase in class I MHC levels relative to wild type transformed cells , confirming BNLF2a's role in inhibiting antigen presentation at this time . When different CD8+ T cell specificities were assayed for their ability to recognize their cognate antigen presented by the ΔBNLF2a LCLs as compared to the wild-type LCLs , variable levels of increased recognition were seen for the different T cell specificities . In some cases why this variability occurs is not clear . The abundance of the source protein does not appear to play a role as T cells specific for the three epitopes derived from BRLF1 namely AEN , YVL and IACP show quite different levels of increased recognition of the ΔBNLF2a LCL . The TAP dependence of the epitopes studied where determined does not appear to correlate with recognition . Furthermore as the hydrophobicity of peptides broadly correlates with the TAP independence [11] , no clear correlation is seen between the hydrophobicity or likely TAP independence and the increase in recognition . The HLA C presented epitope IACP was consistently more greatly recognized when presented by the ΔBNLF2a LCL compared to other epitopes presented from these LCLs . Some immune evasion proteins have been described to have allele specificity , such as the cytomegalovirus encoded US3 protein [15] , however whether BNLF2a shows allele-specificity requires further investigation . When the expression profile of the early protein BMRF1 was examined it showed a delayed pattern of expression relative to BNLF2a and the other early proteins studied . T cell recognition assays with clones specific to epitopes derived from BMRF1 consistently showed the lowest increase in recognition by T cells in BNLF2a-deficient targets , indicating that BNLF2a has some but perhaps a lesser effect on presentation of epitopes from this protein . This raises the possibility that other mechanisms are preventing effective antigen presentation during this later phase of early gene expression . More compelling evidence for other EBV-encoded class I evasion mechanisms comes from the study of the T cell recognition and expression kinetics of late phase protein targets . The expression of the best characterized late protein , BALF4 , was seen to increase in the inducible cell line from 6 hours post induction , with heightened expression occurring at 8–12 hours . At this stage BNLF2a protein levels were decreasing in these cells , yet T cell recognition experiments using late-specific effectors to BALF4 and BILF2 show very poor recognition of wild-type LCL targets . Importantly however , when using the same late-specific effectors in recognition assays of BNLF2a-deleted targets , no increase in detection is seen compared to wild-type targets . Given that the target of BNLF2a is the TAP complex and we have shown previously that this complex is not degraded during EBV lytic cycle replication , at least at 24 hours post-induction of lytic cycle [8] , this would suggest that EBV-encoded mechanisms other than BNLF2a are operating to block antigen presentation during the late phase of replication . Supporting this idea is the observation that BNLF2a-deficient LCLs expressing the late antigen BALF4 show decreased levels of surface class I MHC molecules similar to wild-type virus transformed cells . Evasion of CD8+ T cell recognition is likely to be most efficient when multiple points of the antigen processing pathway are targeted , with BNLF2a being one of potentially several immune evasion proteins . Other proteins potentially involved in this process include the early-expressed gene BGLF5 which functions as an alkaline exonuclease and a host protein synthesis inhibitor . BGLF5's inhibition of global protein synthesis , including that of class I MHC , can inhibit effective CD8+ T cell recognition of cognate targets [16] , [17] . A second candidate recently identified in modulating surface class I levels is the early phase expressed gene BILF1 , whose product acts to promote turnover of surface class I molecules [18] . Conceivably these proteins may act in a complementary manner to BNLF2a at early time points , initially by BILF1 clearing class I complexes containing immediate early epitopes from the surface of the cell that were produced before BNLF2a function was established and then BGLF5 acting to prevent effective class I synthesis . As to BNLF2a's function in vivo , it is difficult to draw direct inferences from animal herpesvirus models in which immune evasion genes have been disrupted since the viruses used , either the β-herpesvirus murine cytomegalovirus ( MCMV ) or the γ-2 herpesvirus MHV-68 , have different in vivo infection biology compared to EBV . Nevertheless , recent work on the β-herpesvirus MCMV has indicated that deletion of viral regulators of antigen processing either has no effect on immunodominance hierarchies or virus loads [19] , [20] , or surprisingly , decreases the size of at least some CD8+ T cell reactivities [21]; perhaps as a consequence of increased antigen clearance . In the case of MHV-68 which has a similar cellular tropism to EBV , deletion of the immune evasion gene mK3 , which is expressed during latency establishment and also during lytic replication , led to increased CD8+ T cell responses to lytic proteins yet had little effect on levels of virus undergoing lytic replication . It did however decrease latent viral loads , suggesting a role for mK3 in amplifying the latent virus reservoir [22] . By contrast , BNLF2a is not expressed during latency and EBV's mechanism of amplifying the latent viral load may come more from its growth transforming ability , by directly expanding latently infected B cells when first colonizing the B cell system . Ultimately , the impact BNLF2a has on immunodominance , viral loads and transmission may be best addressed using the closely related rhesus macaque lymphocryptovirus ( Cercopithicine herpesvirus 15 ) model . This virus has a similar biology to EBV and the same repertoire of genes [23] , including a BNLF2a homologue which has the ability to cause surface class I MHC downregulation when expressed in rhesus cell lines [9] . Overall , these results indicate that BNLF2a functions to protect the immediate early and early proteins from being efficiently processed and presented to CD8+ T cells . We would expect then that in vivo BNLF2a would function to shield virus reactivating from latency or initiating lytic cycle replication . Such stage-specific expression of immune evasion genes is a feature of several herpesviruses . Perhaps the clearest example comes from CMV where multiple proteins involved in disrupting CD8+ T cell recognition of infected cells have been described . During CMV replication the US3 gene , whose product retains class I complexes in the endoplasmic reticulum , is abundantly expressed during the immediate early phase [24]–[26] , while the gene US11 , whose product dislocates class I molecules from the endoplasmic reticulum into the cytosol , is expressed predominantly during early phase replication , and the TAP inhibitor US6 is transcribed in early and late phases [27] . The differential expression of these genes then may be in part why these viruses utilize multiple evasion mechanisms . In the case of EBV replication , as BNLF2a acts in a stage-specific manner we suggest that it will act in concert with other EBV encoded immune evasion genes to reduce efficient T-cell surveillance of reactivating or productively infected host cells .
All experiments were approved by the South Birmingham Local Research Ethics Committee ( 07/Q2702/24 ) . All patients provided written informed consent for the collection of blood samples and subsequent analysis . Wild-type and ΔBZLF1 recombinant EBV BACs used have been previously described [10] . The generation of a recombinant EBV BAC deleted for BNLF2a was performed as follows: a targeting vector containing the BNLF2a region was used to delete BNLF2a from the wild-type B95 . 8 EBV BAC genome . The introduction of a tetracycline cassette , flanked by FLP recombinase target sites ( FRT ) , between a unique XhoI site ( −6 bp from the BNLF2a open reading frame ATG initiation codon ) and AatII site ( 108 bp downstream of the BNLF2a initiation codon ) allowed for the insertional mutagenesis of the BNLF2a ORF . This left a 66 bp 3′ BNLF2a sequence fragment intact that was lacking an initiation codon . Homologous recombination of the target vector , via flanking sequences either side of the truncated BNLF2a , allowed for the introduction of the mutation into the wild-type EBV B95 . 8 BAC sequence . Successfully recombined clones were doubly selected on tetracycline and chloramphenicol ( the latter resistance cassette present in the wild-type backbone sequence ) , followed by removal of the tetracycline cassette through transformation of an FLP recombinase . Bacterial clones that survived this selection process were screened with several restriction enzymes and also sequenced to confirm successful disruption of BNLF2a ( data not shown ) . Wild-type , ΔBNLF2a and ΔBZLF1 recombinant virus preparations were generated by stably transfecting 293 cells with the corresponding EBV BAC genome and inducing lytic cycle replication , as previously described [10] , [28] . B lymphoblastoid target cell lines ( LCLs ) were generated by transformation of laboratory donor B lymphocytes ( isolated by positive CD19 Dynabead® ( Invitrogen ) selection , as per the manufacturer's instructions ) with the following recombinant EBV viruses: wild-type , ΔBNLF2a and ΔBZLF1 . LCLs were maintained in standard medium ( RPMI-1640 , 2 mM glutamine , and 10% [vol/vol] FCS ) . Effector CD8+ T cells were generated as previously described [6] , [29] . CD8+ T cell clones used in this study were specific for the following epitopes derived from the respective EBV gene products: RAKFKQLL from BZLF1 presented by HLA-B*0801 [30] , AENAGNDAC from BRLF1 presented by HLA-B*4501 [6] , IACPIVMRYVLDHLI from BRLF1 presented by HLA-C*0202 [6] , ARYAAYYLQF from BALF2 presented by HLA-B*2705 [6] , TLDYKPLSV from BMRF1 presented by HLA-A*0201 [31] , FLDKGTYTL from BALF4 presented by HLA-A*0201 [6] , RRRKGWIPL from BILF2 presented by HLA-B*2705 [6] , YVLDHLIVV from BRLF1 presented by HLA-A*0201 [32] , GLCTLVAML from BMLF1 presented by HLA-A*0201 [29] , [33] . The capacity of lytic-specific CD8+ T cell clones to recognize lytically replicating cells within LCLs of the relevant HLA type was measured by IFNγ ELISA ( Endogen ) . Briefly , target LCLs ( 5×104 cells/well ) were co-cultured in triplicate with effector CD8+ T cells ( 5×103 cells/well ) in V-bottomed 96-well plates in a total of 200 µl standard media/well and incubated overnight at 37°C with 5% CO2 . After 18 hours 50 µl of culture supernatant from each well was used for IFNγ detection by ELISA AKBM cells and their use have been described previously [8] . Briefly , this EBV infected cell line contains a reporter GFP-rat CD2 construct under the control of an early EBV promoter to allow identification of cells in lytic cycle . Prior to induction , AKBM cells were sorted by FACS to exclude any GFP+ve cells that had spontaneously entered lytic cycle . The GFP-ve fraction was then induced into lytic cycle by crosslinking of surface IgG molecules as previously described [8] . Cells were then harvested at the indicated timepoints post induction for western blotting and qRT-PCR analysis . Total cell lysates were generated by denaturation in lysis buffer ( final concentration: 8 M urea , 50 mM Tris/HCl pH 7 . 5 , 150 mM sodium 2-mercaptoethanesulfonate ) and sonicated . Protein concentration was determined using a Bradford protein assay ( Bio-Rad ) , and 20 µg of protein for each sample was separated by SDS-polyacrylamide gel electrophoresis ( SDS-PAGE ) using a Bio-Rad Mini Gel tank . Proteins were blotted onto nitrocellulose membranes and blocked by incubation for 1 hr in 5% skimmed-milk powder dissolved in PBS-Tween 20 detergent ( 0 . 05% [vol/vol] ) . Specific proteins were detected by incubation with primary antibodies for BZLF1 ( murine monoclonal antibody ( MAb ) BZ . 1 , final concentration 0 . 5 µg/ml , [34] ) , BRLF1 ( murine MAb clone 8C12 , final concentration 2 . 5 µg/ml , Argene , cat . # 11-008 ) , BMLF1 ( rabbit serum to EBV BSLF2/BMLF1-encoded SM , clone EB-2 , used at 1/6000 [35] ) , BMRF1 ( murine MAb clone OT14-E , used at 1/2000 [36] ) , BALF2 ( murine MAb clone OT13B , used at 1/5000 , [37] ) , BNLF2a ( clone 5B9 , used at 1/100 , a rat hybridoma supernatant directed to the N-terminal region of BNLF2a generated by E . Kremmer through immunization of Lou/C rats with KLH-coupled BNLF2a peptides , followed by fusion of rat immune spleen cells with the myeloma cell line P3X63-Ag8 . 653 ) , BALF4 ( murine Mab clone L2 , used at 1/100 , [38] ) BFRF3 ( rat MAb clone OT15-E , used at 1/250 , J . M . Middeldorp , [39] ) and EBNA2 ( murine MAb clone PE-2 , used at 1/50 , [40] ) for 2 hrs at room temperature , followed by extensive washes with PBS-Tween . Detection of bound primary antibodies was by incubation for 1 hr with appropriate horseradish peroxidase ( HRP ) -conjugated secondary antibodies ( goat anti-mouse IgG:HRP ( Sigma , cat . #A4416 ) , goat anti-rat IgG:HRP ( Sigma , cat . #A9037 ) , and goat anti-rabbit IgG:HRP ( Sigma , cat . #A6154 ) . Bound HRP was then detected by enhanced chemiluminescence ( ECL , Amersham ) . Total RNA was extracted from 0 . 5×106 cells using a NucleoSpin® RNA II kit ( Machery-Nagel ) followed by Turbo DNA-free™ ( Ambion/Applied Biosystems ) treatment to remove any residual DNA contamination , as per the manufacturers' instructions . 500 ng of RNA was reverse transcribed into cDNA using a pool of primers specific for BZLF1 , BRLF1 , BMLF1 , BNLF2a , BALF4 and BLLF1 , with GAPDH included as an internal control , followed by subsequent quantitative-PCR ( q-PCR ) . EBV lytic gene primers were as follows ( primer sequences in parenthesis ) : BZLF1 ( cDNA 5′GCAGCCACCTCACG3′ , F 5′ACGACGCACACGGAAACC3′ , R 5′CTTGGCCCGGCATTTTCT3′ , probe 5′GCATTCCTCCAGCGATTCTGGCTGTT3′ ) , BRLF1 ( cDNA 5′CAGGAATCATCACCCG3′ , F 5′TTGGGCCATTCTCCGAAAC3′ , R 5′TATAGGGCACGCGATGGAA3′ , probe 5′AGACGGGCTGAGAATGCCGGC3′ ) , BMLF1 ( cDNA 5′GAGGATGAAATCTCTCCAT3′ , F 5′CCCGAACTAGCAGCATTTCCT3′ , R 5′GACCGCTTCGAGTTCCAGAA3′ , probe 5′AACGAGGATCCCGCAGAGAGCCA3′ ) , BNLF2a ( cDNA 5′GTCTGCTGACGTCTGG3′ , F 5′TGGAGCGTGCTTTGCTAGAG3′ , R 5′GGCCTGGTCTCCGTAGAAGAG3′ , probe 5′CCTCTGCCTGCGGCCTGCC3′ ) , BALF4 ( cDNA 5′CCATCAACAGGCCCTC3′ , F 5′CCAGCTTTCCTTTCCGAGTCT 3′ , R 5′ACACTGGATGTCCGAGGAGAA3′ , probe 5′TCCAGCCACGGCGACCTGTTC3′ ) , and BLLF1 ( cDNA 5′ACTGCAGTACTAGCATGG3′ , F 5′AGAATCTGGGCTGGGACGTT3′ , R 5′ACATGGAGCCCGGACAAGT3′ , probe 5′AGCCCACCACAGATTACGGCGGT3′ ) . cDNA and forward/reverse primers were synthesised by Alta Bioscience ( University of Birmingham ) . Probes were synthesised by Eurogentec S . A and labelled with 5′ FAM fluorophore and 3′ TAMRA quencher . Data was normalised to GAPDH expression , and expressed as relative to the maximal level of transcript for each gene . LCLs were assayed for the percentage of cells spontaneously reactivating into lytic cycle by intracellular staining for BZLF1 . Cells were first fixed using 100 µl of Ebiosciences Intracellular ( IC ) Fixative ( cat . # 00-8222-49 ) for 1 hr on ice , followed by permeabilisation through the addition of 100 µl Triton X-100 ( final concentration 0 . 2% ) and a further 30 minute incubation on ice . After extensive washing with PBS , cells were incubated with 1 µg/ml of either MAb BZ . 1 ( anti-BZLF1 ) or with an IgG1 isotype control antibody for 1 hr at 37°C . Cells were washed twice in PBS and then incubated with 1∶20-diluted R-phycoerythrin-conjugated goat anti-mouse IgG1 antibody ( AbD Serotec , cat . # STAR132PE ) for 1 hr at 37°C . Following further washes cells were resuspended in IC fixative and analysed on a Dako Cyan flow cytometer ( Dako , Denmark ) . LCL surface HLA class I and intracellular lytic-cycle EBV antigens were detected simultaneously by first staining viable cells with 1∶15-diluted allophycocyanin-conjugated-anti-human HLA-A , B , C ( Biolegend , cat . # 311410 ) antibody for 30 minutes on ice . Cells were then washed extensively in PBS and fixed and permeabilised as above , followed by incubation for 1 hr at 37°C with 1 ug/ml of either MAb BZ . 1 ( immediate early antigen BZLF1 ) or L2 ( late antigen BALF4 ) , or IgG1 isotype control . After several washes in PBS cells were incubated for 1 hr with 1∶20-diluted R-phycoerythrin-conjugated goat anti-mouse IgG1 antibody as above . Cells were washed and fixed as above , followed by analysis on a Dako cytometer ( Dako , Denmark ) . All flow data was analyzed using FlowJo software ( Tree Star ) .
|
Epstein-Barr virus ( EBV ) is carried by approximately 90% of the world's population , where it persists and is chronically shed despite a vigorous specific immune response , a key component of which are CD8+ T cells that recognize and kill infected cells . The mechanisms the virus uses to evade these responses are not clear . Recently we identified a gene encoded by EBV , BNLF2a , that when expressed ectopically in cells inhibited their recognition by CD8+ T cells . To determine the contribution of BNLF2a to evasion of EBV-specific CD8+ T cell recognition and whether EBV encoded additional immune evasion mechanisms , a recombinant EBV was constructed in which BNLF2a was deleted . We found that cells infected with the recombinant virus were better recognized by CD8+ T cells specific for targets expressed co-incidently with BNLF2a , compared to cells infected with a non-recombinant virus . However , proteins expressed at late stages of the viral infection cycle were poorly recognised by CD8+ T cells , suggesting EBV encodes additional immune evasion genes to prevent effective CD8+ T cell recognition . This study highlights the stage-specific nature of viral immune evasion mechanisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/antigen",
"processing",
"and",
"recognition",
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"immunology/immune",
"response",
"virology/immune",
"evasion",
"immunology/immunity",
"to",
"infections"
] |
2009
|
Stage-Specific Inhibition of MHC Class I Presentation by the Epstein-Barr Virus BNLF2a Protein during Virus Lytic Cycle
|
Chromatin insulators/boundary elements share the ability to insulate a transgene from its chromosomal context by blocking promiscuous enhancer–promoter interactions and heterochromatin spreading . Several insulating factors target different DNA consensus sequences , defining distinct subfamilies of insulators . Whether each of these families and factors might possess unique cellular functions is of particular interest . Here , we combined chromatin immunoprecipitations and computational approaches to break down the binding signature of the Drosophila boundary element–associated factor ( BEAF ) subfamily . We identify a dual-core BEAF binding signature at 1 , 720 sites genome-wide , defined by five to six BEAF binding motifs bracketing 200 bp AT-rich nuclease-resistant spacers . Dual-cores are tightly linked to hundreds of genes highly enriched in cell-cycle and chromosome organization/segregation annotations . siRNA depletion of BEAF from cells leads to cell-cycle and chromosome segregation defects . Quantitative RT-PCR analyses in BEAF-depleted cells show that BEAF controls the expression of dual core–associated genes , including key cell-cycle and chromosome segregation regulators . beaf mutants that impair its insulating function by preventing proper interactions of BEAF complexes with the dual-cores produce similar effects in embryos . Chromatin immunoprecipitations show that BEAF regulates transcriptional activity by restricting the deposition of methylated histone H3K9 marks in dual-cores . Our results reveal a novel role for BEAF chromatin dual-cores in regulating a distinct set of genes involved in chromosome organization/segregation and the cell cycle .
Chromatin insulators/boundary elements ( BEs ) [1 , 2] are defined as sequences able to insulate a transgene from its chromosomal context and to block promiscuous enhancer–promoter interactions or heterochromatin spreading [1 , 3–5] . These elements are thought to subdivide the genome into functional chromosome domains , through their ability to cluster DNA loops [1 , 2] and to control the deposition of histone epigenetic marks [6–8] to regulate chromatin accessibility for gene expression [9–13] . No common signature and/or mechanism of action has been identified among characterized insulators/boundary elements [2] . Rather , several factors confer insulating activity by targeting different DNA consensus sequences in the known insulators . In Drosophila , insulating factors include dCTCF [14 , 15] , Zw5 [16] , boundary element–associated factor ( BEAF ) [17] , and the well-characterized suppressor of Hairy-wing ( Su ( Hw ) ) [1 , 18 , 19] , which targets hundreds of distinct , largely uncharacterized genomic sites [20–22] . Whether each of these factors and subfamily of insulators might possess distinct cellular functions is of particular interest . BEAF blocks both enhancer–promoter communication [17 , 23–25] and repression by heterochromatin , as shown using reporter transgenes [5 , 25] . This insulating activity of BEAF was also evidenced by a genetic screen in yeast [4] , confirming that , unlike de-silencing activity , BEAF binding sites must bracket a transgene for insulation . The hundreds of BEAF binding sites have not been characterized in situ , however , and the cellular function of BEAF remains to be elucidated in vivo . Here we have combined computational and experimental approaches to address the function of BEAF binding sites in vivo . We have identified ≈1 , 720 BEAF dual-core elements genome-wide that share an unusual organization conserved over 600 bp . The dual-core signature consists of five to six BEAF binding motifs bracketing 200 bp AT-rich nuclease-resistant spacers . BEAF dual-cores juxtapose to hundreds of genes highly enriched in gene annotations regulating chromosome organization/segregation and the cell cycle . Accordingly , BEAF depletion leads to cell-cycle and chromosome segregation defects . Quantitative RT-PCR analyses further show that dual-cores regulate the expression of key cell-cycle genes including cdk7 and mei-S332 . These results are also reproduced in embryos expressing truncated beaf mutants , which abolish the proper targeting of BEAF to dual-cores and its insulating activity . Chromatin immunoprecipitation analyses show that BEAF acts by restricting the deposition of methylated H3K9 marks in dual-cores . Our data reveal a new role for BEAF in regulating chromosome organization/segregation and the cell cycle through its binding to highly conserved chromatin dual-cores .
The DNA-binding activity of BEAF has been well-characterized in vitro [17 , 20 , 23 , 24] . Each subunit of the BEAF complex targets one CGATA motif . Point mutations within this consensus abolish both its binding and insulating activities . Clusters of three to four CGATA motifs can create high-affinity ( Kd ∼ 10–25 pM ) BEAF in vitro binding sites , which we call single elements . A computational scan of the Drosophila genome revealed thousands of single elements , yet immunostaining analysis demonstrated that they were not good predictors for BEAF binding in vivo . For example , Chromosome 4 was found to contain hundreds of single elements , yet immunodetection analysis showed only three major BEAF signals on this chromosome ( Figure 1A ) . Interestingly , statistical analysis showed that single elements were often organized in a pair-wise configuration . Genome-wide , 988 single elements form 494 so-called “dual-cores , ” which harbor two separate clusters of three CGATAs , a statistically significant result ( p-value ∼ 1e-9 ) . Moreover , 1 , 226 additional “dual-core–like” elements have a second cluster of two ( instead of three ) CGATAs . These elements include all characterized BEAF insulators whose activity involves a second , lower-affinity CGATA cluster ( Kd ∼ 400–600 pM ) where BEAF binding is abolished when the first high-affinity cluster is mutated [20 , 23] . Detailed analysis by alignment of all 1 , 720 dual-core and dual-core–like elements showed a highly organized distribution of their 12 , 058 CGATAs , which preferentially segregate into two clusters separated by spacers of approximately 200 bp ( Figure 1B ) . For scs' and other characterized BEAF insulators , these spacers were found to be relatively AT-rich [20 , 24 , 26] . Scanning the 1 , 720 dual-cores for A+T content showed that they all harbor significant AT-rich ( >70% ) sequences in their spacers ( Figure 1C , Figure S1 ) . The remarkably conserved organization of dual-cores indicates that they likely correspond to a highly specific BEAF-binding signature . We tested this possibility by assaying BEAF binding to dual-cores by chromatin immunoprecipitation ( ChIP ) and ChIP-on-chip ( Figures 1D and 2 ) . Based on the signals obtained with anti-BEAF antibodies , dual-cores are expected to be precipitated much more efficiently than single elements ( Figure 1A ) . Indeed , ChIP analysis confirmed that single elements were not bound by BEAF ( Figure 1D ) . In contrast , dual-cores from the 7C locus of the X chromosome were efficiently bound by BEAF ( Figure 1D , probes 4 and 5 ) , while nearby control sequences or single elements were not ( probes 1 , 2 , 3 , and 6 ) . Altogether , 25 out of 25 dual-cores and dual-core–like elements assayed by ChIP were found to be efficiently bound by BEAF in vivo ( Figures 1D and 2; unpublished data ) . The actin promoter region , which contains six unclustered CGATA motifs , was not bound by BEAF ( Figure 1D; last row ) , indicating that the distribution of CGATAs in dual-cores , rather than the number of CGATAs per se , is important for BEAF binding . Furthermore , ChIP-on-chip analysis over 350 Kbp of the X chromosome strengthens our conclusions , as all major peaks corresponding to regions where BEAF binds in vivo fit into a dual-core or a dual-core–like element ( hereafter called “dual-cores” , see black rectangles in Figure 2; see our database at http://www . sfu . ca/~eemberly/insulator/ for a complete listing ) . We note that computer analysis occasionally retrieved minor peaks present in the shoulder of the major BEAF peaks ( enrichment <2; red bars in Figure 2 ) that may be attributed to the cooperative binding of BEAF to additional CGATA motifs present in single elements juxtaposed to dual-cores ( Figure 2 , see black bars for “single” ) . However , no peaks were present in regions corresponding to dispersed single elements ( Figure 2; see our database ) , confirming that they are not sufficient for BEAF binding . These results establish that BEAF elements organized into dual-cores indeed define a characteristic in vivo BEAF-binding signature ( Figure 1E ) . Analysis of the positioning of dual-cores relative to genes showed that they are preferentially associated with gene-dense regions . 545 dual-cores reside within 500 bp of promoter/transcriptional start sites ( TSSs ) ( p-value = 6 . 7e-119 ) ( Figure 3A ) , and more than 850 are within 2 , 000 bp . As dual-cores are preferentially distributed in pairs separated by approximately 5–15 kbp ( p-value = 1 . 01e-33; Figure 3B ) , the remaining elements might be found at the 3′ borders of genes . However , we could not find any specific enrichment for dual-cores in the 3′ UTR of genes ( unpublished data ) , indicating that the clustering of dual-cores can be attributed to the clustering of genes/TSS rather than the bracketing of genes by dual-cores per se . These features ( see our genome-wide database ) raise the possibility that dual-cores might exert a function distinct from that of Su ( Hw ) binding sites , which rarely juxtapose the TSS of genes [21 , 22 , 27] . Strikingly , genes containing a dual-core near their promoter were statistically enriched in gene-class ontology ( GO ) groups that include the cell cycle , chromosome organization/segregation , apoptosis , and sexual reproduction ( p-value < 1e-6; Figure 3C ) . These essential cellular processes require constitutive regulation , whereas genes associated with non-constitutive processes such as sensing and behavior were not enriched for BEAF dual-cores ( Table S1 ) . Inspection of Table S1 also shows that other cell functions enriched in BEAF dual-cores include GOs that can be linked to phenotypes observed in beaf mutants [25 , 28 , 29] , such as chromosome architecture , germ-cell and imaginal-disc development , and eye morphogenesis defects . We asked whether BEAF might be involved in regulating the cell-cycle and/or chromosome organization by siRNA-mediated depletion of BEAF from cells . Reduction of BEAF levels to background occurred from day 3–4 ( Figure 3D ) , when defects in cell growth are first observed ( Figure 3E ) . In addition , FACS and microscope analyses showed that BEAF depletion led to a significant and reproducible increase ( >3× ) in the proportion of cells with 4N DNA content and with phenotypes typical of chromosome segregation defects ( Figure 3F and 3G ) . These observations support our conclusion that the selective association of the corresponding GOs with closely linked dual-cores likely reflects a biologically significant localization . We next asked whether the phenotypes observed upon BEAF-depletion can be attributed to the loss of activity of BEAF dual-cores associated with 160 genes that control cell-cycle chromosome dynamics . These include mei-S332 and cdk7 , two major chromosome-segregation and cell-cycle regulators [30–32] whose promoter regions are bound by BEAF in vivo ( Dual-cores 38/56 , Figure 1D ) . Remarkably , further DNA-motif searches showed that the dual-cores associated with cdk7 and mei-S332 , and more generally with all genes belonging to the cell-cycle and/or chromosome dynamic GOs , also contain the TATCGATA consensus sequence recognized by DREF ( p-value ∼ 2 . 4e-6; Figure 4A ) . DREF activates hundreds of cell-cycle regulatory genes [33] and , importantly , might compete with BEAF for binding to the overlapping consensus [34] . Hence , DREF-regulated dual-cores may define a distinct regulatory subclass ( Figure 4A , right ) . To test how BEAF might affect the expression of genes associated with dual-cores that do or do not contain a DREF consensus site , we performed quantitative RT-PCR expression analysis from BEAF-depleted or control cells ( Figure 4 ) . BEAF depletion did not affect the expression of control genes ( see Figure 4A , left ) , including those located near a single element ( Figure 4B; actin , CG9745 ) where BEAF does not bind ( Figure 1 ) . The expression of all genes associated with a dual core lacking a DREF element was consistently found to be positively regulated by BEAF by approximately 4-fold to 5-fold ( CG1430 , CG10946 , CG1444 , snf , ras , janus; Figure 4B ) . These data are in complete agreement with previous work showing that BEAF has a positive effect on gene expression by de-repressing a transgene from surrounding chromatin [17 , 20 , 23 , 24] . In stark contrast , the expression of all genes associated with a dual-core harboring a DREF consensus , including cdk7 and mei-S332 , specifically increased by approximately 4- to 6-fold upon depletion of BEAF ( Figure 4B; CG32676 , mei-S332 , cdk7 , CG10944 , ser ) . Accordingly , Western blot analysis showed that Cdk7 and Mei-S332 protein levels increased under these conditions ( Figure S2 ) . Therefore , two categories of dual-cores may be found . In those lacking a DREF consensus , BEAF positively regulates gene expression; in those that contain a DREF consensus , BEAF may prevent binding of DREF to its overlapping consensus , thereby controlling the activation of the associated cell-cycle and chromosome organization/segregation GOs . Quantitative RT-PCR analysis showed that DREF depletion resulted in a more than 10-fold down-regulation of cdk7 ( Figure 5 ) , confirming the role of DREF as a transcriptional activator of this gene . To further characterize the respective roles of BEAF and DREF in regulating cell-cycle regulatory genes by binding to dual-cores , we eliminated the DREF consensus from the dual-core associated with cdk7 ( dre mutant , Figure 5A ) and transfected this construct or its wild-type version into cells depleted of BEAF or of DREF by siRNA ( Figure 5B ) . Because the dre mutant does not modify the CGATA BEAF consensus and still harbors the dual-core signature ( 2× 3CGATAs separated by the spacer; Figure S7B ) , this construct may be used to reveal the effect of the BEAF dual-core on the expression of cdk7 independently of DREF . Importantly , mutating the DREF consensus site led to a down-regulation of cdk7 ( Figure 5B , cdk7-mut , blue bar ) , similar to what is found by depleting DREF . Strikingly , BEAF depletion further impaired the expression of cdk7 by approximately 5-fold ( Figure 5B , cdk7-mut , red bar ) compared to the expression of the identical construct in control cells ( Figure 5B , cdk7-mut , blue bar ) . We conclude that , although BEAF regulates DREF-mediated activation , it additionally positively regulates the expression of cdk7 , as found for other genes associated with a dual-core lacking a DREF consensus . In support of this conclusion , we obtained a similar result for snf , which is transcribed in opposite orientation relative to cdk7 ( Figure 5A ) . Snf is under the influence of the same dual-core as cdk7 , yet its expression is not regulated by DREF ( Figure 5B ) . However , BEAF depletion reproducibly impaired snf expression by approximately 6-fold , similar to what we obtained for cdk7 in the absence of DREF . These results show that BEAF has a positive role on the expression of genes associated with dual-cores , in addition to its role in controlling activation by DREF . BEAF insulating activity can protect a transgene from repression by chromatin [5 , 25] . The expression of genes positively regulated by dual-cores might implicate mechanisms similar to those required for insulation , and we asked whether BEAF might control the deposition of epigenetic marks , as shown for other types of insulators [7 , 35 , 36] . We tested this possibility by measuring the levels of histone H3 methylated on lysine 9 ( H3K9me3 ) , a characteristic mark of heterochromatin , as a function of BEAF depletion . The deposition of H3K9me3 was strongly increased upon BEAF depletion ( Figure 6A ) . Double immunostaining analysis showed that this increase was specific , as RNA polymerase II , actin , or unmodified histone H3 levels were unchanged ( Figure 6A and 6B , Figure S3A and S3B ) . Numerous and broader H3K9me3 foci not restricted to heterochromatin regions appeared in BEAF-depleted cells ( Figure 6B , 3× panels; [37] ) , strengthening the view that H3K9me3 also acts to influence gene expression in euchromatin [8 , 38 , 39] . ChIP-on-chip analysis confirmed that discrete H3K9me3 peaks are found in many promoter regions , including those associated with a dual-core ( Figure S3C ) . However , these H3K9me3 peaks appear to be distinct from the broader and larger H3K9me3 peaks found in regions where genes are known to be repressed ( e . g . , eye , Figure S3C ) and where the methylK27 mark is also present ( not shown; B . Schuettengruber unpublished data ) . We further tested if BEAF affects the deposition of H3K9me3 marks into dual-cores by performing ChIP analysis using anti-H3K9me3 antibodies on BEAF-depleted , DREF-depleted , or control cells ( Figure 6C ) . BEAF-depletion led to a significant and reproducible increase of approximately 8-fold in H3K9me3 levels for the dual-cores linked to snf-cdk7 , similar to that obtained for mei-S332 and CG1430 , and in stark contrast to the stable levels found for the actin control ( Figure 6C; unpublished data ) . In contrast , no variation in H3K9me3 levels could be found upon DREF depletion ( Figure 6C ) , showing that this increase is specific to BEAF depletion . This result also rules out that the changes we observe overall could be due to off-target effects . Moreover , CDK7 depletion , which severely impaired cell-cycle progression ( unpublished data ) , did not affect the levels of H3K9me3 ( Figure 6C ) , indicating that their increase is not due to an indirect perturbation of the cell cycle upon BEAF depletion . Finally , H3K9me3 levels did not vary in control regions located a few kbp away from the dual-core , suggesting that BEAF controls the deposition of this mark locally ( unpublished data ) . These results show that BEAF dual-cores are involved in blocking the deposition of H3K9me3 marks , fully consistent with their ability to positively regulate the expression of dual core-associated genes . To confirm that the observed increase in H3K9me3 levels is directly linked to the activity of BEAF , we introduced mutations in two of the CGATA motifs of the dual-core associated with cdk7 ( “beaf-mut” , Figure 7A ) and transfected this construct or constructs harboring a wild-type dual-core or a dual-core mutated in the DREF site ( dre mutant , Figure 7A ) into cells . Quantitative PCR analysis of chromatin immunoprecipitated with anti-H3K9me3 antibodies showed that mutation of the BEAF site led to an increase in H3K9me3 levels of approximately 3 . 8-fold compared to wild-type or dre mutant constructs ( Figure 7B ) , establishing that BEAF directly controls the deposition of H3K9me3 . This did not affect the levels of H3K9me3 in the endogenous cdk7 dual-core , as measured from the same batch of transfected cells , showing that the observed increase is indeed specific for the mutated dual-core . We conclude that BEAF serves to restrict the deposition of H3K9me3 marks into dual-cores . The deposition of epigenetic marks is critical for regulating gene activity at the level of chromatin accessibility [9 , 12 , 13] , which may account for the positive effect of BEAF on gene expression . We sought to determine whether BEAF-regulated deposition of H3K9me3 marks affects the expression of cell-cycle genes . BEAF-depleted or control cells were treated with anacardic acid ( AA ) , a histone acetyltransferase ( HAT ) inhibitor that globally affects gene expression by altering the accessibility of chromatin [40] . AA treatment did not affect the expression of either control genes or dual core-associated genes ( compare grey and black bars in Figure 8 ) . In contrast , AA severely compromised the activation of snf , cdk7 , or mei-S332 upon BEAF depletion compared to untreated BEAF-depleted cells ( Figure 8; unpublished data ) . This result strongly supports a model whereby BEAF restricts the deposition of methylated H3K9 marks , thereby protecting the expression of dual core-associated genes from repression by chromatin ( see Discussion ) . Are these variations in gene expression related to the cooperative binding of BEAF to the two clusters of CGATAs present in dual-cores ? We sought to answer this question by using transgenic fly lines expressing the C-terminal BEAF self-interaction domain ( BID in Figure 9A ) under the control of a GAL4 activator . BID lacks the BEAF DNA-binding domain , impairing the insulating activity of BEAF [25] by preventing its cooperative binding to two nearby CGATA clusters ( Figure 9B ) . Importantly , defects in expression of cdk7 , snf , and/or mei-S332 were highly similar in embryos expressing BID to that observed in BEAF-depleted cells ( compare Figures 9C and 4B ) . This result supports our conclusion that BEAF binding is required to regulate these genes in vivo . It also suggests that the cooperative binding of BEAF to conserved dual-cores , which is abolished by BID , may be important for the regulation of gene expression by BEAF . Accordingly , cell functions enriched in BEAF dual-cores include GOs ( Table S1 ) that correspond to phenotypes observed following expression of beaf mutants , which are lethal to flies [25] , or to GOs found to genetically interact with these mutants [28] .
Results of our in silico analysis reveal ∼1 , 720 BEAF dual-cores in the Drosophila melanogaster genome that share a striking organization ( Figure 1E ) . Genome-wide ChIP-on-chip analysis detects approximately 1 , 800 significant BEAF binding sites ( C . M . Hart , unpublished observations ) , suggesting that our dual-core database encompasses most in vivo BEAF binding sites . The few ( <100 ) additional peaks not included in our database but detected by ChIP-on-chip analysis may correspond to elements initially scored as single elements but whose organization is close to that of dual-cores . These rare exceptions are in part due to the computer stringency of the dual-core signature . For example , BEAF-1255 can be bound by BEAF in vivo ( Figure S4 ) , yet this element could not be scored as a dual-core because one out of five of its clustered CGATA motifs lies 3 bp outside the defined 100-bp window ( ‘out' in Figure S4 ) . Furthermore , approximately 10% of the minor BEAF sites are found in regions lacking any CGATA motifs , including the scs insulator ( unpublished data ) [16] . Since this region is not directly bound by BEAF , it is thus possible that some of the minor BEAF peaks are due to indirect interactions between BEAF and other insulator proteins , as previously suggested for the scs′–scs pair of insulators [16] . Other protein–protein interactions that regulate BEAF binding could also involve the splicing variant of the beaf gene itself , called BEAF-32A [23] , which does not harbor the BEAF DNA-binding domain that recognizes clustered CGATA motifs . ChIP-on-chip analysis using antibodies that also recognize this isoform showed no additional major peaks ( Figure S5 , compare ‘−32A' with ‘+32A' ) , indicating that dual-cores constitute the main binding sites for both BEAF isoforms . Finally , we note that the BEAF-32A isoform is unlikely to play a major role in the activities described here , as its binding is dispensable for the insulating function of BEAF [20] , and its expression is not essential for the development of embryos into adult flies [29] . Taken together , our results show that the BEAF dual-core signature is a bona fide mark that identifies a cis-regulatory element that regulates the expression of nearby genes . Results of our experiments using both BEAF depletion in tissue culture cells and BID expression in vivo provide clear evidence for specific functions of the BEAF dual-cores , reflected by a selective association with genes that control cell-cycle and/or chromosome organization/segregation . The competition between DREF and BEAF for binding to nested consensus sequences is also supported by ChIP analyses showing that DREF targets' identical sites [34] clearly enriched nearby genes associated with the cell cycle and chromosome dynamic GOs ( Figure S6; unpublished data ) . Thus , while DREF levels increase at the G1/S transition to activate mei-S332 and cdk7 within the appropriate window for cell-cycle progression [30–32] , BEAF may further facilitate this activation by restricting the deposition of H3K9me marks . Indeed , over-expressing BEAF was shown to reduce the phenotypes related to cell-cycle progression in flies that over-express DREF [33] , supporting a role for BEAF in controlling the cell cycle . Such a model is also supported by our observation that AA treatment strongly represses these genes in BEAF-depleted cells and that mutation of the BEAF-binding site in a dual-core results in a local increase in H3K9m3 levels . In addition , computer analysis of micro-array expression data for Drosophila embryos during early development shows that the 545 genes associated with dual-cores are positively correlated with beaf expression ( Figure S7A ) , in contrast to genes unlinked to these elements ( p-value ∼ 3e-17 according to the Kolmogorov-Smirnov test ) . This strict correlation further indicates that BEAF has a global positive role on gene expression genome-wide , and similar analyses did not reveal any significant correlation change between genes whose TSS is closely juxtaposed ( <100 bp ) to dual-cores , including snf or cdk7 ( Figure S7B ) , compared to genes whose TSS is more distant ( 500 bp ) . Accordingly , the cell-cycle and chromosome dynamics GOs that include cdk7 and mei-S332 are enriched for positively correlated genes ( see our database for a detailed list ) . Taken together , our results show that BEAF could play an important role in chromosome organization during the cell cycle through a regulated switch involving the BEAF–DREF competition: According to such a mechanism , BEAF would restrict the deposition of H3K9me3 , allowing dual-core–associated genes to remain in a potentially active state , while controlling the time of activation of cell-cycle GOs by DREF . Accordingly , BEAF depletion leads to down-regulation of genes associated with a dual-core lacking a DREF element ( CG10946 , ras , CG1430 , Janus , CG1444 ) , but to increased expression of CG32676 , mei-S332 , cdk7 , CG10944 , and ser , which are under the control of DREF-associated dual-cores ( Figure 4 ) . In the latter case , the apparent contradiction between the positive—restriction of H3K9me3 deposition—and negative effects of BEAF can be reconciled by our results showing that BEAF controls the activation of these genes by DREF . BEAF depletion relieves the competition for binding by DREF , leading to the increased expression of cdk7 or mei-S3332 in spite of an increased deposition of H3K9me3 marks under these conditions . Mutating the DREF or BEAF binding sites of DREF-associated dual-cores ( Figures 5 and 7 ) allows for distinguishing between these different effects on the expression of linked genes . It is intriguing that the spacers of dual-cores are well-conserved . One possibility is that they may be preferentially bound by a nucleosome , as recently shown for CTCF insulators [41] . Supporting this idea , the known dual core-spacers correspond to nuclease-resistant “cores” , between two nuclease-hypersensitive sites ( BE76 , scs′ ) [20 , 24 , 26] ( Figure S8 ) , where a nucleosome may be present ( C . M . Hart , unpublished observations ) . Indeed , we found that dual core-spacers fall within predicted nucleosome-positioning sequence ( NPS ) databases [42–44] , as indicated by NPS/dual-core sequence alignments ( Figure S8; not shown ) , possibly accounting for the conserved organization of dual-cores . Our results further suggest that the cooperative binding of BEAF across these AT-rich spacers may be important for BEAF function . Indeed , expression of BID , which prevents its cooperative binding across the spacers , mimics the effect of BEAF depletion on the expression of dual-core–associated genes , as also found by mutagenesis of two CGATA motifs from one dual-core cluster . However , BEAF still efficiently binds in vivo to the few dual-cores that harbor a shorter spacer ( <150 bp; e . g . , see Dual-core 1 , 254 , Figure 1; unpublished data ) , indicating that the conserved dual-core–spacer is dispensable for BEAF binding . Recent reports have shown that gene expression is differentially regulated through nucleosome positioning in several species [12 , 13 , 42 , 43] . Positioned nucleosomes may restrict promoter accessibility in yeast , and pausing of RNA polymerase II facing the +1 nucleosome may be regulated through nucleosome positioning in Drosophila [44] . Similarly , dual-cores are also closely associated with TSSs , and a potential link to nucleosome positioning strengthens the view that BEAF may regulate chromatin accessibility for gene expression through a restriction of the deposition of methylated H3K9 marks into dual-cores . Our model whereby dual-cores regulate the deposition of specific epigenetic marks is in agreement with the activity of other known insulators [6 , 7 , 9–11] . Variations in H3K9me3 levels might affect the interplay between the deposition of H3K9me3 and acetylated histone H4 ( H4Ac ) marks [45] . However , no variation in the deposition of H4Ac could be found in dual-cores compared to control regions after BEAF depletion ( unpublished data ) . This is not surprising , as BEAF has no de-silencing activity on its own [5 , 25] . Computer analysis failed to reveal any enrichment of dual-cores near the 3′UTR of genes , and the activity of dual-cores may thus essentially play a role in regulating chromatin accessibility near promoter regions , but not within the 3′ border of genes . Furthermore , the insulating activity of BEAF was demonstrated in the context of two dual-cores bracketing a transgene [5 , 25] , and most likely also involved higher-level chromatin organization [2] . Although not enriched near the 3′UTR of genes , dual-cores still bracket/separate groups of genes clustered within 5–15 Kbp , a genomic context that may further require insulating activity to block promiscuous enhancer–promoter interactions and involve DNA looping between distant insulators [2] . It has recently been shown for a Su ( Hw ) insulator that the regulation of gene expression may further depend on its genomic environment [46] . Also , other dual-cores are often found in the vicinity of genes exposed to repression by heterochromatin ( see our genome-wide database ) , and the function of BEAF may be particularly important in this context [17 , 20 , 23 , 24] . We propose that the BEAF dual-cores closely linked to a restricted array of several hundred genes define a family of insulators that provide a link between chromatin organization and the cell cycle .
All genome-wide predictions and analyses are available on our Web site: http://www . sfu . ca/~eemberly/insulator/ . Additional information , including DNA sequences of single elements , dual-cores or dual-core–like elements , and their position relative to genes or other genomic features ( GOs ) can be directly retrieved from our Web site . Each single BEAF element that was not a part of a dual-core element was analyzed for the presence of a “dual-core–like” signature . We define single elements as consisting of three CGATAs within 200 bp , and a dual-core–like element as a single BEAF element ( three CGATAs ) associated with a second nearby ( <800 bp ) cluster of two CGATA sites within a 100-bp window . 1 , 226 BEAF elements fit into this classification , including all previously identified dual-cores ( BE76 , BE28 , BE51 , Jan/Ser ( BE83 ) ) . The position of each CGATA site within a dual-core sequence was analyzed relative to the position of the rightmost site of the first BEAF single element . In Figure S1 , the position of each CGATA motif was measured from the average position ( taken as position 0 on the x-axis ) of all the CGATA locations in the first BEAF single element of the dual-core . This removes any ambiguity in defining the starting position of the sequence , allowing more precise mapping of dual-cores with respect to gene promoters . We predicted dual-cores by pairing together the genome-wide set of 7 , 045 single BEAF elements that were separated by a spacer <L bp . The statistical significance of the number of predicted dual-cores as a function of spacer length L was assessed by comparing it to the expected number for randomly spaced elements . The p-value was found to reach a flat minima for 600 bp < L < 3 , 000 bp . For larger L values , the predictions decreased in significance , eventually becoming no more significant than chance . There are 1 , 720 dual-cores , L = 800 bp with a p-value of 1e-9 , in the sequenced Drosophila melanogaster genome . The statistical significance of the number of dual-cores within +/− d bp of a promoter was assessed by comparing it to the number expected for randomly placed elements . Out of 1 , 720 dual-core elements , 545 fall within +/− 500 bp of a promoter . Beyond this distance , the p-value was found to decrease in statistical significance , yet 850 dual-cores reside within 2 , 000 bp of a promoter . Additional dual-cores are found close to genes or groups of genes ( see our database ) . In order to analyze the distribution of dual-cores , we calculated the statistical significance for a minimum number of dual-cores , 2 , 3 , …x dual-cores ( DC ) to be found along 5 , 10 , …100 kbp of DNA ( W ) . For a given W and DC , we predicted N ( W , DC ) , the frequency of dual-cores for a certain DNA length . To assess the significance of N ( W , DC ) , we compared it to the number of randomly distributed elements for the same DNA length . If the probability of a random dual-core element to occur within a window of size W is p , then the probability that there are ≥DC elements in W is P ( W , DC ) = B ( x > DC , W , p ) , where B is the binomial distribution . The expected number of domain predictions for these random elements is then E ( W , DC ) = Nwin ( W ) *P ( W , DC ) , where Nwin ( W ) is the number of non-overlapping windows of size W in the entire genome . The p-value for N ( W , DC ) can then be evaluated using the expected number E ( W , DC ) as a function of W and DC . We find W = 10 kb and DC = 2 to yield the statistically most significant BEAF dual-core distribution in pairs ( p-value ∼ 1 . 01e-33 ) . The statistical significance of a GO class was assessed using the binomial distribution , p-value = B ( x , N , p ) , where x is the number of genes within the given GO class in a set of N predicted genes , and p is the probability of that GO class in the entire annotation . See our database for a complete listing of all GO analyses of positively correlated genes with or without BEAF dual-cores or DREF elements in their promoters . Genome-wide Drosophila gene expression data ( Figure S7 ) covering the first 12 hours of embryonic development are available from the Berkeley Drosophila Genome Project . Twelve time points were collected , each with three replicates . Each gene g in the genome has an expression profile containing 12 data points ( gi = ( x1 , x2 , … , x12 ) ) . For a given pair of genes , we calculated the Pearson correlation coefficient between their respective expression profiles . We then calculated the correlation coefficient between a given set of genes and a given reference gene . To test whether two sets of genes had statistically different correlation coefficient profiles , we used the Kolmogorov-Smirnov test , which assigns a p-value to the likelihood that two samples of a continuous random variable come from the same parent distribution . Chromatin immunoprecipitation ( ChIP ) was done according to the Upstate protocol using control or beaf siRNA-treated cells . Equivalent amounts of chromatin samples were sonicated using a Diagenode Bioruptor and immunoprecipitated with 4 μl of anti-H3K9me3 ( Abcam ) . Precipitated DNA was analyzed by real-time PCR in parallel with genomic DNA using a Roche Light Cycler and a Light Cycler FastStart DNA Master SYBR green kit . The amplified DNA fragments ( <250 bp ) cover regions corresponding to the indicated elements ( Figures 6 and 7 ) . ChIP with anti-BEAF was performed as previously described [34] with 10 μl affinity-purified anti-BEAF antibodies that recognize ( Figure S5 ) or not ( Figure 2 ) the BEAF-32A isoform or IgG . The immunoprecipitated DNA was analyzed in parallel with input genomic DNA as a standard . For ChIP-on-chip assays using H3K9 antibodies , precipitated DNA was amplified by ligation-mediated PCR ( LM-PCR ) . 4μg of each amplified sample was used to hybridize on 3 × 385 K tiling microarrays representing the euchromatic , non-repetitive regions of the Drosophila melanogaster genome sequence ( Flybase release 4 . 3 ) from Nimblegen Systems ( GEO accessions: GPL3352 , GPL3353 , GPL3354 ) . To calculate whether the levels of enrichment are statistically significant for each array , a normal distribution was calculated , with the assumption that the mode and median absolute deviation of the normalized log2 ratios are the average and the standard deviation of the normal distribution , respectively . Assuming that the normal distribution covers the entire background noise ( non-significant signals ) , a p-value was calculated for each oligonucleotide signal . For the two replicate samples of each profile , each pair of probe p-values were then combined using a Chi Square law with 4 degrees of freedom . Finally , correction for multiple testing [47] was applied to the combined p-values . Only oligonucleotides with final p-values ( for combined replicates ) < 1E-04 were considered to be significantly enriched for the signal . For siRNA treatments , exponentially growing Drosophila Schneider SL2 cells were maintained between 1 and 4 × 106 cells/ml in Schneider's Drosophila medium ( SDM , GIBCO , Invitrogen ) supplemented with 10% Fetal Bovine Serum ( FBS , Sigma ) and 1% penicillin/streptomycin ( GIBCO , Invitrogen ) . Cells were diluted to a final concentration of 1 × 106 cells/ml in SDM without FBS , and 400 μl of 2 μM beaf32 , dref or cdk7 double-stranded RNAs ( dsRNA ) were added directly to 10 ml of cells which were then plated on 75-cm2 T-flasks ( Sarstedt ) , immediately followed by vigorous agitation . dsRNAs were synthesized using full-length cDNAs of the above genes as templates . Primers consisted of a complementary template portion , a floating end with a T7 promoter and an EcoR1 site located at the other end . 5 μg of DNA template were transcribed for 2 hours at 37 °C in the presence of 0 . 5 mM rNTPs , 10 mM DTT , 120 units RNAse inhibitor , 60 units T7 polymerase in its 1× buffer in a 100 μl final volume . cDNA degradation was performed for 30 to 40 minutes at 37 °C in the presence of 4 units RQDNase in a 400 μl final volume of the recommended buffer . dsDNAs were then extracted with phenol/chloroform , ethanol-precipitated , and solubilized in 20 μl TE , pH 7 . 5 . The resulting sequences were checked for potential off-target effects by performing searches with dsCheck [48] ( http://dsCheck . RNAi . jp/ ) . Treated cells were incubated for 2 hours at 25°C , followed by addition of 20 ml of SDM containing FBS , and cells were incubated for an additional 5 days . Depletion of beaf32 mRNA was assayed by RT-PCR at 1 , 3 , or 5 days after treatment . Cells were grown for 5–6 days , and samples were recovered for total RNA , immunostaining , or immunoblotting analysis . FACS analyses were performed after resuspending control or BEAF-depleted cells and staining their DNA with propidium iodide . Analysis of gene expression was performed by quantitative RT-PCR on cDNAs prepared by RT-PCR from BEAF-depleted or control cells ( +5–6 days ) , untreated or treated with AA ( 5 μM ) for 24 hours . Each measurement was reproduced three times and in two independent RNA extraction experiments . For gene expression analysis , cDNAs prepared from control or BEAF-depleted cells were quantified in parallel with genomic DNA by RT-PCR using a Qiagen Light Cycler . Transfections of plasmids were performed using Lipofectamine ( Invitrogen ) for 2 hours according to the manufacturer's instructions , 48 hours before RNA purification . Measurements of gene expression for the transfected ( wild-type or mutant ) constructs were performed using primers that specifically amplify cDNAs from the tags introduced at the 5′ and 3′ borders ( see Figure 5 ) and that were unable to amplify cDNAs from untransfected cells ( unpublished data ) . Expression was normalized to the copy number of transfected constructs estimated by quantitative PCR of input genomic DNA . For endogenous genes , the primer sequences were selected from the coding regions ( ≈1 , 000 bp 3′ from promoter start ) of each gene . For endogenous cdk7/snf , the selected primers lie outside ( 15 bp 5′ or 3′ ) of the tags . For other analyses , two primer pairs were used alone or in combination to confirm the specific increase/decrease in gene expression , using actin as a control . For quantitative RT-PCR analysis in embryos , males with the BID transgene on Chromosome 2 ( CyO/Sp1; BID2B ) were crossed with virgin females harboring an embryonic da-GAL4 driver ( daughterless ) on Chromosome 3 . The corresponding measurements were compared to those from embryos expressing da-GAL4 alone or from BID2B embryos without a da-GAL4 driver . For mutagenesis of Dcore38_D , a genomic DNA fragment harboring the first exons of cdk7 and snf was cloned , and PCR-mediated mutagenesis was performed using primers that contain mismatches as followed: the dre ( DREF site ) mutant sequence is TAgCGATA and disrupts DREF binding but preserves the CGATA consensus of BEAF . The BEAF site mutant was produced by mutagenesis of two of the CGATA consensus in one cluster of the dual-core , using the ttATA mismatches critical for BEAF binding [17 , 23–25] . Immunostaining analyses were performed using affinity-purified mouse or rabbit anti-BEAF-32B ( 1:100 ) as previously described [34 , 49] , using the indicated affinity-purified antibodies or commercially available anti-acetyl-Histone H4 , anti-H3K9me3 , anti-H3 , anti-RNA polymerase II ( Upstate ) , or anti-actin antibodies ( Sigma ) . Double immunostaining of siRNA-treated cells was performed in duplicates and in parallel for control or BEAF-depleted cells treated for 1 , 3 , or 5 days . Each experiment was repeated three times . DNA was stained with 500 ng/ml DAPI or 1 μg/ml Hoechst , and coverslips were mounted with 4 μl of antifading mix and sealed with nail polish . Slides with siRNA control or BEAF-depleted cells were analyzed using the same acquisition parameters using a Leica DMRA2 microscope . Mapping of BEAF dual-cores and immunolocalization of anti-BEAF signals was performed over >10 Mbp for Chromosome 2 and X chromosome , showing striking correspondence ( analysis available upon request ) . For mapping of nucleases-sensitive sites ( Figure S8 ) , freshly isolated nuclei from approximately 108 cells were digested with very low concentrations of either microccocal nucleases or DNAase I essentially as previously described [17 , 20 , 23 , 24] , and the purified DNA was further digested with PvuII and run onto a 1 . 2% agarose gel for Southern blotting . Naked DNA controls were similarly digested . A PvuII-EcoRI end-labeled DNA fragment was used to probe specifically the region containing the dual-core region . Western blotting was performed using anti-actin or anti-BEAF antibodies . As a control , genomic DNA was first purified and then digested with MNase and Pvu II ( +/− EcoRI to mark the 5′ border of the dual-core ) before analysis by Southern blotting . Western blotting was performed as previously described [17 , 24] using anti-actin , anti-H3K9me3 , anti-mei-S332 , or anti-BEAF antibodies .
|
The genome of eukaryotes is packaged in chromatin , which consists of DNA , histones , and accessory proteins . This leads to a general repression of genes , particularly for those exposed to mostly condensed , heterochromatin regions . DNA sequences called chromatin insulators/boundary elements are able to insulate a gene from its chromosomal context by blocking promiscuous heterochromatin spreading . No common feature has been identified among the insulators/boundary elements known so far . Rather , distinct subfamilies of insulators harbor different DNA consensus sequences targeted by different DNA-binding factors , which confer their insulating activity . Determining whether distinct subfamilies possess distinct cellular functions is important for understanding genome regulation . Here , using Drosophila , we have combined computational and experimental approaches to address the function of the boundary element-associated factor ( BEAF ) subfamily of insulators . We identify hundreds of BEAF dual-cores that are defined by a particular arrangement of DNA sequence motifs bracketing nucleosome binding sequences , and that mark the genomic BEAF binding sites . BEAF dual-cores are close to hundreds of genes that regulate chromosome organization/segregation and the cell cycle . Since BEAF acts by restricting the deposition of repressing epigenetic histone marks , which affects the accessibility of chromatin , its depletion affects the expression of cell-cycle genes . Our data reveal a new role for BEAF in regulating the cell cycle through its binding to highly conserved chromatin dual-cores .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"biology",
"computational",
"biology",
"molecular",
"biology"
] |
2008
|
BEAF Regulates Cell-Cycle Genes through the Controlled Deposition of H3K9 Methylation Marks into Its Conserved Dual-Core Binding Sites
|
Behavioral and neuroimaging evidence shows that human decisions are sensitive to the statistical regularities ( mean , variance , skewness , etc . ) of reward distributions . However , it is unclear what representations human observers form to approximate reward distributions , or probability distributions in general . When the possible values of a probability distribution are numerous , it is cognitively costly and perhaps unrealistic to maintain in mind the probability of each possible value . Here we propose a Clusters of Samples ( CoS ) representation model: The samples of the to-be-represented distribution are classified into a small number of clusters and only the centroids and relative weights of the clusters are retained for future use . We tested the behavioral relevance of CoS in four experiments . On each trial , human subjects reported the mean and mode of a sequentially presented multimodal distribution of spatial positions or orientations . By varying the global and local features of the distributions , we observed systematic errors in the reported mean and mode . We found that our CoS representation of probability distributions outperformed alternative models in accounting for subjects’ response patterns . The ostensible influence of positive/negative skewness on the over/under estimation of the reported mean , analogous to the “skewness preference” phenomenon in decisions , could be well explained by models based on CoS .
As Horace Barlow wrote , “the brain has to decide upon actions in a competitive , chance-driven world , and to do this well it must know about and exploit the non-random probabilities and interdependences of objects and events” [1] . In general , the probabilistic information the cognitive system needs to deal with lies in the form of probability distributions of varying kinds: distributions of sensory stimuli in the natural environment [2–4] , distributions of sensorimotor errors for motor actions [5–7] , and distributions of rewards and penalties for alternative choices [8 , 9] . There is evidence for close-to-optimal probabilistic inference in human perception [10] , cognition [11] , and motor control [6] , suggesting that the cognitive system is capable of coding probability distributions to satisfactory precision . Given that an arbitrary distribution can have myriad possible values and render an exact representation unaffordable , what approximations may be used in human representation of probability distributions ? Three general approaches have been proposed for the internal coding of probability distributions [12] . The first is to represent the event probabilities separately [13 , 14] . However , such coding schemes would be practically impossible for continuous probability distributions where the number of potential events is infinite , unless additional discretization procedures are assumed . The second approach is sampling; that is , to represent the values of a set of samples from the underlying distribution [15–17] . Probabilistic inference or decision making , therefore , could be based upon samples harnessed from the underlying distribution [18–21] , analogous to Monte Carlo methods . Indeed , there are circumstances where people seem to base their judgment or decision on a few [22] or even one [23 , 24] random sample taken from the distribution . Third , any probability distribution , in the form of probability density or probability mass functions , may be approximated by the linear combination of a set of basis functions [25–29] , much like the fact that time series can be decomposed into the sum of sine and cosine functions in Fourier analysis . The idea of basis function representations is appealing , because it reduces the whole distribution into a set of coefficients and therefore alleviates the cognitive load subjects would have otherwise undertaken , given that the forms of the basis distributions are known [25] . Recently , Zhang , Daw , and Maloney [30] have provided preliminary behavioral evidence that people might represent their own sensorimotor error distributions with a small number of basis distributions . A fourth approach , which has not been explicitly proposed but has been the foundation of statistical decision theory , is to encode the moments of probability distributions , such as mean ( first ) , variance ( second ) , skewness ( third central moment ) , and so on [31–33] . Mathematically , the whole sequence of moments of a specific distribution contains all the information of the distribution . There have been a number of empirical studies of economic decision-making where efforts have been made to map brain regions dedicated to the calculation of the first three moments [34–38] , with the implicit assumption that different moments might be separately processed by different brain structures . In the present study , we explore the basis function hypothesis where a probability distribution is represented as a set of coefficients of particular basis functions . What is often emphasized in previous theoretical work [25 , 28] is the flexibility of this approach . In theory , any probability distribution can be well approximated as long as enough basis distributions are used . Humans in practice , however , may not be able to afford a large number of basis distributions , and the coefficients they extract from the empirical distribution are error-prone . There is increasing evidence that human representations of prior distributions can deviate significantly from the empirical distribution [30 , 39] and such deviations prove to be an important source of suboptimality in human probabilistic inference [40] . If humans do not necessarily have a lossless representation of the encoded distribution , a natural question arises: What information is extracted from the empirical distribution ? Here we propose the following representation of probability distributions ( Fig 1 ) . After a stochastic clustering process , samples from the distribution are classified into a small number of clusters and the centroids and relative weights of the clusters—{ ( ck , wk ) }k=1K—are maintained for future use . We call it Clusters-of-Samples ( CoS ) representation for which , as in the sampling approach , probabilistic information initially comes from samples and , as in the basis function approach , only a finite set of coefficients needs to be estimated and maintained to approximate the encoded distribution . An idea akin to our proposal was Shelton et al . ’s [41] select-and-sample approach , which assumes that a specific probability distribution is coded by samples but only a small set of pre-selected high-density areas of the distribution may be sampled . Similar to the CoS representation , the select-and-sample approach would reduce an arbitrary distribution to a small number of high-density centers . Shelton et al . [41] proved theoretically that such centers could efficiently approximate multimodal distributions in high-dimensional spaces while retaining the correlations between dimensions . Whereas in our hypothesis , by representing an arbitrary distribution with a set of cluster centers , we are essentially using the clusters as the basis functions to discretize the distribution and obtaining a mixture of Dirac delta functions . The CoS representation can also be extended to the usage of uniform or Gaussian distributions as the bases , with additional coefficients for the spread of the basis distributions . On the one hand , even with just a small number of clusters , a CoS representation reflects the overall shape of the encoded distribution and is capable of capturing multiple modes if the encoded distribution is multimodal . On the other hand , the CoS representation is prone to information loss . With groups of samples summarized by the cluster centers , information about individual samples in each cluster and thus local details about the distribution are lost . Moreover , the stochasticity in the clustering process may cause variations in the weights as well as in the centroids of the clusters . Due to its characteristic lossy coding , CoS can lead to systematic errors in certain tasks , which would allow us to test CoS against candidate representations that predict no such errors or different patterns of errors . Following the reasoning above , a possible testbed for CoS would be processing multimodal distributions . The main goal of our study is to test how humans cope with the structure of multimodal distributions . In Marr’s [42] term , our proposal of the CoS representation resides on the computational level , concerning what statistics for a probability distribution are internally maintained . How CoS is implemented algorithmically or biologically , however , is a separate question . In order to make our arguments concrete and testable , we specified certain computational procedures about the stochastic clustering process . In particular , we implemented the stochastic clustering process as a distance-dependent Chinese Restaurant Process ( ddCRP ) [43] . It has the desirable property that the number of clusters does not have to be specified in advance and is instead determined by a self-adaptive probabilistic process ( see Methods ) . It should also be noted that not all the alternative representations we reviewed earlier are incompatible with CoS on the computational level . For example , a sampling-based representation following the select-and-sample approach [41] may have similar behavioral consequences as CoS . In a series of behavioral experiments , we asked human subjects to report the summary statistics of visually presented distributions . On each trial ( Fig 2A ) , 70 vertical lines , whose horizontal coordinates were samples randomly drawn from a specific underlying probability distribution , were briefly and sequentially presented along the middle axis of the computer screen . Subjects’ task was to move a mouse pointer to locate ( 1 ) the Mean and ( 2 ) the Mode ( location of the highest density ) of the observed distribution of spatial positions . Subjects were not required to memorize the spatial positions of individual vertical lines but rather to report the ensemble statistics of spatial positions [44–48] ( see [49] for a review of ensemble perception ) . The Mean and Mode estimation tasks were specifically chosen to test whether subjects’ representations captured both the global features and local details of the empirical distribution . The underlying distributions were generated as the weighted mixtures of multiple evenly-spaced beta-like distributions ( Fig 2B–2D ) . By varying the relative weight of different beta components , for example by assigning more weights to the left or to the right , we were able to manipulate the global distribution to be more positively or more negatively skewed . In contrast , by varying the shape of individual beta components , we modified the local asymmetry of the distribution . Subjects’ Mean and Mode estimates , therefore , afford a unique opportunity to test different representation hypotheses . Had subjects represented the distribution exactly as it was observed , their Mean and Mode reports would be unbiased estimators about the true values of the Mean and Mode . However , in all four experiments that we tested , systematic deviations between subjects’ estimates and the ground truths of the empirical distributions were detected . We constructed computational models based on the CoS representation and alternative representations and compared different models’ performance in quantitatively predicting subjects’ Mean and Mode estimates . The CoS models outperformed the alternative models for both estimates .
In Experiment 1 , the positions of the 70 vertical lines for each trial were randomly drawn from a mixture of three beta-like distributions that adjoined each other . We call this underlying distribution “3-beta mix” , for which both the shape ( identical for all the components in the same distribution ) and weights of the beta components were varied across trials ( Fig 2D ) . The shape of the beta components could be negatively skewed , symmetric , or positively skewed ( see Methods for details ) . The weights for the three components , from left to right , could be ( 0 . 2 , 0 . 3 , 0 . 5 ) , ( 0 . 3 , 0 . 2 , 0 . 5 ) , ( 0 . 2 , 0 . 5 , 0 . 3 ) , ( 0 . 3 , 0 . 5 , 0 . 2 ) , ( 0 . 5 , 0 . 2 , 0 . 3 ) , or ( 0 . 5 , 0 . 3 , 0 . 2 ) . In what follows , “local skewness” refers to the skewness ( shape ) of the individual beta components in the 3-beta mix . We refer the skewness of the whole 3-beta mix distribution as “global skewness” here to differentiate from “local skewness” , which relies mainly on the weights of the beta components . Thus the effects of shape and weight conditions correspond to the local and global skewness effects , respectively . In the experiment , each of the 18 combinations of shape and weight conditions was repeated for 9 times , resulting in 162 trials . “True mode” and “true mean” refer to the statistics computed from the empirical distribution ( i . e . the 70 samples , see Methods ) . All 16 subjects’ Mode and Mean estimates were positively correlated with the true mode and mean ( Pearson’s correlation , all p < 0 . 001 ) . Besides , subjects’ Mode estimate was closer to the true mode than to the true Mean ( t ( 15 ) = -7 . 78 , p < 0 . 001 ) , and their Mean estimate was closer to the true Mean than to the true Mode ( t ( 15 ) = -26 . 36 , p < 0 . 001 ) , indicating that subjects did report the two statistics as instructed instead of using the same estimates for the two tasks . Meanwhile , the deviations of subjects’ Mode and Mean estimates from the ground truth varied systematically with the shape and weight conditions ( Fig 3A and 3C ) . For subjects’ errors in Mode estimates , a two-way ( 3 shapes × 6 weights ) repeated-measures ANOVA showed significant main effects of shape ( F ( 2 , 150 ) = 71 . 52 , p < 0 . 001 ) and weight conditions ( F ( 5 , 150 ) = 199 . 96 , p < 0 . 001 ) as well as their interaction ( F ( 10 , 150 ) = 3 . 25 , p = 0 . 001 ) . Further post-hoc comparisons indicated that the three shape ( local skewness ) levels differed from each other ( all p < 0 . 001 , Bonferroni corrected for three comparisons; Fig 3B ) . A similar ANOVA on subjects’ errors in Mean estimates showed that the main effect of the weight condition ( F ( 5 , 150 ) = 39 . 13 , p < 0 . 001 ) was significant , and no other effects reached the 0 . 05 significance level . Three patterns emerged from the behavioral data . First , the Mode was overestimated for positive compared to negative local skewness ( Fig 3A and 3B , differences between the three colors ) . That is , though on average still falling within the beta component where the true mode resided , subjects’ Mode estimate was biased towards the center of the beta component . Second , the Mode was overestimated for positive and underestimated for negative global skewness ( Fig 3A , the ascending lines , but note the exceptions at 253 and 352 ) . In other words , the Mode estimate was also biased towards the mean of the empirical distribution . Third , the Mean was overestimated for positive and underestimated for negative global skewness ( Fig 3C , the ascending lines ) . When choosing among probability distributions of rewards , besides the well-known preference for higher mean ( expected gain ) and lower variance [50] , it has been suggested that people tend to prefer positively skewed over symmetric , and symmetric over negatively skewed distributions . This phenomenon is known as “skewness preference” in economic decisions [51–55] , which was considered to be associated with activities in dedicated brain structures devoted to the processing of skewness [34 , 56 , 57] . Our finding that the reported Mean was positively associated with distribution skewness , other things being equal , echoed previous literature on skewness preference and raised the possibility that skewness preference might be due to the mis-estimation of the mean of skewed reward distributions . If subjects had an accurate representation of the empirical distribution and computed the required statistics properly , their Mode and Mean estimates would not systematically deviate from the true mode and mean . What representation of probability distributions could best account for the error patterns described above ? Based on the different representations introduced earlier , we constructed a variety of models for the estimations of the Mode and Mean , and compared different models’ performance in explaining the data . In the CoS representation of a specific distribution—{ ( ck , wk ) }k=1K , detailed information about the individual samples that constitute each cluster has been lost . As a result , the mode of the distribution cannot be exactly recovered from its CoS representation . In the CoS model for Mode estimates ( see Methods ) , we assume that the subject simply reports the centroid of the cluster that is of the highest weight as the Mode estimate . Intuitively , the CoS model can produce the observed two error patterns of Mode estimates ( Fig 4 ) : On the one hand , because the CoS representation is ignorant of the individual samples of each cluster and only identifies their means , the CoS model would predict that the mean—instead of the mode—of the largest beta component mainly drives subjects’ Mode estimates . On the other hand , due to the stochasticity of the clustering process , the largest cluster in the CoS representation occasionally does not correspond to the largest beta component . Thus , on average , the Mode estimate would deviate from the mean of the largest beta component towards the mean of the whole distribution . In addition , the occasional mismatch of the largest cluster in the CoS representation with the largest beta component in the underlying distribution gives rise to two more specific predictions for Mode estimates , both of which are supported by our data . First , it predicts that subjects’ Mode estimates across trials would be multimodally distributed , with the major peak centered at the largest beta component and a minor peak at the second largest beta component ( see Fig 5C for our further specifications of the data and model predictions ) . Second , it can naturally predict the observed non-monotonic increase of errors with global skewness ( Fig 3A ) : Given that the 253 weight condition has its second largest beta component on the right of its largest beta component and the 352 condition has the reverse arrangement , the former is likely to incur a more positive error than the latter , though the former is associated with negative global skewness and the latter with positive global skewness . We considered several alternative models for Mode estimates . One model is the ground-truth model , where the Mode estimate is assumed to be the same as the true mode . Apparently the ground-truth model cannot explain any systematic biases from the true mode and therefore only serves as a baseline for the other models . The second model is a Bayesian ideal observer model ( see Methods ) , in the consideration that even an ideal observer may not be able to build an accurate representation of the empirical distribution from the available samples and thus may show certain biases . Following Orhan and Jacobs’ [58] modeling of working memory , we used a Dirichlet Process Mixture Model ( DPMM ) as the generative model assumed by the ideal observer . That is , the ideal observer assumes a “bumpy” world: Each observed sample descends from a specific cluster and its value is generated from the Gaussian distribution associated with the cluster . The number of clusters and the number of samples in each cluster are assumed to follow a Dirichlet random process . DPMMs are commonly used to model cognitive processes [58–60] , which have the desirable property that the number of clusters need not to be pre-specified but can be estimated from the data . By estimating the parameters of such a generative model from the observed samples , the ideal observer can approximate the empirical distribution with a Gaussian mixture distribution , whose mode would be reported as the Mode estimate . We found that the Gaussian mixture distribution obtained by the ideal observer closely matches the empirical distribution , even for beta mixtures that have skewed beta components ( S4 Fig ) . That is , the behavior responses of the Bayesian ideal observer model would be almost equivalent to those of the ground-truth model and not show systematic errors . The other alternative models are based on a moment-based representation of probability distributions , all of which can qualitatively reproduce , at least part of , the observed error patterns in Mode estimates . The moment representation does not necessarily suggest any biases: In theory , the mode of a distribution can be recovered from the set of moments of the distribution . However , if subjects only represented the first a few moments and used them to estimate the mode , or if subjects had an unbiased internal estimate of the true mode but their responses were biased by certain task-irrelevant moments , their responses might show systematic errors . To test these possibilities , we constructed a series of moment representation models where the Mode estimate is a weighted average of the true mode and moments of the distribution plus random noises . In particular , the models were mean+skewness , mode+mean , mode+skewness , and mode+mean+skewness . We did not include variance as a predictor in these models since all the distributions were generated with equal variance . All models share a common assumption that the final Mode response undergoes an additional linear transformation and contains a Gaussian random noise , due to the imperfect mapping from perception to motor response . We fit each model to each subject’s Mode estimates using maximum likelihood estimates and computed the Akaike information criterion corrected for small sample-size ( AICc ) [61 , 62] as the metric of goodness-of-fit for model comparison . The ΔAICc of a specific model for a specific subject is defined as the difference between the AICc of the model and the lowest AICc for the subject . According to the summed ΔAICc across subjects ( Fig 5A ) , the CoS model was the best predictor of Mode estimates . A group-level Bayesian model selection [63 , 64] showed that the protected exceedance probability of the CoS model , i . e . the probability for the CoS model to outperform all the other models' predictions of Mode estimates , was close to 100% . The CoS model well predicted the systematic errors in subjects’ Mode estimates , including the non-monotonic increase of errors with global skewness and the effect of local skewness ( Fig 6A ) . On each trial , subjects reported both the mean and mode of the empirical distribution . It is reasonable to assume that the two estimates are based on a shared CoS representation . The exact CoS representation for a specific trial is not deterministic because the stochastic clustering process may end up with different clustering results and thus different CoS representations on different runs . However , subjects’ Mode estimates had allowed us to infer a probability distribution over different CoS representations for each trial ( see Methods ) and we used this information to model subjects’ Mean estimates . Given a specific CoS representation { ( ck , wk ) }k=1K , we assume that the Mean estimate is a weighted average of all ck , where the subjective weight for ck is a transformation of wk that reflects probability distortion [65] and lateral inhibition [66 , 67] . We hypothesize that the systematic errors in subjects’ Mean estimates arise as a consequence of CoS representations followed by further transformations upon CoS . The CoS representation itself would not lead to any systematic errors in Mean estimates , because the relative weight of each sample in computing the mean is faithfully transferred to the cluster it is assigned to and thus effectively not altered by the clustering process . Probability distortion and lateral inhibition , as we specify later in a model lesion analysis , would cause overweighting or underweighting of different parts of the distribution and thus biases in Mean estimates . To exclude the possibility that the observed biases are solely induced by the additional transformations , we constructed an alternative model for Mean estimates with similar transformations but no clustering—the subjective weighting model ( see Methods ) , and compared its performance with that of the CoS model ( Fig 5B & Fig 6B and 6C ) . A Bayesian ideal observer model and several moment representation models for Mean estimates were defined in a similar way as their counterparts for Mode estimates ( see Methods ) . Parallel to the models for Mode estimates , all models for Mean estimates include a linear transformation and Gaussian noise . The model fitting and comparison procedures were the same as those of Mode estimates . The CoS model outperformed the other models for Mean estimates in the summed ΔAICc . According to a group-level Bayesian model selection , it had a 99 . 5% probability to excel all the other models ( Fig 5B ) . Note that the mean+skewness model—Mean estimate as a weighted average of the mean and skewness of the distribution—was among the models that were inferior to the CoS model , though it seems to provide a straightforward explanation for the “skewness preference” ( Fig 3C ) . Similarly , though assuming a lateral inhibition between samples can cause samples underweighted in dense areas and overweighted in sparse areas and thus explain the “skewness preference” ( S5 Fig ) , the subjective weighting model without clustering fit worse to the data than the CoS model did . The CoS model for Mean estimates ( but not that for Mode estimates ) includes additional transformations . In a model lesion analysis , we tested further how the additional transformations as well as the clustering process of the CoS model contributed to its performance in Mean estimates . When lateral inhibition , power transformation , both the transformations , or clustering were removed from the CoS model ( the CoS model w/o clustering is equivalent to the subjective weighting model in Fig 5B ) , the resulting model fit worse to subjects’ Mean estimates than the CoS model did ( Fig 6B , also see S7 Fig for similar results of further experiments ) . Compared to its lesioned versions , CoS had a smaller summed ΔAICc and a protected exceedance probability of 95 . 6% . The CoS model well predicted subjects’ errors in Mean estimates ( Fig 6A ) , whereas the lesioned models failed quantitatively or qualitatively ( Fig 6C ) . The failure of the CoS model without both transformations ( i . e . the CoS w/o both model ) is straightforward since the CoS representation alone does not introduce biases into Mean estimates , as we mentioned earlier . The intuitions for the other two lesioned models are as follows . When there is no lateral inhibition ( i . e . the CoS w/o LI model ) , power transformation leads to a re-distribution of cluster weights that depends only on the values of the weights but not on how the clustered are aligned . As we elaborate later , the major clusters in subjects’ CoS representations closely followed the beta components in the empirical distribution . Therefore , the weight re-distributions would be similar for the 235 and 325 weight conditions ( weights moving from the rightmost cluster to the left two clusters ) and for the 523 and 532 weight conditions ( weights moving from the leftmost cluster to the right two clusters ) . As the result , the CoS w/o LI model predicted an S-shaped error pattern ( left panel of Fig 6C ) . In contrast , when there is lateral inhibition but no power transformation ( i . e . the CoS w/o PT model ) , the re-distribution of cluster weights depends on the alignment of the clusters , for which 235 and 325 , or 523 and 532 would be rather different . Consequently the CoS w/o PT model predicted a close-to-linear error pattern ( central panel of Fig 6C ) . The error patterns predicted by the CoS model as well as that of the data lay between those of the two lesioned models . To see whether subjects really , as we assumed , reported their Mode and Mean estimates based on a common CoS representation , we constructed an additional lesioned CoS model for Mean estimates that does not use the representational information inferred from the Mode estimate on the same trial . If two distinct CoS representations had been used for Mode and Mean estimates , the CoS representation inferred from the Mode estimate would be non-informative for predicting the Mean estimate and the lesioned model would perform equally well as the original model . Instead , we found that this lesioned model was inferior to the original CoS model in fitting subjects’ Mean estimates ( S6 Fig ) , thus providing evidence for a shared CoS representation across the two estimation tasks . Though more than one model can qualitatively predict the error patterns of Mode and Mean estimates in Fig 3 , the CoS models outperformed the alternative models in predicting the full distributions of Mode and Mean estimates . Fig 5C shows the joint distributions of Mode and Mean estimates , collapsed across subjects and separately for the six weight conditions , compared between data and model predictions . In most of the data plots , the joint distributions appeared to be multimodal: We can see at least two separate peaks , one dominant and the other minor ( see S9 Fig for statistical evidence ) . The major peak for each weight condition corresponds to the beta component of the largest weight , while the minor peak corresponds to the second-largest beta component . The CoS models predicted such multimodality , while the moment representation models predicted only unimodal distributions . The CoS representation could lead to the observed multimodality in Mode estimates because of the stochasticity inherent in its clustering process so that the same empirical distribution may be parsed into different partitions in different runs . To illustrate how the clusters in subjects’ CoS representations can capture the statistics of the empirical distribution , we plot the relative frequencies of different cluster sizes ( i . e . number of samples per cluster , see Fig 5D ) , which were averaged across trials and possible CoS representations on each trial . The frequencies for 14 , 21 , and 35 samples per cluster—which correspond to the relative weights of 0 . 2 , 0 . 3 , and 0 . 5 that were used in our stimuli—were much higher than those of their neighbors , indicating that the clustering process could partly recover the multimodal structure of the empirical distribution . To test whether our findings in Experiment 1 can generalize to decision contexts other than spatial position judgments , we performed Experiment S1 ( see Methods and S8 Fig ) , which used the same design as Experiment 1 but replaced spatial positions with orientations—another widely used stimuli in ensemble perception [49] . The results of Experiment S1 ( S3 Fig ) replicated the major findings of Experiment 1 , including the three error patterns in Mode and Mean estimates , the superiority of the CoS models over the alternative models , and the multimodal distributional structure captured in subjects’ CoS representations . We also noted a slight difference between the results of the orientation and the spatial position experiments: the CoS model for Mean estimates in Experiment S1 had a considerably lower protected exceedance probability compared to its counterpart in Experiment 1 . In Experiment 1 , we found that subjects might be sensitive to the multimodality of the distribution and formed clusters accordingly . To further test this hypothesis , we conducted two new experiments , where the task structures were similar to Experiment 1 but the number of modes in the underlying distributions were varied . In Experiment 2 , we increased the number of modes to four . In Experiment 3 , we had separate mini-blocks where the numbers of modes were different ( three or four ) . Sixteen new subjects took part in Experiment 2 , where the underlying distribution in each trial was a mixture of four beta-like distributions , abbreviated as 4-beta mix ( Fig 2C ) . As in Experiment 1 , we found systematic deviations of the Mode and Mean estimates from the true mode and mean ( S1 Fig ) : Both errors were significantly influenced by the weight condition , F ( 23 , 644 ) = 53 . 27 , p < 0 . 001 , and F ( 23 , 644 ) = 13 . 58 , p < 0 . 001 , according to repeated-measures ANOVAs . For Mode estimates , the main effect of the shape condition ( F ( 2 , 644 ) = 4 . 83 , p = 0 . 008 ) and its interaction with the weight condition ( F ( 46 , 644 ) = 2 . 87 , p < 0 . 001 ) were also significant . Post-hoc comparisons ( Bonferroni corrected for three comparisons ) showed that the errors for locally-negatively-skewed distributions were significantly more negative than those of the locally-symmetric ( t ( 14 ) = -3 . 684 , p = 0 . 008 ) and locally-positively-skewed distributions ( t ( 14 ) = -4 . 481 , p < 0 . 001 ) , while the difference between the latter two conditions was insignificant ( t ( 14 ) = 0 . 011 , p = 1 . 000 ) . There were no other significant effects for Mean estimates . Again , the Bayesian ideal observer model was able to perfectly recover the underlying 4-beta mix distribution ( S4B Fig ) , which could not explain the systematic errors in Experiment 2 . Among the models developed for Experiment 1 , the CoS models still performed the best in predicting subjects’ Mode and Mean estimates in Experiment 2 , according to summed ΔAICc ( S1 Fig ) . The probabilities for the CoS models to excel the alternative models ( i . e . protected exceedance probability ) were 100 . 0% and 93 . 6% , respectively for the Mode and Mean estimates . Similar to Experiment 1 , subjects’ CoS representations could capture the multimodal structure of the empirical distribution: Clusters of 7 , 14 , 21 , and 28 samples , which correspond to the relative weights of 4-beta mix , stood out from the histogram ( S1F Fig ) . In Experiment 3 , we tested whether our CoS model is flexible enough to adapt to a dynamic environment where 3-beta mix and 4-beta mix distributions were presented in alternating mini-blocks of 10–14 trials . As in Experiments 1 and 2 , all subjects’ Mode and Mean estimates were significantly correlated with the true mode and mean ( Pearson’s correlation , all p < 0 . 001 ) but had systematic errors ( S2 Fig ) , with the error patterns of the 3-beta and 4-beta trials respectively resembled those of Experiments 1 and 2 . We performed 2 ( shape conditions ) × 2 ( weight conditions ) repeated-measures ANOVAs separately for 3-beta and 4-beta trials . For 3-beta trials , the main effects of the shape condition ( F ( 2 , 150 ) = 9 . 43 , p < 0 . 001 ) and the weight condition ( F ( 5 , 150 ) = 82 . 73 , p < 0 . 001 ) were significant for Mode estimates , while only the main effect of the weight condition was significant for Mean estimates ( F ( 5 , 150 ) = 22 . 78 , p < 0 . 001 ) . For 4-beta trials , the main effects of the shape condition ( F ( 2 , 690 ) = 3 . 6 , p = 0 . 028 ) and the weight condition ( F ( 23 , 690 ) = 32 . 48 , p < 0 . 001 ) , and their interaction ( F ( 46 , 690 ) = 1 . 95 , p < 0 . 001 ) were significant for Mode estimates , while only the main effect of the weight condition ( F ( 23 , 690 ) = 4 . 36 , p < 0 . 001 ) was significant for Mean estimates . The results of model comparisons replicated those of Experiments 1 and 2: The CoS models fit best to both the Mode and Mean estimates , according to summed ΔAICc ( S2 Fig ) . On the group level , the probabilities for the CoS models to outperform the alternative models approached 100% and were 99 . 9% , respectively for the Mode and Mean estimates . The frequency statistics of cluster sizes of the 3-beta and 4-beta trials mimicked those of Experiments 1 and 2 .
The fact that probability distributions can be represented by their central moments—mean , variance , skewness , etc . —has gained increasing popularity in cognitive and decision neuroscience research . Though it was not among the theoretical possibilities formally proposed [12] , the moment representation was implicitly assumed in the decision making studies that attempted to separate the brain regions for mean , variance , and skewness [34 , 37 , 38] . These studies tested subjects’ preferences for different reward distributions to see how the choice-related neural activities may vary with specific moments of the reward distribution . However , concerning the coding of the moments higher than the mean ( i . e . variance and skewness ) , the brain regions identified by recent neuroimaging studies were rather inconsistent: Variance representation was associated with anterior cingulate cortex [34] or ventral striatum and anterior insula [38]; skewness was associated with dorsal insula [34] , ventral striatum [38] , or anterior insula and dorsomedial prefrontal cortex [37] . These conflicting findings raised the possibility that variance and skewness might not be the variables actually encoded in the brain . Instead , their influences on human decisions may come through another set of more basic variables humans adopt to appraise uncertainty . Indeed , we found that the moment-based models were inferior to the CoS models in fitting both the Mode and Mean estimates , even though the coding of task-irrelevant skewness seems to be a natural explanation for the increase of the Mode and Mean estimates with the skewness of the distribution . An obvious failure of the moment-based models was their inability to capture the multimodality of subjects’ responses . The phenomenon of skewness preference—positively skewed reward distributions are favored over symmetric , and symmetric over negatively skewed reward distributions—have been widely reported in animal studies [57] and in economics and finance [34 , 53 , 54 , 56] . Recent studies started to look into the neural basis of skewness preference , in an attempt to identify the brain regions dedicated to the processing of skewness [34 , 37 , 38] . However , as discussed earlier , identification of brain regions dedicated in skewness processing is still actively debated and the proposition of skewness ( or variance ) preference was inconsistent with emerging behavioral results . For example , Strait and Hayden [35] reported a non-monotonic preference ranking of reward distributions in monkeys where weakly negatively skewed reward distributions were less preferable to weakly positively skewed reward distributions and the latter were further less preferable to strongly negatively skewed reward distributions . Alternative explanations other than explicit representation of skewness have been proposed for skewness preference . For example , Genest , Stauffer , and Schultz [57] measured the utility function for individual monkeys and found that the skewness preference in monkeys’ choices can be accounted for by utility maximization , given that the monkeys’ utility functions are convex . This explanation , however , might have trouble generalizing to the skewness preference observed in human choices [34 , 56] , because humans’ utility functions are typically concave [68 , 69] ) and would predict the opposite behavior . Indeed , a recent empirical study of decision under risk in humans [70] reported the coexistence of skewness preference with concave utility functions . The findings of the present study suggest a new possibility: skewness preference can be the epi-phenomenon of mis-estimating the mean—the expected value of the distribution . We found that subjects overestimated the mean of positively skewed distributions and underestimated the mean of negatively skewed distributions . This would appear to be skewness preference if subjects had been asked to choose between two distributions to maximize the expected value of their choice . In accordance with our conjecture , one recent neuroimaging study showed that higher skewness of reward distributions would lead to stronger activation in ventral striatum [38] , a brain region involved in the representation of expected value [71 , 72] . Since no preference judgment was involved in our task , the patterned errors we found in subjects’ Mean estimates cannot be an effect of non-linear utility functions . Instead , we showed that an approximate representation of probability distributions along with subsequent distortions of probabilities , as implemented in the CoS model for Mean estimates , may give rise to the “skewness preference” . Representing probability distributions in the real world , which are often high-dimensional and multimodal , confronts human cognition with potential problems such as the curse of dimensionality [28 , 73–76] . The CoS representation proposed here provides a simplified representation of probability distributions by reducing an arbitrary distribution to a few pairs of summary statistics { ( ck , wk ) }k=1K . Though coming at the cost of information loss , such simplification is likely to alleviate the mnemonic and computational load in probabilistic inference and decision making . As a basis-function representation [25 , 29 , 30] , CoS adds to extant sparse-coding approaches to simplifying the representation and computation of probabilistic information [75 , 77 , 78] . Zhang , Daw , and Maloney [30] investigated subjects’ internal representation of their own visuo-motor error distributions in a motor choice task and found empirical evidence for the basis-function representation . Though the objective distribution was unimodal and close to Gaussian , they found that subjects’ internal representation was multimodal and , among a variety of distribution families , was best fit by the mixture of a small number of non-overlapping basis functions . What remains unknown , however , is how subjects’ internal representation arises from the empirical distribution . Here , with the CoS representation , we attempted to bridge the gap between an empirical distribution and its internal representation through a stochastic clustering process . We showed that a Bayesian ideal observer that is unaware of the generative process of the distribution stimuli used in our experiments can form an accurate representation of the empirical distribution based on Gaussian mixtures ( S4 Fig ) . As a result , the ideal observer model failed to account for the systematic errors in subjects’ Mode and Mean estimations , but rather performed close to the ground-truth model ( Fig 5A and 5B ) . The CoS model differs from the ideal observer model in several aspects . First , CoS does not involve Bayesian inference and the stochasticity inherent in clustering may not be eliminated even with a large number of samples observed . Second , by only keeping the cluster centers , CoS loses higher-order information about each cluster such as the local skewness . As we reasoned earlier , these characteristics allow CoS to explain the specific error patterns in subjects’ Mode estimates . The combination of CoS and additional transformations but not the transformations alone can also explain subjects’ Mean estimates . By comparing CoS with the ideal observer model in predicting human data , we have obtained evidence for the two key assumptions of the CoS representation: stochastic clustering and the loss of local information . Our results raise the possibility that inferring the generative process from the observed samples might not occur for a complicated generative process . Even when the form of the generative model is simple and known , a recent study [79] found that people tend not to estimate the generative model . On each trial of their task , subjects saw an array of four dots distributed around a vertical line and were explicitly informed that the horizontal coordinates of the dots were generated from a Gaussian distribution centered at the line . Subjects were required to locate the range that would include 65% of the distribution . Despite comprehensive feedbacks after each trial , subjects’ behavioral patterns systematically deviated from those predicted by a Gaussian internal model . Instead , subjects’ internal model was well approximated by a kernel density estimation based on the four samples , which was consequently multimodal . Though there is doubt whether the kernel density representation holds for distributions presented by more than four samples ( see [30] for opposing evidence ) , the results of [79] as well as our own study suggest that humans may not function as Bayesian ideal observers in probability density estimation . The CoS representation we proposed also echoes the spontaneous clustering process theorized in the memory literature , for both working memory [58 , 80] and long-term memory [81 , 82] . According to Orhan and Jacobs [58] , people assume the world is “bumpy” and try to infer the clusters from which individual items have been generated . Though their task was to memorize individual items , subjects’ biases during retrieval suggest that the inferred clusters were also maintained and used to compensate for perceptual and mnemonic noises . What we considered here is a different situation , where the task was not to memorize individual items but to report the summary statistics of a distribution . That is , subjects were nudged to extract a representation of the distribution from the samples . While an ideal observer can almost perfectly recover the underlying distribution used in our experiments ( S4 Fig ) , human behavioral data suggested that the local features of the distribution were lost . For Gaussian or any symmetric unimodal distributions , mean , median , and mode are all the same . In such cases , it would be theoretically difficult to tell apart different hypotheses about human representations of probability distributions . That is why highly skewed [40 , 83 , 84] or multi-modal [6 , 40 , 85] distributions have been used to investigate how people represent probability distributions . Indeed , the specific one-dimensional multimodal distributions we used in the present study revealed diagnostic error patterns , which provided preliminary evidence in support of the CoS representation . But it is still an empirical question whether CoS well describes human representations of probability distributions that are beyond multimodal distributions . In theory , the CoS representation is applicable to distributions of a higher dimension . It can also be extended to accommodate modulations from top-down cognitive processes by assuming that the parameters controlling the clustering process may be modulated by prior knowledge . Further empirical tests in a broader range of tasks would be required to establish the CoS representation as a general heuristic in representing probability distributions .
The experiments had been approved by the Institutional Review Board of School of Psychological and Cognitive Sciences at Peking University . Informed consent was given by all subjects prior to the experiments . Sixty-four paid subjects ( 19–25 years old , 36 females ) participated in the four experiments , with 16 subjects for each experiment . All of them were naïve to the goal of our study . Subjects whose Mode or Mean estimates failed to show significant correlations with the corresponding true statistics were excluded from further data analysis . Only one subject from Experiment 2 was excluded . Stimuli were presented on black backgrounds on a 52 . 0×32 . 5-cm computer screen ( 1920×1200 px , refresh rate 60 Hz ) controlled by Matlab and Psychophysics Toolbox [86–88] and were viewed by subjects from a distance of approximately 65 cm . The experimental procedure was the same for all the experiments , with spatial positions used as stimuli for Experiments 1–3 and orientations used for Experiment S1 . On each trial of Experiments 1–3 ( Fig 2A ) , following a 1-s fixation cross , a 40-cm white horizontal axis appeared in the middle of the screen . In the subsequent 25 seconds , 70 red vertical lines were sequentially presented on the axis at different horizontal positions , each for 0 . 18-s and separated by 0 . 18-s intervals . The tasks were to report the Mean and Mode of the observed horizontal distribution of the red lines: Subjects first saw a blue vertical line and were required to move it along the axis to locate the Mean ( the average horizontal position of the red lines ) ; after completing the estimation of the Mean , they saw a blue box and were instructed to locate it at the position where it would catch the largest number of red lines . To help subjects understand the task , graphed illustrations of mean and mode were given during the instructions . The initial position of the blue line or box was randomly chosen . Subjects used the mouse cursor to move it and left clicked to confirm . No time limit was imposed on either task . The horizontal coordinates of the 70 red lines on each trial were sampled from a specific underlying distribution , which was a linear combination of multiple beta-like distributions ( Fig 2B–2D ) . Experiments 1–3 differed in the number of components that consisted of the beta-mix distributions: three components for Experiment 1 ( “3-beta mix” ) , four components for Experiment 2 ( “4-beta mix” ) , and a combination of 3-beta and 4-beta mix for Experiment 3 . Each beta-mix distribution had two sets of parameters: shape and weight . The shape parameters , ( α , β ) , controlled the shape of individual beta components and were the same for all the components in the same distribution . The ( α , β ) could be ( 3 . 1 , 1 . 1 ) , ( 2 . 9 , 2 . 9 ) or ( 1 . 1 , 3 . 1 ) , respectively corresponding to negatively-skewed , symmetric , and positively-skewed local components of equal variance . In contrast , the weight parameters , ( φ1 , φ2 , … , φm ) , with ∑i=1mφi=1 , referred to the relative weight of each component in the mixture distribution , ordered from left to right . The different beta components of a beta-mix distribution had equal widths and joined each other’s ends , whose standard deviations were 0 . 19 times of their widths . In Experiment 1 , where the distributions were 3-beta mix , the relative weights could be ( 0 . 2 , 0 . 3 , 0 . 5 ) , ( 0 . 3 , 0 . 2 , 0 . 5 ) , ( 0 . 2 , 0 . 5 , 0 . 3 ) , ( 0 . 3 , 0 . 5 , 0 . 2 ) , ( 0 . 5 , 0 . 2 , 0 . 3 ) , or ( 0 . 5 , 0 . 3 , 0 . 2 ) , that is , the full permutation of ( 0 . 2 , 0 . 3 , 0 . 5 ) . Each combination of the 3 shape and 6 weight conditions was repeated for 9 times , resulting in 3×6×9 = 162 trials . In Experiment 2 , the relative weights for the 4-beta mix were the full permutation of ( 0 . 1 , 0 . 2 , 0 . 3 , 0 . 4 ) . Each combination of the 3 shape and 24 weight conditions was repeated twice , resulting in 3×24×2 = 144 trials . Experiment 3 was a combination of 72 trials of 3-beta mix from Experiment 1 ( 3 shapes × 6 weights × 4 repetitions ) and 72 trials of 4-beta mix from Experiment 2 ( 3 shapes × 24 weights × 1 repetition ) . The 144 trials were divided into 12 mini-blocks of 10–14 trials , with each mini-block devoted to either 3-beta or 4-beta mix and the two types of mini-blocks interleaved . The existence of mini-blocks was unbeknown to the subject . The 70 samples for each trial ( i . e . the horizontal coordinates of the red lines ) were first generated by random and independent draws from its underlying beta-mix distribution , and subsequently scaled to a specific standard deviation and jittered around the center of the screen . The standard deviations of the samples for Experiments 1 , 2 , and 3 were respectively set to be 7 . 27 , 7 . 20 and 7 . 55 cm . Given the screen center as the origin , the samples were allowed to range from –20 to 20 cm and the mean of the samples was within the range of –3 . 8 to 3 . 8 cm . In a specific experiment , the same set of samples was used for all subjects , but the order of samples within each trial and the order of the trials were randomized for each subject . Experiment S1 was a conceptual replication of Experiment 1 where orientations instead of spatial positions were used as stimuli ( S8 Fig ) . Samples were lines of 8 cm long , starting from the center of the screen and pointing to various directions . For a specific subject , all sample lines pointed towards either the upper or lower half of the screen so that the whole range of the stimuli was within 180 degrees . Subjects rotated a line or bar around the origin to report the Mean or Mode of the orientations , analogous to the responding procedures in Experiments 1–3 . Half of the subjects reported the Mean first and half reported the Mode first . We did not find any significant differences between these two task orders in subjects’ error patterns , no matter for Mode or Mean estimates . There were three practice trials preceding the formal experiment . Each experiment took approximately 1 . 5 hours .
|
Life is full of uncertainties: An action may yield multiple possible consequences and a percept may imply multiple possible causes . To survive , humans and animals must compensate for the uncertainty in the environment and in their own perceptual and motor systems . However , how humans represent probability distributions to fulfill probabilistic computations for perception and action remains elusive . The number of possible values in a distribution is vast and grows exponentially with the dimension of the distribution . It would be costly , if not impossible , to maintain the probability of each possible value . Here we propose a sparse representation of probability distributions , which can reduce an arbitrary distribution to a small set of coefficients while still keeping important global and local features of the original distribution . Our experiments provide preliminary evidence for the use of such representations in human cognition .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"statistical",
"noise",
"decision",
"making",
"statistics",
"social",
"sciences",
"vertebrates",
"neuroscience",
"animals",
"mammals",
"primates",
"probability",
"distribution",
"mathematics",
"cognitive",
"psychology",
"cognition",
"skewness",
"gaussian",
"noise",
"statistical",
"distributions",
"monkeys",
"behavior",
"probability",
"theory",
"psychology",
"eukaryota",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"cognitive",
"science",
"amniotes",
"organisms"
] |
2019
|
Human representation of multimodal distributions as clusters of samples
|
Of the membrane proteins of known structure , we found that a remarkable 67% of the water soluble domains are structurally similar to water soluble proteins of known structure . Moreover , 41% of known water soluble protein structures share a domain with an already known membrane protein structure . We also found that functional residues are frequently conserved between extramembrane domains of membrane and soluble proteins that share structural similarity . These results suggest membrane and soluble proteins readily exchange domains and their attendant functionalities . The exchanges between membrane and soluble proteins are particularly frequent in eukaryotes , indicating that this is an important mechanism for increasing functional complexity . The high level of structural overlap between the two classes of proteins provides an opportunity to employ the extensive information on soluble proteins to illuminate membrane protein structure and function , for which much less is known . To this end , we employed structure guided sequence alignment to elucidate the functions of membrane proteins in the human genome . Our results bridge the gap of fold space between membrane and water soluble proteins and provide a resource for the prediction of membrane protein function . A database of predicted structural and functional relationships for proteins in the human genome is provided at sbi . postech . ac . kr/emdmp .
The structural space of soluble proteins has been extensively explored . Indeed , most single-domain soluble proteins now appear to have at least one structural homolog in the current PDB database [1] , [2] . In contrast , the exploration of membrane protein fold space lags far behind [3]–[5] . Moreover , much more work has been directed at soluble proteins , so functional annotations are much more extensive for soluble proteins as well . Membrane proteins reside in a hydrophobic lipid-bilayer , but their extra-membrane regions are exposed to same folding environment as soluble proteins [5] . Thus , fold space of membrane proteins may be connected with soluble proteins through the extra-membrane portions . Indeed , many membrane proteins contain large extracellular domains that can be separated from the membrane embedded domain and they behave as stable soluble proteins . We therefore examined how much overlap exists between the structure spaces of soluble proteins and membrane proteins . If there is extensive domain sharing , it may be possible to use the vast data on soluble proteins to provide information on their membrane protein relatives . Here , we used a large-scale structure comparison to explore domain sharing between membrane and soluble proteins . We found that: ( i ) a large fraction of membrane proteins share structural similarities with soluble proteins , ( ii ) the domain exchanges between membrane and soluble proteins are particularly frequent in eukaryotes , ( iii ) in many cases , residues in functional sites are conserved between membrane and soluble protein pairs . These results imply that we can use the extensive knowledge of soluble protein function , to infer previously uncharacterized membrane protein functions . We therefore employed structure guided sequence alignment to elucidate the functions of membrane proteins in the human proteome .
We compared the structures of the extramembrane domains of 558 membrane proteins with 43 , 547 soluble protein structure in the PDB by using TM-align [6] which is a suitable tool for large-scale structural comparisons . We found that structure comparison results from various tools were similar ( Figure S1A and S1B ) , but TM-align was faster than other structure alignment programs . Domain structures were considered to be similar if the RMSD was less than 5 Å over an aligned length of more than 100 residues , and a confidence score of more than 0 . 5 [6] . In the current PDB library , 67% ( 376 ) of the membrane proteins share a domain structure with soluble proteins ( Figure 1A ) . Moreover , 41% ( 17 , 858 ) of soluble proteins share structural similarity with the already known membrane protein structures . The structurally similar membrane and soluble proteins have a mean RMSD of 3 . 9 Å and a mean aligned length of 162 residues . Furthermore , we found that a large fraction of non-redundant membrane protein structures shared extramembrane domains with soluble proteins . We applied PISCES [7] with sequence identity threshold 30% to remove the redundant sequences . Among the 160 non-redundant membrane protein structures , 68% ( 106 ) of membrane proteins share extramembrane domains with soluble proteins ( Figure S2 ) . As shown in Figure 1B , the distribution of structural relatives is skewed toward distant relationships with low sequence identity . Thus , most of these relationships would have been undetectable by sequence methods alone , which explains why the high degree of overlap between membrane and soluble protein structures has not been previously observed to our knowledge . The structure alignment data between membrane and soluble proteins are available at: sbi . postech . ac . kr/emdmp . In the web-server , users can search membrane and soluble proteins by PDB ids or Pfam domains and download all structure alignment results ( Figure S3 ) . We found that majority of globular domains shared between membrane and soluble proteins are located at the ‘outside’ region of membrane proteins . We mapped the topology information ( i . e . inside and outside regions ) onto membrane protein structures aligned with soluble proteins . Among the 376 membrane protein structures , we found that 95 . 7% ( 360 ) structures are located at the ‘outside’ region , whereas only 4 . 3% ( 16 ) structures are located at the ‘inside’ region , suggesting that domain exchange were much more frequent at the outside region of membrane proteins . Interestingly , structures located at the outside region of membrane proteins had larger alignment than structures of inside region . Shared domains located at the outside region have a mean aligned length of 163 residues , whereas domains located at the inside region have a mean aligned length of 116 residues ( Figure S4 ) . The extramembrane domains that have soluble counterpart appear to be less intimately associated with the membrane or membrane embedded domains . To assess the degree of membrane association , we determined the average membrane distance of extramembrane domain , measured by the z-coordinate information from PDBTM database [8] ( detailed description in Material and Methods section ) . The average membrane distance of extramembrane domains that have soluble counterpart was 25 . 9 Å , whereas the distance without soluble counterpart was 20 . 7 Å ( p-value = 0 . 0088 , Mann-Whitney U ) ( Figure S5 ) . This result may reflect the more facile exchange of domains that are not deeply entwined within the membrane protein structure . The structural relatives do not appear to be restricted to any particular type of fold as they span many SCOP classes , including all alpha , all beta , alpha+beta and alpha/beta classes ( Figure 2 ) . The aligned pairs share 352 different fold types ( Table S1 ) which is roughly a quarter of the 1 , 200 total fold types in SCOP [9] . These results indicate that diverse fold types performing various biological functions are shared between membrane and soluble proteins . We conducted a comprehensive gene ontology analysis for 29% membrane proteins that have no counterpart in the soluble proteins . It turned out that these membrane proteins were GPCRs families and sensory receptors families ( G-proteins coupled receptor protein signaling pathway; p = 6 . 78e-54 , sensory perception of chemical stimulus; p = 3 . 15e-49 , sensory perception of smell; p = 6 . 58e-48 ) ( Table S2 ) . They usually have short extra-membrane regions and tend not to share globular domains with soluble proteins [10] . Figure 3A shows the distribution of sequence identities between soluble and membrane proteins grouped into archaea , bacterial and eukaryotes . High sequence identities reveal the soluble/membrane domain exchanges that occurred relatively recently in evolutionary history . The high sequence identities are dominated by eukaryotes , suggesting that many of the soluble/membrane protein exchanges in eukaryotes are relatively new developments . Figure 3B compares sequence identity distributions according to their functional ontologies . The basal cellular functions have the lowest sequence identities between membrane and soluble proteins , consistent with their ancient origin , whereas the more complex functions associated with eukaryotic organisms have higher sequence identities . These results suggest that as life became more complex , recombination of membrane and soluble proteins became more common and important . Proteins that share similar domain structures often have similar functions even with very low sequence similarity . For example , the nicotinic acetylcholine receptor and acetylcholine-binding protein , which both bind acetylcholine , are found to share a domain that aligned with 2 . 94 Å RMSD over 173 residues , but shares only 17 . 3% sequence identity ( Figure 4A ) [11] . The chloride intracellular channel and glutathione S-transferase ( GST ) can be aligned with 3 . 43 Å RMSD over 159 residues , but share only 2 . 9% sequence identity ( Figure 4B ) . Both proteins share a glutathione S-transferase function [12]–[14] . Thus , structural similarity can often suggest a functional similarity that cannot always be detected by sequence similarity . It therefore seems possible , given the extensive domain sharing noted above , to learn more about membrane protein functions by employing the annotations available for soluble proteins . The soluble protein knowledge base could provide a rich source of information for membrane proteins as soluble proteins have generally been studied more extensively . Consistent with this history , only 26% of membrane protein domains that we found to align to soluble domains have annotated biochemical functions ( 109 of 414 proteins ) . In contrast , 72% ( 13 , 044 of 17 , 972 proteins ) of soluble proteins that share domain structure with membrane proteins have domain annotation in the aligned regions ( Figure 4C ) . A common structure does not always imply a common function , however , so we examined the degree to which functional annotations might be transferrable from soluble proteins to membrane protein extracellular domains . To test the possibility of functional overlaps , we asked whether residues known to be critical for function were conserved in the structurally aligned pairs . For proteins with catalytic residues defined in the Catalytic Site Atlas ( CSA ) database [15] we found that 56% ( 114 of 211 proteins ) of aligned structures share identical functional residues ( Figure 5A ) . For example , the functional residues of bovine heart phosphotyrosyl phosphatase ( soluble protein ) are found to be conserved in envelope structure-factor ( membrane protein ) , although their sequence identity is only 4 . 7% over 116 residues ( Figure 5B ) . Bovine heart phosphotyrosyl phosphatase has a tyrosine phosphatase domain with the catalytic site residues , Cys12 and Cys17 . Envelope structure-factor currently has no domain annotation , but the conserved catalytic sites as well as the aligned domain structures suggest that they may share a general biochemical function . Also , Penicillin-binding protein ( membrane protein ) and Oxa-10 β-lactamase ( soluble protein ) share identical functional residues although they only share 13 . 2% overall sequence identity over 218 residues ( Figure 5C ) . Both apparently interact with β-lactam antibiotics . These results suggest that structure-guided alignments between membrane and soluble proteins can be useful for inferring unknown functions of extra-membrane domains . We analyzed sequence identity of the first and second shell residues around the common functional sites compared to the rest of the residues . We defined the first shell residues as those within a distance of 5 Å of a known functional residue . The second shell residues were defined as the group of residues within 5 Å from the first shell residues . Sequence similarity scores were calculated using a PAM-250 matrix with the gap penalty of −11 . We found that the first and second shell residues showed higher sequence similarity ( Figure S6 ) . Among the 471 structure pairs of membrane and soluble proteins , 412 structure pairs have higher sequence similarity at the first and second shell residues than other regions ( Table S3 ) . For example , the first and second shell residues around common functional sites of envelope structure-factor ( 1BHY ) and bovine heart phosphotyrosyl phosphatase ( 1PNT ) have higher sequence similarity than the rest of the residues ( Figure S7A ) . The first and second shell residues of functional sites have a sequence similarity score of 123 , whereas other residues have a sequence similarity score of 51 . 3 . Also , the first and second shell residues of the functional sites of penicillin-binding protein ( 1K25 ) and Oxa-10 β-lactamase ( 1E4D ) had higher sequence similarity than the rest of the residues ( Figure S7 ) . We compared the functional annotations of membrane and soluble protein domains that share conserved functional residues . We discovered that 41% ( 28 ) of membrane protein domains share same the functional annotations with soluble domains and 31% ( 21 ) of membrane protein domains do not have functional annotation ( Figure S8 ) . Thus , these membrane protein functions can be inferred from the functional annotation of soluble proteins . But , 26% ( 18 ) of membrane protein domains turned out to have ambiguous functional annotations whose annotation were dissimilar but somewhat related . For example , membrane protein 1NRF has been annotated as beta-Lactamase/D-ala carboxypeptidase and soluble counterpart 2G2U has been annotated as beat-lactamase-inhibitor protein . We provide the list of functional annotation of membrane and soluble protein domains that share common functional residues ( Table S4 ) We examined how frequently shared domains between membrane and soluble proteins were found from same SCOP folds . Of 87 structurally similar domains , 60 ( 68 . 9% ) extramembrane domains and soluble protein domain shared same SCOP folds , whereas 27 ( 31 . 1% ) domains appeared in different SCOP folds ( Figure S9A and Table S5 ) . The number of fold types annotated for membrane proteins is much smaller than that of soluble proteins ( Figure S9B ) . Specifically , structural pairs that share same SCOP fold were usually found from the extramembrane regions of membrane proteins . Meanwhile , structural pairs with different SCOP folds were mostly found from fold annotations assigned to whole membrane protein structures including both transmembrane and extramembrane regions . We examined what kinds of membrane protein functions can be inferred from our work and to what extent . We classified membrane protein functions into 3 large families , such as receptors , transporters and enzymes , and divided into 16 sub families . We found that extramembrane domains shared between membrane and soluble proteins were mainly found from the enzyme family . Specifically , about 50% of the enzyme family of membrane proteins shares extramembrane domains with soluble counterparts , whereas less than 25% of the receptor family shares extramembrane domains with soluble counterparts ( Figure S10 ) . It suggests that function of membrane proteins in the enzyme family can be more likely inferred from the structural comparisons with soluble counterparts . The results described above indicate that membrane and soluble proteins extensively exchange domains and that soluble domain annotations can be useful for suggesting functions of the membrane domains . There are relatively few membrane protein structures , however , and the vast majority of structurally related proteins show little detectable sequence similarity . We therefore sought to expand the utility of the soluble domain structure database using both sequence and structural information . To detect distant relationships that are not apparent by sequence similarity alone , we employed the secondary structure element alignment method ( SSEA ) [16] . To test the effectiveness of the SSEA method for detecting distant relationships and to identify appropriate cutoffs , we generated training sets . A positive set included 923 similar membrane and soluble protein structures with less than 5 Å RMSD and sequence identity ranging from 5 to 15% . The negative set included 210 dissimilar structure pairs with greater than 10 Å RMSD and sequence identity ranging from 5 to 15% . As shown in Figure 6A and 6B , the SSEA method can effectively separate the two training sets at an SSEA score of 50 ( P-value<1 . 0×10−100 ) [16] . Moreover , we calculated the probability of finding structure pairs with RMSD<5 Å and discovered that it was dramatically increased over SSEA score 50 ( Figure S11 ) . Thus , the SSEA method can allow us to detect many more relationships than would be possible by sequence similarity alone . We searched for soluble/membrane protein structural relationships in the human proteome ( Figure S12 ) . Of 5003 membrane proteins in the human genome , we found that 1 , 155 showed clear sequence similarity to soluble proteins of known structure . Moreover , of 1 , 155 TM proteins , 449 TM proteins were aligned with soluble domains bearing SwissProt domain annotations ( Table S6 ) . Employing the SSEA method , we could assign an additional 1 , 129 proteins as probable relatives of soluble proteins of known structure . Thus , a detectable structural relative exists for ∼45% of the membrane proteins in the human genome ( Figure 6C ) . An example of the type of information that can be derived is shown in Figure S13 . Monoacylglycerol lipase ABHD6 ( membrane protein ) and epoxide hydrolase 2 ( soluble protein ) aligned well with the SSEA score of 66 . 91 and shared experimentally verified active site residues , Asp495 and His523 , suggesting that these proteins may have a common hydrolase function . We believe the list of identified structural relationships will be a useful resource for developing functional hypotheses and the list is provided at sbi . postech . ac . kr/emdmp .
Our results show that membrane proteins quite commonly acquire or share functions by domain exchange with soluble proteins . There has been a controversy over whether membrane or soluble proteins have emerged first during evolution and several reports support the idea that membrane proteins may have come first [17]–[19] . They argue that membrane proteins require less extensive sequence optimization for folding than soluble proteins because they reside in a more restrictive membrane environment . However , we suggest that the evolutionary paths of membrane proteins might be more diverse . For example , we found that a soluble protein , 3-hydroxy-3-methylglutaryl-CoA ( HMG-CoA ) reductase , exists in all three kingdoms , whereas the membrane form of HMG-CoA reductase only exists in eukaryotic species ( Figure S14A ) [20] , [21] . This suggests that the evolutionary origin of HMG-CoA reductase may be a soluble form and the membrane form was created by acquiring transmembrane domains . Alternatively , the membrane variants in prokaryotes could have been lost at some point in evolution . On the other hand , acetylcholine-binding proteins may have emerged from eukaryotic species by losing the transmembrane domains of nicotinic acetylcholine receptors ( Figure 4A ) . Nicotinic acetylcholine receptors exist in all three kingdoms , but acetylcholine-binding proteins only exist in eukaryotes . Thus , it seems reasonable to suggest that membrane and soluble proteins exchange domains and functionalities in both directions over the course of evolution ( Figure S14B ) . The fact that the more recent exchanges have occurred in eukaryotes suggests that this became a particularly important evolutionary mechanism as life became more complex . Regardless of the evolutionary origins , it is clear that many membrane and soluble proteins share structural similarity . Similar folds do not always imply similar function , but in many cases , structural similarities of proteins have been used to discover functional similarities [22]–[25] . This is based on the notion that sequence and structure similarities between gene products infer functional similarities [26]–[28] . We can therefore utilize structural and functional information obtained from one class to report on the other .
We collected 558 membrane and 43547 soluble protein structures from the PDB library [29] . We included only structures solved by X-ray and NMR , and excluded structures solved by EM ( electron microscopy and cryo-electron diffraction ) , Fiber ( fiber diffraction ) , IR ( infrared spectroscopy ) , Model ( predicted models ) , Neutron ( neutron diffraction ) . Only experimentally confirmed membrane protein structures from the SwissProt and PDB databases were included . Proteins annotated as single-/multi-pass membrane proteins or membrane proteins were included , but peripheral membrane proteins were excluded . We collected soluble protein structures by excluding membrane proteins and putative membrane proteins . The SCOP database ( release 1 . 75 ) was used to examine the fold and class diversity of structures . The current SCOP database lists only 58 folds of membrane proteins , whereas more than 1000 folds are listed for soluble proteins . We compared structure pairs of membrane and soluble proteins using TM-align , a structure comparison algorithm which uses dynamic programming and alignment confidence score rotation matrix [6] . TM-align is a suitable tool for large-scale structural comparisons . The calculation time of TM-align was faster than other structure alignment programs , such as CE and DALI [30] , [31] . The average CPU time per pair by TM-align was 0 . 3s , which was 40 time faster than CE ( P-value = 1 . 65e-56 , t-test ) . For the calculation , we randomly selected structure pairs of membrane and soluble proteins 1 , 000 times . Calculations were performed on 2 . 66 GHz hexa core CPU LINUX machine . We compared structural superimposition of TM-align with other tools by using 10 , 000 random pairs between membrane and soluble proteins . We found that CE and DALI gave equivalent results of structural alignments compared with TM-align . Particularly , RMSD values from each tool are highly correlated for the same structure pairs ( Figure S1A and S1B ) . We applied a strict cutoff of RMSD , aligned length , and alignment confidence score to select only significantly aligned structure pairs between membrane and soluble proteins . Structure pairs with RMSD<5 Å , aligned length >100 residues , and alignment confidence score ( TM-score ) >0 . 5 were selected . Structural alignments of relatively shorter sequence ( less than 100 residues ) gave somewhat dissimilar results ( Figure S1C and S1D ) when we applied different tools . Thus , we chose aligned length >100 residues as a length threshold . These selection criteria have been found to filter out dissimilar structures in other high-throughput structural comparison studies [1] , [6] , [32] , [33] . We applied PDBTM database to measure whether structural similarity occurred in the extramembrane or transmembrane regions of membrane proteins . Membrane proteins that shared structural similarity within transmembrane region were removed . Furthermore , structure pairs that have several disconnected extramembrane loops were removed since these short loops cannot act as independent domains . We mapped the topology information ( i . e . inside and outside regions ) of membrane proteins onto the structural alignment results using TMHMM [34] , [35] . The procedure of structure comparisons between membrane and soluble proteins is described in Figure S15 . We classified structurally similar membrane and soluble proteins into four classes; all alpha , all beta , alpha+beta , and alpha/beta based on SCOP classifications [9] . SCOP database is a comprehensive ordering of all proteins of known structures according to their structural relationships . Because structural information of membrane proteins is lacking , we utilized class information of soluble proteins to identify the class of structurally aligned membrane and soluble protein pairs . We used the domain information from the SCOP database to assign domain boundaries of the structurally aligned regions of membrane and soluble proteins . We assigned a domain annotation if an aligned region covered more than the 90% of domain length . We used 120 fully sequenced genomes of archaea , bacteria and eukaryotes to compare orthologs of soluble proteins aligned with membrane proteins . The 120 genomes are comprised of 9 archaea , 80 bacteria and 21 eukaryotic species . InParanoid was used to detect the orthologs of query proteins [36] . For functional enrichment analysis , we used a function annotation tool , DAVID [37] . Among the 31 biological process terms in the level 1 of gene ontology hierarchy , we found 14 terms in which at least one protein is involved . We collected 504 extramembrane domains which have soluble counterparts and 102 extramembrane domains which don't have soluble counterparts . We transformed molecular coordinate of each membrane protein structures to be parallel with the membrane plane by using the PDBTM database . Membrane distance of extramembrane domains was measured between the average of all the coordinates of domains and the surface of membrane bilayer . We applied the SSEA method that can detect possible structural homology in the absence of strong sequence similarity by including secondary structure pattern information [16] . Secondary structures of membrane and soluble proteins were predicted by PSIPRED [38] . To set a reliable cut-off value for the structural comparisons , we evaluated SSEA score based on a positive and a negative set . The positive set includes structure pairs of membrane and soluble proteins with <5 Å RMSD and sequence identity range from 5 to 15% . The negative set includes dissimilar structure pairs of membrane and soluble proteins with >10 Å RMSD and sequence identity range from 5 to 15% . We selected 100 pairs from each positive and negative set by random sampling . We compared the SSEA score of these pairs from each group and repeated the process 1 , 000 times . We found that SSEA score of 50 works best for separating the positive set from the negative set ( P-value<1 . 0×10−100; Figure 6AB ) . Furthermore , we analyzed the correlation between SSEA score and the probability of finding structure pairs with RMSD <5 Å ( Figure S11 ) . To calculate the probability , we randomly selected 10 , 000 structure pairs of membrane and soluble proteins from all ranges of RMSD values . We found that the probability of finding structure pairs with RMSD <5 Å was dramatically increased with an SSEA score over 50 . We compared the structure-guided sequence alignment results of SSEA with HHpred [39] . We found that SSEA and HHpred gave similar alignment results except for the positive prediction rates . SSEA provided more positive sets than HHpred for the structural comparisons ( Figure S16 ) . The domain structures shared between membrane and soluble proteins usually have low sequence identity and it has been shown that the HMM method tends to have difficulties detecting distant homologs [40] , [41] . Therefore , for the comparisons of membrane and soluble domains with very low sequence identity , the SSEA method was chosen .
|
Membrane proteins play important roles in cellular communication and molecular transport . However , experimental difficulties and lack of structural information have limited the functional characterization of membrane proteins . In this study , we find that over 60% of the extramembrane domains were structurally related to proteins of known structure . The exchanges between membrane and soluble proteins are particularly frequent in eukaryotes , indicating that this is an important mechanism for increasing functional complexity . This result has important implications for the evolution of membrane and soluble proteins . Beyond that , it provides a previously untapped resource for predicting the functions of many membrane proteins without a known function . Based on these results , we provide a new database of predicted functional and structural overlaps for all membrane proteins in the human genome .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"computational",
"biology"
] |
2013
|
Rampant Exchange of the Structure and Function of Extramembrane Domains between Membrane and Water Soluble Proteins
|
The emergence of variant Creutzfeldt Jakob Disease ( vCJD ) is considered a likely consequence of human dietary exposure to Bovine Spongiform Encephalopathy ( BSE ) agent . More recently , secondary vCJD cases were identified in patients transfused with blood products prepared from apparently healthy donors who later went on to develop the disease . As there is no validated assay for detection of vCJD/BSE infected individuals the prevalence of the disease in the population remains uncertain . In that context , the risk of vCJD blood borne transmission is considered as a serious concern by health authorities . In this study , appropriate conditions and substrates for highly efficient and specific in vitro amplification of vCJD/BSE agent using Protein Misfolding Cyclic Amplification ( PMCA ) were first identified . This showed that whatever the origin ( species ) of the vCJD/BSE agent , the ovine Q171 PrP substrates provided the best amplification performances . These results indicate that the homology of PrP amino-acid sequence between the seed and the substrate is not the crucial determinant of the vCJD agent propagation in vitro . The ability of this method to detect endogenous vCJD/BSE agent in the blood was then defined . In both sheep and primate models of the disease , the assay enabled the identification of infected individuals in the early preclinical stage of the incubation period . Finally , sample panels that included buffy coat from vCJD affected patients and healthy controls were tested blind . The assay identified three out of the four tested vCJD affected patients and no false positive was observed in 141 healthy controls . The negative results observed in one of the tested vCJD cases concurs with results reported by others using a different vCJD agent blood detection assay and raises the question of the potential absence of prionemia in certain patients .
The emergence of variant Creutzfeldt Jakob Disease ( vCJD ) is considered a likely consequence of human dietary exposure to the Bovine Spongiform Encephalopathy ( BSE ) agent [1] . Both primate and sheep experimental models rapidly indicated that vCJD/BSE could be transmitted by blood transfusion [2] , [3] . To date , three vCJD cases and one vCJD infected but asymptomatic individual have been identified in the United Kingdom ( UK ) , in patients that received Red Blood Cell units from donors who developed symptoms of vCJD 17 months to 3 , 5 years after donation [4] , [5] . More recently , one preclinical vCJD case was reported in the UK in a haemophiliac patient . This patient had been treated with one batch of FVIII that was manufactured using plasma from a donor who developed vCJD six months after donating blood [6] . The total number of vCJD clinical cases identified so far remains limited ( 225 patients worldwide at the time of writing ) . However the prevalence of vCJD infected and asymptomatic individuals in the BSE exposed population remains extremely uncertain [7] . A first retrospective analysis of stored lymphoid tissues indicated that vCJD prevalence in the UK could approach 1 out of 4000 individuals , though with wide confidence intervals [8] . More recently 32 , 441 appendix samples , collected during surgery on patients born between 1941 and 1985 were tested for abnormal prion protein accumulation . This study indicated a likely vCJD prevalence estimate of 1 in 2 , 000 in these age cohorts ( 95% Confidence Interval ranging from 1 in 3 , 500 to 1 in 1 , 250 ) [9] . In addition , human PrP transgenic mouse models indicated that the BSE agent can colonize lymphoid tissues without propagating to detectable levels in the brain and causing clinical disease . This suggests the possibility of silent carrying by vCJD infected individuals [10] . This data raised major concerns about the possible occurrence of inter-individual iatrogenic vCJD transmission in particular by blood and blood products . Despite a decade of efforts , there is currently no validated test that would allow the identification of vCJD infected but asymptomatic individuals or the screening of blood donations for the presence of the vCJD agent [11] . There is currently limited information related to the infectivity level and distribution in the blood components of vCJD affected patients . Bioassay testing of blood fractions from a single vCJD affected patient indicated an infectious titer of 4 . 45 ID per mL of blood which was approximately equivalent to the infectivity found in 1 µg of a reference vCJD brain sample [12] . Such low infectious titer makes the direct detection of prion in blood difficult to achieve . Like in various TSE animal models ( mice , hamsters , sheep and cervids ) , a substantial part of the infectivity in this patient was associated with white blood cells ( WBC ) [13]–[16] . This suggests that WBC could be an appropriate target to detect endogenous vCJD agent presence in human blood . Prions are primarily composed of multimers of a misfolded form ( PrPSc ) of the host-encoded prion protein ( PrPC ) . They propagate by recruiting and converting PrPC into PrPSc and fragmentation of PrPSc multimers is thought to provide new PrPSc seeds for the conversion reaction . The Protein Misfolding Cyclic Amplification ( PMCA ) technology is aimed at replicating this phenomenon in vitro , allowing amplification of minute amounts of prions [17] . It facilitates the combining of a PrPC-containing substrate with previously undetectable amounts of PrPSc by repetitive cycles of incubation and sonication leading to amplification of abnormal PrPSc . With this potential high sensitivity , PMCA has been proposed for prion detection in blood , and studies have been carried out in scrapie-infected hamsters and sheep that validated the concept that blood associated PrPSc can be amplified by PMCA [14] , [18] , [19] . However , despite the ability to amplify brain-derived vCJD agent by PMCA , the reported amplification performance was considered too limited for reliable detection in blood [20] , [21] . In this study , we first identified PMCA substrate and conditions that allow highly efficient and specific amplification of the vCJD/BSE prions . We then show , using white blood cells as a template , that this method enables the identification of vCJD/BSE in asymptomatic experimental animals in the early phase of the incubation period .
All animal experiments have been performed in compliance with institutional and French national guidelines , in accordance with the European Community Council Directive 86/609/EEC . Primates were housed and handled in accordance with the European Directive 2010/63 related to animal protection and welfare in research , under constant internal surveillance of veterinarians , in level-3 confined facilities entirely dedicated to prion research ( agreement numbers A 92-032-02 for animal care facilities , 92-189 for animal experimentation ) , where cynomolgus macaque is the only housed animal species . Primates were placed in individual cages ( a maximum of 20 cages per room ) in six separate rooms , taking into account different parameters including the experiment they belong , their ages , their sex , their affinities to each other and their hierarchical status . Social enrichment was a constant priority , through individual activities and feeding according to the infectious risk . Animals were handled under anaesthesia ( including blood sampling ) to limit stress and avoid injury of handlers , and euthanasia ( barbiturate overdose ) was performed for ethical reasons when animals lost autonomy . The blood donor animals used in this study were included in experiments that were approved by the CETEA ethical committee ( French Ethical Committee N°44 , approvals 12-020 ) . Sheep were housed in level -3 containment animal care facilities ( agreement numbers C-31-555-227 for animal care facilities , 31-09-555-47 for animal experimentation ) . The experimental protocol ( oral challenge and blood collection ) was approved by the Comité d'éthique Midi Pyrénées ( ref MP/05/05/01/12 ) . Each healthy blood donor was individually informed and gave his/her written consent for using the collected samples in a scientific study . One vCJD blood sample included in the first panel of human blood samples was collected from a French patient at the clinical stage of the disease . According to French regulation , written informed consent for the use of this sample was obtained from the next of kin . Collection , storage and use of blood samples from vCJD patient included in this study was approved by national ethical authority ( PHRC ref 2004-D50-353 ) . The use of the second human blood sample panel that was provided by the MRC Prion Unit , London , ( United Kingdom ) was approved by a UK national ethical committee ( authorization number 03N/022 ) . These samples were analyzed anonymously . Finally , the experimental protocol on animals and the use of human samples was examined and approved by the INRA Toulouse/ENVT ethics committee . TSE free sheep were produced in the Defra ‘New-Zealand flock’ which was a unique source able to provide animals that can be considered free from classical scrapie [22] . The animals included in our experiments were imported into France and housed in dedicated scrapie free facilities before their use in experiments . In all cases , PrP genotype was obtained by sequencing the Exon 3 of the PRNP gene as previously described [23] , [24] . Four ARQ/ARQ sheep ( 6–10 months old ) were orally challenged with 5 g equivalent of brain material ( 1% brain homogenate in glucose ) . The inoculum was prepared using brain from an ARQ/ARQ sheep experimentally challenged with cattle BSE . Animals were then observed until the occurrence of clinical signs and euthanized when exhibiting locomotor signs of the disease that impaired their ability to eat . White blood cells ( WBC ) from age and breed matched uninoculated TSE free ARQ/ARQ control animals ( n = 60 ) were obtained by osmotic lysis of the buffy coat ( one volume ) with ACK solution ( one volume ) ( NH4Cl 0 , 15 M , KHCO3 1 mM , Na2EDTA 0 , 1 mM , pH 7 . 4 ) for 5 min RT . WBC were washed 3 times with 50 mL of PBS before being pelleted and stored at −80°C . Captive-bred 2 . 5 year-old male cynomolgus macaques ( Macaca fascicularis ) were provided by Noveprim ( Mauritius ) . Primates were checked for the absence of common primate pathogens before importation , and handled in accordance with national guidelines . One animal ( Macaque 6 ) was transfused with 40 mL of blood from a vCJD-infected primate sampled at the terminal stage of the disease . The other primates were intravenously inoculated with clarified supernatants ( obtained by centrifugation at 1 , 500 g for 10 minutes after extensive sonication ) derived from 10 or 100 mg of brains from BSE- or vCJD-infected primates . Such intravenous inoculation route is likely to mimic the contamination as it occurs in post-transfusion vCJD secondary cases . Primate blood samples were drawn into sodium citrate and fractionated by centrifugation at 2 , 000 g for 13 minutes according to the techniques classically applied in human transfusion . WBCs were obtained by osmotic lysis of buffy coat ( one volume ) with Easy-lyse ( Dako , 9 volumes ) for 10 minutes RT . WBCs were washed three times with 50 mL of PBS . Animals were handled under anesthesia to limit stress , and euthanasia was performed for ethical reasons when animals lost autonomy . The majority of the blood samples used for vCJD agent detection were obtained from archive collections . No influence was possible on the design of blood sampling plans . The possibility of collecting multiple samples from each animal was limited by ethical constraints ( reduction of stress to the primates ) . All samples were encoded before dispatch and tested blind . None of the primates that were involved in this experiment suffered from the myelopathic syndrome recently described in primates challenged with human and primates blood products [25] . In a first experiment related to human blood , WBC from 135 healthy volunteer human donors were prepared using the same protocol as in primates . In addition a vCJD blood sample collected in a French patient at clinical stage of the disease was tested . This blood sample was the same as the one used to measure vCJD infectivity in blood components by bioassay in a recently published study [12] . In this patient , vCJD was confirmed by both neuropathological examination and Western blot . All these samples were encoded before dispatch and testing . In a second experiment , a panel of nine buffy coat samples was provided by the MRC Prion Unit ( London , UK ) . This panel comprised material collected and prepared more than 10 years ago . It included three vCJD affected patients , and nine healthy patients . The blood volume that was used to prepare the buffy coat of each healthy patient varied between 3 . 5 to 8 mL . For one of the vCJD cases buffy coat samples were prepared using 3 . 5 mL of blood . In the two other vCJD cases the initial blood volume was undocumented . The nature of the anticoagulant used to collect the blood samples , the purity and the final number of WBC in the samples was not available . None of the vCJD samples that were included in this panel had been tested using the MRC vCJD blood assay described by Edgeworth et al . [26] . WBC were received as a frozen cell suspension ( in 50 µL of PBS ) . They were re-suspended in 200 µL of PMCA amplification buffer before homogenization . The homogenates were then split in two and tested in parallel in INRA UMR 1225 ( Toulouse , France ) and INRA UR 892 ( Jouy en Josas , France ) . Brain material from vCJD ( n = 4 ) , sCJD ( one MM1 , one MM2 , one VV1 , one VV2 , and one MV2 ) and Alzheimer's disease ( n = 3 ) affected patients were obtained from the National Creutzfeldt-Jakob Disease Surveillance Unit ( UK-Edinburgh ) or from the French CJD national reference laboratories network [27] . For testing the inhibitory impact of red blood cells on vCJD amplification , red blood cells from a healthy human donor were separated from plasma and buffy coat by centrifugation ( 2000 g-13 min ) and washed twice in PBS . Red cells were then submitted to two freezing/thawing cycles . The obtained red blood cell lysate was then used in the experiment . Transgenic mice lines that express PrPC of different species were used to prepare substrates: tgBov ( Bovine PrP , line tg110 ) , tga20 ( murine PrP ) , tg338 ( ovine V136R154Q171 PrP ) , tgShXI ( ovine A136R154Q171 PrP variant ) and tg650 ( Human Met129 variant of the human PrP ) . All but the bovine PrP expressing mice ( tgBov ) were established on the same mouse PrPKo background ( Zurich I ) [28]–[30] . In each of these mouse lines relative PrP expression level in the brain , by comparison to the natural host species , was described ( tga20: 10-fold–tg338: 6–8 fold , tgBov/tg110: 8-fold , tgShpXI: 3–4-fold , tg650: 6-fold ) [28] , [31]–[34] Mice were euthanized by CO2 inhalation and perfused ( intra-cardiac ) with PBS pH 7 . 4/EDTA 5 mMol ( 40–60 mL per mouse ) . The brains were then harvested and snap frozen in liquid nitrogen . 10% brain homogenate was prepared ( disposable UltraTurax – 3 min ) in 4°C PBS pH 7–7 . 65+0 . 1% Triton X100+ 150 mM NaCl ( 10% Weight/vol ) . The substrate was then aliquoted and stored at −80°C . In order to check the PrPC protein level in the PMCA substrates , total protein from an aliquot of each type of substrate was quantified by bicinchoninic acid ( BCA , Pierce ) . Five µg of proteins were mixed with an equal volume of 2X Laemmli's buffer before Western Blotting and PrPC immunodetection ( see Western blot section below , supplementary figure 1 ) . The desired amount of WBC or Buffy coat were resuspended in 200 µL of 4°C PBS pH 7 . 4+150 mMoL NaCl+ 0 . 1 TRITON X100 and homogenized at high speed ( Precess 48 , Bertin , France ) . Samples were then spun down at 15000 g for 20 seconds and then stored at −80°C or used fresh . 7 µL of the seed were mixed with 63 µL of substrate in 0 . 2 mL ultrathin wall PCR tubes or 96 well microplates that contained five to eight 1 mm diameter silica/zirconium beads ( Biospec Cat . No . 11079110z ) . Amplification was performed in a modified Misonix 4000 cup horn ( see below ) , using a water recirculation system ( 39 . 5°C ) . The reaction tubes/microplates were then submitted to 96 cycles of 30 seconds sonication ( power 70% ) followed by a 29 minutes and 30 seconds incubation period . After the PMCA round , 7 µL of the reaction product were added to a new tube containing fresh substrate and a new round ( 96 cycles ) was performed . In order to limit the cross contamination risks that are linked to serial PMCA , procedures were employed that are similar to those in place for nested PCR . In particular , PMCA substrates , amplification and handling of amplified products were performed in different rooms using dedicated material . On each PMCA run , a standard 1/10 dilution series ( ovine BSE , 10% brain homogenate , 10−5 to 10−9 diluted ) was included to check the amplification performance . A large batch of these controls was prepared and stored at −80°C as single use aliquots . Similarly unseeded controls ( 1 unseeded control for 5 seeded reactions ) were included on each run . A total of 68 PMCA runs were performed in the framework of this study . Contamination of some negative control reactions ( false positives ) was observed in 4 runs that had been performed in individual PCR tubes . In two of these runs , contamination was a likely consequence of a fault in the tube caps ( obvious loss of reaction mixture in the tube ) . In two other cases the source of the contamination remained unclear , but the WB PrPres pattern in false positive reactions was typical of a vCJD/BSE prion , making a cross contamination between tubes a likely explanation . No false positive reaction was observed in PMCA runs that were performed in 96 well PCR microplates . When a false positive was observed , the complete PMCA runs were discarded and restarted from the first amplification round . Modifications consisted of the enlargement ( 5 mm inner diameter ) of existing holes and creation of new holes for water recirculation in the crown surrounding the plate horn . These holes allowed a closed water circulation system in the horn delivering over 1 . 5 liters per minute of water . Permanent water re-circulation was ensured by a peristaltic pump ( Watson Marlow 520 U ) and deflectors were added to the horn to avoid water projection . The water circuit consisted of 10 metres of flexible tygon tube ( diameter 9 . 2 mm ) placed in a water bath . This system allowed the temperature of the water in the horn to return to its nominal value ( 39 . 5°C ) within 20–40 seconds following the sonication burst and also maintained the water level in the horn at a constant level . The bottom of the reaction tubes or 96 well microplates were positioned at a height of 2 mm above the horn plate and the water level in the horn was adjusted ( before each PMCA round ) to be at the same level as the reaction mixture in the tubes . Finally the acoustic protection box containing the sonicator horn was placed in an environment ( temperature regulated room or incubator ) maintaining the air temperature between 35°C and 40°C ( limit of condensation ) . PK resistant abnormal PrP extraction ( PrPres ) and Western blot were performed as previously described [35] , using a commercial extraction kit ( Biorad , France ) . For PMCA products the equivalent of 20 µL of reaction product were loaded on to each lane . PrP immunodetection was performed using either Sha31 monoclonal antibody ( 0 , 06 µg per mL , epitope: YEDRYYRE , amino acid 145–152 ) or 12B2 ( 4 µg/mL ) ( epitope WGQGG , amino acid sequences 89–93 ) . Both Sha31 and 12B2 antibodies have been described in previous studies to bind the mouse , ovine , bovine , porcine and human PrPC and PrPres in WB [27] , [36]–[39] .
The first goal of the study was to identify a substrate and experimental conditions that together would enable a highly efficient PMCA amplification of vCJD/BSE agent . For that purpose , brain material from different transgenic mouse lines expressing ovine ( A136R154Q171 and VRQ variants ) , bovine , human ( Met129 variant ) and murine PrPC were used to prepare substrates . Reactions were then seeded with ten-fold dilution series of brain homogenate from vCJD/BSE-affected humans , primates , pigs , cows and sheep ( figure 1 ) . After six PMCA rounds , no PK resistant abnormal PrP ( PrPres ) could be detected by Western blot ( WB ) in un-seeded reactions ( figure 1 , 2 ) or in those seeded with healthy brain material ( data not shown ) . Whatever the substrate , no PrPres was detected in reactions seeded with brain material from Alzheimer affected patients ( figure 2A , B ) . All the tested substrates allowed the amplification of vCJD/BSE , but displayed dramatically different detection limits . Whatever the origin ( species ) of the BSE/vCJD agent , the ovine PrP substrates ( ARQ and VRQ ) provided the best detection performances , i . e . positive for reactions seeded with a 10−6 to 10−8 dilution of the original brain homogenates ( figures 1 , 2C ) . No amplification was observed in ovine substrate reactions seeded with sCJD brain homogenates ( figure 2B ) . All these results support the view that the homology of PrP amino-acid sequence between the seed and the substrate may not be the crucial determinant for vCJD/BSE agent PMCA amplification . Strikingly , the capacity of the human PrP substrate to amplify the vCJD/BSE agent varied greatly according the infectious source species ( figure 2D ) . Human vCJD , porcine BSE and ovine BSE prions were amplified using human PrP as a substrate but in contrast vCJD/BSE from cattle or primates was barely or not amplified . For all vCJD/BSE agent source/substrate combinations , the PrPres WB pattern ( glycoprofile and mobility ) observed after PMCA amplification was indistinguishable from that observed in the brains of the transgenic mouse line used to prepare the PMCA substrate ( figure 3A–D ) . In particular , the PrPres obtained after PMCA displayed the same low/null immunoreactivity to 12B2 antibody ( epitope WGQGG , amino acid sequences 89–93 ) as the original vCJD/BSE isolates ( figure 3E ) . These results indicate that whatever the substrate , the amplified prion displays a PrPres molecular signature consistent with BSE/vCJD . In order to establish the capacity of the assay to detect endogenous vCJD/BSE agent in the blood , WBCs from 4 sheep orally challenged with BSE and 60 healthy control sheep were tested using the ovine ARQ substrate . In that experiment , the BSE infected sheep had developed disease 20 months post inoculation ( mpi ) ( table 1 ) . For all the symptomatic sheep , reactions seeded with WBC were shown to be positive after two PMCA rounds with a typical BSE PrPres WB pattern . After four rounds , reactions seeded with WBCs collected at 6 mpi from some animals and at 12 , 16 and 20 mpi from all animals were positive ( table 1 and figure 4A ) . The WBC from the 60 TSE-free controls remained negative after 6 PMCA rounds ( figure 4B ) . These promising results enabled us to test blood samples collected from vCJD-infected primate experiments ( figure 5 ) . This model is considered to be the closest to infection in humans [2] . Buffy coat ( BC-n = 33 ) and WBC ( n = 14 ) obtained by red cell lysis of BC from vCJD-infected ( n = 8 ) and control ( n = 15 ) cynomolgus macaques were tested . The animals had been challenged by the intravenous route using either brain homogenate ( n = 7 ) or blood from a vCJD-affected primate and developed the disease with incubation periods ranging from 33 to 61 months ( figure 5 ) . All samples were encoded before dispatch and were tested blind . After 4 PMCA rounds , blood from all the clinically affected primates was positive ( figures 5 , 6A ) . All the reactions seeded with BC or WBC ( n = 17 ) from unchallenged primates remained negative after 6 PMCA rounds ( figure 6A ) . In four vCJD-infected primates ( macaques 2 , 4 , 6 and 7 ) , BC had been collected at different times during the asymptomatic phase of the incubation period . The reactions seeded with BC collected from 10 mpi to 14 mpi ( more than 32 months before clinical onset ) were positive after five PMCA rounds ( figures 5 , 6B ) . These data indicate that vCJD infection can be detected in the early preclinical stage in primates . The comparison of PMCA reactions seeded with BC and WBC prepared from the same blood samples suggested the presence of amplification inhibitor ( s ) in the BC ( figure 6C and table 2 ) . The negative effect of red blood cell presence on the vCJD amplification by PMCA was demonstrated by spiking a vCJD brain dilution series with red blood cell lysate ( figure 7A ) . The addition of red blood cells resulted in a lack of amplification in reactions seeded with low dilutions of vCJD brain material ( figure 7B , C ) . This loss of sensitivity in the vCJD amplification was not compensated by a higher number of PMCA rounds . However this inhibitory effect was compensated for/attenuated by diluting the red blood cell tainted seed in PMCA buffer prior to amplification . To limit inhibition , BC had to be diluted at least fifty-fold before being processed ( figure 6C , table 2 ) . This phenomenon could impact on the final sensitivity of the assay when applied to BC samples and could explain some of the negative results obtained in samples from asymptomatic but infected primates ( figure 5 -macaque 2 ) . The results obtained in vCJD infected primates allowed access to a first panel of human blood samples that included WBCs from one French vCJD affected patient and 135 healthy controls . Samples were received encoded and tested blind . After 6 PMCA rounds , no PrPres was detected in reactions seeded with WBC from human healthy controls ( figure 8A ) . In contrast , two PMCA rounds ( figure 8A ) were sufficient to detect PrPres in reactions seeded with the vCJD affected patient's WBC . In order to test additional samples from vCJD infected patients we next contacted the MRC Prion Unit ( London UK ) . They provided us with a panel of nine buffy coat samples that included three vCJD cases ( confirmed by neuropathology and Western blot ) and six healthy controls . The samples were received encoded and tested blind in two laboratories ( UMR INRA ENVT 1225 , Toulouse and UR 982 Jouy en Josas ) using the same methodology . In both laboratories , the PMCA results were identical . After six PMCA rounds no PrPres was detected in reactions seeded with BC from the healthy controls . Two PMCA rounds were sufficient to detect PrPres in PMCA reactions seeded with the buffy coat from two of the vCJD cases ( figure 8B and 9A ) However , even after these six PMCA rounds , no PrPres was detected in reactions seeded with buffy coat prepared from the third vCJD affected patient . The rarity of blood samples collected in vCJD affected patients and the lack of samples from infected patients at preclinical stage of the disease are two major limitations for the development and performance assessment of vCJD blood detection assays . To model the capacity of this assay to detect lower amounts of blood vCJD agent ( as expected in patients at preclinical stage ) a ten-fold dilution series of WBC and BC samples from the three positive vCJD patients was made . Using the WBC sample from the French vCJD affected patient , three amplification rounds allowed PrPres detection in one out of two PMCA reactions seeded with material equivalent to 0 . 05 µL of starting whole blood ( figure 9A ) . Similarly , after three PMCA rounds , PrPres was detected in reactions seeded with 10−3 to 10−5 diluted buffy coat homogenates from two UK vCJD affected patients ( figure 9B ) . Under the assumption that 3 . 5 mL of whole blood were used to prepare these BC ( see method ) , these results indicate that less than 0 , 5 nL of whole blood equivalent material was sufficient to detect endogenous vCJD agent in the blood of these two patients . Finally , the WBC homogenate from the French vCJD affected patient were mixed with WBC from either eleven ( 8 different pools ) , twenty-three ( 4 different pools ) , forty-seven ( 2 different pools ) or ninety-five ( 1 pool ) healthy donors plus the WBC from the vCJD affected patient ( figure 9C ) . After three PMCA rounds , reactions seeded using a pool constituted with up to forty-seven healthy donors plus the vCJD affected patient's WBC were PrPres positive . All the reactions seeded with pools containing only WBC from healthy donors were negative .
Cell free conversion assays have been extensively used to investigate PrPSc induced PrPC to PrPSc conversion . Combinations of PrPSc and PrPC from different species have provided insight into the molecular basis for barriers to the transmission of TSEs between species ( species barriers ) and same-species hosts with different PrP genotypes ( polymorphism barriers ) . Results obtained in this system , indicate that the reactions between PrPSc and PrPC molecules of the same sequence are more efficient than heterologous sequence conversion . These results provided strong support for the concept that the sequence specificity in the conversion of PrPC to PrPSc modulates the interspecies or intraspecies transmissibility of TSE agents [40]–[46] . The results obtained here when amplifying by PMCA vCJD/BSE agents originating from different species are not fully consistent with those findings . The observation that human PrPC substrate support better PrPSc amplification when seeded with human vCJD agent than with any other source of vCJD/BSE agents , and that the murine PrPC substrate was poorly efficient at amplifying non-murine vCJD/BSE agents , concur with the general conclusions derived from cell free conversion assay . However , the fact that whatever the considered source of vCJD/BSE agent ( human , bovine , porcine etc… ) , the Q171 ovine PrPC substrates provide better amplification than homologous PrPC sequence substrates was unexpected . PrPSc amplification levels in cell-free conversion assays remain very limited . This is a likely consequence of the fact that the newly formed PrPSc has either no or very limited seeding activity in this type of assay . In PMCA each sonication cycle is believed to create new seeding sites , including in the bulk of newly converted PrPSc . These new seeds have the same amino acid sequence as the PrPC substrate and therefore the efficacy of the PrPC conversion could be enhanced . These differences might explain the discrepancies between our results and those previously reported using cell free conversion assay . In addition , it is worth noting that whereas conventional mice are poorly susceptible to sporadic Creutzfeldt Jakob , they propagate variant CJD isolates prepared from patients displaying identical ( Methionine homozygous at codon 129 ) PrPC sequence [47] . This illustrates that rather than depending solely on the donor/recipient host PrP sequence homology the capacity of a prion to propagate efficiently in a host and in PMCA is also directly dependant of its strain properties . Similarly while human PrP substrate supported amplification of BSE adapted in ARQ sheep in PMCA , it did not allowed the amplification of ARQ sheep scrapie [20] , [48] . This phenomenon could also contribute to an explanation for the results we obtained when amplifying vCJD/BSE by PMCA in different substrates . Whether , at the molecular level , the species specificity of PMCA faithfully mimics the species barrier as observed in ‘living hosts’ remains to be thoroughly assessed . Interestingly prion strains amplified by PMCA using a homologous PrP amino acid sequence as the substrate share identical biological properties to the parental strain , e . g . in bioassay [49] . In addition , propagation of a prion by PMCA using a substrate with a heterologous PrP sequence , can result in an evolution of its strain properties identical to that observed after in vivo propagation of this strain in the heterologous host ( i . e . PMCA can reproduce the transmission barrier phenomenon ) [50] . Here , the vCJD/BSE agent amplification obtained with different PMCA PrPC substrates paralleled to some extent the propagation efficiency already reported in vivo . For instance , ovine BSE propagates with an apparently similar efficiency to cattle BSE prions in bovine transgenic mice ( tgBov ) [36] and with an higher efficiency in human transgenic mice ( tg650 ) [51] . BSE/vCJD agents propagate with little or no transmission barrier in transgenic mice expressing the ovine ARQ PrP [33] , [52] , [53] and can be passaged in those expressing the ovine VRQ PrP variant ( tg338 mice ) [54] . However , in our opinion , there are still missing elements to establishing whether the PMCA amplification efficiency of an isolate/substrate combination is systematically correlated to the corresponding bioassay sensitivity . In this context , a end-point titration of the vCJD/BSE isolates used in the different transgenic PrP mouse lines ( tga20 , tgBov , tg338 and tgShXI ) has been initiated . Despite the limited number of vCJD clinical cases observed so far ( n = 177 ) in the United Kingdom , the most recent epidemiological studies indicate that , in this country , 1 out 2000 people could carry the vCJD agent . In the absence of validated vCJD screening assay , UK like most of the developed countries apply systematic measures aiming at mitigating the blood borne transmission risk of the disease . These measures represent a substantial cost and increase the difficulty met by the blood banking system to provide certain blood products . In that context the added value from a vCJD blood detection assay is obvious . The absence of human blood samples that would have been collected in infected individuals at asymptomatic stage of the disease represents a major limitation for developing and validating such assay . Using the two animal models that are considered as the most relevant for vCJD agent infection ( sheep and primates ) , our study demonstrates that an assay based on the in vitro amplification of BSE/vCJD by PMCA allows an early and specific detection of infected animals . The blind testing of two sample panels , that included a limited number of vCJD cases ( n = 4 ) and a substantial number of healthy controls ( n = 141 ) , provides evidence that PMCA can be used for detecting vCJD agent in blood in human . These results also demonstrate the very high sensitivity of the PMCA method for detecting the endogenous vCJD agent associated to WBC/buffy coat in three vCJD affected patients , as detection can be possible with the equivalent of 0 . 5 nL of infected whole blood . However , despite its sensitivity , our assay failed to amplify PrPres in the reactions seeded with buffy coat from one of the vCJD affected patient . This failure might be the consequence of several non-exclusive phenomena . First , it might be due to the sample processing . Indeed our experiments in vCJD infected primates clearly highlighted that buffy coat can contain PMCA inhibitors Alternatively , this negative result might be due to a lower or absent prionemia in certain vCJD affected patients . This explanation would fit with the results reported by the MRC unit in the UK using a different vCJD blood detection assay . Rather than amplifying abnormal PrP , this assay is based on the capture of non PK digested disease-associated PrP on a solid-state binding matrix . Like our PMCA method , the MRC vCJD blood assay displayed an excellent analytical sensitivity and specificity . However about a third of the vCJD blood samples tested so far were score negative ( 6 out the 21 vCJD affected cases ) [26] , [55] , [56] . The idea of a lower/absence of prionemia in certain vCJD cases is also indirectly supported by the observations recently reported by Mead et al . This author reported that in a vCJD affected patient that was negative using the MRC vCJD blood detection assay , the lympho-reticular tissues displayed unusually low PrPSc accumulation levels [57] . Beyond this , a low level or an absence of infectivity in the blood of certain vCJD infected patients could also explain the lack of disease transmission observed so far in certain patients who received blood from donors who later developed vCJD [58] . To date , the presence of vCJD endogenous infectivity in human blood has been formally established ( bioassay ) in a single affected patient [12] . In that context , measuring through bioassay the infectivity level in blood from a panel of vCJD affected patients ( including if possible vCJD blood samples that were scored negative for PrPSc presence ) would be highly valuable . For more than a decade PMCA has been reputed to be a highly sensitive but unreliable technique [59] . Even if there is still a need for standardisation of protocols and for an optimisation of hardware , in our opinion , the reliability of the technique has now reach an acceptable level . Moreover , the recent progress in the miniaturisation of the method [60] and the demonstration that brain homogenate can be replaced by cell lysate [61] should further facilitate the use of this technique . Over the last few years alternative methods to PMCA for in vitro amplification and detection of prions have been developed . The quaking induced conversion ( QuIC ) and the real time QuIC ( RT-QuIC ) are based on fibrillation of a recombinant PrP ( rec-PrP ) substrate triggered by the presence of a minute amount of PrPSc [62] , [63] . QuIC already allowed highly sensitive detection of abnormal PrPSc in various biological fluids and some studies reported its capacity to detect brain derived vCJD PrPSc in plasma [64]–[66] . The possibility of using bacterial rec-PrP and the apparent simplicity of these methods are quite attractive . However , at this stage , in case of a positive reaction , the assay does not offer the opportunity to confirm directly the nature of the TSE agent that triggered conversion . In contrast , since PrPSc amplified with PMCA has all the biochemical characteristics of the original seed ( in our case BSE/vCJD ) this method allows the direct identification of the vCJD agent signature in positive reactions . Despite all the remaining difficulties , the results obtained so far by two different methodologies ( PMCA as presented here and the abnormal PrP capture ) , and the rapid progress of QuIC derived technologies , allow potential new possibilities for vCJD screening and the prevention of its iatrogenic transmission .
|
Variant Creutzfeldt Jakob Disease ( vCJD ) cases were identified in patients who received blood products that had been prepared from donors who later developed the disease . The blood borne transmission of vCJD is a major concern for blood transfusion banks , plasma derived products manufacturers and public health authorities . A vCJD blood screening test would represent an ideal solution for identifying donors/blood donations that might be at risk . In this study , we describe a blood assay which is based on the in vitro amplification of vCJD agent by Protein Misfolding Cyclic Amplification ( PMCA ) . In vCJD animal models ( sheep and primate ) , the assay enabled the identification of infected individuals in a very early stage of the asymptomatic incubation phase . We also provide evidence of the high specificity and the high analytical sensitivity of this assay using blood samples from vCJD affected and healthy patients .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"biotechnology",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"biology",
"and",
"life",
"sciences"
] |
2014
|
Preclinical Detection of Variant CJD and BSE Prions in Blood
|
The scarcity of information on the immature stages of sand flies and their preferred breeding sites has resulted in the focus of vectorial control on the adult stage using residual insecticide house-spraying . This strategy , along with the treatment of human cases and the euthanasia of infected dogs , has proven inefficient and visceral leishmaniasis continues to expand in Brazil . Identifying the breeding sites of sand flies is essential to the understanding of the vector's population dynamic and could be used to develop novel control strategies . In the present study , an intensive search for the breeding sites of Lutzomyia longipalpis was conducted in urban and peri-urban areas of two municipalities , Promissão and Dracena , which are endemic for visceral leishmaniasis in São Paulo State , Brazil . During an exploratory period , a total of 962 soil emergence traps were used to investigate possible peridomiciliary breeding site microhabitats such as: leaf litter under tree , chicken sheds , other animal sheds and uncovered debris . A total of 160 sand flies were collected and 148 ( 92 . 5% ) were L . longipalpis . In Promissão the proportion of chicken sheds positive was significantly higher than in leaf litter under trees . Chicken shed microhabitats presented the highest density of L . longipalpis in both municipalities: 17 . 29 and 5 . 71 individuals per square meter sampled in Promissão and Dracena respectively . A contagious spatial distribution pattern of L . longipalpis was identified in the emergence traps located in the chicken sheds . The results indicate that chicken sheds are the preferential breeding site for L . longipalpis in the present study areas . Thus , control measures targeting the immature stages in chicken sheds could have a great effect on reducing the number of adult flies and consequently the transmission rate of Leishmania ( Leishmania ) infantum chagasi .
Members of the Lutzomyia longipalpis ( Lutz & Neiva ) species complex are the main vector of Leishmania ( Leishmania ) infantum chagasi ( Cunha & Chagas ) , the causative agent of the visceral leishmaniasis ( VL ) in Brazil and Latin America [1]–[3] . Its recent introduction and adaptation to domiciliary habitats in the urban and peri-urban areas of municipalities in every region of Brazil , including São Paulo state , has resulted in higher incidences of human and/or canine visceral leishmaniasis [4]–[8] . From 1995 to 2010 , Brazil recorded 53 , 633 new cases of the disease , with an annual mean of 3 , 352 new cases [9] . From 1999 to 2011 , São Paulo state recorded 1 , 927 new cases of the disease , with 169 deaths [10] . In Brazil , the VL control program focuses on the treatment of human cases , the euthanasia of seropositive infected dogs and insect vector control [5] , [11] , [12] . Since this disease is zoonotic , treatment of human cases does not affect transmission . The incidence of human infections seems to be associated with the number of infected dogs and vectors [13]–[15] . Culling dogs is unpopular and its impact on the reduction of the incidence of human VL is contradictory [12]–[14] , [16]–[18] . Vectorial control is focused on the sand fly's adult stage through residual house-spraying insecticide [1] , [5] , [11] , [12] , [19] . Insecticide application is generally accepted by residents in affected areas , is more practical and , in theory , more effective than the other methods at controlling transmission [13] . However , due to the irregularity and low coverage of application , possible repellent action of insecticide , high cost when used on a large scale and its short residual effect this method is considered inefficient in the control of VL [12] , [14] , [19] , [20] . In Brazil , the high abundance of L . longipalpis in conjunction with domestic animals , particularly dogs , which act as the amplification hosts for L . ( L ) . i . chagasi , means that transmission occurs within relatively small areas represented by peridomiciles in rural and urban areas [19] , [21]–[23] . In addition , dispersion studies have shown that the movements of L . longipalpis individuals are spatially focal [24] , [25] . Such observations , coupled with the high frequency of males and females of different physiological age ( empty , engorged and gravid ) present in collections carried out in urban areas of several municipalities of São Paulo state ( unpublished ) , indicate that the complete life cycle of L . longipalpis occurs in peridomicile habitats of these areas . Nevertheless , finding the preferred breeding microhabitat of the L . longipalpis and other sand flies species is a difficult task [26]–[28] . It is generally assumed that the immature stages of sand flies develop in shaded and moist terrestrial microhabitats , rich in organic nutrients , such as in or near leaf litter , bases of trees , animal burrows , animal sheds , and rock crevices [27] , [29]–[32] . Nevertheless , there is no precise information on the preferred breeding sites of these important vectors . Among the techniques used to identify the natural breeding sites of sand flies , soil emergence traps are the preferred indirect method employed [26] , [27] , [29] , [30] , [33]–[37] . In this study , a new soil emergence trap design was used to identify the natural breeding sites of L . longipalpis in urban and peri-urban areas of two municipalities identified as endemic for VL in São Paulo state . A more detailed knowledge of the immature stages and their preferential breeding sites is essential to understand the vector's population dynamics and could be used to develop novel control strategies .
The municipality of Promissão ( 21°32′S 49°51′W ) and Dracena ( 21°28′S 51°31′W ) are located in the Western region of São Paulo state , Brazil ( Fig . 1 ) . Promissão has a total area of 787 km2 and approximately 37 , 570 inhabitants . Dracena has a total area of 489 km2 and approximately 41 , 000 inhabitants . According to Köeppen's climate classification , these areas are identified as Aw - Tropical wet and dry [38] . Lutzomyia longipalpis was first detected in Promissão and Dracena in 1999 and 2003 , respectively . At the time of the present study , canine and human transmissions have been established in both municipalities . The houses in the urban and peri-urban areas of these municipalities usually have non-paved peridomicile with bushes and trees ( e . g . fruit trees ) , and domestic animals shelters for dogs and chickens are common . In both municipalities , of the 20 blocks where the study was performed , chickens and dogs were present in approximately 10% and 30% of the dwellings respectively . Two emergence trap designs were used in this study . The first one was that described by Casanova ( 2001 ) and a new one ( described below ) was developed by the same author ( Fig . 2A–2D ) through modifications in the polyvinyl chloride ( PVC ) pipe tube trap described by Ferro et al . ( 1997 ) [26] , [35] . The emergence traps were 20 cm tall , with varying diameters of 10 . 16 , 20 . 32 , 25 . 40 and 44 . 60 cm , sampling 0 . 008 , 0 . 032 , 0 . 051 and 0 . 156 m2 of substrate respectively ( the diameter reduced where necessary due to site conditions ) . The bottom edges of the PVC tubes were serrated to allow insertion at least 5 cm into the soil ( Fig . 2A ) of different microhabitats . After fixing it to the ground , the inner surface of the superior part of the tube was totally covered with a 4 cm wide adhesive paper ( Fly – Catcher “The Stable” , Silva – made in Sweden ) , which was attached to the tube wall using paper clips ( Fig . 2B ) . To prevent sand flies escaping , the top of the tube was covered with voile ( a fine-mesh , fabric gauze ) fixed with rubber bands ( Fig . 2C ) . This way , the sand flies which emerge and fly inside the PVC tube end up landing on the adhesive paper , which works as a sticky trap . Finally , to protect the trap from the sun and rain , a small tent was placed over them , taking care to leave approximately 5 cm clearance between the trap and the tent walls ( Fig . 2D ) . Two series of continuous soil emergence trap collections were carried out from March 2005 to February 2006 and from September 2006 to January 2007 in the urban and periurban area of the municipalities . The first period was exploratory and its main objective was to identify possible L . longipalpis breeding sites and the fauna associated with them . The second period aimed to evaluate the spatial distribution pattern of immature stages in the two principal microhabitats positive to L . longipalpis in Promissão , during the first study period . Chi-square analysis , used to compare the proportion of positive microhabitats for L . longipalpis in each municipality , and Pearson correlation coefficient , calculated to examine the relationship between the number of L . longipalpis collected in emergence and CDC traps , were carried out using BioEstat ( version 5 . 0; Mamirauá/CNPq , Belém , PA , Brazil ) . To evaluate the spatial distribution pattern of the immature stage , the variance-to-mean ratio ( s2/ ) , with chi-square statistic test to determining significantly departure from randomness χ2 = SS/ where s2 is the variance and is the sampling mean and SS is the sum of squares ) and the Morisita's Index ( Id = nΣ x2−N/N ( N−1 ) , with chi-square statistic test to determine significant departure from randomness χ2 = ( nΣ x2/N ) −N , where n = number of samples; N is the total number of individuals collected , and Σ x2 is the squares of the number of individuals per sample , summed over all samples ) were used [42] . In these indexes , values equal the unity indicate a random disposition , values smaller than the unity show a regular or uniform distribution , and values significantly higher than one indicate an aggregated or contagious disposition . The frequency of sand flies in CDC traps was obtained by month using William's geometric mean [43] .
The new design of emergence trap used in the present study is easy to construct , very straightforward to install and allows for longer periods between visits to the traps to remove sand flies ( every 20 days , approximately ) . Clearly , the chicken shed was the microhabitat that contributed the most to the high positivity of investigated dwellings , for the high proportion of positivity of emergence traps and to the increased number of L . longipalpis collected in the municipality of Promissão . In Dracena , chicken sheds were also the most productive microhabitat for L . longipalpis , however with a much lower number of positive emergence traps and sand flies than found in Promissão . Probably , this was due to both the smaller number of chicken shed investigated ( about 50% smaller ) and sampled area in the chicken shed microhabitat ( about three times smaller ) than that in Promissão . Compared to the leaf litter under tree microhabitat , the chicken shed and uncovered debris areas are much smaller , and the population boundaries are readily apparent . This facilitates sampling of the population and estimating its size , particularly if the population shows contagious spatial distribution . In this sense , although in the present study the leaf litter under tree microhabitat has contributed only 5 . 40% of the total L . longipalpis collected , one should not ignore the possibility of a high production of L . longipalpis in this microhabitat , because this species is by far the most common in the peridomiciles of the studied areas . However , the differences in microhabitat size were most likely not the reason for the great difference observed between the positivity and productivity of these microhabitats . The chicken sheds are probably more favourable breeding sites because they are important blood feeding and resting sites for females of L . longipalpis and the abundance of faeces can provide a source of organic material for larval food . One advantage of employing emergence traps to detect sand fly breeding sites is that they allow estimates of population densities from the observed productivity of breeding sites , expressed in adults/area/time [29] , [45] . Considering the period of investigation , the number of the traps and the sampled area , the estimated production of L . longipalpis in the chicken shed microhabitat was by far the highest among all the researched microhabitats in the present study . For instance , the density of 17 . 29 sand flies per m2 or 41 . 17 sand flies per 100 m2 per day estimate for the total of chicken shed microhabitat in Promissão is a much higher value than found for L . longipalpis and other species in Neotropical region . In emergence traps set near pigsty microhabitats Ferro et al . ( 1997 ) estimated a density of 4 . 96 L . longipalpis per m2 sampled [26] . Considering other sand fly species , Rutledge & Ellenwood ( 1975 ) in Panama , and Arias & Freitas ( 1982 ) , Casanova ( 2001 ) and Alencar ( 2007 ) in Brazil , estimated from the total of sand flies species captured in the litter of the forest floor , a production of 24 . 4 , 4 . 1 , 24 . 0 and 5 . 8 sand flies per 100 m2 per day , respectively [29] , [33] , [35] , [36] . Notably , the 34 L . longipalpis collected in one emergence trap with 20 . 32 cm of diameter ( 0 . 032 m2 ) , set in a chicken shed in Promissão , corresponded to a density of 1 , 062 . 5 sand flies per m2 . Besides that , the high density of 50 . 7 sand flies per m2 obtained in chicken shed microhabitats during August 2005 in Promissão allowed to estimate a production of 121 . 7 L . longipalpis per 100 m2 per day . This high potential production of L . longipalpis agrees with the high abundance of adults in CDC traps set in the peridomicile . The sexual ratio pro males may be a result of a possible greater male vagility after emergence facilitating their contact with the adhesive paper . Some of the male flies collected in the emergence traps still showed partial or no rotation of their genitalia . However , there is still the necessity to evaluate the efficiency of the traps of different dimensions to collect the emergence population inside them . This can be tested by releasing a known number of pupae or newly emerged adults into the traps , preferably at the central point [45] . Although the sampling of the microhabitats in the first period of study had been done with an uneven number of traps of different sizes , the intensive sampling , the concentration of a great number of L . longipalpis in few traps and the great number of traps with few or no flies seemed to be a strong indication of its contagious spatial distribution pattern . During the second study period , designed to identify where the concentrations occurred , and how great and how frequent they were , the indices used also showed a contagious distribution pattern . However , the abundance of L . longipalpis was low and new long term experiments , with greater number of traps and an exact positioning in the grid are necessary to allow the use of more accurate methods . These results suggest that females lay their eggs in clutches in restricted microhabitats , as indicated by Ferro et al . ( 1997 ) [26] . Probably the female sand fly in this , and other , species has the ability to detect food , shade , humidity and physical-chemical soil constitution , which are generally graded in a habitat . Some studies have shown that female sand flies locate an appropriate site by orienting towards semiochemical oviposition attractant components of eggs and animal faeces [46] . Contagious spatial distribution is the pattern commonly reported for insects in natural environments [45] and has already been suggested for sand flies species in forest floor microhabitats [29] . The contagious spatial distribution pattern can be responsible for the commonly reported difficulty in finding immature forms in soil samples or adult forms in emergence traps [27] , [28] . The associated fauna may offer important information on species which might act as a sand fly breeding site indicator . Detecting larvae from a Diptera predator group in the same chicken shed sample where L . longipalpis were found is interesting from the biological control perspective . Collecting E . cortelezzii , E . lenti , E . termithophila and N . neivai in chicken shed together with L . longipalpis also points to the importance that this environment can have in the ecoepidemiology of cutaneous leishmaniasis . The high abundance of L . longipalpis detected in the CDC traps fixed in peridomicile associated with animal pens , especially chicken sheds , has been frequently observed in several other rural and urban areas of Brazil [21] , [47]–[49] . The lack of correlation between the number of adult sand flies collected in CDC traps and the number of immature stages in soil samples , or adults in emergence traps , is not uncommon [26] , [50] , [51] . This could be due to an accumulation of multiple generations of flies in the CDC trap , in addition to the fact that adults are attracted to the host from an area considerable larger than the emergence microhabitats that are sampled . Probably the combined effects of rainfall , air temperature and evapotranspiration , which determine the soil water balance , influence the quality of the breeding site microhabitats and consequently determine the adult sand fly population fluctuations [26] , [29] , [48] , [52] , [53] . In the present study , moderate water deficit periods seem to be favorable for population increase and periods of high water surplus seem to negatively affect the immature and adult populations of L . longipalpis . For Promissão , the rising curve for adults from October on , which led to a peak in December , may have been influenced by moderated water surplus periods between October and December , which only happened in this municipality and not in Dracena . On the other hand , longer periods of intense draught , like the one which happened more evidently in Dracena ( water balance deficit from March to December ) , may be detrimental to the immature stages . The same has been observed with L . longipalpis and other sand fly species in urban and rural areas of São Paulo state [48] , [53] . Both the high proportion of positive sites and high density of individuals in emergence traps indicate that the chicken shed microhabitat is the preferential breeding site of L . longipalpis in the study areas . In this sense , a new item – act as breeding sites to L . longipalpis - can be added to list of Alexander et al ( 2002 ) as factor associated to chicken sheds that increases the risk of transmission of L . ( L . ) i . chagasi to humans in Brazil [22] . Raising chickens is relatively common and both culturally and socio-economically important in urban , peri-urban and rural areas of the municipalities in all regions of Brazil . Its importance in the ecoepidemiology of zoonotic visceral involves some type of balance between zooprophylaxis , maintenance of sand fly populations and attraction of reservoir hosts [22] . Spatial analysis studies to detect areas at increased risk for visceral leishmaniasis such as those developed by Cerbino-Neto et al ( 2009 ) , could evaluate the presence of chicken in the neighbourhood as an environmental factor associated with incidence of the disease [54] . It would be of interest to evaluate the efficiency of vector control interventions , such as environmental change and insecticide ( chemical or biological ) , in chicken shed microhabitats , with the aim of reducing the adult population . The abundance of the female population size is a critical parameter of vectorial capacity [53] , [55] . If the chicken shed , as a preferential breeding site , has an important role in determining the population abundance of adult forms of L . longipalpis , control strategies aimed at immature stage population in this microhabitat could be cheap , practical , and effective at reducing the populational abundance of females and consequently , the transmission rate . Furthermore , the efficacy of residual insecticide and others potential control strategies against adult forms - i . e . impregnated dog-collar [17] , [56] , zooprophylaxis [22] synthetic sand fly pheromone in conjunction whit insecticide [47] , [57] – could be improved through of the control of immature stages of L . longipalpis population in chicken shed microhabitats .
|
Sand flies are the vectors of a number of pathogens that infect man , especially cutaneous and visceral leishmaniasis . The control of sand flies through residual house-spraying insecticide has proven inefficient in preventing the spread of these diseases in several areas of the globe . Sand flies have a life cycle including eggs , larvae and pupae that are found in the ground , and winged adults . We developed a soil emergence trap that allowed us to identify chicken sheds as the preferred breeding sites for L . longipalpis that is the vector of visceral leishmaniasis in urban areas of municipalities in South-eastern region of Brazil . This finding opens up the possibility of controlling the immature stages which could complement the usual control strategies that focus on the adult fly .
|
[
"Abstract",
"Introduction",
"Methods",
"Results"
] |
[] |
2013
|
Larval Breeding Sites of Lutzomyia longipalpis (Diptera: Psychodidae) in Visceral Leishmaniasis Endemic Urban Areas in Southeastern Brazil
|
Bacterial biofilms account for a significant number of hospital-acquired infections and complicate treatment options , because bacteria within biofilms are generally more tolerant to antibiotic treatment . This resilience is attributed to transient bacterial subpopulations that arise in response to variations in the microenvironment surrounding the biofilm . Here , we probed the spatial proteome of surface-associated single-species biofilms formed by uropathogenic Escherichia coli ( UPEC ) , the major causative agent of community-acquired and catheter-associated urinary tract infections . We used matrix-assisted laser desorption/ionization ( MALDI ) time-of-flight ( TOF ) imaging mass spectrometry ( IMS ) to analyze the spatial proteome of intact biofilms in situ . MALDI-TOF IMS revealed protein species exhibiting distinct localizations within surface-associated UPEC biofilms , including two adhesive fibers critical for UPEC biofilm formation and virulence: type 1 pili ( Fim ) localized exclusively to the air-exposed region , while curli amyloid fibers localized to the air-liquid interface . Comparison of cells grown aerobically , fermentatively , or utilizing an alternative terminal electron acceptor showed that the phase-variable fim promoter switched to the “OFF” orientation under oxygen-deplete conditions , leading to marked reduction of type 1 pili on the bacterial cell surface . Conversely , S pili whose expression is inversely related to fim expression were up-regulated under anoxic conditions . Tethering the fim promoter in the “ON” orientation in anaerobically grown cells only restored type 1 pili production in the presence of an alternative terminal electron acceptor beyond oxygen . Together these data support the presence of at least two regulatory mechanisms controlling fim expression in response to oxygen availability and may contribute to the stratification of extracellular matrix components within the biofilm . MALDI IMS facilitated the discovery of these mechanisms , and we have demonstrated that this technology can be used to interrogate subpopulations within bacterial biofilms .
In nature , bacteria predominantly exist in a biofilm state [1] forming mutualistic or parasitic associations with other living organisms [2 , 3] . Within vertebrate hosts , the resident microbiota are essentially multi-species biofilms that play a key role in preventing colonization by pathogens [4] . Conversely , pathogenic bacteria exploit biofilm formation to colonize prostheses , catheters , as well as extracellular and intracellular host niches resulting in potentially life-threatening infections that are often difficult to treat [5] . Both single and multi-species biofilms are heterogeneous in nature , comprised of bacterial subpopulations with distinct tasks , such as expression of matrix components or a specific metabolic activity [6–9] . This “division of labor” within the community contributes to recalcitrance of the biofilm to antibiotic treatment . Biofilm subpopulations can be transient in nature , and arise in response to alterations in nutrient and oxygen availability of the surrounding microenvironment that in turn leads to local changes in bacterial gene expression [6–9] . However , little is known about the expression and distribution of individual protein species within a single multicellular community that results from this differential gene expression and how such differences may shape the characteristics and the fate of the biofilm . Traditional techniques used to visualize protein distribution within intact biofilms rely on microscopy-based methods that require the use of either fluorescently labeled proteins or the application of antibodies specific to a protein of interest [10 , 11] . These techniques are limited to previously identified protein targets and can typically only accommodate one or two species in a single analysis . Conversely , more global genomic and proteomic-based analyses necessitate the destruction of biofilm architecture , leading to complete loss of spatial information . Matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry ( MALDI-TOF IMS ) is a surface-sampling technology that can determine spatial information and relative abundance of analytes directly from biological samples [12] . Samples are treated with a matrix that absorbs ultraviolet light from a laser source to ionize analytes of interest . The generated ions are accelerated along a time-of-flight ( TOF ) mass analyzer for separation and detection [13] . Using this technique , spectra are collected in a defined array across the sample , and each peak intensity in the spectra is then extrapolated to generate an ion intensity map , allowing for a two-dimensional representation of analyte distribution within the imaged array ( Fig . 1 and [14] ) . This label-free technology does not require prior knowledge of sample composition or analyte distribution and provides an unbiased approach for the simultaneous localization analysis for multiple analytes within a single biological sample . Here , we used MALDI-TOF IMS to examine the in situ distribution and localization of low molecular weight proteins within biofilms formed by uropathogenic Escherichia coli ( UPEC ) . UPEC , one of the extra-intestinal E . coli pathotypes and the primary cause of urinary tract infections , can form extracellular biofilms on host cells and urinary catheters , as well as intracellular biofilm-like communities within host bladder epithelial cells [15–19] . These UPEC virulence mechanisms dictate multiple disease outcomes [20] , including urosepsis that can have life-threatening complications [21] . MALDI IMS detected distinct protein localization patterns within the surface-associated UPEC biofilms imaged in these studies . Subsequent , conventional proteomic approaches led to the identification of several of the distinctly localized ion species . Among the proteins identified were CsgA and FimA , which comprise the primary structural subunits of curli and type 1 pili fibers respectively . Type 1 pili , encoded by the fim gene cluster , are chaperone-usher pathway ( CUP ) pili [22] that facilitate adherence to mannosylated moieties and are the primary determinant that enables a ) UPEC attachment to the bladder urothelium , and b ) inter-bacterial interactions in both extracellular and intracellular biofilms [15 , 23] . MALDI IMS revealed that , while curli subunit signatures are found at the air-liquid interface of the biofilm , which is consistent with their primary role in extracellular matrix infrastructure , type 1 pili subunit signatures predominantly localize to the air-exposed regions of the biofilm . Subsequent studies investigating the effects of anaerobiosis on expression of type 1 pili in UPEC led to the discovery of two regulatory mechanisms controlling expression of type 1 pili in response to the presence of oxygen . Together , these data demonstrate how MALDI IMS can be used to dissect the spatial proteome of an intact bacterial biofilm , and highlight how the information obtained can provide new insight into protein regulation relating to biofilm infrastructure .
In order to assess the utility of MALDI-TOF IMS for evaluating protein localization within bacterial biofilms , we adapted a simple surface-associated biofilm setup that enabled the sampling of single-species biofilms formed by uropathogenic Escherichia coli ( UPEC ) [24] . We optimized growth conditions to promote biofilm formation onto indium tin oxide ( ITO ) coated glass slides , given that our MALDI IMS must be performed directly from an electrically conductive surface for high voltage analyses [25] . Slides were placed vertically into culture media seeded with bacteria , such that only half of the slide was submerged within the media . This setup created an environmental gradient of oxygen and nutrients that induced biofilm formation at the air-liquid interface ( Fig . 1A ) . We hypothesized that MALDI IMS would enable detection of distinct bacterial subpopulations resulting from the induced gradient ( Fig . 1A ) . MALDI-TOF IMS requires the application of a UV-absorbing matrix for analyte ionization [25] ( Fig . 1B ) . Typical sample preparation methods begin with solvent washes to decrease ion suppression from lipids and salts within the sample in order to enhance protein ionization [25] . Here , we selected a sequential washing procedure of 70% , 90% , and 95% ethanol for 30 seconds each . Following washes , we evaluated biofilm integrity using three different techniques: crystal violet staining , scanning electron microscopy ( SEM ) , and optical profilometry ( S1 Fig ) . SEM analysis of the air-exposed , the air-liquid interface , and liquid-exposed regions of the biofilm indicated that the tertiary structure , along with cell shape and surface features , were preserved post-washing ( S1 Fig ) . Crystal violet staining [26] and subsequent quantitation showed that the preparative ethanol washes did not significantly reduce biofilm levels ( S1 Fig ) . Finally , optical profilometry [27] was used to assess the biofilm depth on the surfaces analyzed by MALDI IMS ( S1 Fig ) . Combined , these approaches indicated that the sample preparation methods for MALDI IMS did not significantly perturb biofilm integrity . A schematic for the MALDI-TOF IMS analysis of UPEC biofilms is shown in Fig . 1B . The MALDI methods and matrix selected for these studies were optimized for lower molecular weight protein species; therefore , all analyses were carried out over an ion range of mass-to-charge ratio ( m/z ) 2 , 000–25 , 000 . Within this range , we observed 60 UPEC protein ion species that were detected reproducibly in at least 5 biological replicates ( S1 Dataset ) . The relative abundance and localization patterns for representative ion species are shown in Fig . 2 . Each panel depicts a heat-map intensity plot for a unique ion species within the biofilm , where red/white indicates the highest levels of relative abundance , and black/blue the lowest levels ( Fig . 2 ) . All observed ion species displayed one of the following localization/distribution patterns: diffuse distribution throughout the biofilm , localization specific to the air-exposed or liquid-exposed region , or localization to the air-liquid interface ( Fig . 2 ) . Overlay analysis of ion images demonstrated that we could differentiate localization patterns for different protein species within the same region of the biofilm ( Fig . 2 , ion overlay of m/z’s 5 , 596-red and 13 , 036-yellow ) . Following MALDI IMS spatial analysis , enzymatic digestion of biofilm lysates and tandem mass spectrometry were used to identify select ion species observed ( Table 1 ) . These analyses identified the histone-like global transcriptional regulators HU-α ( UniProt KB Q1R5W6 , m/z 9 , 535 ) and HU-β ( UniProt KB Q1RF95 , m/z 9 , 226 ) , which co-localized throughout the biofilm and were most abundant in the air-exposed region ( Fig . 3A and S2 Fig ) . The acid stress-response chaperone protein , HdeB ( UniProt KB Q1R595 , m/z 9 , 064 ) , and the uncharacterized protein YahO ( UniProt KB Q1RFK1 , m/z 7 , 718 ) were also identified ( Table 1 ) . HdeB localized to the air-liquid interface and was most abundant towards the liquid-exposed surface , while YahO localized throughout the biofilm ( Fig . 3A and S2 Fig ) . Finally , two of the IMS signals identified by proteomics corresponded to major subunits of two UPEC adhesive organelles: The major curli subunit CsgA ( UniProt KB Q1RDB7 , m/z 13 , 036 ) , an essential determinant for UPEC biofilm formation under the culture conditions used for these studies [7 , 28] , and; the major subunit of type 1 pili , FimA ( UniProt KB Q1R2K0 , m/z 16 , 269 ) . Based on the MALDI IMS results , CsgA signatures were predominantly found at the air-liquid interface of the biofilm ( Fig . 3A-B and S2 Fig ) , consistent with the role of curli as the primary extracellular matrix ( ECM ) component under the biofilm conditions tested . Conversely , FimA localized uniquely to the air-exposed region of the biofilm ( Fig . 3A-B , and S2 Fig ) . Under the biofilm growth conditions used for these studies , type 1 pili have been shown to play an accessory role to biofilm infrastructure , and loss of type 1 pili impairs integrity but does not abolish biofilm formation [7] . Thus , we took advantage of a fim deletion mutant ( UTI89ΔfimA-H ) to validate the identification of the m/z 16 , 269 ion as FimA . MALDI IMS analysis of UTI89ΔfimA-H biofilms showed a loss of the ion at m/z 16 , 269 ( S3 Fig ) , confirming the ion m/z 16 , 269 as FimA . Similarly , the ions m/z 9 , 535 and m/z 7 , 718 were validated as HupA and YahO respectively , through MALDI analysis of UTI89 mutants lacking the respective gene ( UTI89ΔhupA and UTI89ΔyahO ) ( S3 Fig ) . Given that curli are essential for UPEC biofilm formation under the conditions tested , we utilized a more traditional immuno-fluorescence approach with an antibody against CsgA to visualize curli-expressing bacteria within the biofilm and validate CsgA localization to the air-liquid interface . Combining immunohistochemistry with super-resolution structured illumination microscopy ( SIM ) , we observed that the majority of curli-producing bacteria localized to the air-liquid interface of the biofilm , with only sparse populations found at the air- and liquid-exposed regions ( Fig . 4 , S1 Video ) . These data confirmed the IMS observations of CsgA localization to the air-liquid interface of the biofilm . As an orthologous approach , we took advantage of small peptidomimetic molecules that interfere with curli biogenesis in UPEC [29] . We hypothesized that treatment of pre-formed biofilm with one such compound , FN075 [29] , should block curli fiber subunit incorporation leading to an abundance of CsgA monomers within the biofilm that could be detected by IMS . To test this hypothesis we cultured UPEC biofilms for 24 hours , at which time we added FN075 or DMSO ( vehicle control ) at previously reported concentrations [29] . Biofilms were allowed to grow in the presence of compound/vehicle for 24 hours prior to quantitation by crystal violet staining and imaging by MALDI IMS ( S4 Fig ) . Consistent with previous observations [30] , DMSO treatment increased biofilm levels and CsgA expression compared to untreated controls ( S4 Fig ) . Though these experiments were carried out under atmospheric conditions , DMSO can serve as an alternative terminal electron acceptor for E . coli [31] . This ability of DMSO may be contributing to the observed increase in biomass , though additional studies are needed to dissect the basis of biofilm increase in response to DMSO treatment . Colorimetric quantitation of biofilm levels also revealed a significant reduction in biomass with FN075-treatment of biofilms ( p = 0 . 0089 ) , compared to the DMSO-treated controls ( S4 Fig ) . Consistent with the difference in biofilm levels , average MALDI IMS spectra normalized to the total ion current ( TIC ) indicated a higher level of overall signal within the DMSO-treated samples ( S4 Fig ) . To account for the differences in biofilm levels between non-treated 48 hour biofilms , DMSO-treated , and the FN075-treated samples , mMass [32] software was used to normalize the overall intensity of the average spectra of each sample to the most abundant ion in the analysis ( m/z ~7 , 280 ) . These normalization parameters revealed an apparent increase in detection of the ion species corresponding to CsgA ( m/z 13 , 036 ) within the FN075-treated sample , despite the reduction in overall biofilm levels ( S4 Fig ) . IMS ion images for CsgA also appeared to show an increase in detectable CsgA monomers within the liquid-exposed region of the biofilm ( S4 Fig ) . This is consistent with our hypothesis that FN075 treatment of a pre-formed biofilm would lead to an increase in monomeric CsgA , which would be more readily ionized and thus detected . Having validated the identity and localization of CsgA and FimA , we next sought to understand the basis of the spatial segregation of type 1 pili within UPEC biofilms . The observation that type 1 pili-producing bacteria make up the top-most layer of the biofilm led us to the hypothesis that oxygen tension , at least in part , regulates the expression of type 1 pili . The fim gene cluster is under the control of a phase-variable promoter region ( fimS ) , the orientation of which in UTI89 is directed by the action of site-specific recombinases FimB , FimE , and FimX ( Fig . 5A ) belonging to the lambda integrase family [33] . At least two other global transcriptional regulators , Lrp and IHF , have been proposed to bend the fimS DNA in order to bring the invertible repeats in close proximity to each other and allow for recombination [33 , 34] . We used a previously developed PCR-based “phase assay” [35] that can distinguish between the transcription-competent ON ( fimON ) and transcription-incompetent OFF ( fimOFF ) orientations of the fim promoter ( Fig . 5A ) , along with immunoblot analysis and transmission electron microscopy to evaluate whether oxygen is requisite for fim expression . UTI89 was grown statically in either the presence or absence of oxygen in two different growth media ( YESCA and Luria Bertani ( LB ) ) and in two different temperature conditions ( room temperature and 37°C ) to evaluate the possibility that Fim localization to the air-exposed region was due to a nutritional or a temperature cue ( S1 Table ) . Static growth at 37°C in LB media under atmospheric conditions enhances expression of UPEC type 1 pili [36–38]; these conditions were used as a positive control . UTI89ΔfimA-H was used as a negative control . Given the static nature of all culture methods , cultures grown in the presence of oxygen were termed “semi-aerobic” . When starting these experiments from UPEC cultures that were primarily fimOFF , we observed that sub-culturing statically in the presence of oxygen induced expression of type 1 pili ( Fig . 5B—“semi-aerobic” , S5 Fig , S6 Fig ) . However , regardless of growth medium or temperature , the fim promoter remained in the fimOFF orientation when bacteria were cultured in the absence of oxygen ( fermentative conditions ) ( Fig . 5B and S5 Fig ) . When oxygen is not present , E . coli can utilize alternative terminal electron acceptors , such as nitrate , DMSO , TMAO , or fumarate [31] . Given that nitrate is the preferred alternative electron acceptor for E . coli , we assayed how anaerobic growth in the presence of nitrate ( in the form of 40 mM sodium nitrate , NaNO3 ) would impact expression of type 1 pili . We observed that static cultures started fimOFF remained largely fimOFF during anaerobic growth in the presence of NaNO3 similar to what was observed with cultures grown fermentatively ( Fig . 5B ) . When populations grown fermentatively or anaerobically with nitrate were sub-cultured into semi-aerobic conditions for 18 hours , the phase-variable promoter returned predominantly to the fimON orientation , leading to increased FimA protein levels ( Fig . 5B ) . These results suggested that the phase-switch from fimOFF to fimON is affected by the bacterial respiration state , favoring aerobic respiration . Previous studies indicated that multiple static sub-cultures under aerobic conditions enhance expression of type 1 pili by enriching for UPEC populations in which the fim promoter is fimON [36 , 37] . We thus repeated our experiments starting from cultures that were pre-enriched for fimON populations to test whether this would influence piliation in the absence of oxygen . Phase assays , FimA western blot analyses , and transmission electron microscopy ( TEM ) revealed that under fermentative conditions , the promoter actively inverted to the fimOFF orientation ( Fig . 5C ) , leading to significantly fewer pili on the cell surface ( Fig . 5C and S7 Fig ) . These data suggest that under fermentative conditions the phase-switch is preferably in the fimOFF orientation . Interestingly , growth of fimON cells in the presence of nitrate partially preserved the fimON state and production of type 1 pili on the surface ( Fig . 5C and S7 Fig ) . The partial preservation observed under anaerobic growth in the presence of nitrate for populations starting fimON suggests that anaerobic respiration does not impact the fimON to fimOFF phase-switch . Together , these data suggest a regulatory mechanism that actively senses and responds to environmental oxygen levels , and/or bacterial respiration state , to control the expression of type 1 pili in UPEC by altering fimS promoter orientation . In previous studies we created a UPEC strain ( UTI89_LON ) in which the fim promoter element is genetically locked into the transcription-competent fimON orientation [38] . We postulated that if oxygen/respiration state only impacts the phase-state of the fim promoter , then UTI89_LON would be piliated when cultured in the absence of oxygen . When cultured under fermentative conditions , UTI89_LON exhibited a marked reduction in type 1 pili production , similar to wild-type ( WT ) UTI89 , despite the “locked on” position of the promoter ( Fig . 5D and S7 Fig ) . The phase state of the fim promoter in UTI89_LON was verified by phase assays ( Fig . 5D ) to exclude the possibility of mutations affecting the phase state under the conditions tested . These data point towards an additional regulatory mechanism that influences production of type 1 pili in a manner that is independent of the fim promoter switch . Interestingly , anaerobic growth in the presence of nitrate induced fim gene expression in UTI89_LON ( Fig . 5D ) , similar to the fimON population shown in Fig . 5C ( Fig . 5C-D and S7 Fig ) . Taken together , these observations suggest that the absence of oxygen impacts the phase state of the fim promoter element , and demonstrate that if the promoter is found in the fimON orientation , the presence of an alternative electron acceptor is sufficient to induce transcription . Previous studies indicated that reduction in the expression of type 1 pili induces the expression of S pili under type 1 pili-inducing conditions [39–41] . We therefore evaluated the presence of S pili on the surface of the cell . Type 1 pili are characterized by their ability to bind mannosylated moieties [42] . An assay to evaluate the extent of type 1 pili in a UPEC population involves the agglutination of guinea pig red blood cells in the presence and absence of mannose . In bacteria that solely express type 1 pili , hemagglutination can be abolished by the addition of mannose to the agglutination reaction [42] . S pili bind sialic acid residues; therefore desialylation of red blood cells using neuraminidase prior to the agglutination assay abrogates S pili-dependent hemagglutination [43 , 44] . We combined these two approaches to establish the identity of the pili produced by UTI89 under anaerobic growth with cultures started from populations primarily fimON . As expected , when WT UTI89 was grown statically in the presence of oxygen , hemagglutination ( HA ) was abolished in the presence of mannose and was unaffected by neuraminidase treatment ( Fig . 6A ) , suggesting high numbers of type 1 pili . However , WT UTI89 grown under fermentative conditions exhibited lower HA titers that were inhibited by both mannose and by neuraminidase treatment ( Fig . 6B ) , indicating that the observed agglutination was mediated by both type 1 and S pili . Given the inverse relationship between these two chaperone usher pathway ( CUP ) pili systems , the observable increase in S pili-mediated agglutination under fermentative growth conditions is an orthologous approach to demonstrate the down-regulation of type 1 pili in response to the lack of oxygen . WT UTI89 grown anaerobically in the presence of nitrate exhibited overall lower HA titers compared to semi-aerobic and fermentative conditions ( Fig . 6C ) . However , this agglutination was inhibited by mannose and was not significantly impaired by neuraminidase treatment , confirming the de-repression of type 1 pili expression by addition of nitrate and the subsequent down-regulation of S pili . UTI89_LON exhibited an HA profile that was similar to WT UTI89 , suggesting that when the fim promoter is genetically locked in the fimON orientation , it exerts a negative effect thereby repressing S pili expression ( Fig . 6 ) . UTI89ΔfimA-H yielded low HA titers under the three growth conditions tested and agglutination was not inhibited by mannose but was abolished when treated with neuraminidase , verifying that pili observed by TEM with the UTI89ΔfimA-H mutant are S pili ( Fig . 6 and S7 Fig ) . These data demonstrate that the inverse relationship previously reported for type 1 and S pili [41] is maintained during growth in the absence of oxygen and that depletion of oxygen does not repress expression of all CUP pili systems .
This work shows MALDI IMS to be a strong analytical technology to study the spatial proteome of intact bacterial biofilms . Using a surface-associated biofilm setup that allowed for the formation of a biomass spanning two environmental niches ( liquid versus air ) , we show that this imaging technology can be applied towards the interrogation of biofilm heterogeneity without a priori knowledge of protein targets of interest . Various mass spectrometric techniques have previously been applied for the study of microbial systems [45] . Laser desorption post-ionization mass spectrometry has been applied to analyze peptides involved in sporulation and bacterial competence [46] , and secondary ion mass spectrometry ( SIMS ) was successfully used to analyze peptides involved in bacterial swarming [47] . MALDI IMS has been used successfully for the analysis of small molecules and metabolites within bacterial communities [48–51] . To date , only one other study has utilized MALDI IMS for the direct analysis of protein species within a bacterial community [52] . M . T . et al . used MALDI IMS for the analysis of peptides and proteins found at the site of interaction between E . coli and Enterococcus faecalis biofilms co-cultured on an agar surface , as well as within each individual biofilm [52] . Other than this initial study , little has been done to define the stratification of proteins within intact biofilms by IMS . Therefore , the application of MALDI IMS for the analysis of the intact spatial proteome of a single-species bacterial community represents an emerging approach that has the potential to offer new insights into the role and regulation of protein stratification within biofilms . One caveat to MALDI-TOF IMS analyses of intact protein localization is that the species observed are typically limited to those most abundant within the sample or those that crystallize and ionize best with the MALDI matrix selected [25 , 53 , 54] . This limitation can restrict the sensitivity and dynamic range of the analytes observed by IMS . In turn , large molecular weight proteins or large polymeric protein complexes vital to biofilm formation , which are harder to ionize by MALDI and detect by time-of-flight mass analysis could be intrinsically excluded from the data . This caveat is exemplified by our curli fiber studies , where FN075 treatment increased the amount of detectable CsgA . Thus , orthologous approaches are still critical for validating MALDI IMS findings . The profile of protein species observed can be expanded by varying the UV-absorbing matrix used for the analysis and by extending the overall m/z ion range analyzed ( i . e . from 2 , 000–25 , 000 m/z to 2 , 000–40 , 000 m/z , and so on ) [55] . The sensitivity of MALDI IMS can be refined further by increasing the spatial resolution at which the biofilm is imaged from the current resolution of 150 μm to as low as 20μm in order to better define stratification of subpopulations . We are currently developing both methods to enhance the number and type of protein species that can be localized within a single biofilm . While our approach clearly did not capture the global biofilm proteome , it simultaneously detected the spatial localization of up to 60 protein species within a single analysis; this represents a significant advancement compared to more traditional antibody- or fluorescent tag-based approaches that have been largely limited in the number of protein species visualized per analysis . In addition , the localization of proteins such as FimA and CsgA , which have been shown to play a crucial role in UPEC biofilm formation and pathogenesis but cannot be epitope-tagged due to their incorporation in macromolecular structures , also highlights the strength of this application . MALDI IMS analyses revealed that type 1 pili-producing bacteria stratify above curli fiber-producing bacteria within the UPEC glass slide surface-associated biofilms interrogated in our studies ( Fig . 3B ) . Similar UPEC biofilms have been previously shown to consist of an extracellular matrix comprised of curli and cellulose [7 , 28] , with type 1 pili playing an accessory role in biofilm tensile strength [7] . The study by Hung et al . , revealed that the bacteria on the air-exposed layer of a floating pellicle biofilm ( formed during growth in the same media used in our studies ) , are morphologically distinct from those at the liquid interface [7] . In the same study , they also reported that disruption of fim-mediated adhesion did not ablate biofilm formation , but rather impaired biofilm integrity through the formation of large holes on the air-exposed side of the biomass [7] . Here , MALDI IMS demonstrated that type 1 pili are produced by the bacteria forming the topmost , air-exposed layer of the biofilm . In our studies , we observed that a pellicle biofilm typically surrounded the UPEC slides cultured for IMS analysis within 72–96 hours of starting the culture . If the slide-associated biofilm analyzed by MALDI IMS , is representative of a cross-section of the growing pellicle biomass biofilm , stratification of type 1 pili observed in surface-associated biofilms by IMS could help to explain the loss in tensile strength upon disruption of fim-mediated adhesion observed by Hung et al . However , it is important to note that the type of surface to which the bacteria adhere and the nutrient or surrounding environmental conditions can alter the genetic expression profiles within the biofilm community . Therefore , we recognize that the conclusions drawn here are representative of biofilms formed on a glass surface in a laboratory setting and may bear differences from cross-sections obtained from floating pellicles . Bacterial biofilms constitute a serious problem in the healthcare setting . The unique heterogeneous architecture of the biofilm , combined with the composition of a self-secreted extracellular matrix , greatly hampers the penetrance and efficacy of bactericidal drugs and limits treatment options in the case of biofilm-related infection [21] . It is thus imperative to identify new strategies to combat or re-program how bacteria form these multicellular structures . Numerous studies identified the presence of bacterial subpopulations within bacterial biofilms and identified that these subpopulations execute unique “tasks” [56 , 57] . For example , in the benign B . subtilis biofilms , specific subpopulations produce extracellular matrix while others undergo sporulation [57 , 58] . Further studies indicated that B . subtilis biofilms are coated with a hydrophobin that renders the biofilm colony impervious to penetration [58] . In E . coli and other pathogens , metabolically inactive “persister” cells within the biofilm re-seed the infection upon cessation of antibiotic treatment [8 , 56 , 59] . Identifying the spatial proteome of biofilms may uncover markers for distinct subpopulations , thereby aiding in the development of new strategies for thwarting biofilm formation . Our analyses so far revealed that induction of type 1 pili expression likely occurs on the topmost layer of the imaged biofilm due to the increased oxygen levels in this region . Previous studies reported that UPEC strains rely on the TCA cycle during infection [39 , 60] and that TCA cycle perturbations lead to a repression of fim gene expression and abrogation of intracellular bacterial community formation [39] . The studies described here show that there are at least two regulatory mechanisms that control expression of type 1 pili in the absence of oxygen; one that exerts its regulatory effect by influencing the fim promoter switch and another that acts independently of the fim promoter switch . Both of these mechanisms are engaged under fermentative growth , strongly suggesting that loss of the ability to use the electron transport processes imposes an energetic cost to the bacteria and necessitates the down-regulation of energetically expensive structures . In probing the basis of these mechanisms , we have found that under fermentative conditions , there is no significant change in steady-state mRNA transcripts of the two main fim recombinases FimB and FimE ( S8 Fig ) . We have also ruled out the involvement of the Anaerobic Respiration Control ( Arc ) two-component system ( S8 Fig ) . It is likely that the effects on the phase-state of the fim promoter result from effects on the function of FimB and/or FimE as previously described [61] . Muller et al . elegantly demonstrated that CRP impacts fim gene expression by interfering with FimB function and repressing the expression of Lrp [61] . Other studies indicated that mutants deleted for the global regulator FNR had increased levels of Lrp under anaerobic growth conditions , suggestive of FNR down-regulating lrp expression in the absence of oxygen [62 , 63] . In the UPEC strain CFT073 , Barbieri et al . have demonstrated that deletion of FNR suppresses expression of the FimB recombinase under atmospheric conditions [63] . We are currently investigating the involvement of FNR on modulating fim promoter switching in UPEC strain UTI89 . Use of alternative electron acceptors affords E . coli the ability to continue the electron transport processes under a variety of growth conditions , extending the range of environmental conditions they can withstand . Here we show that while incorporation of an alternative terminal electron acceptor ( nitrate ) partially preserved piliation in cells that had the promoter fimON , it was unable to restore production of type 1 pili in cells with the promoter in the fimOFF orientation . We have attributed this effect to the ability of nitrate to serve as an alternative terminal electron acceptor . However , it is important to note that nitrate itself , as well as byproducts of nitrate respiration , specifically nitric oxide ( NO ) , can also serve as a signaling molecule within the biofilm community [64–66] . NO has also been shown to have anti-biofilm abilities , suggesting possible role within biofilm signaling and maintenance [67] . We are currently in the process of confirming our results and examining the impact of the other preferred alternative terminal electron acceptors of E . coli ( DMSO , TMAO , and fumarate ) , on type 1 pili expression under oxygen-deplete conditions . Overall , the results of our nitrate studies are in agreement with our previous studies , in which a non-functional TCA cycle threw the fim switch in the fimOFF orientation [39] . Pathogenic extra-intestinal E . coli strains , such as UPEC , typically thrive in the gastrointestinal tract of humans and other warm-blooded animals where oxygen is limited . As UPEC exit the gut and ascend the urethra to eventually colonize the urinary tract , they undergo multiple metabolic transitions between aerobic and anaerobic growth states . Each of these transitions is accompanied by fluctuations in oxygen tension from strictly anaerobic to highly oxygenated , to semi-aerobic . The bacterial cells respond to these fluctuations by modulating central metabolic pathways for carbon and energy flow , which in turn impact expression of a battery of targets including virulence factors . Together with previous reports [39 , 60] , the studies described here corroborate a direct link between respiration state and the expression of adhesive fibers that has multiple regulatory checkpoints , possibly to account for the diverse fluctuations in oxygen tension encountered by UPEC . Our study also suggests that oxygen gradients determine fiber stratification within the biofilm , which may contribute to overall integrity . Collectively , our studies used MALDI IMS to begin to define the spatial stratification of distinct bacterial subpopulations within UPEC biofilms based on differential protein expression profiles . Extrapolating from observations made by MALDI IMS , we discovered that type 1 pili-producing bacteria constitute the uppermost layer of UPEC biofilms under the conditions tested , and we identified two new UPEC regulatory mechanisms that control the expression of type 1 pili in response to oxygen and/or bacterial respiration state . These findings highlight how MALDI IMS can drive the identification and characterization of biofilm subpopulations , leading to a greater understanding of their role and regulation within the biofilm .
For these studies we used the UPEC cystitis isolate UTI89 [24] . Previously constructed UTI89 mutants used in this study are UTI89ΔfimA-H ( gift from Dr . Scott Hultgren ) ; UTI89_LON [38]; and UTI89ΔarcA ( gift from Dr . Matthew Chapman ) . UTI89ΔhupA and UTI89ΔyahO were created using the previously established λ Red recombinase methods [68] and the following primers ( Integrated DNA Technologies ) : hupA_Fwd ( 5’–TTACTTAACTGCGTCTTTCAGTGCCTTGCCAGAAACAAATGCCGGTACGTGTGTAGGCTGGAGCTGCTT–3’ ) / hupA_Rev ( 5’-ATGAACAAGACTCAACTGATTGATGTAATTGCAGAGAAAGCAGAACTGTCCATATGAATATCCTCCTTAG-3’ ) ; yahO_Fwd ( 5’-ATGAAAATAATCTCTAAAATGTTAGTCGGTGCGTTAGCGTTTGCCGTTACGTGTAGGCTGGAGCTGCTTC-3’ ) / yahO_Rev ( 5’-TTACTTCTTCTTATAAATATTTGCCGTGCCGTGAATCTTATTGTCAGTTTCATATGAATATCCTCCTTAG-3’ ) . All strains were grown overnight in Lysogeny broth ( LB ) ( Fisher ) , pH 7 . 4 , at 37°C with shaking , unless otherwise specified . Overnight cultures were then sub-cultured in 1 . 2x Yeast-Extract/Casamino Acids ( YESCA ) broth [43] . Bacterial suspensions were then dispensed in 50 mL conical tubes containing ITO-coated glass slides ( Delta Technologies ) and cultured for 48 hours at room temperature . After culture , slides were removed , rinsed with water to remove non-adherent bacteria and stored at -80°C until analysis . Biofilms were quantified as previously described [43] . Crystal violet stained biofilms were removed from ITO slides using 35% acetic acid and transferred to 96-well plates for absorbance readings . Absorbance at 570 nm was determined using a BioRad Model 680 microplate reader ( BioRad ) . Data are presented as the average absorbance from at least three independent experiments . Statistical analysis was performed using a two-tailed unpaired Student’s t-test ( GraphPad Prism 6 ) . Scanning electron microscopy ( SEM ) . Bacterial biofilms grown as described for MALDI IMS were treated for SEM as previously described [69] . Samples were dried at the critical point , mounted onto aluminum sample stubs and sputter coated with gold-palladium . A small strip of silver paint was applied to the sample edge , and biofilms were imaged with an FEI Quanta 250 Field-emission gun scanning electron microscope ( FEI ) . At least two biological replicates were imaged for each sample preparation and representative images were collected . Transmission electron microscopy ( TEM ) . TEM analyses were performed as outlined previously [40] . Briefly , 100 μL of normalized bacterial cultures ( OD600 = 1 . 0 ) from each condition were centrifuged at 4 , 000 rpm for 10 minutes and resuspended in 50 μL of TEM fixative ( 2 . 5% glutaraldehyde in 100mM sodium cacodylate ( Electron Microscopy Sciences ) ) for 1 hour at room temperature . Samples were then deposited onto glow-discharged formvar-/carbon-coated copper grids ( Electron Microscopy Sciences ) for 60 seconds and stained with 1% uranyl acetate for 90 seconds . Samples were then analyzed on a Phillips/FEI T-12 Transmission Electron Microscope ( FEI ) . Immuno-fluorescence by Super-resolution Structured Illumination Microscopy ( SIM ) . The α-CsgA antibody was provided by Dr . Matthew Chapman at the University of Michigan . UPEC biofilms were grown for 48 hours as previously described . Biofilms were fixed in 4% paraformaldehyde in phosphate-buffered saline ( PBS ) for 30 minutes at room temperature and blocked in 5% BSA overnight at 4°C . Biofilms were immuno-stained with α-CsgA ( 1:1000 ) for 1 hour at room temperature , followed by 3 washes in PBS and secondary detection with Alexa Fluor-555 goat anti-rabbit ( 1:1000 ) ( Life Technologies ) for 1 hour at room temperature . Samples were washed 3 times in PBS and mounted under a 1 . 5 size coverslip ( Fisher Scientific ) using ProLong Gold antifade reagent containing DAPI for DNA counterstain ( Life Technologies ) . Cells were imaged using a GE/Applied Precision DeltaVision OMX in SIM mode with 1 . 516 immersion oil at 63X magnification . Post-data acquisition processing was performed using SoftWorx for OMX . Images were processed for contrast enhancement and cropping in Photoshop . With the exception of x-y sections ( z stacks ) , images are shown as maximum intensity projections through the entire imaged area ( ranging from 3–6 μm in z , 40 μm in x-y ) . Videos depicting three-dimensional reconstruction of biofilms were generated using the Volume Viewer in Progressive mode in SoftWorx for OMX . Surface analysis was performed on crystal violet stained biofilms using a Zeta-20 True Color 3D Optical Profilometer ( Zeta Instruments ) at 20x magnification . Fifty microns were z-stacked to create the profiles at 0 . 2 microns/step . Images were reconstructed using a 10% optical overlap in stitching . Optical images of crystal violet stained biofilms were obtained using a Leica SCN400 Digital Slide Scanner ( Leica Microsystems ) at 20x magnification in manual bright field mode . Biofilms grown on ITO-coated glass slides were washed to remove interfering salts and lipids in sequential 30-second washes of 70 , 90 , and 95% HPLC-grade ethanol ( Fisher Scientific ) . Matrix comprising 15 mg/mL 2 , 5-dihydroxybenzoic acid ( DHB ) ( Fisher Scientific ) and 5 mg/mL α-Cyano-4-hydroxycinnamic acid ( CHCA ) ( Sigma-Aldrich ) was applied using a TM-Sprayer ( HTX Imaging ) , and samples were vapor rehydrated with 10% acetic acid . Samples were analyzed using a Bruker Autoflex Speed mass spectrometer ( Bruker Daltonics ) in linear positive ion mode . Each pixel contains an average of 200 spectra . Images were collected at 150 micron ( μm ) lateral resolution . Data were analyzed using FlexImaging 3 . 0 Build 42 ( Bruker Daltonics ) . Datasets were normalized to total ion current unless otherwise indicated . Ion intensity maps were extracted for each range of interest and were plotted using the maximum intensity within the range . ( Detailed MALDI-TOF IMS methods are found in S1 Methods ) . To identify 48-hour UPEC biofilm m/z ion species observed by IMS , multiple slide-associated biofilms were lysed and pooled together . Lysates were sonicated , centrifuged , and supernatants dried by vacuum centrifugation ( Thermo Scientific ) . Samples were resuspended and fractionated using C8 ( Grace Vydac ) or C18 ( Phenomenex ) reversed-phase high performance liquid chromatography ( HPLC ) ( Waters ) . Fractions were analyzed for m/z ions corresponding to those observed in the IMS analyses , subjected to in-solution tryptic digestion , and submitted to the Vanderbilt University Mass Spectrometry Research Center Proteomics Core for LC-MS/MS identification ( Detailed methods in S1 Methods ) . For validation of FimA protein identification , 48-hour biofilms of the UTI89ΔfimA-H were cultured as described above and analyzed by MALDI IMS . For validation of HupA and YahO protein identifications , 48-hour static liquid cultures ( in 1 . 2x YESCA ) of UTI89ΔhupA and UTI89ΔyahO were grown . After 48 hours , an aliquot of liquid culture was removed and pelleted . Pellets were then lysed with a volume of 35% acetic acid , and centrifuged to pellet debris . Lysates were then analyzed by MALDI-TOF MS ( Bruker Daltonics ) using the same matrix and parameters for IMS analyses . FN075 was prepared and characterized as described previously [29 , 70] . UPEC biofilms were cultured as described above for 24 hours . After 24 hours the preformed biofilm was treated with either 125 μM FN075 dissolved in 100% dimethyl sulfoxide ( DMSO ) , an equivalent volume of 100% DMSO ( vehicle control ) , or an equivalent volume of fresh YESCA media ( negative control ) and allowed to develop for another 24 hours . Slides were then removed and processed as described above . Biofilms were quantified and analyzed by MALDI IMS as described above . WT UTI89 and mutant strains were cultured under media and growth conditions listed in Supplemental Table 1 ( S1 Table ) . Cultures starting fimOFF were begun from overnight shaking cultures , and cultures starting fimON were begun from overnight statically grown cultures , both in LB media at 37°C . Oxygen-deplete cultures were grown in an anaerobic chamber maintained at 0% oxygen with between 2–3% hydrogen . Alternative terminal electron acceptor samples were treated with 40mM sodium nitrate ( NaNO3 ) ( Sigma-Aldrich ) . All cultures were grown for 48 hours to mimic biofilm growth conditions used in IMS analyses . After 48 hours , cultures were normalized to an OD600 of 1 . 0 with sterile PBS for phase assay and immunoblot analysis . Phase assays were performed as previously described [35] using 100 ng of genomic DNA , or an aliquot of normalized cells ( OD600 1 . 0 ) and with the following modifications: Primers Phase_L ( 5’-GAGAAGAAGCTTGATTTAACTAATTG-3’ ) , and Phase_R ( 5’-AGAGCCGCTGTAGAACTCAGG-3’ ) were used and the PCR was performed using the following parameters: 95°C—5min , 30 cycles ( 95°C—45sec , 50°C—20sec , 72°C—45sec ) , 72°C—5min . To determine the proportion of the population fimON vs . fimOFF , mean pixel intensity of the bands at 489 bp ( fimON ) and 359 bp ( fimOFF ) was determined within each sample using Adobe Photoshop CS6 ( Adobe Systems ) . Background taken from a blank area of the gel at a position equivalent to each band , was subtracted . The mean intensity of the fimON and fimOFF band for each sample was then summed , and the percentage ON vs . OFF was then determined for each sample . The percentage of each sample fimOFF was then plotted with GraphPad Prism 6 ( GraphPad Software Inc . ) , and statistical analysis was performed using a one-way ANOVA with Bonferroni’s multiple comparisons test . Immunoblots probing for FimA were performed as previously described [43] . Briefly , cultures were normalized to an OD600 = 1 . 0 and 1 ml of normalized cultures was pelleted by centrifugation . Normalized cell pellets were suspended 1x Laemmli sample buffer ( BioRad ) containing 5% 2-mercaptoetahnol ( Sigma-Aldrich ) . Samples were acidified with 1M hydrochloric acid ( HCl ) , heated at 100°C for 10 minutes , and then neutralized with 1N sodium hydroxide ( NaOH ) . Samples were then resolved on a 16% SDS-PAGE gel . Following SDS-PAGE , proteins were transferred to nitrocellulose using the Trans-Blot Turbo Transfer System ( BioRad ) , ( 7 minute transfer at 1 . 3A and 25V ) . Transfer efficiency was verified with Ponceau S ( Sigma-Aldrich ) . Stains corresponding to blots shown in Fig . 5 are included in S6 Fig . Following transfer , membranes were blocked with 5% non-fat milk in 1x TBST overnight at 4°C . After blocking , membranes were washed 2x with 1x TBST and incubated with primary anti-FimA antibody [1:5 , 000] [43] for 1 hour at room temperature , washed 2x with 1x TBST , and incubated with HRP-conjugated goat—anti-rabbit secondary antibody ( Promega ) for 30 minutes at room temperature . Following secondary antibody application membranes were washed 3x with 1x TBST , treated with SuperSignal West Pico Chemiluminescent Substrate ( Thermo Scientific ) , and bands visualized on x-ray film ( MidSci ) . Immunoblots probing for CsgA were performed in a similar fashion with the exception that cell pellets were first solubilized in 100% formic acid , which was then evaporated prior to re-constitution in 1x SDS sample buffer , as previously described [29] . The anti-CsgA antibody was used at a 1:10 , 000 dilution . Hemagglutination assays were performed as described previously [43] . Guinea pig erythrocytes were obtained from the Colorado Serum Company . Erythrocyte de-sialylation was performed using Clostridium perfringens neuraminidase ( New England BioLabs ) for 2 hours at 37°C with gentle agitation . RNA extraction , reverse transcription , and real-time quantitative PCR were performed as previously described [71] . qPCR analysis was performed with three concentrations of cDNA ( 50 ng , 25 ng , 12 . 5 ng ) each in triplicate for each sample , and internal DNA gyrase ( gyrB ) levels were used for normalization . The following primers ( Integrated DNA Technologies ) were used for amplification; fimB_Fwd ( 5’—GCATGCTGAGAGCGAGTCGGTA—3’ ) , fimB_Rev ( 5’—GGCGGTATACCAGACAGTATGACG—3’ ) , fimE_Fwd ( 5’—ATGAGCGTGAAGCCGTGGAACG—3’ ) , fimE_Rev ( 5’—TATCTGCACCACGCTCAGCCAG—3’ ) , gyrB_L ( 5’—GATGCGCGTGAAGGCCTGAATG—3’ ) , gyrB_R ( 5’—CACGGGCACGGGCAGCATC—3’ ) . The following probes ( Applied Biosystems ) were used for quantitation; fimB ( 5’– 6FAM-TCATCCGCACATGTTAC-MGBNFQ—3’ ) ; fimE ( 5’—NED-CGGACCGACGCTATAT-MGBNFQ—3’ ) ; gyrB ( 5’—VIC-ACGAACTGCTGGCGGA-MGBNFQ—3’ ) .
|
Bacteria are commonly found in multicellular communities known as biofilms . Biofilms can form on a variety of surfaces , both outside and within living things , and can have detrimental effects on human health . The characteristics of bacteria occupying different areas within biofilms are not well understood , and such knowledge is critical for understanding how biofilms form and for developing strategies to treat biofilm-related infections . Here , we adapted a technique to sample how proteins cluster within bacterial biofilms as a means to identify the location of bacteria with differential protein expression within the community . We observed that with uropathogenic E . coli , which is the major cause of urinary tract and catheter-associated urinary tract infections , bacteria close to the air-exposed region of the biofilm expressed different adhesive fibers compared to those at the liquid interface . We went on to show that lack of oxygen shuts down the production of fibers known to be critical for adherence to host bladder cells and to catheter material . This discovery was enabled by a new application of an existing technology that allowed us to gain insights into the spatial regulation of proteins within bacterial biofilms and to elucidate pathways that could be targeted to inhibit bacterial adherence .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Adhesive Fiber Stratification in Uropathogenic Escherichia coli Biofilms Unveils Oxygen-Mediated Control of Type 1 Pili
|
Most cellular processes are conducted by multi-protein complexes . However , little is known about how these complexes are assembled . In particular , it is not known if they are formed while one or more members of the complexes are being translated ( cotranslational assembly ) . We took a genomic approach to address this question , by systematically identifying mRNAs associated with specific proteins . In a sample of 31 proteins from Schizosaccharomyces pombe that did not contain RNA–binding domains , we found that ∼38% copurify with mRNAs that encode interacting proteins . For example , the cyclin-dependent kinase Cdc2p associates with the rum1 and cdc18 mRNAs , which encode , respectively , an inhibitor of Cdc2p kinase activity and an essential regulator of DNA replication . Both proteins interact with Cdc2p and are key cell cycle regulators . We obtained analogous results with proteins with different structures and cellular functions ( kinesins , protein kinases , transcription factors , proteasome components , etc . ) . We showed that copurification of a bait protein and of specific mRNAs was dependent on the presence of the proteins encoded by the interacting mRNAs and on polysomal integrity . These results indicate that these observed associations reflect the cotranslational interaction between the bait and the nascent proteins encoded by the interacting mRNAs . Therefore , we show that the cotranslational formation of protein–protein interactions is a widespread phenomenon .
The majority of cellular proteins function as subunits in larger protein complexes . However , very little is known about how protein complexes form in vivo . One possibility is that proteins are fully translated and released into the cytoplasm before finding their interacting partners ( posttranslational assembly ) . Alternatively , protein-protein interactions could form as one or several of the interacting proteins are being translated ( cotranslational assembly ) . There are indications that some cytoskeletal proteins , including vimentin , myosin and titin , assemble cotranslationally into insoluble filaments [1] . The formation of some multimeric membrane channels also appears to take place cotranslationally [2] , [3] . There are also a few examples of cotranslational assembly of soluble proteins: the p53 and NF-κB transcription factors form homodimers , which are thought to be generated by cotranslational interactions within a single polysome [4] , [5] . Importantly , the majority of these examples involve the assembly of a single protein into higher order structures . A number of recent studies have shown that the use of immunoprecipitation coupled with microarray analysis ( RIp-chip , for Ribonucleoprotein Immunoprecipitation analysed with DNA chips ) can be used to study cotranslational pathways involved in protein biosynthesis [6] , [7] , [8] . In this approach , a protein is purified together with associated RNAs , and the mRNAs are identified using DNA microarrays . When this method is applied to proteins associated with polysomes , it allows the identification of mRNAs cotranslationally associated with the bait protein . Using this technique we recently showed that the Rng3p myosin-specific chaperone associates cotranslationally with all five myosin heavy chains in the fission yeast Schizosaccharomyces pombe [6] . Another study in the budding yeast Saccharomyces cerevisiae found that the SET1 mRNA is part of a complex containing four components of the SET1C histone methyltransferase complex . The protein-RNA interactions were dependent on active translation , suggesting that the complex between these proteins was formed cotranslationally [7] . Apart from these few examples , very little is known about the prevalence of cotranslational assembly in the formation of protein complexes . Importantly , systematic approaches to identify and characterise this phenomenon ( such as RIp-chip ) have not been applied to large numbers of proteins . To address these questions we carried out RIp-chip experiments with 31 proteins with different functions and structures . We found that more than 12 of the proteins interacted specifically with small numbers of mRNAs ( between 1 and 3 ) , most of which encoded proteins that are known or predicted to interact with the bait proteins . We examined the protein-RNA interactions of three proteins in detail: in all cases we found that the interactions required the presence of the protein encoded by the associated mRNA as well as active translation . These data demonstrate that these protein mRNA interactions reflect the cotranslational formation of protein-protein interactions , and suggest that this is a widespread phenomenon .
As part of a project to identify RNAs associated with molecular motors in S . pombe , we performed RIp-chip experiments with the Tea2p kinesin . Surprisingly , Tea2p copurified specifically with only two mRNAs: tea2 and tip1 ( Figure 1A ) . tip1 encodes a protein of the CLIP-170 family that interacts physically with Tea2p and is transported by it along cytoplasmic microtubules [9] . We considered three models that could explain the association between Tea2p and the tip1 mRNA: in models 1 and 2 ( Figure 1B ) , Tea2p could interact with specific sequences on the tip1 mRNA , either directly ( model 1 ) or through a sequence-specific RNA-binding protein ( model 2 ) . In model 3 , Tea2p interacts with the Tip1p nascent peptide , and thus pull downs the tip1 mRNA as part of the whole polysome . In this case , the complex between Tea2p and Tip1p forms cotranslationally . The different models can be distinguished from each other experimentally by their dependence on the Tip1 protein for the interaction: in models 1 and 2 ( recognition of RNA sequences ) , the interaction between Tea2p and tip1 mRNA should be independent of the presence of Tip1p . In model 3 ( cotranslational assembly ) , the association should only occur if Tip1p is present . To discriminate between these possibilities we used a strain expressing tip1 RNA but not Tip1 protein ( Figure 1C , Figure S1 , Figure S2 ) . The strain was generated by mutating a single nucleotide in the initiation codon of tip1 ( −ATG-tip1 ) , and expressing the resulting construct in cells in which the endogenous tip1 gene had been deleted ( see Materials and Methods ) . As a control , a similar strain was constructed expressing wild type tip1 ( +ATG-tip1 ) . While Tea2p and tip1 mRNA copurified in +ATG-tip1 cells , the interaction was completely lost in −ATG-tip1 cells ( Figure 1A and Table S1 ) . By contrast , tea2 mRNA was precipitated to a similar extent in +ATG-tip1 and −ATG-tip1 cells ( Figure 1A and Table S1 ) . These data demonstrate that association of Tea2p with the tip1 mRNA is dependent on expression of Tip1p , and strongly suggests that Tea2p and Tip1p bind to each other cotranslationally . This model makes a further prediction , namely that the interaction between Tea2p and tip1 should be dependent on the integrity of polysomes . To test this idea we performed RIp-chip experiments after disrupting polysomes in vivo and in vitro . Treatment of S . pombe cells with puromycin leads to polysome disassembly and the release of nascent peptides [10] , [11] . In cells incubated with puromycin , the interactions between Tea2p and both tea2 and tip1 mRNAs were entirely lost ( Figure 1A and Table S1 ) . Treatment of extracts with EDTA , which chelates magnesium and causes polysome disassembly , also disrupted the association between Tea2p and tip1 and tea2 mRNAs ( Table S1 ) . All together , these experiments strongly suggest that the complex between the Tea2 and Tip1 proteins forms cotranslationally . To test if this phenomenon is bidirectional ( i . e . if Tip1p interacts with Tea2p as tea2 mRNA is being translated ) , we carried out RIp-chip experiments with Tip1p . In this case , Tip1p coprecipitated with its own mRNA , but not with that of tea2 ( Figure S3 and Table S2 ) . We wondered whether cotranslational assembly is a common mechanism for the formation of protein complexes . To address this question we analyzed 31 proteins by RIp-chip ( Figure S3 and Table S2 ) . We tested a variety of proteins , none of which contained canonical RNA-binding domains . Our baits included protein kinases , transcription factors , components of the proteasome , kinesins and several members of the actin related protein ( Arp ) family . Of the 31 bait proteins probed , 10 showed no significant association with any RNA , 9 proteins coprecipitated with only their own mRNAs , and 12 proteins reproducibly pulled down other mRNAs ( Figure S3 and Table S2 ) . Notably , the majority of associated mRNAs encoded known or suspected protein interactors of the corresponding bait proteins ( Figure 2 and Table 1 ) . A striking example is provided by Cdc2p , the ortholog of CDK1 in higher eukaryotes . Cdc2p is the only cyclin-dependent kinase in fission yeast and is an essential regulator of cell cycle progression . Cdc2p interacted with two mRNAs: rum1 , which encodes a CDK ( Cyclin-Dependent Kinase ) inhibitor that associates with Cdc2p and inhibits its kinase activity [12] , and cdc18 , which encodes an essential DNA replication factor ( a homologue of budding yeast CDC6 ) [13] . Both Cdc18p and Rum1p are also direct targets of Cdc2p . Another protein kinase , Sty1p , which is a MAP kinase that mediates most stress responses in fission yeast , interacted with three mRNAs , pyp2 , cip2 and its own transcript . Pyp2p is a protein tyrosine phosphatase that directly binds and dephosphorylates Sty1p [14] . Cip2p is an RNA-binding protein thought to be regulated by Sty1p ( however , no direct protein-protein interaction has been demonstrated ) [15] . A predicted component of the 19S proteasome regulatory subunit , Rpn12p/Mts3p , associated with mRNAs encoding other subunits of the 19S proteasome ( rpn1301 , rpn1302 ) and a protein required for the assembly of the proteasome core and regulatory subunits ( ecm29 ) . A second component of the 19S proteasome , Rpt2p/Mts2p , interacted with the ubp6 and rhp23 mRNAs . Ubp6p is a proteasome-associated ubiquitin C-terminal hydrolase , while Rhp23p contains a ubiquitin-like N-terminus . The budding yeast orthologs of these two proteins ( Ubp6 and Rad23 , respectively ) copurify with components of the proteasome . We also looked at two transcription factor of the b-ZIP family , Atf1p and Pcr1p , which can form heterodimers with each other [16] . Atf1p interacted with pcr1 and its own mRNA , while Pcr1p only pulled down its cognate mRNA . Finally , Mnh1p , the S . pombe ortholog of the Mago nashi protein ( SPBC3B9 . 08c , a component of the splicing-dependent exon–exon junction complex ) associated with the mRNA of mni1 ( SPBC19C7 . 01 ) , which encodes a protein that contains a Mago nashi-binding domain . These data suggested that the mRNAs associated to a protein can provide insight into their cellular function and the protein complexes they belong to . To test this idea we examined all 10 members of the Actin-related protein family , which are structurally similar to actin but involved in functions as varied as chromatin remodelling , actin polymerisation or microtubular transport ( as part of dynactin ) . These functions are carried out as part of different protein complexes that have been well characterised [17] , [18] , providing an excellent model to test this hypothesis . Half of the probed proteins pulled down specific mRNAs other than their own . The dynactin Arps ( Arp1p and Arp10p ) were the only members of this family that did not to associate with any mRNA . S . pombe nuclear Arps are components of several chromatin-remodelling complexes ( SWI/SNF , INO80 , NuA4 and Swr1C ) . Arp6p , a component of the Swr1C chromatin-remodelling complex , copurified with the alp5 mRNA ( Alp5p is also part of this complex , but also of Ino80 and NuA4 ) [18] . Arp9p and Arp42p are members of the SWI/SNF and RSC complexes [17] , and both proteins associated with mRNAs encoding two SNF helicases ( snf21 and snf22 ) . The Ino80 complex contains three Arps ( Arp8p , Arp5p and Alp5p ) [18] . Of these , Arp8p associated with the ino80 mRNA , while Arp5p and Alp5p pulled down only their own mRNAs . Unexpectedly , Arp2p , a member of the family that regulates actin polymerisation , copurified with the mRNAs of two unrelated Arps ( arp8 and arp9 ) . These proteins have very different functions and are not expected to interact directly with Arp2p . Therefore , the nature of the protein-RNA interactions of the Arps could allow the assignment of one protein ( Arp6p ) to one of several related complexes , and the unambiguous allocation of three proteins to a specific complex ( Arp8p , Arp9p and Arp42p ) . These results demonstrate that a large fraction of proteins copurify with mRNAs encoding interacting proteins . To confirm that , as in the case of Tea2p/tip1 , these interactions reflect cotranslational formation of the corresponding protein complexes , we characterised in more detail the interactions between Sty1p/cip2 and Cdc2p/rum1 . We followed the same strategy described above for Tea2p/tip1 . First , we made non-translatable versions of the rum1 and cip2 mRNAs ( Figure S1 and Figure S2 ) . Second , we performed RIp-chip experiments after in vivo treatment of the cells with puromycin , or after in vitro incubation of the extracts with EDTA . Sty1p did not associate with −ATG-cip2 , while the association with another mRNA ( pyp2 ) was unaffected by the mutation . By contrast , neither cip2 nor pyp2 copurified with Sty1p after puromycin or EDTA treatments ( Figure 3A and Table S1 ) . Similarly , the association between Cdc2p and rum1 was lost in −ATG-rum1 , but the interaction with cdc18 was not . As expected , both associations were disrupted by puromycin and EDTA incubations ( Figure 3B and Table S1 ) .
Our data show that around 38% of a randomly selected set of proteins that do not contain canonical RNA-binding domains specifically copurify with small number of mRNAs ( between 1 and 3 ) . Remarkably , the majority of these mRNAs encode proteins that are closely related to the proteins used as bait , either as known direct interactors or as members of the same multiprotein complex . In some cases ( rum1 , pyp2 ) , the proteins are key regulators of the bait protein . This phenomenon is not limited to a specific type of protein ( we have observed it with protein kinases , transcription factors , actin-related proteins , kinesins , etc ) , nor is it restricted to specific cellular processes ( the proteins we tested function in microtubular transport , cell cycle control , proteolysis , stress responses , chromatin remodelling and splicing ) . In fact , the functional association between bait proteins and their associated mRNAs could conceivably be used to gain insight into the function of the bait protein ( if the proteins encoded by the interacting mRNAs have known functions ) or to identify interacting partners . Although previous work had hinted at the importance of cotranslationally assembly ( especially for homomultimers ) , this is the first demonstration that cotranslational assembly is a widespread and ubiquitous process . We notice that the protein-mRNA interactions we report are extremely specific . For example , Cdc2p interacts with and is regulated by many proteins , including at least four cyclins , protein phosphatases ( Cdc25p ) , protein kinases ( Wee1p ) and kinase inhibitors ( Rum1p ) . In addition , Cdc2p is thought to have dozen of targets . However , Cdc2p associates specifically with two mRNAs . This suggests that only a fraction of all protein-protein interactions are formed cotranslationally . Interestingly , both proteins encoded by the Cdc2p-bound mRNAs are phosphorylated by Cdc2p and degraded through the same mechanism [19] , possibly pointing at a role of cotranslational assembly in the control of protein stability . The preference for certain nascent peptides to undergo cotranslational assembly could simply be a reflection of their levels in the cell ( which , in turn , would depend on the abundance of their cognate mRNAs and their translation rates ) . However , Cdc2p associates with rum1 but not with cdc13 ( which encodes a B type cyclin that binds to Cdc2p ) , despite the fact that rum1 mRNA levels are almost 6-fold lower that those of cdc13 and both mRNAs are associated with similar number of ribosomes ( 3 . 7 for rum1 and 4 . 2 for cdc13 ) [20] . Therefore , the specificity of cotranslational assembly seems to be conferred by factors other than the abundance of the interacting nascent chains . It has recently been shown that many proteins that lack RNA-binding domains can interact directly with specific mRNA sets [21] , [22] , [23] . We do not believe that our observations reflect direct binding: first , most of the proteins identified in those studies interacted with much larger numbers of mRNAs; second , in the few cases in which those interactions were analysed more thoroughly , they were shown to be resistant to EDTA , arguing against cotranslational assembly [23] . By contrast , the three examples we analysed in detail using a variety of approaches were consistent with cotranslational assembly . It is formally possible that our results reflect direct binding to RNA that is dependent on active translation through an unidentified mechanism , but we consider this possibility highly unlikely . The cotranslational formation of protein complexes could be required for multiple reasons in the cell . It is possible that some interactions can only form before a given member of the complex has folded completely . Indeed , it is relatively frequent that recombinant proteins need to be coexpressed in order for them to form a complex . Second , some proteins are unstable in the absence of their partner . In this case , cotranslational assembly would stabilise the protein by reducing the time during which a protein is susceptible to degradation . This explanation has been proposed for the SET1C histone methyltransferase complex [7] . Consistently , Pcr1p is unstable in the absence Atf1p [24] . Finally , some proteins could be toxic when not part of a complex . Again , early formation of a complex would reduce this potential toxicity . Unexpectedly , we have found that a large fraction of protein-protein interactions are likely to be formed cotranslationally , and we demonstrate that the RIp-chip strategy can provide a genome-wide view of this phenomenon . The protein-RNA networks we present here add another layer of complexity to the formation and regulation of protein complexes in eukaryotic cells .
Standard methods were used for fission yeast growth and manipulation [25] . Proteins were TAP-tagged using a one-step PCR method in haploid cells except for alp5 and arp10 , where ∼400 nucleotides of ORF and 3′UTR sequences were cloned into the pFA6a-2xTap-Kan vector [26] , [27] . All construct were transformed into haploid cells with the exceptions of arp1 ( integrated in pat1 diploids ) and alp5 and arp10 ( transformed into wild type diploids ) . Successful tagging was verified by western blot as described below . All epitope-tagged strains grew normally and displayed normal cell shape , showing that the tagged proteins were functional . A complete list of the strains used in this work is presented in Table S3 . Integration of constructs at the leu1 locus was performed as described [28] . All constructs were tagged integrated into the leu1 locus of a strain in which the endogenous copy of the mutated gene had been deleted ( Table S3 ) . The length of the flanking sequences required to include endogenous 5′ and 3′ UTR was estimated from high throughput sequencing data [29] . For every gene two constructs were made , one containing a mutated ATG and a control carrying the normal initiation codon . The presence of the mutations was confirmed by sequencing . In all three cases , the control construct complemented the phenotype of the corresponding deletion strain ( Figure S2 ) . The cip2 ORF and 989 bp downstream were amplified by PCR from genomic DNA using primers that added a SalI and an EcoRI site to the 5′ and 3′ end , respectively , and cloned into pBluescript II . Two constructs were made: in the wild type control the 5′ primer contained the endogenous ATG , while in the −ATG one the ATG was mutated to AGG . The cip2 promoter ( 560 bp ) region was amplified by PCR as a KpnI/SalI fragment and cloned upstream of the ORF . The leu1 gene was amplified by PCR as a XmaI/NotI and cloned into the vectors above . The plasmids were linearised with NruI before transformation into leu1-32 cip2Δ cells . The tip1 ORF and 154 bp downstream were amplified by PCR from genomic DNA as PacI/AscI fragment and cloned into pFA6a . Two constructs were made: in the wild type control the 5′ primer contained the endogenous ATG , while in the −ATG one the ATG was mutated to TTT . The tip1 promoter region ( 865 bp ) was amplified by PCR as a BamHI/PacI fragment and cloned into the vector above . The whole construct containing the tip1 ORF and flanking regions was cloned into pJK148 ( containing a leu1 marker ) as a KpnI/BamHI fragment . The construct was linearised and transformed as described above into leu1-32 tip1Δ cells . For rum1 we used site-directed mutagenesis to mutate the first five ATGs of the ORF to CTGs . However , we found that this construct was still able to complement the sterility phenotype of a rum1Δ strain . This suggested that translation was taking place from a cryptic start site ( different from ATG ) . Therefore , we made a construct in which all seven in-frame ATGs in the rum1 ORF were mutated to stop codons . For this construct a DNA fragment was synthesised ( GENEART ) that contained the whole ORF and flanking sequences ( from the NheI to the SphI restriction sites ) , and in which all 7 in-frame ATGs had been replaced with the stop codon TGA . The digested DNA fragment was used to replace the corresponding section in pJET2 . 1 containing a leu1 marker and 2 . 6 kb of rum1 ORF and UTR sequences . −ATG and +ATG rum1 plasmids were linearised with NruI and transformed into leu1-32 rum1Δ cells . In contrast to the situation with rum1 , mutation of the first ATG of cip2 and tip1 was sufficient to create a loss-of-function phenotype indistinguishable to that of the deletion mutant . As both genes contain several ATGs downstream of the annotated initiation codon , we cannot completely rule out the possibility that in the –ATG mutants there is initiation from downstream ATGs ( or from other cryptic sites different from ATG ) , leading to the formation of N-terminally truncated peptides . However , any such peptides would not be functional ( Table S3 ) . Expression of TAP-tagged proteins was verified by western blot using peroxidase-anti-peroxidase soluble complexes ( Sigma ) to detect the protein A-binding domains of TAP . Myc tags were detected using the 9E11 monoclonal ( Abcam ) . We followed a previously published protocol [6] . Immunoprecipitation of TAP-tagged proteins was carried out using monoclonal antibodies against protein A ( Sigma ) , and myc-tagged proteins were purified using the 9E11 monoclonal antibody ( Abcam ) . All experiments were performed with vegetative cells except those using Arp1-TAP , Alp5-TAP and Arp10-TAP , which were carried out under meiotic conditions . Puromycin experiments were conducted by adding a final concentration of 1 mM puromycin to cell cultures , followed by incubation at 32°C for 15 minutes . In addition , immunoprecipitation buffers contained 1 mM puromycin and 2 mM GTP . In control experiments the buffers contained only 2 mM GTP . EDTA treatment was performed as previously described [6] . Total RNA purified from the cell extract was used as a reference in all experiments . 20 µg of total RNA and all the RNA from the IP were labelled using the SuperScript Plus Direct cDNA Labelling System ( Invitrogen ) . Labelled cDNAs were hybridised to PCR S . pombe DNA microarrays or to custom-designed oligonucleotide microarrays manufactured by Agilent as described [30] , [31] . Microarrays were scanned with a GenePix 4000A microarray scanner and analysed with GenePix Pro 5 . 0 ( Molecular Devices ) . Only probes corresponding to coding sequences were considered . Spots with unreliable signals were removed as follows: for the PCR microarrays , spots that did not show a minimum of 55% of pixels above the median background signal plus two standard deviations in the immunoprecipitate and 90% of pixels in the total RNA ( or at least 90% in the channel of the IP ) were removed; for the Agilent microarrays the corresponding thresholds were 70% , 98% and 98% , respectively . Median log10 ratios were used for the analysis . We compiled a list of common mRNA unspecific contaminants , which were removed from the analysis . Selection of enriched RNAs in the RIp-chip experiments was carried out as described [6] , by choosing genes whose enrichment ratios were at least two standard deviations above the median enrichment of all genes . Only mRNAs that passed this threshold in every independent biological experiment were considered enriched ( Table 1 ) . Assuming a normal distribution of the enrichments , the expected fraction of false positives using this threshold with a single experiment would be ∼0 . 05 . However , as we only selected RNAs enriched in each of 2–4 repeats , this number is reduced to ∼2 . 5×10−3 to ∼6 . 25×10−6 . All RIp-chip experiments in which mRNAs different from the one encoding the bait were detected were carried out at least twice . Other RIp-chips ( negatives or containing only the cognate mRNA of the bait ) were performed once or twice . All repeats were independent biological experiments and dyes were swapped for at least one experiment with each protein . All raw and normalised microarray data have been deposited in ArrayExpress ( accession number E-TABM-1158 ) . Dataset S1 contains normalised data and experimental details for all RIp-chip experiments reported in this manuscript .
|
Most proteins do not function in isolation . Instead , they associate with other proteins to form complexes . Little is known about the assembly of protein complexes within cells . One possibility is that proteins are completely synthesised before they bind to each other . An alternative is that proteins attach to each other as they are being translated in the ribosome ( called cotranslational assembly ) . To investigate if cells use cotranslational assembly to form complexes , we identified mRNAs associated with specific proteins . The expectation is that if protein A binds to protein B as protein B is being translated , A will associate indirectly to the mRNA encoding B . Indeed , we found that for ∼40% of proteins ( out of a sample of over 30 ) this was the case . Proteins associated with a small number of mRNAs , most of which encoded known or predicted interacting proteins . We found examples of this phenomenon in proteins with different functions and structures , indicating that cotranslational assembly is widespread . Cotranslational assembly might be required for certain proteins to associate , or it might be important in cases where the early formation of a protein complex is beneficial , such as when a protein is toxic or unstable unless bound to a partner .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"functional",
"genomics",
"protein",
"interactions",
"proteins",
"genetics",
"biology",
"genomics",
"gene",
"networks",
"genetics",
"and",
"genomics"
] |
2011
|
Widespread Cotranslational Formation of Protein Complexes
|
Schlafen11 ( encoded by the SLFN11 gene ) has been shown to inhibit the accumulation of HIV-1 proteins . We show that the SLFN11 gene is under positive selection in simian primates and is species-specific in its activity against HIV-1 . The activity of human Schlafen11 is relatively weak compared to that of some other primate versions of this protein , with the versions encoded by chimpanzee , orangutan , gibbon , and marmoset being particularly potent inhibitors of HIV-1 protein production . Interestingly , we find that Schlafen11 is functional in the absence of infection and reduces protein production from certain non-viral ( GFP ) and even host ( Vinculin and GAPDH ) transcripts . This suggests that Schlafen11 may just generally block protein production from non-codon optimized transcripts . Because Schlafen11 is an interferon-stimulated gene with a broad ability to inhibit protein production from many host and viral transcripts , its role may be to create a general antiviral state in the cell . Interestingly , the strong inhibitors such as marmoset Schlafen11 consistently block protein production better than weak primate Schlafen11 proteins , regardless of the virus or host target being analyzed . Further , we show that the residues to which species-specific differences in Schlafen11 potency map are distinct from residues that have been targeted by positive selection . We speculate that the positive selection of SLFN11 could have been driven by a number of different factors , including interaction with one or more viral antagonists that have yet to be identified .
RNA viruses pose a major threat to public health because of their potential to rapidly adapt to the human host after transmission from animal reservoirs [1] . In general , the genetic barriers to cross-species transmission of viruses are poorly understood . A notable exception exists in the case of retroviruses , where a number of well-characterized restriction factors have been shown to potently block viral replication in non-native hosts [2] . Restriction factors recognize viruses and directly interfere with , or restrict , viral lifecycles . Restriction factors have been identified to act at several different stages of the retroviral lifecycle including uncoating ( TRIM5α ) , reverse transcription ( APOBEC3s , SAMHD1 ) , import/integration ( MxB ) , and budding ( Tetherin ) [3–11] . These restriction factors all recognize virus-associated molecular patterns , such as the viral capsid or single stranded DNA exposed during reverse transcription . Zoonotic transmission of retroviruses is rare , only occurring when a virus is able to adapt to subvert manifold human innate immune defenses simultaneously . Most notably , simian immunodeficiency viruses ( SIVs ) have on several occasions adapted to humans , becoming the human immunodeficiency viruses HIV-1 and HIV-2 [12–14] . Recently , Schlafen11 ( encoded by the SLFN11 gene ) was shown to restrict HIV-1 replication at the step of protein translation [15] . Interestingly , Schlafen11 was reported to block translation of viral but not host proteins . This was shown to be related to the different patterns of codon usage observed between host and retroviral transcripts . It has long been known that many RNA viruses do not have the same codon bias as their hosts [16] . Reasons proposed for this differential codon usage include constraints imposed by RNA secondary-structure , mutational biases of viral polymerases , and translational rate requirements for proper protein folding [17–19] . Evidence exists to support each of these hypotheses , and all evidence is in line with non-optimal codon usage being generally beneficial for RNA viruses [20] . Thus , the discovery of Schlafen11 yet again revealed the amazing power of the immune system to take advantage of any possible difference between self and non-self as a way to target and destroy viruses . Many primate restriction factor genes with activity against HIV-1 have been shown to be subject to evolutionary “arms race” dynamics [14 , 21–33] . Arms races can occur when a direct interaction exists between a host-encoded protein and a virus-encoded protein . For instance , host restriction factors interact with viral proteins that are either the target of restriction ( e . g . TRIM5α and HIV-1 capsid [34] ) or antagonist proteins that the virus uses to block restriction ( e . g . APOBEC3G and HIV-1 Vif [35] ) . In these instances , both genomes experience constant selection for new allelic protein variants that weaken or strengthen this physical interaction , depending on which is beneficial to each party [36 , 37] . This creates a situation of runaway evolution , where both sides must continually adapt to keep step with the other . Arms races may play out over millions of years of evolution , during which many speciation events may occur . This process leads to restriction factors becoming species-specific , which can be studied in the laboratory to enhance our understanding of zoonosis and disease emergence . When a cross-species viral transmission event occurs , the virus must adapt in order to subvert the restriction factors as they exist in the new host , or perish . In this study , we show that the SLFN11 gene is evolving under positive selection in simian primates and has become species-specific in its activity against HIV-1 . The activity of human Schlafen11 is weak compared to that of some other primate versions of this protein , with the Schlafen11 proteins encoded by chimpanzee , orangutan , gibbon , and marmoset being particularly potent inhibitors of HIV-1 protein production . Interestingly , the strong inhibitors such as marmoset Schlafen11 consistently block protein production better than weak primate Schlafen11 proteins , regardless of the virus being analyzed . Further , we show that the ability of Schlafen11 to decrease protein levels is independent of viral infection altogether: Schlafen11 reduces protein production of viral , non-viral ( GFP ) , and even host proteins ( in particular if they are poorly codon optimized ) , even outside of the context of any infection . Because the activity of each primate Schlafen11 is consistent across the target proteins tested , species-specific differences in Schlafen11 appear to have more to do with differences in the basic function of these homologs rather than their ability to recognize particular viral patterns . In line with this model , we map the residues responsible for species-specific differences in Schlafen11 and find that they are distinct from residues that have been targeted by positive selection . Therefore , because Schlafen11 inhibits protein accumulation from diverse target genes and is upregulated by interferon [38] , it may be better thought of as a classic , antiviral interferon-stimulated gene than a restriction factor that is specific to a given virus or viral family . The positive selection in the SLFN11 gene may have been caused by selective pressure exerted by one or more unidentified antagonists encoded by retroviruses or other viruses , past or present . Why different primate versions of Schlafen11 have such variable potencies at inhibiting protein production remains unknown , but we can speculate that there may be a cost associated with Schlafen11 activity , and that therefore it has been selected for reduced potency in some species .
To investigate the evolutionary history of the SLFN11 gene in simian primates , we aligned SLFN11 orthologs from 18 species , representing approximately 40 million years of primate evolution ( Fig 1A ) . Seven of these sequences were obtained from primate genome projects ( asterisks in Fig 1A ) . The remaining 11 were directly sequenced from cDNA libraries generated using extracted RNA from primate cell lines . We then analyzed this SLFN11 multiple alignment for codon positions enriched for nonsynonymous substitutions relative to synonymous substitutions ( dN/dS>1 ) , indicative of positive , or diversifying , selection in favor of amino-acid altering mutations . Using four common tests , we found strong evidence for positive selection in the SLFN11 gene ( S1 Table ) . For instance , using PAML we found that a model of neutral evolution ( M8a ) was rejected in favor of a model of positive selection ( M8; p < 0 . 0001 ) . In the model of positive selection , many ( 7 . 6% ) of the codon positions in this gene were assigned a value of dN/dS > 1 ( Fig 1B ) . Codons identified to be evolving under positive selection by each of the four tests are listed in S1 Table , and correspond to residues spread throughout the length of the protein ( Fig 1C ) . These findings suggest the SLFN11 gene has encountered one or multiple pressures that have driven selection in favor of protein-altering mutations in this gene . We next investigated whether the evolutionary sequence divergence that has occurred in the SLFN11 gene results in species-specific effects on HIV-1 . We cloned into a mammalian expression plasmid SLFN11 from human and three simian primate species ( bonobo , Pan paniscus; red-cheeked gibbon , Nomascus gabriellae; and the common marmoset , Callithrix jacchus ) . Using the assay that has been previously established for Schlafen11 [15] , we transfected into 293T cells each of these Schlafen11-expressing plasmids , along with a plasmid encoding an HIV-1 proviral clone ( pNL4-3 . Luc . R+E- ) . 293T cells are naturally hypomorphic for Schlafen11 expression compared to other human cell types ( [15] and S1 Fig ) . After 48 hours , cellular extracts were evaluated by immunoblotting for the production of Schlafen11 and an HIV-1 protein ( p24 ) . As shown previously , human Schlafen11 was able to reduce production of p24 encoded by the HIV-1 genome ( Fig 2A ) . Bonobo Schlafen11 had similar activity , consistent with the close evolutionary relationship of human and bonobo . However , both gibbon and marmoset Schlafen11 had dramatically increased activity in this assay , with marmoset Schlafen11 consistently blocking almost all HIV-1 p24 ( Fig 2A ) . None of the Schlafen11 proteins had any effect on the protein levels of a cellular control , GAPDH . Thus , the amino acid differences that have accumulated in the Schlafen11 proteins encoded by different species affect their ability to inhibit HIV-1 protein production . We tested whether Schlafen11 was active against a single HIV-1 protein product , Gag-Pol , independent of other viral components . We transfected into 293T cells a plasmid encoding each primate Schlafen11 along with two plasmids encoding HIV-1 Gag-Pol and Rev ( needed for nuclear export of the Gag-Pol transcript ) . After 48 hours , cell extracts were probed for Schlafen11 and HIV-1 p24 protein production . The species-specific differences in Schlafen11 were the same as before , where gibbon and marmoset Schlafen11 had increased activity at inhibiting HIV-1 p24 production compared to human and bonobo Schlafen11 ( Fig 2B ) . We confirmed that the production of unprocessed Gag was also affected ( S2 Fig ) . Therefore , if the species-specific differences in Schlafen11 activity reflect differential susceptibility to a viral antagonist , this antagonist must be encoded within the Gag-Pol polyprotein or Rev . We next tested the ability of different primate Schlafen11 proteins to inhibit Gag-Pol production from other retroviral genomes: murine leukemia virus ( MLV ) and feline immunodeficiency virus ( FIV ) . Plasmids expressing MLV and FIV Gag-Pol were co-transfected with plasmids expressing various primate Schlafen11 . Immunoblots were used to monitor the production of p30 by MLV and p26 by FIV . Once again , the gibbon and marmoset Schlafen11 were more potent at diminishing protein production than human and bonobo Schlafen11 ( Fig 2C and 2D ) . In these transfection-based experiments , Schlafen11 homologs were sometimes expressed at slightly different levels , although increased expression was not noted to track with the increased suppression of viral protein production . To attempt to quantify the relative activity of the different Schlafen11 homologs , we normalized the amount of viral protein produced to the relative amount of Schlafen11 ( Fig 2E , increasing Schlafen11 activity from blue ( least active ) to red ( most active ) , see materials and methods for an explanation of normalization process ) . In general , the quantification procedure confirms that marmoset and gibbon Schlafen11 are the most active homologs . We also find that when HIV-1 , MLV , and FIV are packaged in cells that are expressing Schlafen11 , fewer infectious virions result ( Fig 2F ) . In both the immunoblotting and packaging assays , we see a nearly identical species-specific pattern of inhibition for all viruses , with marmoset and gibbon Schlafen11 being more potent inhibitors than human Schlafen11 . Thus , the ability of Schlafen11 to suppress viral protein production is specific to each host species but , curiously , virus independent . Given that Schlafen11 restricted viral protein production outside of the context of infection , we wondered if viral proteins were necessary at all . To explore this , we tested the activity of primate Schlafen11 proteins against non-viral proteins . Schlafen11 was previously found to affect the translation of GFP but not the codon optimized version of this gene ( eGFP ) , but these experiments were performed in the context of co-transfection with an HIV-1 proviral clone [15] . We transfected mammalian expression plasmids encoding GFP and eGFP into 293T cells only along with plasmids encoding primate Schlafen11 proteins . After 48 hours , cellular extracts were probed for GFP/eGFP protein production by western blot . We were able to differentiate these two forms of GFP on a western blot by adding a V5 tag to GFP and a codon-optimized myc tag to eGFP . The identical species-specific pattern of Schlafen11 inhibition seen for HIV-1 proteins was also observed for GFP , with marmoset and gibbon Schlafen11 having the most extreme inhibitory effect ( Fig 3A ) . As expected , there were no significant differences in eGFP protein production in the presence of any version of Schlafen11 . We confirmed these results in a flow cytometry based assay , where GFP or eGFP ( both without a tag ) were coexpressed with either Schlafen11 from human or marmoset , or with chloramphenichol acetyltransferase ( Chlor ) as a negative control . Cells showed less fluorescence in the presence of marmoset Schlafen11 than human Schlafen11 , but only for GFP and not for eGFP ( Fig 3B ) . Overall , since there are no virus components of any type in these experiments , this strongly suggests that the species-specific differences in activities of Schlafen11 have little to do with a specific viral target , or with sensitivity to a viral antagonist . It appears that some primate versions of Schlafen11 are simply fundamentally more active than others in their ability to inhibit protein production from non-codon optimized transcripts . It is difficult to speculate further , since the mechanism of Schlafen11 is not yet well understood . Given that Schlafen11 can reduce protein production from gene transcripts outside of the context of infection , it stood to reason that human genes could also be susceptible to Schlafen11 . To test this , we wished to choose a human gene that was relatively non-optimal in its usage of codons . We first calculated the codon adaptation index ( CAI ) of all human genes ( Fig 4A ) . We then overlaid onto this distribution the CAI of each gene analyzed in this study ( human or otherwise ) . GAPDH , used as a control in this study , and eGFP are in the upper tail of the distribution ( most codon optimized ) . We have found that both of these genes are resistant to the effects of Schlafen11 . Notably , eGFP has a CAI that is even higher than any actual human gene . On the other hand , the viral genes and GFP , all of which we have found are susceptible to Schlafen11 , fall in the lower half of the distribution ( least codon optimized ) . To test whether Schlafen11 can inhibit the production of human proteins , we chose three human proteins for which good antibodies exist: Actin , GAPDH , and Vinculin . Actin and GAPDH have a very high CAI ( Fig 4A ) , which we hypothesized would make them more resistant to Schlafen11 . Conversely , Vinculin has a lower CAI ( Fig 4A ) , which should cause it to be more susceptible to Schlafen11 . We first tested Vinculin by transfecting plasmids encoding Schlafen11 from several primate species ( in this case , human , chimpanzee ( Pan troglodytes ) , and marmoset ) alone or with a plasmid encoding V5-tagged Vinculin . Cell extracts were harvested 48 hours later and subject to immunoblotting with a V5 antibody . We found that human Schlafen11 only slightly affected protein levels of Vinculin-V5 ( Fig 4B and 4E ) . However , chimpanzee and marmoset Schlafen11 significantly decreased levels of Vinculin-V5 ( Fig 4B and 4E ) . No Schlafen11 proteins affected the levels of endogenous Vinculin , as determined by immunoblotting with an anti-Vinculin antibody ( Fig 4B ) . This can perhaps be explained by the fact that this protein pre-existed in the cell , while the V5-tagged version did not , and by the half-life of Vinculin which is 12 hours [39] . We cannot rule out that overexpression of genes from plasmids or viral genomes makes them generally more susceptible to Schlafen11 , because we have yet to detect an effect on an endogenous human protein . However , this experiment does tell us that there is nothing particularly special about viral genes over host genes in this system . We next tested a plasmid-expressed , V5-tagged version of GAPDH , which has a much higher codon adaptation index ( Fig 4A ) . The accumulation of GAPDH-V5 was somewhat affected by Schlafen11 ( particularly by the marmoset Schlafen11 ) ( Fig 4C and 4E ) . Again , the endogenous version of GAPDH was quite stable ( Fig 4B and 4C ) . Finally , Actin-V5 , which has the highest CAI of the three host genes tested , was not affected by any Schlafen11 ( Fig 4D and 4E ) . We conclude that Schlafen11 , particularly the version encoded by some non-human primates , generally blocks protein accumulation , including the accumulation of human proteins . However , genes with higher codon adaptation indices ( more optimal codon usage ) appear to be more protected from these effects . We cannot rule out that other genetic features besides codon usage may also be contributing to susceptibility to Schlafen11 . To ensure that the above observations were not specific to 293T cells , we performed Schlafen11 assays in Hut78 ( human T cells; representing the primary replication sites for HIV-1 ) and CHO ( Chinese hamster ovary ) cells . Into each cell line , we cotransfected MLV viral packaging plasmids along with increasing amounts of the mammalian expression vector encoding human or marmoset Schlafen11 ( or Chlor as a negative control ) . The relative amount of MLV particles produced ( standardized to Chlor ) was determined by titration on 293T cells . We found that marmoset Schlafen11 was able to decrease viral production in 293T cells even at the lowest amount of transfection ( 250ng , Fig 5A ) . We found a similar species-specific difference in the ability of human and marmoset Schlafen11 to reduce viral production in Hut78 ( Fig 5B ) and CHO cells ( Fig 5C ) as well . Direct comparison of the levels of inhibition between cell lines cannot be made , because some of these cell lines are more readily transfectable than others . This can be seen in results from a qRT-PCR experiment , where Schlafen11 expressed in 293T and CHO cells with 250ng of plasmid is greater than the amount of Schlafen11 expressed in Hut78 cells with 1000ng of plasmid ( Fig 5D ) . Keeping this in mind , it does appear that Schlafen11 is particularly active in CHO cells and interestingly , even human Schlafen11 showed an ability to reduce viral production in CHO cells ( Fig 5C ) , compared to no effect in 293T cells ( Fig 5A ) , even though expression was not higher in CHO cells ( Fig 5D ) . In all three cell lines , marmoset Schlafen11 continued to be a more potent than human Schlafen11 . Therefore , we have shown that Schlafen11 functions in multiple cell lines , including T cells , and that the species-specific pattern of Schlafen11 activity is constant regardless of the cellular context in which it is functioning . Next , we wanted to assess physiological relevance of the Schlafen11 expression levels achieved in our experiments . In order to assess Schlafen11 expression patterns in vivo , we first performed non-quantitative PCR on cDNA libraries constructed from a number of human tissues ( Fig 5E ) . We found that Schlafen11 appears to be expressed in all tissues tested except for placenta and testes . We then performed quantitative PCR on cDNA from cells transfected with Schlafen11 ( the experimental conditions from Fig 5A–5C ) , and the human cDNA libraries . We found , as expected , that transfection induced much higher levels of mRNA expression than is found naturally in human tissue . However , at the lowest transfection levels in 293T , CHO , and HUT78 cells , we saw a level of Schlafen11 expression that was comparable to that found in natural tissues . Therefore , we conclude that Schlafen11 is active at physiologically relevant levels . It should be noted that the tissue panel analyzed in panels D and E is made from tissues not known to be interferon stimulated , so these data may represent the low end of the physiological expression level for this gene . Many of the active primate Schlafen11 proteins tested have a large number of amino acid differences from the non-active Schlafen11 proteins ( e . g . 123 non-synonymous substitutions between human and marmoset Schlafen11 ) . To further assess the evolutionary divide between active and non-active Schlafen11 proteins , we took a focused look at the great apes . We cloned Schlafen11 from human , chimpanzee , gorilla ( Gorilla gorilla ) and orangutan ( Pongo abelii ) into expression plasmids . We tested these great ape Schlafen11s by cotransfecting them along with plasmids encoding HIV-1 Gag-Pol , GFP , and eGFP . This allowed us to look at the effects of each of the Schlafen11 proteins on three different target proteins in a single experiment ( HIV-1 p24 , GFP , and eGFP ) . Despite the close relationship of these species , and the fact that their Schlafen11 proteins are all greater than 94% identical , there were stark species-specific differences in the inhibition of protein production . Notably chimpanzee and orangutan Schlafen11 were more active than human or gorilla Schlafen11 in this assay ( Fig 6A ) , and inhibited both p24 and GFP protein accumulation . Neither affected eGFP or GAPDH , the more codon optimized targets . Together with bonobo Schlafen11 tested previously , we can now superimpose the activity of different Schlafen11 proteins onto a cladogram of the great apes ( Fig 6B ) . Interestingly , there is no clustering of active ( green ) and inactive ( red ) Schlafen11 proteins , and therefore this protein has been quite dynamic even in this recently diverged clade of primates . Because we have performed an analysis of the codons targeted by positive selection in the SLFN11 gene , we had the opportunity to ask whether amino acid differences at sites under selection dictate the increased activity observed for some primate Schlafen11 proteins . We chose to do site-directed mutagenesis between chimpanzee and bonobo Schlafen11 because of their close phylogenetic relationship ( 99% identity ) as well as differential activity , where chimpanzee Schlafen11 is active ( Figs 4 and 6A ) and bonobo Schlafen11 is not ( Figs 2 and 3A ) . There are only 7 nonsynonymous differences between the bonobo and chimpanzee SLFN11 genes , and 4 of these differences are in codons detected to be evolving under positive selection ( Fig 6C , sites under positive selection are denoted by asterisks and support for positive selection is given in table below ) . We mutated each of these seven sites in bonobo Schlafen11 to encode the amino acid found in chimpanzee Schlafen11 . We did not observe any increase in activity of bonobo Schlafen11 ( resulting in reduced GFP-V5 protein production in a co-transfection assay ) when any of the sites under positive selection were mutated , either individually or all together ( Fig 6D ) . Instead , it was when two of the remaining three differences between chimpanzee and bonobo Schlafen11 , sites 271 or 864 , were muted that we saw a dramatic increase in activity by bonobo Schlafen11 ( Fig 6D ) . These sites are located in the AAA-like domain and at the extreme C-terminus of Schlafen11 ( Fig 6C ) . Interestingly , we noticed that one of these sites , 271 , is polymorphic in bonobos , suggesting this species may maintain SLFN11 alleles with differential activity . No single point mutant made in a human Schlafen11 background resulted in an increase in activity ( S3 Fig ) . While the Schlafen11 proteins encoded by different primate species have differential potencies at suppressing protein production , this pattern does not seem to be resulting from recurrent positive selection of particular codons in this gene .
SLFN11 is an interferon-stimulated gene ( S1 Fig ) [15 , 38 , 40] that encodes a protein with suppressive effects on protein accumulation , suppressing levels of both host and viral proteins . It is curious to consider how HIV-1 bypasses the effects of Schlafen11 . However , because the human Schlafen11 is relatively weak in all of the contexts that we have analyzed , HIV-1 may not require an antagonist . In general , Schlafen11 proteins block protein production from genes containing non-optimized codons to a greater extent than from genes with more optimized codons , although further investigation may reveal other determinants of Schlafen11 suppression . While human Schlafen11 does have mild effects on protein production , these effects are dramatically pronounced in some nonhuman primate versions of Schlafen11 . For instance , Schlafen11 from gibbon , marmoset , chimpanzee , and orangutan are all much stronger inhibitors . Schlafen11 from human , bonobo , and gorilla are much weaker . It remains unknown why there are consistent species-specific differences in the potencies of primate Schlafen11 . For instance , marmoset Schlafen11 is a potent suppressor of protein production for all targets tested: viral proteins ( HIV , FIV , MLV ) , non-viral proteins ( GFP ) , and even host proteins ( Vinculin and GAPDH ) . On the other hand , human Schlafen11 is a weak suppressor of all of these proteins . We can only speculate that perhaps there is a cellular cost to Schlafen11 , and that in certain species the genes encoding these proteins have been selected in favor of inactivating mutations that reduce these costs when pathogen-related pressure is not high . Of course , it may also be relevant that primate genomes contain an entire family of SLFN gene paralogs located in a single genomic cluster [41] , although the functions of others have not been explored . It is possible that functional decay is happening in SLFN11 in some species because the antiviral role has been refined in another gene paralog in that species . The gene encoding Schlafen11 has evolved under recurrent positive selection , but this doesn’t appear to have tailored the relative potencies of different primate versions . Instead , this positive selection may be a result of selection imposed by one or more unidentified viral antagonists . Viruses from the genus Orthopoxvirus encode a truncated SLFN-like gene in their genomes , the result of a horizontal gene transfer event from a mammalian host [41] . This viral protein could act as a mimic to restrict the action of host Schlafen proteins , possibly by inhibiting Schlafen multimerization or via another mechanism . Mimicry of host proteins is a common strategy for immunological evasion employed by pox viruses [42–44] . Given that orthopoxviruses infect a number of primate species [45] , they could theoretically have driven an arms race dynamic with SLFN genes including SLFN11 . It is also possible that the signature of positive selection in SLFN11 is unrelated to viral antagonism . Indeed , some mammalian SLFN genes have been implicated in sperm-egg incompatibility . For example , crossing mice with mismatched SLFN loci results in death of the embryo soon after implantation , referred to as DDK syndrome [46] . Similarly , the SLFN gene locus has also been implicated in the unequal segregation of chromatids during the second meiotic division [47] . Both sperm-egg interactions and meiotic drive can result in a strong signature of recurrent positive selection due to arms race dynamics [48–50] . Finally , it is also possible that the signatures of positive selection are due to selective pressures from both of these forces ( sexual and pathogen conflict ) simultaneously .
Primate SLFN11 gene sequences were retrieved from available primate genome projects , using the BLAT function on the UCSC genome browser [51] . The remaining SLFN11 gene sequences were obtained by direct sequencing of cDNA libraries produced from primary or immortalized primate fibroblast cell lines . Briefly , cells were grown in DMEM ( Cellgro ) supplemented with 15% FBS ( Gibco ) at 37°C and 5% CO2 . RNA was extracted using the AllPrep DNA/RNA extraction kit ( QIAGEN ) . cDNA libraries were generated using SuperScript III first strand synthesis kit ( Invitrogen ) . PCR was performed using PCR SuperMix High Fidelity ( Invitrogen ) . PCR products were directly sequenced . Each primate sequence was used as a query to search the human genome , and human SLFN11 gene was returned as the top hit . SLFN11 gene sequences have been deposited in GenBank ( accession numbers KY204401-KY204411 ) . Sequences aligned to using the MUSCLE algorithm were used for downstream evolutionary analysis [52] . The human Schlafen11 sequence used to query UCSC was obtained from NCBI ( accession number NM_001104587 ) . The RNA used to sequence the other primate sequences was obtained from the following cell lines: Bonobo ( Pan paniscus , Coriell PR00748 ) , Bornean Orangutan ( Pongo pygmaeus , Coriell PR00650 ) , Pileated Gibbon ( Hylobates pileatus , Coriell PR00243 ) , Red-Cheeked Gibbon ( Nomascus gabriellae , Coriell PR00381 ) , Crab-Eating Macaque ( Macaca fasicularis , 103–06 , gift from Welkin Johnson ) , Black Mangabey ( Lophocebus albigena , Coriell PR01215 ) , Olive Baboon ( Papio anubis , Coriell PR00978 ) , Talapoin ( Miopithecus talapoin , Coriell PR00716 ) , Wolf’s Guenon ( Cercopithecus wolfi , Coriell PR01241 ) , Colobus ( Colobus guereza , Coriell PR00980 ) , Squirrel Monkey ( Saimiri sciureus , Coriell PR00603 ) . Additional samples used to amplify functional clones are common marmoset ( Callithrix jacchus , Coriell GM07404 ) , chimpanzee ( Pan troglodytes , Coriell NS06006 ) , gorilla ( Gorilla gorilla , Coriell PR00280 ) and orangutan ( Pongo abelii , Coriell PR01052 ) . The human clone was from [15] . Detection of recurrent positive selection was carried out using several commonly used programs . The codeml program in the PAML 4 . 3 software package was used to fit the SLFN11 multiple sequence alignment to two codon models: M8 and M8a [53] . M8a is a neutral model where all codons are constrained to evolve with a dN/dS ≤ 1 . M8 is a model of positive selection ( M8 ) where some codons are allowed to evolve with a dN/dS > 1 . Using a likelihood ratio test , we found that the positive selection model was a significantly better fit to the data ( p<0 . 0001 ) . In model M8 , 36 codons were assigned to the dN/dS > 1 class with high posterior probability ( p>0 . 9 ) by Bayes Empirical Bayes ( BEB ) analysis ( S1 Table ) . Two ω0 seed values ( 0 . 4 and 1 . 5 ) gave similar results , as did the f61 and f3x4 codon frequency models . The SLFN11 alignment was also analyzed for positive selection using REL , FEL , and MEME as implemented in Datamonkey [54–57] . Sites with dN/dS > 1 identified by each of these methods are indicated in S1 Table . The cutoff was p<0 . 01 for FEL , Bayes factor > 50 for REL , and p<0 . 09 for MEME in S1 Table . Genes encoding primate Schlafen11 , GFP , eGFP , Vinculin , Actin , and GAPDH were TA-cloned into pcDNA6 . 2/GW/D-TOPO mammalian expression vector ( Invitrogen ) . eGFP was cloned with a codon-optimized myc tag on the C-terminus followed by stop-codon to prevent translation of the V5 tag encoded in pcDNA6 . 2 . All other genes were V5 tagged , as this was a property of the vector used ( see product literature for pcDNA6 . 2 ) . Chlor-V5 was provided by Invitrogen in the TA-cloning kit as a control vector . pNL4-3 . Luc . R+E- was obtained from the National institutes of health AIDS reference and reagents program . Plasmids encoding HIV-1 Gag-Pol ( pMDLg/pRRE; from Addgene ) , HIV-1 REV ( pRSV-REV; from Addgene ) , NB-MLV Gag-Pol ( CS2-mGP ) [58] , and FIV Gag-Pol ( pFP93 ) [59] . 293T cells ( ATCC CRL-3216 ) were maintained in Dulbecco’s modified eagles media ( DMEM ) supplemented with L-glutamine , pen/strep , and 10% FBS . Cells were seeded into 6-well dishes at a concentration of 800 , 000 cells/well . About 24 hours after seeding , cells were transfected with 2ug of a plasmid encoding the indicated Schlafen11 or chloramphenichol acetyltransferase ( Chlor ) as a negative control , along with plasmids encoding the pNL4-3 . Luc . R+E- ( 1000ng ) , HIV-1 Gag-Pol ( 500ng ) + RSV-Rev ( 250ng ) , NB-MLV Gag-Pol ( 1000ng ) , GFP ( 500ng ) , human Vinculin ( 500ng ) , human GAPDH ( 500ng ) , human Actin ( 500ng ) , and/or eGFP ( 50ng ) . 48 hours post transfection , cells were lysed in the dish using RIPA buffer . Lysate was loaded onto a 10% SDS-PAGE gel . Protein was transferred to a polyvinyl membrane and blocked with a TBS solution containing 5% milk and 0 . 1% TWEEN20 . Immunoblotting was performed using the following primary antibodies rabbit-anti-V5 antibody ( SantaCruz Biotech G-14 , sc83849 ) , rabbit-anti-GAPDH ( Cell Signaling , 14C10 ) , rabbit-anti-myc ( Abcam , ab9106 ) , mouse-anti-HIV-1 p24 ( NIH Aids Reagents , 183-H12-5C ) , and rabbit-anti-MLV p30 ( Abcam , ab100970 ) . HRP conjugated secondary antibodies used: goat-anti-mouse ( Thermo , 32430 ) , goat-anti-rabbit ( Thermo , 32460 ) , donkey-anti-goat ( SantaCruz biotech , sc-2020 ) . Protein bands were visualized using ECL-prime ( GE , RPN2236 ) . Quantification of westerns was performed as follows . Western blot images were loaded into imageJ . The bands corresponding to GAPDH , Schlafen11 , and the viral protein ( p24 , p30 , or p26 ) were quantified using standard techniques . The relative amount of viral protein was normalized using the following equation: p′= ( QGagPol−SLFNQGagPol−CAT ) ( QSLFN11QGAPDH ) p Where QGagPol–SLFN is the quantification of the viral band in the Schlafen11 lane , QGagPol–CAT is the quantification of the viral band in the Chlor lane , QSLFN11 is the quantification of the Schlafen11 band , and QGAPDH is the quantification of the GAPDH band , and p is the numerator calculated for the human Schlafen11 experiment . Pseudotyped virus was produced using a recombinant retroviral packaging system , which utilized a VSV-G glycoprotein , a retroviral Gag-Pol , and a GFP flanked by UTRs specific for each retrovirus . The amount of each component , and the plasmid name follows . See plasmids and viruses section for more details on plasmids . Pseudotyped MLV was produced by cotransfecting 0 . 2ug pC-VSVG ( VSVG encoding plasmid ) , 1ug CS2-mGP ( NB-MLV Gag-Pol encoding plasmid ) , and 2ug pLXCG ( eGFP ) . Pseudotyped HIV was produced by cotransfecting 0 . 3ug pMD2 . G ( VSVG; addgene plasmid #12259 ) , 0 . 5ug pMDLg/pRRE ( HIV Gag-Pol; addgene plasmid #12259 ) , 0 . 25ug RSV-Rev ( HIV Rev; addgene plasmid #12253 ) , and 1ug pRRLSIN-GFP ( eGFP; addgene plasmid #12252 ) . Pseudotyped FIV was produced by cotransfecting 0 . 2ug pMD2 . G , pGinSin ( GFP ) , and pFP93 ( FIV Gag-Pol derived from the 34TF10 molecular clone ) . FIV reagents were a gift from Dr . Eric Poeschla . Either 1/10th , 1/2 , or the full amounts of plasmid reported above for each viral system was cotransfected into 293T cells along with 2ug Schlafen11 or Chlor plasmid . 48 hours post transfection , supernatant was collected and titrated on 293T cells . 2 . 5x10^5 293T cells were plated in a 24 well dish . 24 hours after plating , these cells were infected with 25ul of virus-containing supernatant . The media was treated with 10ug/mL polybrene and centrifuged for 1 hour at 1 , 200 rpm . 24 hours post infection , cells were measured for GFP expression by flow cytometry . Site directed mutagenesis of plasmids was performed using PFU turbo ( Agilent technologies ) and primers with a built in mutation . Primer sequences available upon request . 18 cycles of PCR was performed with the following conditions: 1min at 95°C , 1min at 57°C , 9min at 65°C . The reaction was digested with DPN-I ( NEB ) for 4hrs followed by transformation into competent DH5α cells ( Invitrogen ) . Correct mutagenesis was confirmed by Sanger sequencing . 48 hours after transfection , cells were trypsinized and resuspended in PBS containing 1% paraformaldehyde for 10 minutes . Cells were washed 3x with PBS and then resuspended in PBS + 2% FBS + 1mM EDTA . Flow cytometry was performed on a BD LSRFortessa Flow Cytometer . Analysis of mean fluorescence intensity ( MFI ) or percent GFP-positive was performed using FlowJo version 10 . 1 . To calculate CAIs , the coding sequences of all human genes ( GRCh38 ) were downloaded from Ensembl . These sequences were then filtered to the 21 , 789 longest isoforms of genes with well-formed ORF sequences and Ensembl biotype annotation indicating the production of actual protein products . CAIs for all human genes , and for our genes of interest , were then calculated using the original formula for CAI given by Sharp and Li [60]: CAI ( g ) = ( ∏k=1Lwk ) 1/L where L is the length of the sequence , and wk is the weight of the k-th codon calculated by: wi , j=xi , jyj Where xi , j is the count of the i-th codon of the j-th amino acid in some reference set and yj is the count of the most used codon of amino acid j in the reference set . We chose the reference set by two different methods . First , we used the recommendation by Sharp and Li , and chose the top 1% of genes found to be most highly expressed . To determine this set of genes , we used publicly available , two replicate HEK293T mRNA-Seq data posted to the Gene Expression Omnibus ( GSM1440304 , GSM1440305 ) . Data was cleaned , mapped to the longest isoforms with Bowtie2 , and used to estimate relative gene expression with RSEM . We also naively chose the entire set of accepted longest isoforms as the reference set . No notable difference in the distribution of CAIs was obvious between these two methods . The results reported use the naïve longest-isoform reference set . Total RNA was harvested from cells transfected with indicated amounts of Schlafen11-V5-pcDNA6 . 2 using phenol-chloroform-isoamyl alcohol ( Sigma ) . RNA was treated with Turbo DNaseI ( Thermo-Fisher ) and re-extracted with phenol-chloroform-isoamyl alcohol . RNA was reverse transcribed using SuperScript III First-Strand Synthesis System ( Thermo-Fisher ) , using 250ng RNA per reaction and random hexamer primers . The tissue panel shown in Fig 5 was obtained from Clontech . Schlafen11 was quantified using the following primers: S11-F 5’CCTCCCCTTAGCAGACCAGT3’ , S11-R 5’TTCCCCGAAAGAAAGGTTG3’ , GAPDH-F 5’ACCGTCAAGGCTGAGAACGG3’ , GAPDH-R 5’GTGGTGAAGACGCCAGTGGA3’ . When quantifying in CHO cells , the GAPDH primers used were 5’GGCTGCCCAGAACATCATCC3’ 5’CTTCCCGTTCAGCTCTGGGA3’ . Delta-Ct values were calculated by subtracting the Ct value of GAPDH from the Ct value of SLFN11 . Variance of this measurement was calculated using propagation of error .
|
Schlafen11 was recently identified as a human antiviral protein with activity against HIV-1 . Here we show that some nonhuman primate versions of Schlafen11 are much stronger at blocking the accumulation of viral proteins than is human Schlafen11 . These relatively larger phenotypes of nonhuman primate Schlafen11 allowed us to explore further into the mechanism of this protein . We present data showing that Schlafen11 may not be a classic restriction factor , but rather an interferon-stimulated gene with broad ability to inhibit protein production from many host and viral transcripts , creating a general antiviral state in the cell .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"molecular",
"probe",
"techniques",
"293t",
"cells",
"pathogens",
"biological",
"cultures",
"microbiology",
"vertebrates",
"marmosets",
"immunoblotting",
"animals",
"mammals",
"retroviruses",
"primates",
"immunodeficiency",
"viruses",
"viruses",
"animal",
"models",
"bonobos",
"rna",
"viruses",
"experimental",
"organism",
"systems",
"molecular",
"biology",
"techniques",
"research",
"and",
"analysis",
"methods",
"monkeys",
"new",
"world",
"monkeys",
"medical",
"microbiology",
"hiv",
"cho",
"cells",
"microbial",
"pathogens",
"hiv-1",
"cell",
"lines",
"molecular",
"biology",
"viral",
"pathogens",
"biology",
"and",
"life",
"sciences",
"apes",
"lentivirus",
"chimpanzees",
"amniotes",
"organisms"
] |
2016
|
Non-human Primate Schlafen11 Inhibits Production of Both Host and Viral Proteins
|
The fungal cell wall not only plays a critical role in maintaining cellular integrity , but also forms the interface between fungi and their environment . The composition of the cell wall can therefore influence the interactions of fungi with their physical and biological environments . Chitin , one of the main polysaccharide components of the wall , can be chemically modified by deacetylation . This reaction is catalyzed by a family of enzymes known as chitin deacetylases ( CDAs ) , and results in the formation of chitosan , a polymer of β1 , 4-glucosamine . Chitosan has previously been shown to accumulate in the cell wall of infection structures in phytopathogenic fungi . Here , it has long been hypothesized to act as a 'stealth' molecule , necessary for full pathogenesis . In this study , we used the crop pathogen and model organism Magnaporthe oryzae to test this hypothesis . We first confirmed that chitosan localizes to the germ tube and appressorium , then deleted CDA genes on the basis of their elevated transcript levels during appressorium differentiation . Germlings of the deletion strains showed loss of chitin deacetylation , and were compromised in their ability to adhere and form appressoria on artificial hydrophobic surfaces . Surprisingly , the addition of exogenous chitosan fully restored germling adhesion and appressorium development . Despite the lack of appressorium development on artificial surfaces , pathogenicity was unaffected in the mutant strains . Further analyses demonstrated that cuticular waxes are sufficient to over-ride the requirement for chitosan during appressorium development on the plant surface . Thus , chitosan does not have a role as a 'stealth' molecule , but instead mediates the adhesion of germlings to surfaces , thereby allowing the perception of the physical stimuli necessary to promote appressorium development . This study thus reveals a novel role for chitosan in phytopathogenic fungi , and gives further insight into the mechanisms governing appressorium development in M . oryzae .
All fungal cells are encased within a cell wall . This complex and dynamic structural barrier is composed of interwoven polysaccharides and proteins . Indeed , the polysaccharide moiety makes up the majority of the fungal wall , being comprised of chitin ( a polymer of β1 , 4-N-acetylglucosamine ) , β/α1 , 3-glucans and mannans [1] . There is , however , considerable variation in the proportion of these wall components between different cell-types and between fungal species [1] . The cell wall plays an essential role in maintaining cellular integrity in the face of the challenging and varied environmental conditions to which fungi are exposed . Indeed , the cell wall plays a far greater role than simply being the extracellular “coat of armour” [2] . Firstly , morphogenesis is heavily influenced by cell wall composition , and , conversely , localized changes in composition allow fungal cells to undergo morphogenesis , whilst maintaining cellular integrity [3] . This is illustrated by many studies , where chemical or genetic perturbation of cell wall composition has led to gross defects in fungal growth and morphogenesis ( reviewed in [1 , 4] ) . Secondly , the cell wall forms the interface between the fungal cell and its immediate environment . As a result , the structure and composition of the cell wall influences both physical and biological interactions which occur over the life-cycle of the fungus , as , for example , during host infection [2] . The hemibiotrophic fungus Magnaporthe oryzae causes significant losses of rice [5] . It is thus a notable pathogen but is also considered as a model organism to study appressorium formation in pathogenic fungi [6] . Disease occurs when three–celled asexual conidia land on the host , differentiate a short germ tube and thence an infection structure ( the appressorium ) . This developmental progression occurs in response to hard , hydrophobic surface and following the perception of host-derived surface chemistries , such as cutin [7] . The maturing appressorium generates a considerable turgor pressure , a penetration peg emerges and penetrates the leaf cuticle [8] . Subsequently , invasive hyphae ramify though the host . Throughout this process the fungal wall undergoes extensive remodelling during rapid growth [9] . For successful infection M . oryzae must remain undetected by its host: but how does the fungus do this ? Plants readily detect key molecular signatures of fungal cells , that is , Pathogen Associated Molecular Patterns ( PAMPs ) . Fungal cell wall polysaccharides in particular represent a major source of PAMPs [10] . The best characterized of these are chitin oligomers , which are released from the fungal wall either as a result of endogenous cell wall remodelling , or due to the action of plant chitinases . Chitin oligomers are recognized in rice plants by the Pattern Recognition Receptor ( PRR ) CEBiP ( Chitin Elicitor Binding Protein ) [11] . Binding of chitin oligomers to CEBiP induces its dimerization and , in association with CERK1 ( Chitin Elicitor Receptor Kinase-1 ) , results in phosphorylation and activation of CEBiP [12] . This triggers PAMP Triggered Immunity ( PTI ) [13] . This response includes callose deposition , the production of degradative enzymes ( chitinases and glucanases ) , and a burst of reactive oxygen species [14 , 15]—all of which serve to restrict or kill the invading fungus . Phytopathogenic fungi have evolved a number of strategies to avoid triggering PTI . The first is by secretion of chitin-binding effectors , which compete with the chitin receptors to bind the oligomers , and thus enable full pathogenesis , as in Cladosporium fulvum and M . oryzae [16 , 17] . The second strategy is to change cell wall composition to either alter identity , or to mask the presence of the PAMPs . For example , during infection-related development , M . oryzae synthesises α1 , 3-glucan as a component of the cell walls of germ tubes , appressoria and invasive hyphae [9] . Deletion of the sole gene encoding α1 , 3-glucan synthase ( AGS1 ) results in enhanced susceptibility to enzymatic degradation , the triggering of PTI and thus loss of pathogenicity [18] . This suggests that α1 , 3-glucan acts as a 'stealth' mechanism , cloaking the fungus from recognition by the plant . We postulated that chitin deacetylation may play a similar role—as it constitutes another infection-related cell wall alteration , observed to occur in a number of fungal plant pathogens , including Uromyces fabae , Colletotrichum graminicola , Puccinia graminis and M . oryzae [9 , 19] . Chitin deacetylation is catalyzed by a family of conserved carbohydrate esterase enzymes ( CE-4 family ) known as chitin deacetylases ( CDAs ) ( E . C . 3 . 5 . 1 . 41 ) . The removal of acetyl groups from N-acetylglucosamine residues of chitin results in the formation of chitosan , typically a heterogeneous polymer of β1 , 4-glucosamine and β1 , 4-N-acetylglucosamine [20] . The deacetylation of chitin confers two hypothetical benefits upon the invading fungus . Firstly , unlike chitin , chitosan cannot cause activation of CEBiP [12] . In addition , although various defence responses have been observed in plants treated with chitosan ( reviewed in [21] , it is unclear whether chitosan is as effective as chitin in this respect . Secondly , chitin deacetylation likely provides protection from plant chitinases , since chitosan is a poor substrate for these enzymes [22] . However , such roles for chitosan , acting as either a protectant or as a 'stealth' mechanism are not yet supported by direct experimental evidence . Moreover , chitin deacetylases genes are present in the genomes of all fungi [23] . Thus , chitosan may be a broadly-occurring cell wall component , with roles beyond those we hypothesize for plant pathogens . Indeed , such additional roles may relate to the different physical and chemical properties of chitin and chitosan . Chitin occurs in fungal cell walls as antiparallel chains , forming microfibrils with immense strength and rigidity [24] . However , chitin deacetylation creates primary amine groups , which are largely protonated and charged at physiological pH . Consequently , chitosan is polycationic , more hydrophilic and an amorphous polysaccharide , in stark contrast to its parent polymer chitin . Thus , deacetylation could have profound consequences on the structure and physical properties of the fungal cell wall , which , in turn , will impact on fungal growth and morphogenesis . There is , however , little published data defining the role ( s ) of chitosan in fungal cell walls per se . The role of chitin deacetylation was first described in S . cerevisiae [25] Here , two functionally redundant CDAs deacetylate chitin specifically in the ascospore cell wall—the double knockout mutant Δcda1Δcda2 strain results in complete loss of chitosan from the ascospore wall . Whilst spore viability was unaffected , spores of the Δcda1Δcda2 strain were more susceptible to treatment with lytic enzymes , ether and heat shock , suggesting possible disorganization and increased permeability of the wall [25 , 26] . Further experimentation showed that the outer dityrosine layer was absent in CDA deletion strains [27] . It is hypothesized that the dityrosine is cross-linked to the amine groups of the chitosan , thereby creating a rigid and impermeable ascospore cell wall [28] . Similarly , chitosan was shown to be a component of spore wall in Ashbya gossypii—deletion of the single CDA resulted in a complete loss of sporulation , suggesting that chitosan is required for spore development [29] . In the Basidiomycete fungus Cryptococcus neoformans , chitosan is a major component of the vegetative cell wall [30] . Deletion of 3 of the 4 CDA genes ( CDA1-3 ) abolished chitosan synthesis [31]—these triple deletion strains were hypersusceptible to various cell wall perturbants , showed attenuated virulence and “leaked” melanin from their cell walls [32] -Chitosan is thus required for cell wall integrity in this fungus . . Deacetylation of chitin may play critical roles in fungal development , integrity and pathogenesis . Thus the two central hypotheses regarding its role are: i ) chitin deacetylation is important for cellular development and morphogenesis and ii ) chitosan acts mechanisitically as a 'stealth molecule' during plant infection in pathogenic fungi . Appressorium development in M . oryzae represents an amenable and relevant system to test these hypotheses . Firstly , chitosan is known to be a component of the germ tube and appressorium cell wall [9] . Secondly , protective cell wall components ( α1 , 3-glucan ) in appressoria are required for pathogenicity [18]; this may hold true for chitosan . Lastly , appressorium development likely requires increased cell wall flexibility—chitin deacetylation may enable such plasticity . Indeed , this may be the most likely scenario as deletion of a putative chitin deacetylase , CBP1 resulted in defective appressorium formation on artificial surfaces [33] . Yet , deletion of CBP1 also prevented hyphopodium and pre-invasive hypha formation on artificial surfaces ( but not root surfaces ) , suggesting that chitosan plays a role in multiple infection-related cellular differentiation events [34] . However , chitosan synthesis was not consistently reduced in the cbp1 mutant [34] and neither was the chitin deacetylase activity of Cbp1 proven [33] and so the role of chitin deacetylation remains inconclusive . A comprehensive characterization of chitin deacetylases operating during appressorium development should provide a valuable insight into the roles of chitin deacetylation during infection in plant pathogenic fungi .
The localization of chitosan during appressorium development in M . oryzae has previously been investigated using Eosin Y ( 9 ) . Whilst this dye binds to chitosan , via an electrostatic interaction , the specificity of this interaction has not been directly proven . We sought an alternative , more specific method , that is by immune-detection , with mAbG7 , a monoclonal anti-chitosan antibody [35] , and a polyclonal anti-chitosan antibody [19] . These antibodies revealed that chitosan localizes to the cell wall of the germ tube and the appressorium ( Fig 1A & 1B ) , and is consistent with previous observations made with Eosin Y [9] . However , we wished to determine the precise timing of chitin deacetylation during appressorium development in order to assess whether it is , indeed , required for this process . To do this , we invoked use of the probe OGA488 . This recently developed and highly specific probe [36] stains chitosan rapidly ( within 15 minutes ) , and is thus ideally-suited to studying germlings in vivo at different stages of morphogenesis . Using this probe , germling development was tracked on an artificial hydrophobic surface inductive to appressorium formation [37] . After 1–2 hpi ( hours post inoculation ) the germ tube was weakly labelled , labelling strengthened upon onset of germ tube hooking ( 2–3 hpi ) , and intensified further during appressorium development ( 4 hpi ) . At this stage , the entire germ tube and appressorium wall were labelled—this being consistent with the immune-localisation data , ( Fig 1C ) . Next , we asked which of the 10 putative chitin deacetylase genes residing in the M . oryzae genome ( http://www . broadinstitute . org/annotation/genome/magnaporthecomparative/MultiHome . html ) are responsible for chitin deacetylation occuring prior to and during appressorium development . The 10 M . oryzae CDAs carry a polysaccharide/chitin deacetylase domain ( Pfam 01522 ) . All , except Cda9 , have a predicted N-terminal signal peptide . Putative chitin binding domains ( CBD; Pfam 00187 ) are present in Cbp1 , Cda1 , Cda2 and Cda7 . Cbp1 also has a serine/threonine-rich repeat region at its C-terminus [33] . Cda4 , Cda5 and Cda8 have putative single transmembrane domains , suggesting that these proteins localise to the membrane ( Fig 2A ) . Chitin deacetylation in Colletotrichum lindemuthianum is dependant upon a zinc-binding Asp-His-His triad , and four key active site residues: a catalytic base residue ( Asp ) and a catalytic acid residue ( His ) , which interact with Arg and Asp residues respectively [38 , 39] . Sequence alignment using Clustal Omega [40] , revealed conservation of the zinc-binding triad and active-site resides in all M . oryzae CDA sequences , with the exception of a non-catalytic Asp in Cbp1 ( MGG_12939 ) at position 153 of the alignment ( S1 Fig ) . To determine which of the 10 CDAs are expressed during appressorium development , we performed qRT-PCR analysis . Total RNA was extracted from rice leaves inoculated with WT conidia , at 5 hpi . Appressorium development was confirmed at this timepoint by microscopic observation ( S2 Fig ) . Relative quantification of transcript abundances for all 10 CDAs revealed that CDA3 is the most highly expressed , followed by CDA2 and CBP1 ( Fig 2B ) . Low levels of expression were observed for the remaining genes . In addition to this , we also analyzed the expression of the CDAs during later stages of leaf infection , at 36 hpi . In this case , CDA1 and CDA6 showed the highest transcript abundance ( S2 Fig ) . These data align with published transcriptomics datasets [41 , 42] . The combined transcriptional profiling data reveals that three particular CDA genes are likely involved in chitin deacetylation during appressorium development: CBP1 , CDA2 , and CDA3 . CBP1 has previously been partially characterized [33] , but we wished to further this work , in combination with CDA2 , and CDA3 and thus generated single , double and triple deletion strains by a targeted gene replacement approach . All such mutants generated were confirmed by both PCR and by Southern Blot analysis ( S3 and S4 Figs ) . Deletion strains are listed in S1 Table . To compare appressorium development , conidia of the mutant and WT strains were inoculated onto an artificial hydrophobic glass surface . In the WT strain , over 80% of conidia developed appressoria after an 8 hr incubation ( Fig 3A ) . However , very few appressoria formed in cbp1 at 8 hpi ( 3% ± 3% ( SD n = 3 ) ) —germ tubes were abnormally elongated and failed to hook at the end distal from the spore ( Fig 3B ) . This is consistent with the previous report [33] . Germination of cbp1 conidia was significantly higher than the WT strain at 1 hpi ( Welch's ANOVA , p < 0 . 05 , Fig 3A ) and thus failure to form appressoria is not due to delayed or reduced germination . By 24 hpi , 83% ± 4% of cbp1 germ tubes formed appressoria ( Fig 3A ) that were misshapen in appearance ( S6 Fig ) . Appressorium development is therefore both delayed and defective in cbp1 . In the cda2 , cda3 and cda2/cda3 strains , appressorium development and conidial germination progressed similarly to WT . However , deletion of CDA2 together with CBP1 resulted in much more severe defects in appressorium development than observed in the single deletion strain cbp1 . Germlings of strain cda2/cbp1 failed to form appressoria by 8 hpi , as in cbp1 , but extending the incubation period to 24 hpi resulted in only rare occurrences of appressorium development ( 27% ± 20 of germ tubes ) , in contrast to cbp1 ( Fig 3A ) . The germ tubes of cda2/cbp1 appeared even more elongated ( 487 ± 42 μm in cda2/cbp1 compared with 264 ± 37 μm in cbp1 ( S3 Table ) ) , yet remained undifferentiated . This phenotype was also observed when conidia were germinated on other hydrophobic surfaces , including plastic and Parafilm ( S5 Fig ) . CDA2 and CBP1 therefore exhibit partial redundancy . Conversely , deletion of CDA3 had no additive effects on appressorium development under the conditions tested: cbp1/cda3 demonstrated an identical phenotype to cbp1 , and cda2/cbp1/cda3 a similar phenotype to cda2/cbp1 . To determine whether chitin deacetylation is reduced in the cda mutants , germlings of the deletion strains were stained with OGA488 ( Fig 3B ) . WT germlings developed on an artificial hydrophobic surface showed strong staining of the cell wall in germ tubes and appressoria at 16 hpi , as described previously . Similar staining was observed in cda2/cda3 . In cbp1 and cbp1/cda3 , appressoria labelled with OGA488 , but little fluorescence was observed on their elongate germ tubes . However , no chitosan staining was observed in strains cda2/cbp1 and cda2/cbp1/cda3 , suggesting complete loss of chitin deacetylation activity , and providing further evidence of functional redundancy between cbp1 and cda2 . By 24 hpi a small proportion of germ tubes in strains cda2/cbp1 and cda2/cbp1/cda3 formed appressoria . However , no chitosan was detected ( S6A Fig ) , suggesting that it may not be an absolute requirement for appressorium morphogenesis . Yet , the presence of low concentrations of chitosan , or chitosan in cell wall regions inaccessible to the OGA488 probe cannot be completely discounted . The highly elongate germ tubes observed in strains cbp1 , cda2/cbp1 and cda2/cbp1/cda3 are a curious feature . Calcofluor White staining revealed that the mutant germ tubes are septated , with cross wall distributed along the elongate germ tubes of cbp1 , cda2/cbp1 and cda2/cbp1/cda3 at regular intervals ( S6B Fig ) . Taken together , these data suggest a clear link between the loss of chitin deacetylation , and loss of appressorium development . We created fluorescent protein fusions , to better understand how chitin deacetylation by Cbp1 and Cda2 promotes appressorium development . C-terminal mCherry fusions of CBP1 and CDA2 were made , under the control of their respective native promoters , and transformed into their respective deletion background strains . Several independent transformant lines were characterized for each fusion and which exhibited identical patterns of fluorescence ( S1 Table ) . Strains expressing Cbp1:mCherry show fluorescence at all stages of germling morphogenesis ( Fig 4A–4C ) , and demonstrate fully restored appressorium development ( Fig 4D ) , indicating that the Cbp1:mCherry fusion protein is fully functional . Unexpectedly , fluorescence was observed in the cell wall at conidial apices , even in conidia which had not yet been harvested from the mycelium ( Fig 4A ) . Substantial intracellular fluorescence was also observed at this stage , most likely localized to vacuoles . During initial stages of germ tube growth ( 1–2 hpi ) , weak fluorescence was observed at the lateral walls of the germ tube , but not the tip ( Fig 4B ) . In addition , small punctae of intracellular fluorescence were sometimes observed along the length of the germ tube . At later stages of appressorium development ( 3–5 hpi ) , wall-localized fluorescence became stronger and localized to the entire germ tube and appressorium , although intracellular fluorescence was still apparent in some appressoria ( Fig 4C ) . The appressorial wall remained fluorescent even at later stages ( 8–16 hpi , S7 Fig ) , although intensity decreased noticeably . Similarly to Cbp1:mCherry , strains expressing Cda2:mCherry exhibited fluorescence during appressorium development , but with fluorescence typically appearing after hooking of the germ tube ( Fig 4E ) . Strong fluorescence was also observed during subsequent appressorium formation ( Fig 4F ) , and remained present at later stages ( 8 hpi ) , as observed with Cbp1:mCherry ( S7 Fig ) . Unlike Cbp1:mCherry however , fluorescence was only very rarely observed intracellularly . Since the cda2 strain was apparently identical to the WT strain , functionality of the fusion protein could not be determined in the complemented strain . Attempts to create a Cda3:eGFP fusion protein did not yield fluorescent transformants , suggesting that the fusion protein is unstable . The defects in appressorium development observed upon deletion of CDA genes was similar to that resulting from deletion of the hydrophobin gene MPG1 [43] . It was reported that appressorium development could be rescued in the mpg1 mutant by co-inoculation with WT conidia , suggesting that Mpg1 could act in trans [44] . To test whether chitosan could rescue the cda mutants , exogenous chitosan was added to conidia during germination on an artificial hydrophobic surface . Addition of 0 . 01% ( w/v ) or 0 . 001% ( w/v ) chitosan , surprisingly , restored appressorium development in all cda deletion strains ( Fig 5A & 5B ) . Between 70–85% of cbp1 , cda2/cbp1 and cbp1/cda3 conidia formed appressoria after an 8hr incubation in the presence of 0 . 01% or 0 . 001% chitosan , compared with 0–10% in the control treatment ( water ) . In the triple deletion strain ( cda2/cbp1/cda3 ) rescue was slightly lower ( 55% ± 17 ( SD , n = 3 ) and 70% ± 16 ( SD , n = 3 ) appressoria at 8 hpi for 0 . 01% and 0 . 001% chitosan , respectively . In the WT strain , 70–85% of conidia formed appressoria in all treatments . To further investigate this , different derivatives and formulations of chitosan were tested for their ability to restore appressorium development in the deletion strains ( structures are shown in S8 Fig ) . Glycol chitosan , a soluble derivative of chitosan restored appressorium development in cda2/cbp1/cda3 at a concentration of 0 . 01% but not 0 . 001% . Carboxymethylchitosan ( in which chitosan has been modified by the addition of an anionic carboxymethyl group ) did not restore appressorium formation at either concentration . Lastly , the addition of chitosan in oligomeric form ( oligochitosan , with a degree of polymerization of 22–33 residues ) partially restored appressorium formation at 8 hpi , whereas glucosamine showed no restorative effects at all . Taken together , these data suggest that the cationic nature of exogenous chitosan is the most important factor determining rescue of the mutant phenotype , with steric factors also having some influence . Polymer length appears to be of little importance , given the broad ranges capable of restoring appressorium development . To investigate how exogenous chitosan restores appressorium development in the cda mutants , fluorescently-labelled chitosan was used . FITC-chitosan , synthesized according to Qaqish et al [45] , was added to conidia of the WT and cda2/cbp1/cda3 strains on an artificial hydrophobic surface . After 16 hr incubation , the FITC-chitosan was removed and the germlings imaged by confocal microscopy . The FITC-chitosan restored appressorium development in cda2/cbp1/cda3 , and clear localization to the cell wall of germ tubes and appressoria ( Fig 6A ) . In the WT strain , weak fluorescence was occasionally observed in the germ tubes . Exogenous chitosan is therefore associated with the cell wall of the deletion strains . Chitosan localizes to the germ tube prior to appressorium development ( Fig 1C ) . To determine whether this germ tube-localized chitosan is sufficient to induce appressorium development , FITC-chitosan was added to germinating conidia of WT or cda2/cbp1/cda3 strains for 2 hr only , before being washed off , and the germlings left to develop for a further 16 hr . In this way , chitosan was only present during the initial germ tube stage of germling morphogenesis , and was removed before appressorium development occurred . Appressorium development was restored in the deletion strain by this short incubation with FITC-chitosan . Here , fluorescence was only observed in the germ tube , and not the appressoria of cda2/cbp1/cda3 germlings , with similar localization in the WT ( Fig 6B ) . Thus germ tube-localized chitosan seems sufficient to induce appressorium development . Lastly , to investigate whether chitosan restores appressorium development at later stages of germling morphogenesis , conidia were incubated for 16 hr , FITC-chitosan added , and the conidia incubated for a further 8 hr . Again , appressorium development was restored in the cda2/cbp1/cda3 strain , although here the germ tubes were highly-elongate ( Fig 6C ) . Fluorescence was observed along the entire length of these elongate germ tubes , but appeared more intense at regions proximal to its point of emergence from the conidium . In this case , WT germlings also exhibited weak fluorescence in both germ tubes and appressoria . Appressorium development is highly defective in cda mutants on artificial hydrophobic surfaces , inductive to appressorium formation in the WT strain . To determine whether this results in a reduction in pathogenicity , equal concentrations of conidia of the deletion strains were inoculated onto detached rice leaves . As reported previously for cbp1 [33] and cda3 [46] , and as expected for cda2 and cda2/cda3 , pathogenicity was unaffected in these strains ( S9 Fig ) . Surprisingly however , the double and triple deletion strains were also able to successfully infect detached rice leaves or whole plants , causing similar lesion numbers to the WT strain ( Fig 7A and S9 Fig ) . Previously , appressorium development was found to be restored in the cbp1 strain on rice leaves [33] . To see if this was also the case in the other deletion strains , rice leaf sheaths were inoculated with conidia of the cda2/cbp1/cda3 strain , and incubated for 24 hr . This revealed that appressorium development was , indeed , restored upon germination on a leaf surface ( Fig 7B ) . Not only this , but the appressoria of cda2/cbp1/cda3 were able to penetrate rice cells with similar efficiency to the WT: 67% ( ±7 , ( SD ) n = 3 ) of WT appressoria had invasive hyphae at 24 hpi , compared with 47% ( ±10 , ( SD ) n = 3 ) of cda2/cbp1/cda3 appressoria . To see if this effect was specific to the rice leaf surface , the experiment was repeated on onion epidermis . Again , appressorium development was restored in the cda2/cbp1/cda3 strain and invasive hyphae were observed at 24 hpi ( Fig 7B ) . The artificial hydrophobic surface lacks the laminated layer of cutin sandwiched between wax on the rice leaf surface cuticular rice leaf [47] . To test whether wax is sufficient to restore appressorium development in the cda2/cbp1/cda3 strain , hydrophobic glass coverslips were coated with the wax molecule 1-octacosanol , which has previously been used to induce appressorium development in M . oryzae [48] . Conidia of strain cda2/cbp1/cda3 germinated on this surface for 24 hr demonstrated partially restored appressorium development ( 65 . 6 ± 6 . 4% on 1-octacosanol compared with 4 . 6 ± 3 . 6% on the control surface ) ( Fig 8 ) . However , germ tubes of the triple deletion strain remained extremely elongated ( S10 Fig ) . Cutin monomers are known to induce appressorium development in M . oryzae [7] , To determine whether cutin restores appressorium development in the cda mutants , conidia of cda2/cbp1/cda3 were germinated on an artificial hydrophobic glass surface , in the presence of 1 , 16-hexadecanediol ( HDD; cutin monomer ) either on its own , or in combination with 1-octacosanol . HDD did not induce appressorium development in germlings of the cda2/cbp1/cda3 strain , and no synergistic effect was observed when HDD was used in combination with 1-octacosanol ( Student's T-test , no significant difference at p < 0 . 05 , compared with 1-octasosanol treatment alone ) ( Fig 8 and S10 Fig ) . The rescue of appressorium development in the cda2/cbp1/cda3 strain on plant surfaces is perplexing . We hypothesized that chemical factors present on the leaf surface , such as wax , trigger upregulation of additional , functionally redundant chitin deacetylases , and restore chitin deacetylation . To test this , conidia of the cda2/cbp1/cda3 and WT strains were germinated on rice leaf sheaths or onion epidermis' , and , subsequently stained with OGA488 . Chitosan remained completely undetectable in the triple deletion strain , whilst strong labelling of germ tubes and appressoria was observed in the WT ( Fig 9A ) . This suggests that restoration of appressorium development in cda2/cbp1/cda3 on plant surfaces is not due to the upregulation of redundant chitin deacetylases . Chitosan is thus not an absolute requirement for appressorium development; instead , this requirement appears to be surface dependant . The appressorial cell wall is impregnated with an impermeable layer of melanin . This may mask detection of chitosan in the appressoria of cda2/cbp1/cda3 . Mutant strain germlings were also stained with OGA488 at 4 hpi , prior to melanization of the appressoria . However , no OGA488 staining was detected in unmelanized cda2/cbp1/cda3 germlings , suggesting the melanin does not prevent the detection of chitosan ( Fig 9B ) . Appressorium development in M . oryzae is controlled by a number of regulatory pathways , which have been shown to respond to the physical and chemical cues present on the leaf surface [49] . In order to determine if there is interplay between chitosan and these particular relays , appressorium development assays were performed in the presence of chemical inducers of these pathways . Previously , appressorium development in cbp1 has been shown to be restored by the addition of cAMP , 1 , 16 hexadecanediol or diacylglycerol [33] . We tested the cda mutant strains by germinating conidia on a hydrophobic glass surface in the presence of 2 . 5 mM IBMX ( a phosphodiesterase inhibitor which increases intracellular levels of cAMP ) , 200 mM 1 , 16 hexadecanediol ( HDD , a cutin monomer ) or 58 mM diacylglycerol ( DAG , an activator of protein kinase C ) . Consistent with the previous report , all treatments restored appressorium development in cbp1 ( Fig 10 ) . Although the proportion of germ tubes forming appressoria was unchanged in this case ( since observations were made at 16 hpi ) , germling morphology of cbp1 was now similar to WT , as evidenced by a significant reduction in germ tube lengths ( Mann-Whitney U-test , p < 0 . 001 S3 Table ) . cbp1/cda3 showed a similar profile of sensitivity to the chemical inducers as cbp1 . Intriguingly however , cda2/cbp1 was considerably less affected by all three chemical inducers: neither HDD or DAG caused a significant increase in appressorium development in this strain ( Student's T-test , p < 0 . 05 ) , whilst IBMX effected only a partial rescue towards the WT phenotype . In cda2/cbp1/cda3 , IBMX treatment led to a small proportion of germ tubes forming aberrant appressoria by 16 hpi , although this increase was non-significant ( Student's T-test , p < 0 . 05 ) . Rescue of cbp1 by HDD and DAG is therefore dependent upon CDA2 , and rescue by IBMX is dependent upon both CDA2 and CDA3 . Hydrophobicity is one of the key physical cues inducing appressorium development , and is perceived in a cAMP-dependant manner [50] . In order to further examine the relationship between hydrophobic surface sensing , chitosan and the chemical inducers , germling differentiation assays , with the WT and mutant strain cda2/cbp1/cda3 , were performed on both hydrophobic and a hydrophilic glass surfaces . At 24 hpi on the hydrophobic surface , the response of cda2/cbp1/cda3 germlings to the inducers was much the same as at 16 hpi , although the proportion of appressoria formed with IBMX was higher ( Fig 11A ) . As with the appressoria formed on the plant leaf surface , it was important to determine whether this rescue could be explained by restored chitin deacetylation . IBMX-treated germlings of the WT and cda2/cbp1/cda3 strains were stained with OGA488 at 24 hpi . This revealed that chitosan was indeed present on the germ tubes and appressoria of the triple deletion strain , albeit to a much lesser extent than the WT germlings ( Fig 11B ) . This suggests that rescue by IBMX may simply be due to upregulation of redundant chitin deacetylases , as observed in cbp1 , rather than the activation of an otherwise inactive signalling pathway . Germinations were repeated on a hydrophilic glass surface ( Fig 11C ) . No appressorium development was observed in the WT strain after a 24 hr incubation ( consistent with previous observations [37] ) , but it was restored by the addition of IBMX or HDD . Both such inducers remained ineffective on cda2/cbp1/cda3 . As exogenous chitosan fully restores appressorium development in the CDA deletion strains on a hydrophobic surface we extended this work to evaluate its effect on a hydrophilic surface . WT and cda2/cbp1/cda3 conidia , germinated in the presence of 0 . 01% ( w/v ) chitosan for 24 hr did not form appressoria . Next , combined treatments of chitosan and IBMX or with HDD were applied . Interestingly , these combinatorial treatments induced appressorium development in the cda2/cbp1/cda3 mutant . IBMX and HDD added in combination did not , however , induce appressorium development in the triple deletion strain . These experiments reveal two important facts: i ) Chitosan acts independently of surface hydrophobicity , and ii ) Chitosan relays through a separate pathway from IBMX or HDD . During the course of these assays it was observed that CDA deletion strain germlings washed off artificial surfaces more readily than the WT strain . The percentage of WT and cda2/cbp1/cda3 germlings adhering to hydrophilic and hydrophobic glass surfaces was quantified at 2 hpi , at which point appressorium development had not yet occurred . Germlings of the triple deletion strain demonstrated significantly lower adhesion than the WT , on both surfaces ( Student's T-test , p < 0 . 05 ) ( Fig 12A & 12B ) . This loss of adhesion could not be explained by lower conidial germination in the cda2/cbp1/cda3 strain; 95% ( ±2 ( SD ) , n = 3 ) of WT conidia had germinated by 2 hpi , compared with 98% ( ±1 ( SD ) , n = 3 ) in cda2/cbp1/cda3 . Remarkably , however , adhesion was fully restored on both surfaces by germinating the conidia in the presence of 0 . 01% chitosan ( w/v ) , ( Fig 12A & 12B ) . The adhesion of fungal cells to surfaces such as glass or plastic is mediated by cell wall glycoproteins [51] . These can be detected by staining with the fluorescently-labelled mannose-binding lectin Concanavalin A ( FITC-ConA ) . Staining of WT or cda2/cbp1/cda3 germlings at 2 hpi with FITC-ConA revealed that staining intensity was often reduced in cda2/cbp1/cda3 strain ( S11 Fig ) . Quantification of fluorescence intensity revealed significant ( p < 0 . 01 ) differences between the WT and cda2/cbp1/cda3 germlings in 3 of the 4 experiments ( 2-way ANOVA with post-hoc Tukey test ) ( S11 Fig ) .
Chitin deacetylases are a conserved family of enzymes in fungi . They catalyze the removal of acetyl groups from chitin , forming chitosan . It has been hypothesized that the deacetylation of chitin may either act as a 'stealth mechanism' to prevent the activation of a PAMP-triggered immune response in the host plant , or result in crucial alterations to the physical and chemical properties of the cell wall necessary for cellular development and morphogenesis . In this study , we present evidence to refute these hypotheses . Instead , we show that chitin deacetylation during germling morphogenesis is not an absolute requirement for either appressorium development or for pathogenicity . Instead , chitin deacetylation plays a role in perception of physical stimuli during the early stages of germling morphogenesis . Appressorium development is induced by the perception of physical and chemical stimuli present on the plant surface , with signals then relayed via two key regulatory pathways in M . oryzae . The Pmk1 pathway operates at the earliest stage of appressorium development , and is required for sensing of wax , and possibly for surface attachment . The cAMP pathway , is hypothesized to operate at the commitment phase of appressorial development , and is required for sensing hydrophobicity and cutin monomers [49] . Considerable cross-talk exists between these two pathways , but activation of both is required for appressorium development . Many previous studies have been devoted to characterizing the proteins operating in these pathways ( reviewed in [49] ) . Significantly , deletion of the genes encoding such proteins results in phenotypes that are similar to those reported for the cda mutants . The data from our study , set in the context of previous data on signal perception in M . oryzae [49] allows the role played by chitosan to be elucidated . When germinated on an artificial hydrophobic surface , conidia of the CDA deletion strains cbp1 , cda2/cbp1 and cda2/cbp1/cda3 failed to undergo early differentiation events , and appressorium development was either severely delayed ( in cbp1 ) or rarely occurred at all ( in cda2/cbp1 and cda2/cbp1/cda3 ) . Failure to differentiate appressoria on artificial surfaces is a phenotype shared by a number of other deletion strains with putative roles in surface sensing . For example , deletion strains of the signalling mucin Msb2 , produce highly elongated , undifferentiated germ tubes on an artificial hydrophobic surface [48] , but appressorium development is fully restored when inoculated onto a plant surface , just as in the cda strains described herein . In addition , cutin monomers were also unable to rescue msb2 , although the effect of cAMP was not determined . Is there a functional link between Msb2 and chitosan ? Msb2 has a single transmembrane domain , which anchors the protein to the plasma membrane , whilst the extracellular portion of the protein resides within the cell wall . This extracellular domain alone can partially suppress the defects associated with MSB2 deletion [52] . Chitosan may be required for cell wall-localization of Msb2; the absence of chitosan may result in loss of Msb2 from the cell wall , resulting in the observed phenotype . However , the precise mechanism by which Msb2 mediates surface sensing remains elusive and this limits speculation on the putative functional link between this protein and chitosan . However , in a recent study , combined deletion of CBP1 and MSB2 had additive effects—virulence and Pmk1 phosphorylation ( in vegetative hyphae ) were shown to be more reduced in msb2/cbp1 strains than in msb2 strains [52] . Loss of chitosan may therefore affect multiple surface-sensing related processes at the earliest stage of germling morphogenesis , which converge on the Pmk1 pathway . In support of this hypothesis , cbp1 germlings have also been shown to be unable to form hyphopodia on artificial hydrophilic surfaces , a phenotype shared by the pmk1 mutant , but not the cpka mutant [34] . Other proteins with putative roles in surface sensing are the hydrophobin Mpg1 [43 , 53] , and the putative G-protein coupled receptor Pth11 [54] . The mpg1 and pth11 mutants demonstrate similar defects in appressorium development . That is , germlings produce elongate germ tubes on hydrophobic surfaces , and fail to develop appressoria , as in the cda mutants . There are , however , several important differences in the phenotypes: Firstly , appressorium development is also defective on plant surfaces in mpg1 and pth11 . Secondly , germ tubes of mpg1 and pth11 do undergo early differentiation events ( germ tube hooking ) . Thirdly , cAMP can restore appressorium development in both mpg1 and pth11 [53 , 54] , but not in the cda mutants , although this is complicated by the presence of redundant CDAs that appear to be upregulated by cAMP [55 , 56] . This suggests that Mpg1 and Pth11 act upstream of the cAMP pathway , whereas chitosan may not . This hypothesis is supported by the fact that chitosan and IBMX/cutin had synergistic effects on appressorium development on a hydrophilic surface . Yet , the fact that germination in the presence of 1-octacosanol , which acts upstream of the Pmk1 pathway ( perceived by the ShoI receptor protein ) [48] , could only partially restore appressorium development in the cda2/cbp1/cda3 strain , may suggest that activation of the cAMP pathway is also defective in this mutant . On the other hand , cutin monomers , which act through the cAMP pathway [54] , did not have a synergistic effect with wax , which may suggest the cAMP pathway is already active in the cda2/cbp1/cda3 strain . Partial rescue with 1-octacosanol could instead be explained by the composition of the wax , which may not effectively mimic the mixture present on leaf surfaces [57] . An alternative explanation for the observed phenotypes in the cda mutants , although one that is not mutually exclusive with those proposed above , is that the lack of germling adhesion is responsible . Germlings of cda2/cbp1/cda3 were much more easily detached than those of the WT , even under relatively gentle washing conditions . This suggests that the adhesive properties of the germ tube are defective in the absence of chitosan . In this scenario , germ-tube localized chitosan is required for the adhesion of the germ tube to the surface itself , and is therefore required for surface sensing , since such sensing mechanisms presumably require close contact between the germ tube and the surface in question . Thus , chitosan may act directly as a polysaccharide adhesin , analagous to the role of galactosaminogalactan ( GAG ) in the cell wall of Aspergillus fumigatus , which mediates adhesion to hydrophobic surfaces and host cells [58 , 59] . Alternatively , chitosan may be required for the attachment of various adhesin-like proteins to the cell wall ( or a combination of both ) . Lack of chitosan in cda2/cbp1/cda3 did result in a reduction of Concanavalin A staining , suggesting a reduction of mannan in the cell wall , but there are several possible explanations for this: i ) Chitosan is directly required for attachment of mannoproteins to the cell wall , ii ) Loss of chitosan results in large-scale changes in cell wall composition , affecting attachment of mannoproteins or resulting in a reduction in non-protein associated mannan , iii ) Mannoproteins are produced , but secretion to the outer wall is affected by loss of chitosan or iv ) Lack of surface sensing and activation of Pmk1 and/or cAMP pathways means that production of the mannoproteins does not occur in the first place , i . e . reduced germling adhesion is a consequence , and not the cause of defective surface sensing . Further investigations are required to distinguish between these possibilities . The rescue of appressorium development in the cda mutants by exogenous chitosan and its association with the cell wall was an unexpected result , and it is unclear exactly how this occurs . Because the charge of the exogenous chitosan was of great importance , this may indicate that an electrostatic interaction is responsible for its association with the cell wall . This , in turn , suggests the existence of an anionic cell wall component , which may form a complex with the cationic chitosan . At present , little is known of anionic cell wall components in fungi . Uronic acids are known to be present in the cell walls of some fungi [60–63] , as is phosphomannan [64–66] , but their presence in M . oryzae has not been investigated . In addition to an electrostatic association , the possibility of enzymatic incorporation of exogenous chitosan must also be considered . CRH transglycosylases have previously been shown to be able to incorporate exogenous fluorescently-labelled oligosaccharides in S . cerevisiae [67] . It is therefore possible that other enzymes with similar activities may be responsible for the hypothesized incorporation of exogenous chitosan . The role of chitosan in appressorium development is an intriguing one , and there remain several unresolved issues , as discussed above . Despite this , the model presented in Fig 13 is consistent with all of the data presented herein , and with previous studies characterizing surface sensing proteins in M . oryzae . In this model , germ-tube localized chitosan is required for the activation of the Pmk1 MAP kinase pathway in response to a surface , but independently of surface hydrophobicity . Optimal activation of the cAMP pathway may also require chitosan , but it is not possible to conclusively determine whether just one or both of the signalling pathways are defective in cda mutants from the current data . The deacetylation of chitin was hypothesized to cause profound changes to the chemical and physical properties of the cell wall , which could be crucial for cellular morphogenesis . However , no evidence for this has been found in the present study . Germlings of the cda2/cbp1/cda3 strain developed appressoria that were morphologically indistinguishable from the WT strain on plant surfaces , despite the absence of detectable chitosan . Additionally , appressorium development in cda2/cbp1/cda3 was restored by a short incubation with FITC-chitosan that was only incorporated into the germ tube . Not only was chitosan not required for morphogenesis of appressoria , but appressorium function also seemed to be unaffected by the absence of chitosan , since plant penetration continued to be achieved successfully . Chitosan is therefore also unlikely to be a key structural component of the cell wall , in contrast to Cryptococcus neoformans where loss of chitosan impaired cellular integrity [31] . On the other hand , it is also possible that compensatory changes in cell wall composition occurred in the absence of chitosan , which were not investigated . The upregulation of other cell wall components may be sufficient to maintain cell wall integrity in the cda mutants . In a similar vein , it is not inconceivable that compensatory changes were also responsible for allowing appressorium morphogenesis to occur in the absence of chitosan i . e . there are multiple , redundant morphogenic mechanisms operating during appressorium development . A second hypothesized role of chitosan is in the protection from plant chitinases . However , the findings presented here , together with evidence from previous studies do not support this role for chitosan , at least in germlings . Germlings of cda2/cbp1/cda3 in which chitosan was absent remained intact on leaf surfaces , in contrast to those of the ags mutant which lack α1 , 3-glucan and were destroyed , presumably by degradative enzymes on the leaf surface [18] . This suggests that α1 , 3-glucan may be the cell wall component with the protective role in M . oryzae germlings . It would also be valuable to investigate the hypothesized protective effects of chitin deacetylation during in planta growth . Chitosan staining was not performed on invasive hyphae in the cda mutants , although qRT-PCR analysis suggested that the chitin deacetylases operating during appressorium development are not highly expressed during invasive growth ( S2 Fig ) . Instead , CDA1 and CDA6 appear to be involved in chitin deacetylation at this stage of infection . Further investigations should therefore focus on the characterization of these genes to determine the role of chitosan in invasive hyphae . The deacetylation of chitin is hypothesized to require a degree of collaboration between the membrane-localized chitin synthases , and cell wall or membrane-associated chitin deacetylases [31 , 68] . Evidence from this study may lend further support to this hypothesis . Deletion of CHS7 results in an almost identical phenotype to multiple CDA deletion [69] , suggesting that Cbp1 , Cda2 and Cda3 may deacetylate the chitin synthesized by Chs7 . Further investigation is required to determine the relationship between chitin synthesis and deacetylation during appressorium development in M . oryzae . In summary , the investigation of chitin deacetylation during appressorium development in M . oryzae has yielded unexpected and intriguing data . The deacetylation of chitin by at least three chitin deacetylases , with overlapping roles , is required for surface sensing in germlings . Yet , this requirement is surface dependant , due to the multiple , redundant mechanisms by which appressorium development can be induced in M . oryzae . Nevertheless , this study provides a novel insight into the mechanisms behind the perception of physical stimuli in M . oryzae , and also demonstrates a novel way in which the cell wall is crucial in acting as an interface between fungal cells and their environment . Importantly , evidence in support of the long-standing hypothesis regarding the role of chitin deacetylation was not found; chitosan is not required as a 'stealth' mechanism in germlings of M . oryzae , and so the role of this cell wall component in the interactions between phytopathogenic fungi and their hosts may need to be reconsidered . However , further work is required to determine whether or not chitosan acts as a 'stealth mechanism' during in planta growth of M . oryzae , as this was not determined in this study .
The wild-type ( WT ) rice pathogenic Magnaporthe oryzae strain Guy11 and mutant strains were cultured at 24°C with a 14 h light 10 h dark cycle . Strain maintenance and composition of media were essentially as described by Talbot et al [43] . RNA was extracted from rice leaves inoculated with conidia of the Guy11 strain using the Qiagen RNeasy RNA extraction kit , according to manufacturer's instructions . RNA concentration was determine using a ThermoScientific ND-1000 NanoDrop spectrophotometer , integrity was evaluated by gel electrophoresis . Genomic DNA was removed by using an RNase free DNase set ( Qiagen ) , according to manufacturers’ instructions . Reverse transcription of 1 μg of RNA into cDNA was performed by using the Maxima First Strand cDNA synthesis kit ( ThermoFisher Scientific ) , according to manufacturers instructions , using random hexamer primers . qRT-PCR was performed with Power SYBR Green PCR master mix ( ThermoFisher Scientific ) , on an Applied Biosystems 7300 Real-time PCR system . Primers are listed in S2 Table ( 37–58 ) . Primers were designed to span an intron where possible ( not all CDA genes have introns ) , and efficiency of the primers was 85–104% ( average efficiency 93 . 6% ) . No amplification was observed in samples that were not reverse transcribed ( -RT control ) , in samples without RNA ( NTC control ) , or in RNA extracted from mock-inoculated leaves . Relative transcript abundance was calculated by the efficiency correction method [70] as follows: Abundance = Etarget ( Ct ( reference ) -Ct ( target ) ) . Single CDA deletion strains were generated by replacing the coding sequences of CBP1 ( MGG_12939 ) , CDA2 ( MGG_09159 ) and CDA3 ( MGG_04172 ) with a hygromycin resistance cassette [71] . Briefly , sequences flanking the target genes ( ~1 . 5kb upstream , and ~1 . 1kb downstream ) were amplified by PCR , using primers 7–10 ( for CBP1 ) , 13–16 ( for CDA2 ) and 17–20 ( For CDA3 ) ( primers are listed in S2 Table ) . These fragments were joined to the hygromycin resistance gene by overlapping PCR , using primer pairs 7/4 and 3/10 ( for the CBP1 deletion construct ) , 13/4 and 3/16 ( for the CDA2 deletion construct ) , 17/4 and 3/20 ( for the CDA3 deletion construct ) . The final , complete construct was made by overlapping PCR , amplifying the products from the previous reactions using primer pairs 7/10 ( for CBP1 ) , 13/16 ( for CDA2 ) and 17/20 ( for CDA3 ) . The final PCR product was used directly for DNA-mediated protoplast transformation of WT Guy11 strain following protocols described by Talbot et al ( 39 ) . Putative transformants were selected on minimal medium ( MM ) supplemented with 300 μg ml-1 hygromycin B ( Calbiochem , Merck , Darmstadt , Germany ) . Deletion of the target gene was confirmed by both PCR and Southern Blot analysis , as described in Samalova et al [72] . Double CDA deletion strains were generated in the cda2 or cbp1 background strains . The coding sequences of CBP1 and CDA3 were replaced with a bialaphos resistance cassette . The deletion construct was made as described above , except primers 8 & 9 were substituted for 11 & 12 ( for CBP1 ) , and primers 18 & 19 for 21 & 22 ( for CDA3 ) . The CBP1 deletion cassette was transformed directly into protoplasts of the cda2 strain , to generate the cda2/cbp1 mutant . The CDA3 deletion cassette was transformed into both the cda2 and cbp1 background strains , to generate the cda2/cda3 and cbp1/cda3 mutants . Putative transformants were selected on defined complex medium ( DCM ) supplemented with 60 μg ml-1 Bialophos ( Goldbio , St Louis , MO , USA ) . Deletion strains were confirmed as above . To generate the triple cda2/cbp1/cda3 mutant , CDA3 was deleted in the cda2/cbp1 mutant . In this case , the coding sequence of CDA3 was replaced by a sulphonylurea resistant allele of the M . oryzae ILV gene ( MGG_06868 ) . Since the sulphonylurea transformation constructs were too large to be generated by over-lapping PCR , GAP-repair S . cerevisiae cloning [73] was used to assemble the constructs in pNEB1284 vector . Primers 5 , 6 and 23–26 were used to amplify the required DNA fragments . Putative transformants were selected on BDCM medium supplemented with 100 μg ml-1 chlorimuron ethyl ( Sigma Aldrich , UK ) and confirmed as specified above . Standard molecular techniques [74] were used to prepare the complementation constructs with fluorescently tagged CDAs . A set of transformation vectors based on pUCAP was generated as described in Samalova et al . [75] . The vectors contain polyadenylation signal pATrpC and either bialophos or hygromycin resistance marker that was cloned into re-created SalI sites using primer pairs 1/2 or 3/4 respectively ( see S2 Table and S12 Fig ) . For PCR amplification of CBP1 and CDA2 , primer pairs 27/28 and 29/30 were used , respectively . Genomic DNA from the WT strain Guy11 was used as a template , and amplified using Herculase DNA polymerase ( Agilent ) . This resulted in amplification of the coding sequence of the genes ( without stop codons ) , together with 2 kb of native promoter sequence for CBP1 and 1 . 3 kb for CDA2 . The PCR products were cloned into the AscI sites of the vector described above ( S12 Fig ) , creating C-terminal mCherry fusions . Conidia ( 2 . 5 x 105 ml -1 ) of Guy11 and complemented strains were collected from 10 day old plates and inoculated in 50 μl droplets onto hydrophobic glass cover-slip , ; onion peels , or rice leaf sheaths; as described in Samalova et . al . , [75] and incubated for specified times in the growth chamber . For viewing mCherry fluorescence , the samples were viewed using the C-Apochromat 40x/1 . 2 water corrected objective lens of a Zeiss LSM 510 Meta confocal microscope at 543 nm excitation from the HeNe laser and emission collected with BP565-615 filter for mCherry . Calcofluor White staining was performed as follows: Conidia ( 2 . 5 x 105 ml -1 ) of the Guy11 and mutant strains were inoculated in 50 μl droplets onto hydrophobic glass cover-slips , and incubated for specified times in the growth chamber . After the incubation , the water droplet was removed from the cover-slip , and replaced with 100 μl of 0 . 05% Calcofluor White solution ( Sigma-Aldrich , UK ) , and incubated for 20 min . Samples were then washed briefly with dH2O , and viewed using the Zeiss LSM510 microscope as above , with 405 nm excitation and emission collected with an LP420 filter . OGA488 staining was performed essentially as described in [36] . Briefly , Conidia ( 2 . 5 x 105 ml-1 ) of the Guy11 and mutant strains were inoculated in 50 μl droplets onto hydrophobic glass cover-slips , onion epidermis , or rice leaf sheaths and incubated for specified times in the growth chamber . Samples were washed briefly with 25 mM MES ( pH 5 . 6 ) , and incubated with OGA488 ( a generous gift from William Willats [36] ) ( diluted 1/1000 in 25 mM MES ) for 15 min on ice . This was followed by 2–3 brief washes with 25 mM MES , after which the samples were viewed using the C-Apochromat 40x/1 . 2 water corrected objective lens of a Zeiss LSM 510 Meta confocal microscope , at 488 nm excitation and emission collected with an LP505 filter . Staining of germlings with FITC-labelled Concanavalin A ( ConA-FITC ) ( Sigma-Aldrich , UK ) was performed as follows: Conidia ( 2 . 5 x 105 ml-1 ) of the Guy11 and mutant strains were inoculated in 50 μl droplets onto hydrophobic glass cover-slips , and incubated in the growth chamber for 2 hr . Cover-slips were washed briefly with PBS , then incubated with 40 μg/ml ConA-FITC for 20 min on ice , then washed briefly with PBS 2–3 times . Samples were viewed using the C-Apochromat 40x/1 . 2 water corrected objective lens of a Zeiss LSM 510 Meta confocal microscope , at 488 nm excitation and emission collected with an LP505 filter . Fluorescence intensity was quantified using ImageJ . Staining with the monoclonal anti-chitosan antibody mAbG7 ( a generous gift from Stefan Schillberg [35] ) was performed as follows . Conidia ( 2 . 5 x 105 ml-1 ) of the Guy11 strain were inoculated in 50 μl droplets onto hydrophobic glass cover-slips , and incubated in the growth chamber for the specified time . Samples were first blocked by incubation with 2% BSA ( w/v ) in PBS for 1 hr at room temperature . Samples were washed 3 times with PBS/T ( PBS +0 . 05% Tween 20 ) , for 5 min each on an orbital shaker ( ~70 rpm ) and then incubated with the primary antibody ( mAbG7 , at 10 μg/ml in PBS ) for 1 . 5 hr at room temperature . This incubation was followed by 3 more washing steps as described above , and incubation with the secondary antibody ( FITC-labelled anti Mouse IgM , 5 μg/ml in PBS ) at room temperature for a further 1 . 5 hr . Finally , the secondary antibody was removed by 3 more washing steps with PBS/T as described previously and viewed under using the Zeiss LSM510 microscope , as described above for ConA-FITC staining . A negative control was included in all experiments , in which samples were only incubated with the secondary antibody . Staining with the polyclonal anti-chitosan antibody ( a generous gift from Holger Deising [19] ) was performed as for the mAbG7 , except that the antibody was used at a dilution of 1/100 from the original antiserum , and the secondary antibody ( a FITC-labelled anti-rabbit IgG ) was used at 10 μg/ml ( in PBS ) . Conidial germination and appressorium development were assessed at 1 , 8 , 16 or 24 hpi by following germling differentiation on hydrophobic glass cover-slips ( Gerhard Menzel , Glasbearbeitungswerk GmbH & Co . , Braunschweig , Germany ) . Conidia ( 2 . 5 x 105 ml-1 ) of the Guy11 and mutant strains were inoculated in 50 μl droplets onto hydrophobic glass cover-slips , and incubated in the growth chamber for the specified time . Samples were viewed under an Olympus BX50 microscope , and ~500 germlings in 3 independent experiments counted per strain/timepoint . For germinations in the presence of chemical inducers of appressorium development , IBMX ( Sigma-Aldrich , UK ) was used at 2 . 5 mM ( from a 250 mM stock in DMSO ) , 1 , 16 hexadecanediol ( Sigma-Aldrich , UK ) at 200 μM ( from a 50 mM stock in ethanol ) and diacylglycerol ( 1 , 2-dioctanoyl-sn-glycerol ) ( Enzo Life Sciences ) at 58 μM ( from a 7 . 25 mM stock in DMSO ) . Chitosan and its derivatives were used at a final concentration of 0 . 01% or 0 . 001% , from a 1% stock ( except for FITC-chitosan which was from a 0 . 08% stock ) . For germinations in the presence of wax ( 1-octacosanol ) ( Sigma-Aldrich , UK ) , the wax was first dissolved in chloroform to a concentration of 4 mg/ml . In a laboratory fume hood , 100 μl of this stock was pipetted onto a hydrophobic glass coverslip and the chloroform allowed to evaporate , leaving a layer of wax on the coverslip . Conidia were then inoculated onto this surface , as described above . For germinations on hydrophilic glass coverslips ( Heathrow Scientific ) the protocol was identical except that only 20 μl of conidial suspension was used . Coverslips were placed in square Petri dishes with damp filter paper and sealed with Parafilm to prevent evaporation . Cuticle penetration was assessed by scoring the frequency with which appressoria formed penetration pegs and intracellular infection hyphae on rice leaf sheaths , after incubation at 24°C for 24 h . Leaf infection assays were performed on blast-susceptible , 21-day-old seedlings of rice ( Oryza sativa L . ) cultivar CO39 . Assays on detached leaves were performed as described in [72] . For assays on whole plants , 21-day-old seedlings of rice ( Oryza sativa L . ) cultivar CO39 were spray inoculated with 4 ml of conidial suspension at three different concentrations ( 1 . 25 x 105 , 6 . 25 x 104 or 3 . 13 x 104 conidia/ml , in 0 . 2% ( w/v ) gelatine water ) . A mock inoculation of 0 . 2% ( w/v ) gelatine water was included as a negative control . Infection was assessed 4 days later . Adhesion assays were performed on both hydrophobic and hydrophilic glass coverslips . 20 μl droplets of conidial suspension ( either with or without 0 . 01% chitosan ) of each strain were pipetted onto the coverslips , which were placed into a humidity chamber and incubated at 24°C for 2 hr to allow germination . At 2 hpi , half of the coverslips were removed and placed into a 50 mm Petri dish containing 5 ml dH2O , and shaken on an orbital shaker at 100 rpm for 5 min . Coverslips were then removed and mounted on slides for viewing under the 10X objective lens of an Olympus BX50 microscope . Conidia were counted from one field of view ( for hydrophobic coverslips ) or three fields of view ( for hydrophilic coverslips ) , with a conscious effort made to locate the area of the coverslip with the highest conidial density . For each strain , the number of conidia counted on the washed coverslips was compared with those counted on the unwashed coverslips , and percentage conidial adhesion calculated as: 100— ( ( conidia on unwashed coverslip—conidia on washed coverslip ) /conidia on unwashed coverslip ) x 100 . FITC-labelled chitosan was synthesized exactly as described previously [45] .
|
Magnaporthe oryzae is a filamentous fungal pathogen which causes devastating crop losses in rice . Successful invasion of the host is dependent upon the ability of the fungus to remain undetected by the innate immune system of the plant , which recognizes conserved components of the fungal cell wall , such as chitin . Previous studies have demonstrated that infection-related changes in cell wall composition are necessary to allow the fungus to remain undetected during infection . One such change that has long been hypothesized to have a role as a 'stealth mechanism' is the deacetylation of the polysaccharide chitin by enzymes known as chitin deacetylases . The deacetylation of chitin produces a polysaccharide known as chitosan , which has previously been shown to accumulate specifically on infection structures in plant pathogenic fungi . However , in this study , we show that germling-localized chitosan is not required for pathogenicity , arguing against a role as a 'stealth mechanism' at this stage . Instead , chitosan is required for the development of the appressorium , a critical fungal infection structure required for the penetration of plant cells . This requirement can be attributed to chitosan mediating the adhesion of germlings to surfaces , which is required for the perception of physical stimuli .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"chitin",
"cell",
"walls",
"plant",
"anatomy",
"plant",
"cell",
"biology",
"fungal",
"structure",
"plant",
"cell",
"walls",
"cereal",
"crops",
"fungi",
"plant",
"science",
"rice",
"model",
"organisms",
"developmental",
"biology",
"materials",
"science",
"crops",
"appressoria",
"plant",
"pathology",
"plants",
"macromolecules",
"cellular",
"structures",
"and",
"organelles",
"morphogenesis",
"materials",
"by",
"structure",
"research",
"and",
"analysis",
"methods",
"polymers",
"polymer",
"chemistry",
"mycology",
"grasses",
"crop",
"science",
"chemistry",
"leaves",
"agriculture",
"plant",
"cells",
"plant",
"and",
"algal",
"models",
"cell",
"biology",
"plant",
"fungal",
"pathogens",
"plant",
"pathogens",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"organisms"
] |
2016
|
Chitosan Mediates Germling Adhesion in Magnaporthe oryzae and Is Required for Surface Sensing and Germling Morphogenesis
|
The reports on the origin of human CD8+ Vα24+ T-cell receptor ( TCR ) natural killer T ( NKT ) cells are controversial . The underlying mechanism that controls human CD4 versus CD8 NKT cell development is not well-characterized . In the present study , we have studied total 177 eligible patients and subjects including 128 healthy latent Epstein-Barr-virus ( EBV ) -infected subjects , 17 newly-onset acute infectious mononucleosis patients , 16 newly-diagnosed EBV-associated Hodgkin lymphoma patients , and 16 EBV-negative normal control subjects . We have established human-thymus/liver-SCID chimera , reaggregated thymic organ culture , and fetal thymic organ culture . We here show that the average frequency of total and CD8+ NKT cells in PBMCs from 128 healthy latent EBV-infected subjects is significantly higher than in 17 acute EBV infectious mononucleosis patients , 16 EBV-associated Hodgkin lymphoma patients , and 16 EBV-negative normal control subjects . However , the frequency of total and CD8+ NKT cells is remarkably increased in the acute EBV infectious mononucleosis patients at year 1 post-onset . EBV-challenge promotes CD8+ NKT cell development in the thymus of human-thymus/liver-SCID chimeras . The frequency of total ( 3% of thymic cells ) and CD8+ NKT cells ( ∼25% of NKT cells ) is significantly increased in EBV-challenged chimeras , compared to those in the unchallenged chimeras ( <0 . 01% of thymic cells , CD8+ NKT cells undetectable , respectively ) . The EBV-induced increase in thymic NKT cells is also reflected in the periphery , where there is an increase in total and CD8+ NKT cells in liver and peripheral blood in EBV-challenged chimeras . EBV-induced thymic CD8+ NKT cells display an activated memory phenotype ( CD69+CD45ROhiCD161+CD62Llo ) . After EBV-challenge , a proportion of NKT precursors diverges from DP thymocytes , develops and differentiates into mature CD8+ NKT cells in thymus in EBV-challenged human-thymus/liver-SCID chimeras or reaggregated thymic organ cultures . Thymic antigen-presenting EBV-infected dendritic cells are required for this process . IL-7 , produced mainly by thymic dendritic cells , is a major and essential factor for CD8+ NKT cell differentiation in EBV-challenged human-thymus/liver-SCID chimeras and fetal thymic organ cultures . Additionally , these EBV-induced CD8+ NKT cells produce remarkably more perforin than that in counterpart CD4+ NKT cells , and predominately express CD8αα homodimer in their co-receptor . Thus , upon interaction with certain viruses , CD8 lineage-specific NKT cells are developed , differentiated and matured intrathymically , a finding with potential therapeutic importance against viral infections and tumors .
NKT cells are unconventional T cells that bridge the innate and adaptive immune systems [1]–[4] . Unlike conventional T cells , which recognize MHC-molecule-presented peptide antigens via their αβTCR , NKT cells recognize CD1d-presented glycolipids . Two subsets of functionally distinct CD1d-dependent NKT cells have been identified based on whether the cells express the semi-invariant Vα24-Jα18 TCR ( Vα14-Jα18 in mice ) [1] , [2] , [5]–[12] and whether they recognize the exogenous NKT cell ligand α-GalCer . Other NKT-like cells have been reported based on their CD1d-independence and CD161 ( NK1 . 1 in mouse ) or CD56 expression [12]–[16] , or other semi-invariant Vα7 . 2-Jα33/Vβ2 , 13 TCR expression ( Vα19/Vβ6 , 8 in mouse ) [12] . In mice , conventional αβT cell development in the thymus proceeds through three major stages , i . e . CD4−CD8− ( DN ) , CD4+CD8+ ( DP ) , and CD4+CD8− or CD4−CD8+ ( SP ) [17] . The developing αβT cells undergo positive and negative selection based on TCR affinity of MHC expressed on antigen presenting cells . By contrast , the semi-invariant αβTCR DP NKT precursors interact with the CD1d-ligand complex either on cortical thymocytes to undergo positive selection [1]–[2] , or on thymic dendritic cells ( DCs ) to undergo negative selection [18] . Positively selected DP NKT cell precursors mature by down-regulating CD8 to reach a CD4+CD44lo stage [1]–[2] . Unlike conventional T cells , which emigrate from the thymus as naïve cells , CD44lo NKT cells remain in the postnatal thymus and undergo a linear differentiation program including the expression of the terminal differentiation marker NK1 . 1 [19] , [20] . However , a proportion of the immature NKT cells remains NK1 . 1− and leaves the thymus [19] , [20] . The final NKT-differentiation step takes place in both thymus and periphery [21] , [22] . Peripheral NKT cells reside preferentially in the liver [23] , [24] , but are also present in the spleen , lymph nodes , bone marrow , lung , and gut [1] , [2] . Human NKT cells have not been detected in engrafted fetal thymus tissue in a hu-thy/liv-SCID model , leading to a presumption that the development of peripheral NKT cells is thymus independent [25] . In later studies , it was proposed that the human thymus has little or no role in generating peripheral NKT cells after birth . This hypothesis is based on the inverse correlation between NKT cell frequency in fetal thymus and gestational age , and on the lack of a clear NKT cell population in postnatal thymus but their definite presence in adult blood [26]–[28] . However , reports on the origin of human CD8+ Vα24+TCR NKT cells are still controversial . In mice , it is believed that there are essentially no CD8+ NKT cells [1] , [2] , [29] . However , recent report shows that IL-15 expands CD8ααNK1 . 1+ cells [30] . In humans , the existence of CD8+ NKT cells in thymus and periphery is an area of controversy . CD8+ αβTCR NKT cells expressing CD8αα homodimer are reported in human PBMC [31] . While there are several reports questioning the existence of these cells [26] , [32] , it is widely believed that CD8 is expressed on a minor proportion of human NKT cells , and that the CD8 marker is usually acquired after egress from the thymus [27] , [28] , [33]–[37] . The finding of a limited correlation between human thymic CD4+ NKT cells and peripheral CD8+ NKT cells has raised the question of what is the origin of CD8+ NKT cells [27] , [28] . As accumulation of findings on NKT cell development , the underlying mechanisms that control CD4-CD8 differentiation of human NKT cells are becoming better characterized .
We studied 177 eligible patients and subjects including 128 healthy latent EBV-infected subjects [EBV+ ( La ) ] , 17 newly-onset acute infectious mononucleosis patients [EBV+ ( IMa ) ] , 16 newly-diagnosed EBV-associated Hodgkin lymphoma patients [EBV+ ( HL ) ] , and 16 EBV-negative normal control subjects ( NS ) ( Table S1 ) . None of the individuals had received treatment with anti-virals , antibiotics , or corticosteroids before entry into this study . The race of all individuals was Han as determined and registered by the physicians in this study . None of the individuals had other complicating clinical infectious symptoms when the study samples were taken . The average frequency of total NKT cells in PBMCs from the 128 EBV+ ( La ) subjects ( 1 . 5±0 . 5% ) was significantly higher than that from 16 EBV-negative NS subjects ( 0 . 18±0 . 2% ) , 17 new-onset EBV+ ( IMa ) patients ( 0 . 15±0 . 1% ) and 16 newly-diagnosed EBV+ ( HL ) patients ( 0 . 1±0 . 1% ) ( Figure 1B ) . The frequency of total NKT cells in the EBV+ ( IMa ) patients dramatically increased at year 1 post-onset [EBV+ ( IMy ) , 1 . 6±0 . 6%] ( Figure 1B ) . The frequency of the CD8+ subset of NKT cells in PBMCs from the EBV+ ( La ) subjects ( 17±4% ) was remarkably higher than from EBV-negative NS subjects ( 2 . 1±0 . 3% ) , new-onset EBV+ ( IMa ) patients ( 1 . 9±0 . 4% ) and EBV+ ( HL ) patients ( 1 . 1±0 . 2% ) ( Figure 1B ) . The frequency of CD8+ NKT cells in the EBV+ ( IMa ) patients was significantly increased at year 1 post-onset [EBV+ ( IMy ) , 20±5%] ( Figure 1B ) . However , the average frequencies of total T cells and the ratios of CD4+ versus CD8+ T cells in PBMCs among the EBV+ ( La ) , EBV+ ( HL ) , EBV+ ( IMy ) and NS subjects were not significantly different ( Figure 1C ) , except for a slight and temporary increase in the frequency of total T cells in the EBV+ ( IMa ) patients ( Figure 1C , some data not shown ) . These observations clearly indicate that the EBV status affects the frequency of NKT cells , particularly , the appearance of CD8+ NKT cells in PBMC . To investigate the mechanism of total and CD8-lineage differentiation of human NKT cells in the context of EBV , we established hu-thy/liv-SCID chimeras . The chimeras were challenged i . t . with EBV , a dsDNA virus , or with human T-cell leukaemia virus type 1 ( HTLV-1 ) , a retrovirus . The EBV-challenge efficiently promoted the generation of total NKT cells , whereas HTLV-1-challenge had no effect , but instead promoted a significant increase in the frequency of αβTCR thymocytes and spleen T cells ( Figure 2A ) . The frequency of total NKT cells reached more than 3% of thymic cells and more than 2% of hepatic cells by week 5 post-challenge with EBV . By contrast , the total thymic and hepatic NKT cells were less than 0 . 01% within 5 weeks post-challenge with HTLV-1 , comparable to the frequencies in unchallenged chimeras ( Figure 2A ) . The frequencies of total thymic or hepatic T cells at week 5 were slightly but significantly increased following HTLV-1 infection . There were approximately 30 , 000–35 , 000 total NKT cells per million thymic cells , and 20 , 000–23 , 000 total NKT cells per million hepatic cells at week 5 in EBV-challenged chimeras ( Table S2 ) . The EBV-challenge did not significantly alter the generation of total mainstream αβT cells , whereas HTLV-1-challenge did promote the generation of the T cells , compared with those in unchallenged chimeras ( Figure 2B ) . The frequency of total T cells reached ∼28% of thymic cells and ∼32% of spleen cells at week 5 in the chimeras challenged with EBV or HTLV-1 , as well as in the unchallenged chimeras ( Figure 2B ) . Cell phenotyping based on CD4 and CD8 expression ( Figure 2C ) revealed that EBV-challenge significantly promoted the generation of thymic CD8+ NKT cells and the appearance of hepatic CD8+ NKT cells in the chimeras transplanted i . t . with total thymocytes ( NKT cell-depleted ) plus thymic stromal cells , a population that includes DC ( Figure 2E ) , compared to unchallenged chimeras ( Figure 2D ) . By contrast , HTLV-1-challenge had no effect on the frequency of CD8+ NKT cells ( data not shown ) . The frequency of CD8+ cells in the chimeras reached more than 25% of thymic NKT cells and more than 23% of hepatic NKT cells at week 5 post-EBV challenge ( Figure 2E ) . The CD8+ cells were essentially undetectable among thymic and hepatic NKT cells at 5 weeks in the unchallenged chimeras ( Figure 2D ) . The different thymic or hepatic NKT cell populations ( DN , CD4+ , and CD8+ ) in the HTLV-1-challenged chimeras were comparable to those in the unchallenged chimeras ( data not shown ) . The frequencies of total and CD4/CD8 co-receptor-expressing NKT cells in peripheral blood correlated well with those in thymus and livers in both unchallenged and EBV-challenged hu-thy/liv-SCID chimeras ( data not shown ) . An important role for DCs in the generation of thymic and hepatic CD8+ NKT cells is suggested by the finding that the frequency of these cells was rather low in both unchallenged and EBV-challenged chimeras transplanted i . t . with total fetal thymocytes plus DC-depleted thymic stromal cells ( data not shown ) , an issue explored further below . The different thymic and spleen co-receptor-expressing mainstream T cells ( DN , CD4+CD8lo , CD4+ , CD8+ ) in the EBV- or HTLV-1-challenged chimeras were comparable to those in the unchallenged chimeras ( Figure 2D , 2E , and some data not shown ) . The absolute numbers of NKT cells and αβT cells ( thymocytes ) in the organs from various hu-thy/liv-SCID chimers were shown in the Table S2 . Thymic CD8+ NKT cells from EBV-challenged hu-thy/liv-SCID chimeras displayed an activated memory phenotype ( CD69+CD45ROhi ) , compared with thymic CD4+ NKT cells in same chimeras ( Figure S1A ) . Hepatic CD8+ NKT cells expressed higher amounts of CD62L than thymic CD8+ NKT cells in the EBV-challenged chimeras , probably attributable to their egress from the thymus toward secondary lymphoid organs , whereas these hepatic CD8+ NKT cells expressed similar amounts of CD69 and CD45RO as thymic CD8+ NKT cells ( data not shown ) . CD161 , a maturation marker for human NKT cells , was uniformly highly expressed on thymic and hepatic CD8+ NKT cells in EBV-challenged chimeras; the frequency of CD8+ NKT cells in the unchallenged chimeras was too low to evaluate CD161 expression . On CD4+ NKT cells , CD161 expression was independent of EBV challenge and revealed two populations , CD161hi and CD161lo , although the former population expressed much higher levels of CD161 in the EBV treated mice ( Figure S1A and some data not shown ) . In parallel , we also examined the expression of CD69 , CD62L and CD45RO on thymic and spleenic CD4+ , CD4+CD8lo , CD4+CD8+ , and CD8+ T cells in EBV-challenged or unchallenged chimeras . Both CD4+ and CD8+ T cells in thymus and spleen from EBV-challenged hu-thy/liv-SCID chimeras displayed an activated memory phenotype ( CD69+CD45ROhi ) , compared with T cells from unchallenged chimeras ( Figure S2B and some data not shown ) . Spleen CD4+ and CD8+ T cells in EBV-challenged chimeras expressed higher amounts of CD62L ( data not shown ) than the thymic CD4+ and CD8+ T cells ( Figure S1B ) , which might be attributable to their egress from the thymus to secondary lymph organs . We established different hu-thy/liv-SCID chimeras by i . t . transplantation with purified human fetal DP thymocytes plus thymic stromal cells ( either DC-containing or DC-depleted ) . In chimeras transplanted DN thymocytes only , the frequency of total NKT cells was very low ( <0 . 01% of thymic or hepatic cells ) at week 5 in both unchallenged and EBV-challenged mice ( Figure 3B , 3C ) . The great majority of cells were CD4-expressing in both chimeras . Less than 0 . 5% of thymic and hepatic CD8+ NKT cells were detected in EBV-challenged chimeras ( Figure 3C ) . Both the frequency of total αβTCR-expressing T cells and the ratio of CD4+ to CD8+ T cells in thymus and spleen from EBV-challenged hu-thy/liv-SCID chimera transplanted with DP thymocytes only ( Figure 3B , 3C ) were comparable to those in EBV-challenged or unchallenged chimeras transplanted with total thymocytes ( Figure 2 ) . We further examined the ontogeny and distribution of NKT cells and T cells in hu-thy/liv-SCID chimeras transplanted i . t . with fetal DP thymocytes plus DC-containing thymic stromal cells . The frequency of total NKT cells was still rather low ( ∼0 . 02% of thymic or hepatic cells ) at week 5 post-establishment in the unchallenged chimeras ( Figure 3D ) , but was substantially increased ( ∼3% of thymic or hepatic cells ) at week 5 post-establishment in EBV-challenged chimeras ( Figure 3E ) . Up to 25% of thymic or hepatic NKT cells expressed CD8 in EBV-challenged chimeras , whereas the frequencies of thymic or hepatic CD4+ NKT cells were correspondingly lower than those in the unchallenged chimeras ( Figure 3D , 3E ) . The frequency of total mature αβTCR-expressing T cells and the ratio of CD4+ to CD8+ T cells in thymus and spleen in EBV-challenged hu-thy/liv-SCID chimeras were comparable to those in unchallenged chimeras ( Figure 3D , 3E ) . The development of thymic or hepatic CD8+ NKT cells was severely impaired by the DC-deletion . The frequency of thymic and hepatic CD8+ NKT cells was essentially below the level of detection in both unchallenged and EBV-challenged chimera transplanted i . t . with DP thymocytes plus DC-depleted thymic stromal cells ( Figure 3F , 3G ) . We also examined the ontogeny and distribution of NKT cells and T cells in the hu-thy/liv-SCID chimeras transplanted i . t . with DP thymocytes plus purified thymic DC . The frequency of total thymic and hepatic NKT cells and the ratio of CD4+ to CD8+ NKT cells in EBV-challenged or unchallenged chimeras were comparable to those in the counterpart chimeras transplanted with DP thymocytes plus DC-included thymic stromal cells ( data not shown ) . We further established hu-thy/liv-SCID chimeras by transplantation i . t . with fetal DP thymocytes plus i . v . syngeneic fetal BM-derived DCs . The frequency of total NKT cells was substantially increased to ∼2% of thymic or hepatic cells at week 5 post-establishment in the EBV-challenged chimeras ( Figure 3I ) , compared to the unchallenged chimeras ( ∼0 . 01% of thymic or hepatic cells ) ( Figure 3H ) . Up to 23% of thymic and hepatic NKT cells expressed CD8 in the EBV-challenged chimeras , whereas the levels of thymic and hepatic CD4+ NKT cells were correspondingly lower than those in the unchallenged chimeras ( Figure 3H , 3I ) . The frequency of total mature αβTCR-expressing T cells and the ratio of CD4+ to CD8+ T cells in thymus and spleen from EBV-challenged hu-thy/liv-SCID chimeras were comparable with those in the unchallenged chimeras ( Figure 3H , 3I ) . The absolute numbers of NKT cells and αβT cells ( thymocytes ) in the organs from various hu-thy/liv-SCID chimers were shown in the Table S2 . We next performed various RTOC of human fetal DP thymocytes reaggregated with syngeneic fetal thymic stromal cells ( thymic DC-included ) , purified thymic DCs , or BM-derived DCs . Various stimuli ( EBV-epitopes , infectious EBV or α-GalCer ) were applied during the culture . The frequency of total NKT cells was low ( <0 . 01% of RTOC cells ) in the EBV- or α-GalCer-challenged RTOC established with only DP thymocytes ( Figure 4B ) . In RTOC of DP thymocytes reaggregated with syngeneic fetal thymic stromal cells ( thymic DC-included ) , purified thymic DCs , or BM-derived DCs , the EBV-challenge significantly promoted the generation of total NKT cells . After 14-day-culture , the frequency of total NKT cells was up to 2 . 5–2 . 8% of RTOC cells , whereas treatment with α-GalCer had no such effect ( Figure 4B ) . Addition of HLA-matched or unmatched EBV-epitopes ( BMLF1+EBNA1 ) had no significant effect on the frequency of total NKT cells ( <0 . 01% of RTOC cells ) compared to the un-stimulated RTOC ( Figure 4B and data not shown ) . In RTOCs where thymic or BM-derived DCs were present , EBV-challenge substantially promoted the development of CD8+ NKT cells . Up to 25% of NKT cells expressed CD8 in EBV-challenged RTOCs ( Figure 4C ) . In this case , HLA-matched EBV-epitopes moderately and significantly increased the development of CD8+ NKT cells ( 2 . 5% of NKT cells ) , compared with those in un-stimulated RTOCs ( Figure 4C ) . In RTOC of DP thymocytes reaggregated with DC-depleted thymic stromal cells , the EBV-induced increase in CD8+ NKT was almost completely abolished ( data not shown ) . Both the frequency of total mature αβTCR-expressing T cells and the ratio of CD4+ to CD8+ T cells in the different RTOC conditions were comparable ( Figure 4B , 4D ) . To further confirm that EBV mediated intrathymic CD8-lineage differentiation of human NKT cells , we focused our attention on detecting the actual EBV- or HTLV-1-infection of human progenitor thymocytes , thymic NKT cells and thymic DCs in the virus exposed hu-thy/liv-SCID chimeras . For detection of EBV-infection , five transformation-associated EBV-genes , LMP1 , EBNA1 , BZLFl , BALF2 , and RAZ were examined . By Southern blot and Q-PCR , a high level of viral genes and mRNA transcripts were detected in EBV-exposed human DCs in the chimeras . Since mice are not the natural EBV host and their DCs are well-known to be unsusceptible to EBV , these results indicated that EBV infected only the transplanted human DCs in EBV-exposed hu-thy/liv-SCID chimeras during NKT cell development and differentiation . There was no evidence of EBV viral genes in EBV-exposed human chimeric DP thymocytes or mature CD4+ and CD8+ NKT cells ( Figure S2A ) . For detection of HTLV-1-infection , 2 highly conserved viral X-region DNA sequences , SK43 and SK44 , were examined . By Q-PCR assay , high levels of SK43 and SK44 were detected in human chimeric HTLV-1-exposed DP αβTCR-expressing T cells ( Figure S2B ) as well as in chimeric hepatic T cells ( data not shown ) . There was no detectable HTLV-1 in thymic CD4+ and CD8+ NKT cells or in DCs in the HTLV-1-exposed hu-thy/liv-SCID chimeras , indicating that HTLV-1 virus does not correlate with NKT cell differentiation in EBV-exposed hu-thy/liv-SCID chimeras . IL-7 and IL-15 were known survival factors for T cells , and enhancers of NKT cell homeostatic proliferation [21] , [26] , [38] , [39] . Both cytokines were used in attempts to differentiate and activate NKT cells from human peripheral and cord blood [35] , [40] , [41] . DP thymocytes expressed an increased level of IL-7Rα in EBV-challenged hu-thy/liv-SCID chimeras compared to unchallenged chimeras . The thymic DCs produced a considerable amount of IL-7 mRNA in unchallenged chimeras , and the levels increased substantially with EBV-challenge ( Figure S3A ) . The thymic CD8+ NKT cells expressed a very high level of IL-7Rα mRNA in EBV-challenged hu-thy/liv-SCID chimeras compared with other types of thymic NKT cells and αβTCR-expressing T cells in both unchallenged and EBV-challenged chimeras ( Figure S3A ) . The thymic DCs produced a considerable amount of IL-15 mRNA in both unchallenged and EBV-challenged chimeras . The thymic CD4+ and DN NKT cells expressed higher levels of IL-15Rα mRNA in both unchallenged and EBV-challenged hu-thy/liv-SCID chimeras compared with other types of thymic NKT cells and αβTCR-expressing T cells in both chimeras ( Figure S3A ) . In a time course study , the freshly isolated fetal thymic DCs were found to express a low level of IL-7 mRNA . Thymic DCs in unchallenged chimeras expressed comparable levels of IL-7 mRNA at the different time intervals examined , 1 , 3 , and 5 weeks . By contrast , the thymic DCs rapidly increased the expression of IL-7 mRNA by week 1 post-EBV challenge , and maintained high levels throughout the course of the analysis ( Figure S3B ) . The IL-15 mRNA was uniformly expressed in the thymic DCs of both unchallenged and EBV-challenged chimeras ( Figure S3B ) . Thymic stromal cells ( DC-depleted ) expressed a uniformly low level of IL-7 and IL-15 mRNA in both unchallenged and EBV-challenged chimeras ( data not shown ) . These observations on cytokine and cytokine receptor mRNA expression were confirmed at the protein level by intracellular flow cytometry ( for IL-7 and IL-15 ) and conventional flow cytometry ( for IL-7Rα and IL-15Rα ) ( data not shown ) . Thus , the thymic DCs are a major source of IL-7 during the thymus-dependent development of NKT cells . The frequency of total NKT cells were low ( <0 . 01% of FTOC cells ) after 14-days of culture without adding any stimuli in FTOC ( Figure 5B ) . Nearly all of the NKT cells were CD4-positive and CD8+ cells were undetectable in these conditions ( Figure 5C ) . By contrast , in FTOC with added EBV , the frequency of total NKT cells was increased ( 1 . 5% of FTOC cells ) , and of these , 15% of cells expressed CD8 , whereas , in FTOC with added HLA-matched EBV-epitopes , neither total nor CD8+ NKT cells were changed ( <0 . 01% of FTOC cells , of which <1% were CD8+ ) ( Figure 5C ) . Exogenous IL-7 or IL-15 alone slightly but significantly increased the total , but not CD8+ NKT cell differentiation in the FTOCs ( <0 . 01% of FTOC cells ) ( Figure 5C ) . HLA-mismatched EBV-epitopes were non-functional in FTOCs ( data not shown ) . In EBV-challenged FTOCs , exogenous IL-7 , but not IL-15 , could significantly further enhance the total and CD8+ NKT cell differentiation ( ∼2 . 5% of FTOC cells , of which 20% were CD8+ ) , compared to the EBV-challenged but non-IL-7-stimulated FTOC and un-stimulated FTOC ( Figure 5B , 5C ) . A mAb against IL-7 completely abolished the effect of IL-7 on the differentiation of CD8+ NKT cells in the EBV-challenged FTOCs , indicating an essential role of the cytokine in the differentiation of CD8+ NKT cells . In FTOCs containing added α-GalCer , IL-7 slightly enhanced total , but not CD8+ NKT cell differentiation ( ∼1% of FTOC cells , of which <0 . 8% were CD8+ ) ( Figure 5B , 5C ) . The mAb against IL-7 completely inhibited the effect of IL-7 on the differentiation of total NKT cells in the FTOCs stimulated with α-GalCer . IL-15 had no such effect on the development of total NKT cells in the FTOCs stimulated with α-GalCer . The frequency of total mature αβTCR-expressing T cells , but not ratios of CD4+ to CD8+ T cells , was enhanced by IL-7 or/and IL-15 in the various FTOCs ( Figure 5B , 5C ) . Consistent with the above in vitro findings in the FTOCs , administration of exogenous IL-7 further enhanced development of thymic and hepatic total and CD8+ NKT cells in the in vivo EBV-challenged hu-thy/liv-SCID chimeras ( Figure 6B ) , compared to chimeras given exogenous IL-7 but not EBV ( data not shown ) and to EBV-challenged chimeras not treated with exogenous IL-7 ( Figure 2E ) . The frequency of total NKT cells was significantly increased ( ∼3 . 8% of thymic and ∼3 . 2% hepatic cells ) ( Figure 6B ) . About 28% of thymic and hepatic NKT cells expressed CD8 ( Figure 6B and Table S2 ) . The administration of mAb against IL-7 ( plus exogenous IL-7 ) completely blocked the function of IL-7 in vivo ( Figure 6C and Table S2 ) , indicating an essential role of the cytokine in the EBV-induced development of CD8+ NKT cells . The administration of exogenous IL-7 plus isotype-matched control Ab had no the blocking effect ( Figure 6D and Table S2 ) . Thus , EBV-induced increase in CD8+ NKT cell development is IL7-dependent . To track NKT cells more accurately , we applied 6B11 mAb , which recognized TCR Vα24JαQ junction CDR3-loop , combined with mAb against Vα24TCR for gate of NKT cells by flow cytometry . The outcome of frequencies of total and co-receptor-expressing NKT cells gated by either CD1d tetramers vs . anti-αβTCR mAb or by anti-Vα24 mAb vs . 6B11 mAb were comparable in different human normal subjects or EBV-infected patients ( Figure S4A ) . Comparable sets of data on frequencies of total and co-receptor-expressing NKT cells were also obtained in thymus and liver from EBV-challenged hu-thy/liv-SCID chimeras ( Figure S4B ) , as well as in EBV-exposed RTOCs and FTOCs ( data not shown ) , gated by either CD1d tetramers vs . anti-αβTCR mAb or anti-Vα24 mAb vs . 6B11 mAb by flow cytometry . These data confirmed the observations on EBV-induced development of CD8+ NKT cells , and ruled out possible contamination of activated conventional CD8+ T cells during the flow cytometry analysis . In our recent study [42] , frequencies of CD8+ NKT cells in patients with EBV-associated malignancies were found significantly lower than those in healthy EBV carriers . CD8+ NKT cells in tumor patients were functionally impaired in terms of cytokine production and cytotoxicity . In hu-thy/liv-SCID chimeras , EBV-exposure efficiently augmented the generation of IFN-γ-biased CD8+ NKT cells , which were strongly cytotoxic to EBV-associated tumor-cells . IL-4-biased CD4+ NKT cells were predominately generated in unchallenged chimeras , which were non-cytotoxic . In tumor-transplanted hu-thy/liv-SCID chimeras , adoptive transfer with EBV-induced CD8+ NKT cells remarkably suppressed tumorigenesis by EBV-associated malignancies . CD4+ NKT cells were synergetic with CD8+ NKT cells , leading to a more pronounced T-cell anti-tumor response in the chimeras co-transferred with CD4+ and CD8+ NKT cells . In the present study , we further investigated the perforin expression in NKT cells ( Figure 7 ) . CD8+ NKT cells from healthy EBV+ humans , EBV-challenged hu-thy/liv-SCID chimeras , or EBV-exposed RTOCs and FTOCs produced much higher amount of perforin ( Figure 7B ) than that in counterpart CD4+ NKT cells ( Figure 7A ) , indicating that high production of perforin in EBV-induced CD8+ NKT cells was an additional reason for their high cytotoxicity to EBV-associated tumor cells , besides their biased IFN-γ-production [42] . In the analysis , we applied flow cytometry using the gating of either CD1d tetramers vs . anti-αβTCR mAb and anti-Vα24 mAb vs . 6B11 mAb . Two groups of results were comparable ( Figure 7 ) . Moreover , we further analyzed the CD8α and CD8β expression on NKT cells . Data revealed that CD8αα homodimer was predominately expressed on CD8+ NKT cells in PBMCs from healthy latent EBV-infected subjects and IM patients at year 1 post-onset , as well as from normal control subjects ( Figure 8B and 8C ) , which were consistent with the previous reports [31] . CD8αα homodimer was also expressed in the majority of CD8+ NKT cells in thymus and liver from hu-thy/liv-SCID chimeras challenged i . t . with EBV ( Figure 8D and 8E ) and in EBV-exposed RTOCs and FTOCs ( data not shown ) . In the analysis , we applied flow cytometry using the gating of either CD1d tetramers vs . anti-αβTCR mAb and anti-Vα24 mAb vs . 6B11 mAb . Two groups of results were comparable ( Figure 8 ) .
Taken advantage of the hu-thy/liv-SCID chimeric mouse model [43] , [44] , we have found that a sizable CD8+ fraction ( up to 25% ) of total human thymic NKT cells is generated in vivo after EBV-challenge . The development of CD8+ NKT cells is promoted in the thymus at the DP precursor stage , and requires participation of thymic DCs . CD4 versus CD8 lineage commitment is controlled by the EBV-challenge . The findings provide a crucial access point for unraveling the mechanism for NKT cell development and differentiation . This study led to two important insights . First , we provide direct evidence that certain pathogens , EBV in this case , are important contributors to CD8-lineage commitment of NKT cells . Second , we demonstrate that differential CD4 versus CD8 lineage commitment can be controlled not only by some known classical endogenous elements [1] , [2] , but also by exogenous pathogenic element ( s ) such as EBV . The impact of different viral pathogens on NKT cell frequencies has been investigated . In humans , infection by HIV and HTLV-1 results in a decrease in NKT cells [45]–[52] . In mice , LCMV induces long-term loss of NKT cells since induction of apoptosis [53] , [54] . The patients with severe immunodeficiency ( XLP ) , lacking of NKT cells , is characterized by an extreme sensitivity to EBV infection [55] , [56] . Our previous [42] and current works show that EBV-infection promotes generation of IFN-γ- and perforin-biased CD8+ NKT cells , and IL-4-biased CD4+ NKT cells . A protective role of CD8+ NKT cells synergized with their counterpart CD4+ NKT cells against EBV-associated malignancies has been verified . The beneficial role against the persistence of EBV-infection could be speculated . The observation on induction of dominate populations of CD8αα+ NKT cells by EBV-infection is in agreement with previous report on that CD8αα+ NKT cells control expansion of total and EBV-specific T cells in humans [31] , and is supporting the observation in mice [30] . There remain several controversial issues concerning CD8+ NKT cell development . For example , why does the frequency of human CD8+ NKT cells show such a limited correlation between different sites ( thymus and blood ) and among different ages ( fetus , neonate and adults ) , and why are CD8+ NKT cells the most numerically variable NKT cell subset in humans , particularly under pathophysiological circumstances [1] , [2] , [26]–[28] , [33]–[37] . In the present study , we were able to monitor the intrathymic and extrathymic development of human NKT cells in different organs in hu-thy/liv-SCID chimeric mice . Since all mature NKT cells were depleted from the thymocytes prior to cell transplantation ( Figure S6 ) , any co-receptor-expressing human NKT cells detected in the mice should have developed and differentiated post-cell-transplantation . Since all fetal samples have been from non-EBV-infected mothers , the EBV-challenge in this animal model accurately reflects the viral effects on the differentiation of CD8+ NKT cells . Nevertheless , more evidences are needed to rule out the possibility that EBV is capable of influencing NKT cell expansion/differentiation in the periphery . Given the functional distinction between CD4+ and CD8+ NKT cells [26]–[28] , [33]–[37] and their potential therapeutic importance such as in cancer treatment [42] , it is crucial to identify the factors that induce the development and differentiation of these cells in order to fully understand the causes of NKT cell subset deficiency and dysfunction , particularly of the CD8+ NKT cells . Some investigators hold the opinion that the immature NKT cells undergo extrathymic differentiation in adult blood [27] , [28] , [33]–[37] . Our studies have demonstrated that the frequency of total NKT cells and the different subsets in thymus , liver and peripheral blood from unchallenged and EBV-challenged chimeras is highly correlated , clearly indicating an intrathymic developmental and differentiation step for human CD8+ Vα24+NKT cells . Moreover , the hu-thy/liv-SCID chimeras have provided an in vivo model to investigate cell development and differentiation of human NKT cells under both physiological and pathophysiological circumstances . In mice , IL-15 plays an essential role in the maturation and overall population size of NKT cells in the thymus and periphery [21] , [22] , [30] . On the other hand , IL-7 is critical for the development of NKT cells , but plays a minor role in regulating their maturation and homeostasis [21] . In humans , IL-7 dominates the CD4+ NKT cell development process in the fetus , neonate , and adult [26] , [39] , whereas IL-15 has a selective and age-specific role in vitro in the expansion and homeostasis of the DN and CD8+ NKT cell subsets [27] . We show here that IL-7 is a major and essential enhancer of EBV-induced development of thymic CD8+ NKT cells in vivo , in the hu-thy/liv-SCID chimeras , and in vitro in FTOCs . We still need to define the role of IL-7 in the continuous NKT cell division in the periphery of adults , if it indeed exists , for instance , in secondary lymphoid organs where IL-7 is available . Taking the fact that EBV causes asymptomatic life-long infection in ∼90% of adults worldwide into consideration , the experimental design for developmental studies of NKT cells should pay special attention to the EBV status of the donors of any human samples , since we have shown here that EBV-infection status of the hu-thy/liv-SCID chimeras and the human donors can directly affect the frequency of total and co-receptor-expressing populations of NKT cells . More importantly , the present study has raised several interesting questions , such as how the semi-invariant canonical αβTCR is expressed on DP thymocyte precursor before commitment to the CD4 versus CD8 lineage differentiation of NKT cells , as well as what and how the ligand is presented by thymic DCs to the semi-invariant αβTCR-expressing DP thymocyte precursor causing the preferential CD8 differentiation .
The latent EBV-infected [referred to as EBV+ ( La ) ] or normal control subjects ( NS ) were healthy EBV seropositive or seronegative individuals , respectively . The patients with EBV-associated acute infectious mononucleosis [lytic phase , referred to as EBV+ ( IMa ) ] were diagnosed by a monospot test and the detection of capsid-specific serum IgM [56] , and followed-up at 1 year [latent phase , referred to as EBV+ ( IMy ) ] . The patients with EBV-associated Hodgkin lymphoma ( HL ) were diagnosed according to the WHO criteria , and staged according to the Ann Arbor classification ( Table S1 ) . All EBV+ ( La ) and NS individuals were healthy volunteers . All patients eligible for this study were in- or out-patients in different Hospitals in Hubei Province in China . HLA typing was performed using the Lymphotype Class I system ( Biotest ) and an Olerup SSP kit ( GenoVision ) . The clinical information of all patients and healthy EBV-infected and normal control subjects is listed in Table S1 . All patients were newly-diagnosed and had no previous treatment before entry into this study . All patients provided informed consent according to the institutional guidelines and protocol titled “The study on the frequency and subset distributions of human peripheral NKT cells in normal and EBV-infected subjects” that was approved by The Wuhan University Ethical Committee . The written informed consent from each patients and subjects was obtained . Human fetal thymic cells , bone marrow ( BM ) cells , liver and PBMCs were anonymously obtained from voluntarily elective pregnancy terminations ( <24-wk-gestation; HLA typing matched HLA-A2 and HLA-DRB1 ( *03 ) , the most prevalent HLA-types for Eastern and Southern Chinese populations , and mismatched HLA-A11 , -B8 and HLA-DQ5 ) . The mothers were excluded if lytic and latent EBV- and HTLV-1-infections were detected by Q-PCR and serologic determination [57] . Thymic cells , BM cells and PBMCs were isolated , aliquoted , cryopreserved and maintained in the vapor phase of liquid nitrogen for further use . Viability of thawed cells was evaluated by Trypan blue dye exclusion before use . Thymic dendritic cells were separated from the thymocytes by adhesion onto plastic culture dishes . For transplantation , NKT cells were positively depleted from thymic cells by MACS beads based on staining with α-GalCer-loaded CD1d tetramers [58] . For functional studies , NKT cells were purified from human PBMCs or chimeric thymic cells by flow cytometry cell sorting or a MACS bead system based on staining with α-GalCer-loaded CD1d tetramers [58] , [59] . Synthesized peptides ( proteins ) were EBV-epitopes , GLCTLVAML ( HLA-A2-restricted , derived from the lytic cycle protein BMLF1 ) , AVFDRKSDAK ( HLA-A11-restricted , derived from nuclear antigen EBNA3B ) , RAKFKQLL ( HLA-B8-restricted , derived from the lytic cycle protein BZLF1 ) , TSLYNLRRGTAL ( HLA-DRB1-restricted , derived from nuclear antigen EBNA1 ) , SDDELPYIDPNM ( HLA-DQ5-restricted , derived from nuclear antigen EBNA3C ) [60] . Recombinant peptides ( proteins ) were verified free of pyrogenicity ( endotoxin <10 units/ml , no bacterial or fungal contamination ) according to the certifications from the manufacturer . The α-GalCer-loaded CD1d tetramers were synthesized as previously described [58]–[60] . For preparation of viral stocks , a highly productive EBV-producer cell line P3HR-1 ( American Type Culture Collection , ATCC , Manassas , VA ) was treated with 12-O-tetradecanoyl-phorbol-13-acetate ( TPA , 30 ng/ml ) for 14 days . The virus was then pelleted from the culture supernatant . The residual TPA in the viral suspension for final use had no significant promoting effect on cell proliferation in the in vivo human-thymus-SCID chimeras , based on our preliminary experiments . Recombinant human ( rh ) IL-7 ( Roche ) and rhIL-15 were purchased from R&D Systems . All mouse anti-human monoclonal antibodies were purchased from BD PharMingen , San Diego , CA , USA , except mAbs against human Vα24 or Vβ11 , which were from Immunotech , Marseille , France . To establish the human-thymus/liver-SCID ( hu-thy/liv-SCID ) chimeras , 8-wk-old female SCID mice ( NOD/LtSz-prkdcscid/prkdcscid strain , the Jackson Laboratory ) were irradiated ( 300 cGy/mouse ) prior to cell-transplantation . Human fetal thymic cells were depleted of immature and mature NKT cells based on their reactivity with α-GalCer-loaded CD1d tetramers . Then , 1×107 thymocytes , thymocytes: thymic stromal cells including dendritic cells = 1∶1 ( Figure S5 ) , were transplanted into the thymus of anaesthetized SCID mice [43] , [44] , [58] , [59] . Syngeneic human fetal liver tissue ( equivalent to 1×107 fetal liver cells ) was simultaneously implanted under the mouse kidney capsule , unless otherwise noted . The chimeras were then intrathymically challenged with EBV ( 107 pfu ) [60] or HTLV-1 ( 107 pfu ) , and the challenge was repeated after 6 days . The chimeras were maintained for 4 wks , unless otherwise stated [43] , [44] . In some cases , chimeras were established by transplantation with human fetal thymic cells ( thymocytes plus thymic stromal cells ) , but without implantation of fetal liver tissue referred to as human-thymus-SCID ( hu-thy-SCID chimera ) . The mice were housed in a pathogen-free environment in the Animal Research Institute , Wuhan University . The protocol for animal study titled “The study on the frequency and subset distributions of NKT cells in human-thymus/liver-SCID chimeras” was approved by The Wuhan University Ethical Committee in accordance with the current Chinese laws . FTOC was carried out as described previously [61] . Briefly , fetal thymus tissue was dissected into pieces of ∼2 mm3 . Three pieces of tissue were placed into 24-well plates with culture medium containing various stimuli as indicated . On day 7 , the cultured thymus fragment was dispersed into a single-cell suspension , and cells were stained and analyzed by flow cytometry . RTOC experiments were performed as previously described [61] . Briefly , thymic stromal cells were prepared by disaggregating fetal thymic lobes . DP thymocytes were obtained by gently grinding freshly fetal thymus lobes . The resulting suspensions were sorted for DP thymocytes using CD4 and CD8 labeling . Reaggregates were formed by mixing together the desired thymic stromal cells and DP thymocytes at 1∶1 cell ratio with other stimuli as indicated . After pelleting the cells by centrifugation , the cell mixture was placed as a standing drop on the upper membrane surface , and incubated for 5–12 days . The α-GalCer-loaded CD1d tetramer and αβTCR ( Immunotech , clone BMA031 ) was used to define total NKT cells . For tetramer staining , the cells were incubated with the tetramer labeled with fluorochromes at 37°C for 15 min . The appropriate isotype Ab ( αβTCR mAb isotype mouse IgG2b ) and empty CD1d tetramer conjugated with a fluorochrome was used to establish negative staining gates . The representative experiments for NKT cell gate negative staining were illustrated in Figure 1 and Figure S6 . The αβTCR and other relevant mAbs were used to identify the different subsets of T cells . In some cases , mAb against human CDR3 loop of invariant TCR Vα24 ( 6B11 , Immunotech ) and mAb against human Vα24 ( including isotype controls ) were used for gating NKT cells . For analysis of co-receptor-expressing NKT cells , single cell suspensions were stained with mAbs to human CD4 and CD8α ( R&D Systems , clone 11830 and 37006 , isotype mouse IgG2a and IgG2b ) , unless otherwise noted . In some cases , NKT cells were stained with mAbs to CD8α and CD8β ( Abcam , clone 2ST8 . 5H7 , isotype mouse IgG2a ) , simultaneously . In intracellular staining for detection of perforin , different cells were resuspended in cold Dulbecco's PBS , and then permeabilized by Cytofix/Cytoperm solution ( 15 min , 4°C , in the dark; BD Pharmingen ) according to the manufacturer's protocol . These permeabilized cells were stained with mAb specific for human perforin ( FITC-conjugated G9 , mouse IgG2b , BD Pharmingen ) , or isotype control , and analyzed by flow cytometry . All analyses were performed with a FACSCalibur ( BD Biosciences ) . Four- and five-color analysis was done using CellQuest software . All Q-PCR reactions were performed as described elsewhere [62] . Briefly , total RNA from purified cells ( 1×104 , purity >99% ) or cell lines was prepared by using Quick Prep® total RNA extraction kit ( Pharmacia Biotech ) according to the manufacturer's instructions . RNA was reverse transcribed by using oligo ( dT ) 12-18 and Superscript II reverse transcriptase ( Life Technologies , Grand Island , USA ) . The real time quantitative PCR was performed in special optical tubes in a 96 well microtiter plate ( Applied Biosystems , Foster City , CA ) with an ABI PRISM® 7700 Sequence Detector Systems ( Applied Biosystems ) . By using the SYBR® Green PCR Core Reagents Kit , fluorescence signals were generated during each PCR cycle via the 5′ to 3′ endonuclease activity of AmpliTaq Gold to provide real time quantitative PCR information . Primers used in Q-PCR are listed in Table S3 . Statistical analyses were performed using the Student′s t test . Values of p<0 . 05 were considered statistically significant .
|
We show that the average frequency of total and CD8+ NKT cells in PBMCs from 128 healthy latent EBV-infected subjects is significantly higher than in 17 patients with acute lytic EBV infection , 16 EBV-associated HL patients , and 16 EBV-negative normal subjects . The frequency of total and CD8+ NKT cells is remarkably increased in the lytic EBV-infected patients at year 1 post-onset . EBV-challenge promotes total and CD8+ NKT cell development in the thymus and liver of human-thymus/liver-SCID chimeras , compared to those in the unchallenged chimeras . After EBV-challenge , a proportion of NKT precursors diverges from DP thymocytes , develops and differentiates into mature CD8+ NKT cells in thymus in EBV-challenged human-thymus/liver-SCID chimeras or reaggregated thymic organ cultures . Thymic EBV-infected dendritic cells are required for this process . IL-7 is an essential factor for CD8+ NKT cell differentiation . EBV-induced CD8+ NKT cells produce remarkably more perforin , and predominately express CD8αα homodimer . CD8 lineage-specific NKT cells are developed and differentiated intrathymically upon EBV-exposure , a finding with potential therapeutic importance against viral infections and tumors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/cell",
"differentiation",
"immunology/leukocyte",
"development",
"immunology/immunity",
"to",
"infections",
"immunology/innate",
"immunity"
] |
2010
|
EBV Promotes Human CD8+ NKT Cell Development
|
African trypanosomes are extracellular parasitic protozoa , predominantly transmitted by the bite of the haematophagic tsetse fly . The main mechanism considered to mediate parasitemia control in a mammalian host is the continuous interaction between antibodies and the parasite surface , covered by variant-specific surface glycoproteins . Early experimental studies have shown that B-cell responses can be strongly protective but are limited by their VSG-specificity . We have used B-cell ( µMT ) and IgM-deficient ( IgM−/− ) mice to investigate the role of B-cells and IgM antibodies in parasitemia control and the in vivo induction of trypanosomiasis-associated anemia . These infection studies revealed that that the initial setting of peak levels of parasitemia in Trypanosoma brucei–infected µMT and IgM−/− mice occurred independent of the presence of B-cells . However , B-cells helped to periodically reduce circulating parasites levels and were required for long term survival , while IgM antibodies played only a limited role in this process . Infection-associated anemia , hypothesized to be mediated by B-cell responses , was induced during infection in µMT mice as well as in IgM−/− mice , and as such occurred independently from the infection-induced host antibody response . Antigenic variation , the main immune evasion mechanism of African trypanosomes , occurred independently from host antibody responses against the parasite's ever-changing antigenic glycoprotein coat . Collectively , these results demonstrated that in murine experimental T . brucei trypanosomiasis , B-cells were crucial for periodic peak parasitemia clearance , whereas parasite-induced IgM antibodies played only a limited role in the outcome of the infection .
African trypanosomes are extracellular protozoa that cause chronic infections in humans and livestock and are predominantly transmitted by the bite of the haematophagic tsetse fly [1] . Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense are the causative agents of West/Central- and East-African Sleeping Sickness respectively , also called Human African Trypanosomiasis ( HAT ) , and are responsible for an estimated 500 , 000 infection cases per annum [2] . Trypanosoma congolense , Trypanosoma vivax and Trypanosoma b . brucei are considered the main cause for livestock infections . These infections have striking effects on economic growth , with losses exceeding 1 billion US $/year in Africa [3] . Livestock trypanosomiasis moreover affect public health , as infected animals serve as a reservoir for tsetse transmission to humans [2] , [4] . The main mechanism generally considered to mediate parasitemia control in a mammalian host , is the continuous interaction between antibodies and the parasite surface , covered by variant-specific surface glycoproteins ( VSG ) [5] . Trypanosomes undergo antigenic variation by either changing VSG expression sites , known as in situ switching of transcriptional control , or by gene replacement resulting in a switch of the terminal telomeric VSG gene itself [6] , [7] . Studies in experimental rodent infection models have implicated T-cell-independent anti-VSG IgM responses to be the first line of host defence against proliferating parasites [8] . Experimental approaches using mice depleted of B-cells by polyclonal antibody treatment [9] , or infections followed by drug-treatment [10] , have shown that B-cell responses can be strongly protective but are limited by their VSG-specificity . This has recently been confirmed in a Cape Buffalo model for natural trypanosomiasis resistance [11] . Recently however , using a chimera bovine model , it was shown that trypanosomiasis sensitivity or resistance was not solely linked to the haematopoietic background of the host , suggesting that other additional host derived factors might also play an important role in the determination of bovine resistance phenotypes [12] . Apart from immune mediated control of infection , the initial setting of parasitemia levels and waves of successive parasitemia peaks are regulated by trypanosomes themselves . This coincides with the differentiation of actively dividing ‘long slender’ parasites into non-dividing ‘short stumpy’ parasites [1] , [13]–[15] . In experimental murine trypanosomiasis infection models , pleomorphic parasite populations consist of both ‘long slender’ and ‘short stumpy’ differentiation forms whereas monomorphic populations consist of ‘long slender’ forms only . The latter is highly virulent and kills mice rapidly due to the exponential growth of the ‘long slenders’ . The interaction between the different trypanosome forms and the host immune system can therefore be studied by performing experimental infections using pleomorphic and monomorphic trypanosomes that upon infection initially express the same VSG coat ( clonal ) . One of the most detrimental consequences of trypanosomiasis is anemia , which has been described in experimental mouse models [16]–[18] and livestock [19] , [20] . In cattle , monitoring of the dramatic decrease in packed red cell volume ( PCV ) is the main tool for diagnosis of animal trypanosomiasis , only followed later by parasite detection in the circulation [21] . In human infections , in particular during the hematolymphatic stage of disease , blood and serum anomalies including anemia are also commonly present [22] . However , there is currently a lack of data to explain the occurrence of trypanosomiasis-associated anemia . Some studies have suggested infection-induced anti-VSG antibodies are involved in an erythrolytic process [23] , whereas other studies have suggested that trypanosomes release toxic components which directly lyse red blood cells ( RBC ) [24] . However , we have recently shown that the severity of anemia did not correlate with the actual parasite load [18] . Considering ( i ) the limited knowledge of the role of individual antibody isotypes in trypanosomiasis control , and ( ii ) the unclear role of B-cells in the in vivo induction of trypanosomiasis-associated anemia , we used B-cell ( µMT ) and IgM-deficient ( IgM−/− ) mice to address these points . Our results showed that although B-cell- and IgM-deficient mice infected with the clonal T . brucei AnTat 1 . 1E parasites exhibited a reduced life span and impaired parasitaemia clearance , infection-induced IgMs played only a limited role in host survival during non-clonal infections . In addition , the presence or absence of B-cells or infection-induced IgM antibodies did not change the rate of trypanosome surface antigenic variation nor did it affect the occurrence of infection-associated anemia . Therefore , our results show that in mice the overall importance of the infection-induced IgM response is limited .
Eight to 12 week old female BALB/c mice , BALB/c µMT [25] mice and IgM-deficient BALB/c ( IgM−/− ) mice [26] , as well as C57BL/6 and C57BL/6 µMT mice were obtained from an SPF breeding facility at the University of Cape Town . All mice were housed in filter-top cages and maintained in SPF barrier facilities in individual ventilated cages at the University of Cape Town , or at the Institute for Tropical Medicine Antwerp . Tsetse flies ( Glossina morsitans morsitans ) were available from the insectaria at the Prins Leopold Institute of Tropical Medicine Antwerp ( ITMA ) , originating from puparia collected in Kariba ( Zimbabwe ) and Handeni ( Tanzania ) . Flies were fed on rabbits and maintained at 26°C and at a relative humidity of 65% . Animal ethics approval for the tsetse fly feeding on live animals was obtained from the Animal Ethical Committee of the Institute of Tropical Medicine , Antwerp ( Belgium ) . The T . brucei brucei AnTat ( Antwerp Trypanosoom antigen type ) 1 . 1E , the T . brucei brucei AnTat 1 . 1 and the non-cloned reference T . brucei TSW196 parasite stocks were kindly provided by Dr . N . Van Meirvenne and E . Magnus ( Lab . Serology , Institute of Tropical Medicine ( ITG ) , Antwerp , Belgium ) and the MITat 1 . 2 and 1 . 4 parasite stocks by Dr . M . A . J . Ferguson ( Dept . of Biochemistry , The University of Dundee , Scotland ) . The T . b . brucei AnTat 1 . 1 clonal , monomorphic parasite strain was originally generated as a daughter clone of the AnTat 1 . 1E clonal , pleomorphic parasite strain . This was done by 25 cycles of syringe passing on day 4 of infection . Due to the short interval between passages , the AnTat 1 . 1 parasite is highly virulent and expresses only one type of VSG throughout infection , which is homologous to the initial VSG expressed by the AnTat 1 . 1E clone . Similarly , the T . brucei brucei MITat ( Molteno Institute Trypanozoon antigen type ) 1 . 2 and 1 . 4 clonal , monomorphic parasites are highly virulent and express only one VSG that is non-homologous to the initial VSG expressed by the AnTat 1 . 1E clone . The TSW196 T . b . brucei non-clonal , pleomorphic parasite stock was isolated in 1978 in Côte d'Ivoire , from infected pig and was transferred to an OF1 mice in order to make stabilates for storage at −80°C . It is catalogued at the ITG as ITMAS300500A . The AnTaR 1 ( Antwerp Trypanozoon antigen repertoire ) non-clonal , pleomorphic parasite strain was isolated in 1966 in Uganda from the bloodstream of infected bushbuck . Pleomorphic parasite populations are defined as consisting of both ‘long slender’ and ‘short stumpy’ differentiation forms , whereas monomorphic parasite populations consist of only ‘long slender’ forms . The terms clonal and non-clonal indicates that the parasite population used to infect mice initially express identical or multiple , different VSGs respectively . Infections were initiated by intraperitoneal ( i . p . ) injection of 5×103 parasites , unless stated otherwise . Parasites and red blood cells in a 2 . 5 µl blood sample 1/200 diluted in PBS , were counted every 2 to 4 days , using a light microscope . For super-infection experiments , mice were initially infected with 5000 T . brucei AnTat 1 . 1E parasites by i . p . injection . On day 6 or day 10 post primary infection , mice were super-infected with 5000 homologous highly virulent monomorphic AnTat 1 . 1 parasites or equally virulent monomorphic MITat 1 . 4 parasites . These monomorphic parasites were capable of expressing only one type of VSG during infection which were either homologous ( AnTat 1 . 1 ) or non-homologous ( MITat 1 . 4 ) to the VSG initially expressed during primary infection . For tsetse fly challenge experiments , freshly emerged tsetse flies were infected by feeding on AnTaR 1 infected mice at the peak of parasitemia . In order to obtain a pleomorphic trypanosome population at high titer , these mice were immune suppressed with cyclophosphamide ( 20mg/kg ) . 28 days after the infecting blood meal , flies were screened for a mature salivary gland infection by induced probing on pre-warmed glass slides followed by a microscopic analysis for the presence of metacyclic trypanosomes in the saliva . Infection of tsetse flies with T . brucei parasites was performed in compliance with the regulations for biosafety and under approval from the Environmental administration of the Flemish government . To initiate a natural infection , one individual tsetse fly with a mature salivary gland infection was allowed to feed per mouse . To avoid interrupted tsetse feeding , mice were anesthetized prior to the tsetse exposure . Parasite burden of all mice was monitored every day for 1 week following super-infection and survival of all mice was recorded . The experiment was performed in WT BALB/c mice as well is in BALB/c µMT , and IgM−/− mice . 10 mice per group were used . Infection-induced anti-VSG titers were determined by ELISA as described before [27] . Briefly , anti-VSG mouse antibody isotypes were detected using biotin coupled goat-anti-mouse Ig isotype antibodies ( Pharmingen ) in combination with streptavidin alkaline-phosphatase ( AP ) for development . Optical density was determined at 450 nm using a VERSAmax ELISA reader ( Molecular Division ) . Washed DE52 purified trypanosomes were lysed in TriReagent ( Molecular Research Centre ) and total RNA extracted according to the manufacturer's instructions . Contaminating genomic DNA was digested using molecular grade RNase-free DNAse I ( Promega ) . Reverse transcriptase and cDNA quantification by real-time PCR on the Lightcycler ( Roche Diagnostics ) was performed as recently described [28] using the DNA Master Hybridization Probe Kit ( Roche Diagnostics ) according to the manufacturers instructions . Amplification primers used were: VSG AnTat 1 . 1E ( GenBank accession X01843 ) : ( forward 5′-GAA TGC GAC ACG GAA AGC G-3′ , reverse 5′-CGT CGT TGG CTG CTT GGA G; 399 base pair product ) , VSG MITat 1 . 2 ( forward 5′-ATG GAC ACC AGC GGA ACA AAC-3′ , reverse 5′-TCC AGG CGT CGA TCC ACG-3′; 259 base pair product ) , gene tubulin zeta ( GenBank accession AF241275 ) ( forward 5′-TCC CGT CCA TTT CAG GTC C-3′ , reverse 5′-GTG CAT CAG CAT ACC ATC CAG T-3′; 294 base pair product ) . Specific hybridization probes for the three genes were respectively VSG AnTat 1 . 1E flourescein labeled 5′-CTG CT TGC TTG TAG GTG CTG CCG-3′ , LC640 5′-CGT TAC AGT TGC CAG TTT AGC TGC GA-3′ , VSG MITat 1 . 2 ( GenBank accession X56762 ) flourescein labeled 5′-TAG CGA ACA GCC AAA CAG CCG TCA C-3′ , LC705 5′- GTC CAG GCG CTC GAT GCA TTA CAG-3′ , and ( Tubulin zeta ) flourescein labeled 5′-TGC CTG TAC CAC CAG CTA AAC TGT GT-3′ , LC640 5′-TAC CAA AAT TGC CTC AAA CTC CTC CG-3′ ) . A standard curve for AnTat 1 . 1E , MITat 1 . 2 and tubulin zeta , was established by 10-fold dilutions of a positive sample and included as a standard and a “calibrator control” . Target mRNA levels in IgM+/+ , µMT and IgM−/− derived trypanosomes were determined by comparing the sample threshold cycle number against the target gene standard curve using the Second Derivative Maximum function of the Lightcycler software ( Roche Diagnostics ) . The same dilutions of the samples were used for the AnTat 1 . 1E , MITat 1 . 2 and tubulin zeta RT-PCR . AnTat 1 . 1E VSG levels for each sample ware normalized by dividing the calculated AnTat 1 . 1E value by the calculated tubulin zeta value . The MITat 1 . 2 RT-PCR served as negative control on AnTat 1 . 1E derived mRNA . Fluorescent data for each sample was detected at the annealing step at 60°C and hybridization probe specificity cross-checked . All graphic result presentations were prepared using GraphPad Prism software . The same software was used for statistical analysis of data . Comparative analysis of survival data was done using a designated GraphPad Prism statistical module using a Logrank test .
To address the role of B lymphocytes during experimental trypanosomiasis , comparative infection studies were performed in B-cell-deficient ( µMT ) and WT C57BL/6 mice , using the pleomorphic T . b . brucei AnTat 1 . 1E clone . This parasite clone has been used in the past as a well established model for experimental trypanosomiasis in mice , and produces a relatively chronic infection that lasts usually over a month , before killing the host . The pleomorphic annotation refers to the fact that blood parasite populations comprise both proliferating ‘long slender’ parasites and ‘short stumpy’ non-proliferating parasites , which are awaiting transmission to the tsetse fly vector . Figure 1A shows that in C57BL/6 mice , the absence of B-cells had neither an influence on the initial growth rate of the trypanosomes , nor on the height of the parasitemia at 6 days post infection , corresponding to the first peak in WT mice . Subsequently , post-peak parasite removal and prolongation of the mean survival of the infected mice were clearly B-cell dependent ( Fig . 1A , B ) . Important to note is that even in the absence of any anti-trypanosome antibody response , mice controlled a slowly but gradually increasing level of infection for up to 4 weeks . The eventual death of all µMT and WT mice was associated with exponential parasite proliferation growth , resulting in parasitemia levels up to 2×109 parasites/ml . High parasitemia before death was largely independent of the genetic background of the mice , since comparative experimental infections in WT and µMT deficient mice on either a BALB/c or C57BL/6 background yielded similar results ( Fig . 1 C , D ) . As parasite VSGs rapidly change , primary immunity provided by T-cell independent IgMs , is believed to confer a crucial impact on trypanosomiasis control [8] , [27] , [29] . In order to test this in vivo , IgM−/− and control WT mice were infected with clonal , pleomorphic T . b . brucei AnTat 1 . 1E parasites as described above . Interestingly , in the absence of IgM , mice were able to reduce peak parasitemia levels , albeit with delayed kinetics and reduced efficacy as compared to WT controls ( Fig . 2A ) . The reduced capacity to clear successive parasitemia waves resulted in a slight , but significant , accelerated mortality ( Fig . 2B ) . To evaluate whether changes in intrinsic parasite virulence affects the importance of antibodies in trypanosomiasis control , and to mimic natural conditions , tsetse fly infections were used . Surprisingly , the absence or presence of IgM's had no impact on the parasitemia development and survival of mice infected with AnTaR 1 parasites ( Fig . 2C and 2D ) , which are a non-clonal , pleomorphic T . b . brucei stock . In a second attempt to mimic a more natural infection , usually characterized in cattle by a non-clonal , low-level parasitemia , mice were infected with non-clonal , pleomorphic T . b . brucei TSW196 parasites derived from a field isolate . When infected with TSW196 parasites , a low virulent infection was obtained in mice . Here again , no significant differences in parasite burden or mortality were observed between WT and IgM−/− mice ( Fig . 2E and 2F ) , suggesting no significant role for IgM responses against the non-cloned T . b . brucei TSW196 parasite strain . Antibody binding of the parasite surface is considered to be a crucial step in protective host mechanism used to neutralize blood borne parasites . Here , the ability of mice to produce VSG-binding antibody responses was measured in the presence or absence of IgM . In WT mice , infection with clonal , pleomorphic AnTat 1 . 1E parasites induced rapid VSG-binding IgM antibody serum titers ( Fig . 3 ) with increasing concentrations towards peak parasitemia ( day 7 ) . VSG-binding IgG2a and IgG3 antibody titers were detected with a delay of 2 days . Other VSG binding antibody isotypes were hardly detectable ( data not shown ) . A similar kinetic for IgG2a and IgG3 was seen in infected IgM−/− mice . However , in these mice IgM titers were replaced by compensatory VSG-binding soluble IgD titers . Previous studies have suggested that during pleomorphic infections , parasitemia peak clearance involves mainly macrophage-mediated removal of non-dividing ‘short stumpy’ parasites that predominate at this time point [13] , [30] , [31] . The antibody results presented above may suggest that infection-induced IgMs do contribute to peak parasitemia clearance of clonal parasites . In order to evaluate the functional activity of antibodies in control of actively proliferating ‘long slender’ parasites , an infection model was established using slow killing pleomorphic and fast killing monomorphic ‘long slender’ T . brucei clones , expressing either homologous or non-homologous VSGs . First WT , µMT and IgM−/− mice were infected with 5000 AnTat 1 . 1E parasites , resulting in relatively slow mortality kinetics ( Fig . 4A , combining results of Fig . 1D and 2B ) . In parallel , the same 3 mouse strains were infected with 5000 highly virulent monomorphic AnTat 1 . 1 parasites ( expressing only a VSG that was homologous to the initial VSG of the AnTat 1 . 1E clone ) or with 5000 non-homologous VSG monomorphic MITat 1 . 4 parasites . Figure 4B shows that both monomorphic parasite clones kill their WT host within 5 days . Identical results were obtained in µMT and IgM−/− mice ( data not shown ) , suggesting that through their accelerated exponential growth , these parasites have reached a level of virulence that is lethal before an in vivo efficient B-cell response can be initiated . Next , a combination infection model using pleomorphic parasites for a primary infection , and monomorphic parasites for a re-challenge infection was established . WT mice were infected with pleomorphic AnTat 1 . 1E parasites ( Fig . 4C ) and on day 6 after primary infection , when the parasite load was greater than 108 parasites/ml ( data not shown ) , they were re-challenged with 5000 monomorphic AnTat 1 . 1 or MITat 1 . 4 parasites . These monomorphic parasites expressed VSGs that were either homologous ( AnTat 1 . 1 ) or non-homologous ( MITat 1 . 4 ) to the initial VSG expressed during primary infection . This resulted in significant earlier mortality of re-challenged mice , as compared to primary infected mice , irrespective of the homology of the VSG being expressed by the parasite during re-challenge ( Fig . 4C ) ( p<0 . 0001 ) . This earlier mortality was triggered by uncontrolled growth of ‘long slender’ forms of the monomorphic clones ( Fig . 4C ) , as mice died with high parasitemia exceeding 109 parasites/ml blood after temporary remission of their primary pleomorphic infection ( parasitemia data not shown ) . However , when re-challenged at day 10 with monomorphic AnTat 1 . 1 parasites , expressing homologous VSGs , rapid host killing was not observed demonstrating VSG-specific resistance ( Fig . 4D ) . Indeed re-challenge at day 10 with non-homologous VSG expressing parasites ( MITat 1 . 4 ) resulted in significantly accelerated mortality ( M . S . = 18 days ) as compared to control WT and AnTat 1 . 1 infected mice ( M . S . = 34 days for both groups ) ( Fig . 4D ) . However , the observed host killing occurred with slower kinetics than observed in Fig . 4B . VSG-specific resistance at day 10 was absent in µMT mice ( Fig . 4E ) which displayed a mean survival of 15 days . This was not unexpected since both monomorphic parasites killed WT mice within 4–5 days ( Fig . 4B ) . VSG-specific resistance in IgM−/− mice re-challenged at the same time point with the same parasite was significantly impaired as compared to WT mice ( p<0 . 0001 ) ( Fig . 4D ) . However , IgM−/− mice infected with AnTat 1 . 1 survived 1 week longer ( Fig . 4F ) than the 15 days observed in µMT mice ( Fig . 4E ) . This earlier mortality of IgM−/− mice as compared to WT mice , showed that IgM's play only a minor role in protection against homologous re-challenge infection . In contrast T-cell deficient mice , which would be expected to produce T-cell independent anti-VSG IgM antibodies , were fully protected against homologous challenge ( Fig . 4G and 4H ) . Antigenic variation is a characteristic feature of trypanosomes allowing escape from protective host immune responses . It is suggested to result in characteristic waves of parasitemia through interplay with anti-VSG-specific antibodies [32] ) . Interestingly , the presence of VSG switching during in vitro cultivation of trypanosomes under selective drug pressure , demonstrated that antigenic variation can occur in the absence of antibody mediated events [33] . However , this is not conclusive for in vivo conditions in mammals . Due to B-cell mediated selection pressure , one might expect differences in the overall VSG-specific RNA species . Quantification of VSG AnTat 1 . 1E specific RNA by Real Time RT-PCR from AnTat 1 . 1E infected B-cell deficient ( µMT ) or wild-type mice showed similar VSG AnTat 1 . 1E specific RNA concentrations during the first parasitemia peak ( Fig . 5A , B ) . Using irrelevant VSG MITat 1 . 2 specific primers , no amplification signal was obtained under identical real time RT-PCR conditions , demonstrating the VSG specificity of the assay ( data not shown ) . VSG AnTat 1 . 1E specific RNA was undetectable in parasites isolated from the second peak of parasitemia of WT mice , due to efficient elimination of the VSG AnTat 1 . 1E parasites . Interestingly in µMT mice , VSG AnTat 1 . 1E specific RNA from second peak parasites was strikingly reduced despite exponential parasite growth ( Fig . 5B ) , which suggests that the main evasion mechanism is an intrinsic genetic program . The finding of 3 . 5 % remaining VSG AnTat 1 . 1E specific RNA in µMT-derived parasites compared to the sensitivity level of >1% , may reflect a slight reduction in efficiency of clonal parasite elimination in the absence of antibody-mediated selection pressure . The possibility that a reduction of VSG AnTat 1 . 1E specific RNA was due to a proliferation/growth arrest of parasites could be ruled out as the total amount of VSG-specific RNA species using quantification by Uni-primers ( able to amplify all VSG RNA species ) was similar during the first and second peak , shown in Fig . 5C . This was confirmed by microscopy analysis of infected blood of µMT mice ( day 14–22 ) , that contained around 50% dividing ‘long slender’ parasites and 20% non-dividing ‘short stumpy’ parasites ( Fig . 5D ) . Infection-induced anemia is an important morbidity factor in African trypanosomiasis and has been linked to B-cell responses , due to lysis of red blood cells after opsonization with VSG-specific antibodies [23] . However , trypanosomiasis-induced anemia was as severe in µMT mice as in WT mice ( Fig . 6 ) . This was independent of the mouse genetic background , as similar patterns of anemia development were recorded in C57BL/6 ( Fig . 6A ) and BALB/c mice ( Fig . 6B ) . Furthermore , trypanosomiasis associated anemia also occurred in the absence of a functional IgM response ( Fig . 6B ) .
African Trypanosomiasis is a textbook example of an extracellular parasitic infection where antigenic variation of the variant-specific surface glycoprotein ( VSG ) and subsequent antibody responses of the host result in a prolonged period of parasitemia control [1] , [3] , [4] , [32] . For the parasite , this lengthened time is required to increase the chance of successful parasite transmission through the tsetse vector . While resistance to trypanosomiasis in some mammals such as the Cape Buffalo is linked to their capacity to mount an efficient anti-parasite antibody response , trypano-tolerance in cattle can also develop independently of their genetic haematopoietic background [34] . These observations suggest that besides antibodies , additional host factors can contribute to parasite control . During the chronic phase of trypanosomiasis , the continuous interplay between the parasite and its host's immune system results in infection-associated pathology , including anemia [14]–[20] , [31]–[35] . Despite the importance of anemia , the exact mechanisms underlying its induction remained unsolved [35] . Some studies , showing that in vitro VSG-sensitized RBC can be lysed by VSG-specific antibodies , have suggested that anemia may be linked to B-cell responses and that antibody mediated lysis may be a contributing factor in vivo [23] . Other studies have suggested that trypanosomes themselves release components which directly lyse RBC [24] . Based on in vitro observations , it was proposed that destruction of RBC's could occur as a result of an active GPI-VSG transfer between trypanosome and erythrocyte membranes , followed by the anti-VSG mediated complement-dependent lysis [23] . However no in vivo data was obtained to support this hypothesis . Moreover , we have previously not been able to find any significant correlation between the severity of anemia and parasite load [18] . Using gene deficient mouse models , we have now dissected the role of B-cells and IgM-responses in parasite elimination , host survival , antigenic variation and in Trypanosoma-induced anemia . We have clearly shown that infected µMT mice still developed anemia , suggesting that B-cells are not involved in the induction of trypanosomiasis-associated anemia . This B-cell independent trypanosomiasis-induced anemia may be mediated by TNF [18] , [36] , [37] , although the exact mode of action remains to be elucidated . TNF could have a crucial impact on anemia through its capacity to modulate the activation , growth and the phagocytic potential of macrophages involved in antibody independent erythrophagocytosis [38]–[40] , a process which is dependent on IFNγ [41] . Infections with clonal , pleomorphic T . brucei parasites ( AnTat 1 . 1E ) , demonstrated an essential role for B-cells in host protection and corroborated previous B-cell depletion studies [9] , [10] . However , IgM was found to play only a limited role in primary infection with clonal , pleomorphic parasites , perhaps due to the presence of VSG-specific IgG2a and IgG3 and compensatory IgD antibodies or other Ig isotypes . Interestingly , our results in BALB/c µMT and C57BL/6 µMT mice showed that the genetic background of the host has an impact on parasite growth and differentiation , independent of the B-cell compartment . Indeed , in C57BL/6 µMT mice the first parasitemia peak was consistently lower than the BALB/c µMT , albeit in both strains the peak is reached at the same time point . Also , since T-cell deficient mice were found to be fully protected against re-challenge with a homologous parasite , the antibody mediated VSG-specific protection observed on day 10 was T-cell independent . However , the significance of the extended survival of the T-cell deficient mice is beyond the scope of this paper . Re-challenge studies with the homologous monomorphic AnTat 1 . 1 strain demonstrated that while VSG-specific IgM's were induced early during infection , they were not able to efficiently protect against monomorphic parasites during the first peak of infection . This was evidenced by the impaired protection in wild-type mice when re-infected with the homologous VSG-expressing AnTat 1 . 1 strain early during infection at day 6 ( see Fig . 4C ) . In contrast , VSG-specific IgM responses were able to fully protect WT mice against re-infection at day 10 after primary infection . However , efficient elimination of actively proliferating monomorphic parasites occurred only when the parasites used for re-challenge expressed a VSG that was homologous to those expressed during primary infection . These observations support the notion that antibodies can only exert their full anti-trypanosome activity in an immunological environment that develops later than day 6 . At this early time point , activated macrophages play a crucial role in parasite destruction via the secretion of inflammatory molecules such as nitric oxide ( NO ) and TNF , and through phagocytosis of damaged and opsonized parasites . Although IgM−/− mice survived a few days longer when re-infected on day 10 with the homologous VSG-expressing AnTat 1 . 1 parasite , as compared to re-infection using non-homologous VSG-expressing MITat 1 . 4 parasites ( Fig . 4F ) , this prolonged survival in IgM−/− mice ( MS = 12 days ) was much less pronounced than the prolonged survival obtained in WT mice , where re-challenge on day 10 did not result in accelerated mortality at all ( Fig . 4D ) . This demonstrated that VSG-specific IgD and/or IgG responses were not able to efficiently compensate for IgM immunity , irrespectively of the homology of the VSG being expressed by the parasite during re-challenge . Because MITat 1 . 4 re-challenged IgM−/− mice ( Fig . 4F ) survived longer than MITat 1 . 4 primary infected IgM−/− mice ( Fig . 4B ) , we concluded that a primary host immune responses from day 10 , had a negative effect on parasite growth rate , which seems to be VSG-independent . A similar observation has been reported in natural infections where relative resistance or susceptibility to trypanosomiasis in cattle could not always be correlated to the host's capacity of mounting an efficient anti-VSG response . Instead trypano-tolerance was found to be associated with elevated Type I cytokine production ( IFNγ & TNF ) , increased macrophage activation and NO production [34] , [42] . Similarly , during initial exponential parasite growth phase , SCID mice displayed a lymphocyte-independent parasite growth regulating mechanism which gradually triggered the differentiation of proliferating ‘long slender’ parasites into non-dividing ‘short stumpy’ forms causing the infection to reach a plateau [13] , [43] . This mechanism of B-cell independent parasite control may therefore involve an intrinsic regulation of parasite growth/differentiation by a parasite released short-stumpy inducing factor [44] . In contrast to the results from infection using clonal , pleomorphic parasites , infections with a non-clonal , pleomorphic field isolate ( TSW196 ) showed that IgM−/− mice were able to remit parasitic waves as efficiently as wild-type control mice . Infection-induced IgM's may therefore be compensated for , perhaps by IgG's or other Ig isotypes , during host protective responses . Similarly , no prominent role for IgM was observed in non-clonal tsetse fly initiated infections . However , in this case it should be noted that the AnTaR 1 T . brucei parasite used for tsetse fly transmission caused a highly virulent infection in all experimental groups . Moreover , we can not exclude the possibility that WT mice do not make sufficient IgM in response to tsetse fly initiated infections and that IgM's are important for wave remission . These results may also suggest that either tsetse fly saliva components , or other factors involving intrinsic characteristics of tsetse fly transmitted parasites , actively prevent the induction of efficient B-cell responses in mice as a defence mechanism against the host immune system . Although , previous in vitro experiments indicated that trypanosomes in culture may undergo VSG switching in the absence of antibody-mediated pressure [33] , this has never been demonstrated in vivo . We have shown that antigenic variation occurring in vivo is independent of antibody induction or selective pressure . Although clonally infected µMT mice had overall impaired parasite clearance and shortened survival , the virtual absence of VSG AnTat1 . 1E specific RNA in µMT mice 10 days after first peak of infection , demonstrated that parasites switched their VSG independently of antibody selective pressure . Moreover , the remaining 3 . 5% VSG AnTat1 . 1E specific RNA in µMT mice and 3% VSG AnTat1 . 1E specific RNA in IgM-deficient mice , indicated that antibodies are mostly needed for complete elimination of the remaining non-switched parasites . Interesting to note is the apparently unaltered VSG switching rate between parasites growing in WT and µMT mice . This observation suggests that antibody independent host or parasite factors drive the actual switching process . While the production of a VSG-specific stumpy-inducing factor by the parasite itself appears unlikely , it is feasible that inflammatory mediators such as NO , oxygen radicals or inflammatory cytokines such as TNF and IFNg , produced by activated macrophages or T-cells could impact on the activation of antigenic variation . Future repetitions of the experiments presented here in BALB/c nu/nu and/or RAG−/− mice could potentially shed more light on the mechanisms at work here . In summary , the genetic approach of our study using applicable gene deficient mice has allowed us to discriminate the in vivo roles of B-cells as well as IgM-responses in parasite growth control , host survival and pathology associated with trypanosomiasis . Our report shows that B-cells are not involved in the induction of trypanosomiasis-associated anemia . Moreover , although infections with the clonal , pleomorphic T . brucei AnTat 1 . 1E parasites in µMT and IgM−/− mice indicated a limited role for infection-induced anti-VSG antibodies in parasitemia control and host survival . Moreover , non-clonal , low-virulent infections originating from field populations , as well as tsetse fly exposure experiments , indicated that IgM responses have no decisive role on either disease progression or host survival . Finally , this study demonstrated that in vivo parasite VSG switching operates as an intrinsically programmed genetic process that is independent of B-cell or antibody pressure , with the function of antibodies mainly limited to the elimination of the remaining non-switched parasites .
|
African trypanosomiasis is a disease caused by different species of extracellular flagellated protozoan trypanosome parasites . Trypanosomes have developed a mechanism of regular antigenic variation of their variant-specific surface glycoprotein ( VSG ) coat which allows chronic infection . Replacement of this coat occurs at rapid regular time intervals , allowing the parasite to escape from an effective host antibody responses . So far , primary T-cell independent antibody responses have been described to constitute the main host defense mechanism , relying largely on IgM antibody induction . Using genetically engineered B lymphocyte- or IgM-deficient mouse strains , we show that lack of B-cells or IgM did not prevent infection-associated anemia . More importantly , we show that in the absence of IgM , parasitemia was controlled almost as well as in wild-type mice , with only slightly increased mortality . In addition , we show in vivo that antigenic variation is not affected by the lack of IgM .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/protozoal",
"infections",
"immunology/immunity",
"to",
"infections"
] |
2008
|
The Role of B-cells and IgM Antibodies in Parasitemia, Anemia, and VSG Switching in Trypanosoma brucei–Infected Mice
|
Single-molecule tweezers measurements of double-stranded nucleic acids ( dsDNA and dsRNA ) provide unprecedented opportunities to dissect how these fundamental molecules respond to forces and torques analogous to those applied by topoisomerases , viral capsids , and other biological partners . However , tweezers data are still most commonly interpreted post facto in the framework of simple analytical models . Testing falsifiable predictions of state-of-the-art nucleic acid models would be more illuminating but has not been performed . Here we describe a blind challenge in which numerical predictions of nucleic acid mechanical properties were compared to experimental data obtained recently for dsRNA under applied force and torque . The predictions were enabled by the HelixMC package , first presented in this paper . HelixMC advances crystallography-derived base-pair level models ( BPLMs ) to simulate kilobase-length dsDNAs and dsRNAs under external forces and torques , including their global linking numbers . These calculations recovered the experimental bending persistence length of dsRNA within the error of the simulations and accurately predicted that dsRNA's “spring-like” conformation would give a two-fold decrease of stretch modulus relative to dsDNA . Further blind predictions of helix torsional properties , however , exposed inaccuracies in current BPLM theory , including three-fold discrepancies in torsional persistence length at the high force limit and the incorrect sign of dsRNA link-extension ( twist-stretch ) coupling . Beyond these experiments , HelixMC predicted that ‘nucleosome-excluding’ poly ( A ) /poly ( T ) is at least two-fold stiffer than random-sequence dsDNA in bending , stretching , and torsional behaviors; Z-DNA to be at least three-fold stiffer than random-sequence dsDNA , with a near-zero link-extension coupling; and non-negligible effects from base pair step correlations . We propose that experimentally testing these predictions should be powerful next steps for understanding the flexibility of dsDNA and dsRNA in sequence contexts and under mechanical stresses relevant to their biology .
Nucleic acids play central roles in biological processes including transcription , translation , catalysis and regulation of gene expression [1] , [2] . Double-stranded RNA and DNA ( dsRNA and dsDNA ) stretch and twist when interacting with proteins [3] , [4] and when forming compact structures such as nucleosomes [5] and packaged viruses [6] , [7] . Understanding such deformations is critical for a fundamental understanding of nucleic acids in their biological contexts and for efforts to rationally engineer nanostructures built from dsRNA and dsDNA helices . High precision experimental data are becoming increasingly available from measurements using optical and magnetic tweezers [8]–[20] that measure end-to-end lengths and linking numbers of kilobase-length single molecules upon variation of solution condition , sequence , applied force and torque . In principle , these data offer rigorous challenges that can falsify or validate – and thereby advance – models of nucleic acid flexibility . However , such direct comparison of model predictions and experimental observables remains incomplete . On one hand , fits to analytical equations based on worm-like chain ( WLC ) or elastic rod models are in common use for interpreting single-molecule manipulation data [14] , [21]–[24] , but they lack the power of predicting new experimental results and involve numerous approximations ( see below ) . On the other hand , high-resolution approaches that integrate all-atom energy functions and crystallographic knowledge [25]–[32] offer the prospect of predictive calculations , but the computational costs to simulate kilobase-scale helices remain prohibitively large . Coarse-grained models , such as the base-pair level models ( BPLMs ) pioneered by Olson and colleagues [33] as well as models that use reduced representations for each base ( rather than base-pair ) [34]–[36] , provide mesoscopic “bridges” between simple analytical models and atomic-level simulations . In this work , we focus on BPLMs as they have fewer degrees of freedom than single-base level models , enabling efficient calculations , and their parameterization can be more easily refined by the growing data of crystallographic structures [33] , [37]–[47] . It is worth noting that BPLM is only expected to be applicable to duplexes at low-to-medium tension . Structural transitions involving breaking of base-pairs or formation of non-canonical base-pair interactions , typical at very high tension , are better modeled with single-base level models [48]–[52] . Despite continuing advances , BPLM simulation methods have not yet been used to make direct comparisons with single-molecule experiments . BPLM simulations have focused on helices up to hundreds of base-pairs , significantly smaller than the kilobase lengths probed in single-molecule experiments at which helix bending and twisting may play significant roles in the measured properties . In addition , BPLM calculations have been primarily developed for B-DNA duplexes; growing crystallographic knowledge for dsRNA helices has not been integrated into the BPLM framework . Finally , accurate methods for computing and constraining the twist , writhe , and link of discrete , open-ended helices have not been established until recently [53]–[56] and have not been integrated into BPLM modeling . Here , we describe a blind prediction challenge , where developers of modeling algorithms ( FCC , RD ) predicted unreleased data on the mechanical properties of dsDNA and dsRNA helices measured by a team of experimenters ( Lipfert et al . , unpublished data ) . More specifically , the torsional properties and stretch modulus of dsRNA have not been previously reported ( only the bending persistence length of dsRNA was measured previously [13]; the stretch modulus of dsRNA was published during the modeling [20] ) . This challenge motivated the development of a software package HelixMC , first presented in this work , to close the methodological gaps described above and thus enable simulations of force vs . extension , effective torsional persistence vs . force , link vs . force , and extension vs . link experiments . The goal of calculating actual experimental observables necessitated several systematic studies to check widespread but poorly tested modeling assumptions , including simulation-based validations of the Moroz-Nelson formula for torsional persistence length [21] , [22] . Most importantly , the rigorous comparison between blind predictions and data revealed how current BPLMs largely succeed in modeling stretching and bending but apparently miss physics necessary for understanding dsDNA and dsRNA torsional properties . Finally , HelixMC predictions for previously unmeasured properties of two biological important variants , poly ( A ) /poly ( T ) dsDNA and Z-DNA , delineate future experiments that will allow incisive evaluation and revision of current modeling approaches .
Before presenting the results of the blind prediction , we present an overview of the simulation system and algorithm . Detailed descriptions are given in the Methods section . BPLMs [33] , [37]–[46] abstract the entire duplex into multiple base-pairs stacking on top of each other . The coordinate transformation between two neighbor base-pairs ( i . e . a base-pair step ) is conventionally described with six standard step parameters ( shift , slide , rise , tilt , roll , and twist ) . The internal interactions between neighbor base-pairs can therefore be described using the distribution of these parameters drawn from the Protein Data Bank ( PDB ) in six-dimensional ( 6D ) space . Typically , these 6D distributions are approximated with 6D multivariate Gaussians to allow continuous sampling of the conformation space . We also tested an alternative scheme which samples directly from existing parameters in the database , without assuming Gaussianity . The duplexes , represented in BPLM , are then simulated with a Metropolis Monte Carlo ( MC ) method , with stretching forces and torsional constraints incorporated into the energy function . By default we simulated dsDNA/dsRNA of 3 , 000 base-pairs at room temperature ( 298K ) . At the end of each cycle of Monte Carlo updates , the helix extension and the linking number are recorded . For direct comparison to single molecular tweezers analysis , these data from simulations at different forces and torsional constraints are then used to compute global mechanical properties including bending persistence length , stretch modulus , torsional persistence length and link-extension coupling , by fitting to analytical equations based on the elastic rod model . Single-molecule tweezers experiments allow accurate measurements of the extension and the linking number of long molecules under externally applied stretching forces and torques . Typical experiments include force vs . extension , effective torsional persistence vs . force , link vs . force , and extension vs . link measurements . The published literature on dsDNA mechanical measurements is extensive ( see e . g . [10] , [11] , [18] , [19] ) , but magnetic tweezers data directly probing the torsional properties of dsRNA had not been published at the time of this study ( only the bending of dsRNA has been previously studied [13] ) . Instead , a comprehensive experimental portrait ( Lipfert et al . , unpublished data ) had been acquired by one of us with colleagues but was not publicly released . This situation therefore permitted blind prediction tests of the BPLM approach . Our modeling challenges were to simulate the different experimental setups , to test the applicability of phenomenological formulae used for curve-fitting , and to make quantitative predictions with estimated errors for the following standard constants: bending persistence length A , stretch modulus S , torsional persistence length C , and link-extension coupling g . Drawing on extensive prior work [33] , [54] , [56] , we were able to simulate dsDNA ( for validation of the algorithm ) and dsRNA ( for blind prediction ) under applied force using HelixMC . Fig . 1 gives example simulation frames with random sequences , with BPLMs parameterized on crystallographic data with diffraction resolutions better than 2 . 8 Å and without proteins . ( Other BPLM variants are described below . ) For both dsDNA and dsRNA , higher stretching force leads to longer end-to-end extensions and smaller fluctuations orthogonal to the stretching direction , qualitatively consistent with theoretical predictions and experimental observations . Measurements of the mean end-to-end extension as a function of force give quantitative data for how nucleic acid helices bend , and we first tested if HelixMC recovered the bending persistence length seen in experiments for dsDNA . The simulated data fit well to standard models used in interpreting tweezers experiments , including the extensible worm-like chain ( WLC ) model proposed by Bouchiat et al . [23] ( Fig . 2A; A = 54 . 7±0 . 6 nm ) , the inextensible WLC model [23] ( A = 53±1 nm ) , and an alternative extensible WLC fitting model developed by Odijk [57] ( A = 55±1 . 0 nm ) ; see Table S1 and Fig . S1 . The agreement of all three fits to each other and to more direct estimates of A by averaging the base-pair step transforming matrix [33] ( A = 53 . 0±1 . 0 nm ) confirmed the robustness of A as a comparison metric between experimental and simulated data . To bracket systematic error , we further performed simulations using BPLMs with a high-resolution subset of crystallographic data ( 2 . 0 Å vs . 2 . 8 Å diffraction resolution cutoff ) , without using a Gaussian approximation for the BPLM distributions , and symmetrizing the base-pair step parameters; these variations gave less than 10% changes in A ( Table 1 and S2 ) . We did however find that inclusion of protein/DNA crystallographic structures , which include more distorted helical conformations , led to reduction of A by 30% to 39 nm . Given this level of systematic error , the agreement of the HelixMC calculation and the experimental value for dsDNA ( A in the range of 44–49 nm at near-physiological salt concentrations [16] , [20] , [58] , [59] ) was reasonable . The agreement for dsDNA suggested that the prediction of the dsRNA bending would be similarly accurate . The HelixMC prediction for dsRNA was 66 nm , greater than the value for dsDNA , with a systematic error of ∼30% , again based on an alternative BPLM parameterization including protein/RNA crystallographic models ( Table 2 and Fig . 2B ) . Experimental dsRNA tweezers measurements gave values of A = 57±2 nm ( Lipfert et al . , unpublished data ) and 59±3 nm [20] , greater than the value for dsDNA and in quantitative agreement with the HelixMC value . In addition to enabling fits of the bending persistence length A , force/extension curves give estimates of the stretch modulus S , particularly at high force where the helix is pulled straight without bends . For dsDNA simulations with several variations , the HelixMC calculations gave estimates of S = 2000 pN . As with the bending behavior , inclusion of protein/DNA structures produced lower stretch modulus values , corresponding to more flexibility ( S = 1500 pN; Table 2 ) . These calculations overestimated the experimentally measured value for dsDNA of S in the range of 900–1400 pN [20] , [60] , [61] , slightly beyond our estimated error . The HelixMC prediction for the stretch modulus of dsRNA was S = 980 pN , with a systematic error of 25% . This estimate was also supported by using an alternative model to fit the simulation stretch modulus ( Table S3 and Fig . S1 ) . Given the dsDNA results above , we expected this HelixMC value to overshoot the experimental measurement . Nevertheless , beyond this error in absolute values , we strongly expected that dsRNA would give a relative stretch modulus significantly lower than dsDNA . Unlike the nearly straight axis curve of dsDNA , the base-pair centers of dsRNA trace a ‘spring-like’ axis curve , twirling in circles of radius 8 Å . We developed a novel “springiness” hypothesis , that this “spring-like” property of dsRNA would render it more pliable to stretching , analogous to a spring's lower stretch modulus compared to a straight wire ( Fig . 3 ) . Indeed , the experimental measurements for the dsRNA stretch modulus was 350±100 pN ( Lipfert et al . , unpublished data ) , more than two-fold less than for dsDNA , in agreement with our prediction . An independent experimental dsRNA measurement released at the time of modeling gave a similar value lower than dsDNA ( 500–683 pN ) [20] . Additional simulation-based tests of the ‘springiness’ hypothesis are described in Supplementary Results and Table S4 , S5 . The development of magnetic tweezers with increasingly sophisticated geometries has enabled torsion-sensitive measurements of dsDNA [16] , [62]–[64] and , most recently , measurements on dsRNA that were included in our blind challenge . Before describing the blind comparison , we present HelixMC simulations that were necessary to shed light on puzzling prior results on dsDNA torsional stiffness . Measurements based on topoisomer distributions of closed dsDNA circles , fluorescence polarization anisotropy of intercalated dyes , and x-ray scattering of tethered gold nanoparticles give lower values for torsional persistence length ( C = 25–80 nm [47] , [65]–[68] ) than measurements from optical and magnetic tweezers experiments ( C = 100–120 nm [12] , [16] , [17] , [21] , [59] ) from several different laboratories and with different tweezers geometries . One potential resolution to these discrepancies is that the apparent torsional stiffness of dsDNA is enhanced beyond its intrinsic value due to tethering constraints that attenuate torsional fluctuations in single-molecule experiments [44] . However , testing this hypothesis has been complicated by a prior inability to integrate link ( number of helix turns ) in base-pair-level simulations . Additional concerns have stemmed from the poor quality of fits to infer C from single molecule experiments with the analytical Moroz-Nelson formula [21] , [22] , which assumes the Fuller writhe expression and negligible self-avoidance effects . To address these problems , we reasoned that the direct simulations enabled by HelixMC would reveal any systematic overestimation of intrinsic torsional persistence length due to tethering constraints or to the inaccuracy of the Moroz-Nelson model . First , we simulated link fluctuations in dsDNA helices as a function of force , analogous to experiments in references [12] , [17] , and computed the effective torsional persistence length Ceff by dividing the contour length of the polymer by the variance of the link ( Table 2 and Fig . 4A–B ) . We first observed that the asymptotic value of Ceff ( 29–40 nm ) in our simulation was within error of the ‘intrinsic’ value computed from a normal mode analysis ( 37 . 5 nm [43] ) , suggesting that C is not overestimated due to the tethering setup in single molecule experiments . We also tested the effects of x-y constraints ( perpendicular to the direction of pulling ) that might dampen torsional fluctuations , although such constraints are negligible in magnetic tweezers setups ( and would also be expected to have a suppressive effect on bending fluctuations ) . Applying a harmonic x-y restoring force with strength of 0 . 025 pN/nm gave no significant change in Ceff ( Fig . S2 ) , disfavoring tether constraints as an explanation for the high C anomaly . Second , to test the use of the Moroz-Nelson formula , we fit these simulation data to the Moroz-Nelson model , and found excellent agreement with the same C values as described above . The rarity of self-clashing conformations ( Supplementary Results and Table S6 ) and validity of the Fuller writhe formula above 0 . 4 pN further supported the use of this analytical fit . As a final crosscheck , we also computed the torsional persistence length using the slope of torque vs . number of turns in independent link-constrained simulations at 7 pN , analogous to an alternative experimental approach [16] , [59] , [62] ( Fig . 4C–D , Supplementary Methods ) . This second simulation method gave torsional persistence length values that agreed well with the first method ( within 1% , Table S7 ) , confirming the robustness of the simulation method and Moroz-Nelson fits for inferring C in a way that matches experimental procedures . Given the checks above , the discrepancy between the simulated dsDNA torsional persistence length C = 28 . 8 nm and the value in single molecule experiments C = 109 nm cannot be easily explained by systematic errors in the modeling . Furthermore , the deviation of experimental measurements from the Moroz-Nelson formula [16] , [17] does not appear to be due to inaccuracies in this phenomenological model , given the successful fits of the model to simulated data . The discrepancies in C value and fitting curve strongly indicate either missing physics in modeling dsDNA in both the BPLM and simpler elastic-rod frameworks or currently unknown systematic errors in the experiment ( see below , Discussion ) . Given these issues , we expected that our blind prediction for the torsional persistence length of RNA ( C = 53 nm ) might be an underestimate of the value measured from magnetic tweezers experiment . Indeed the experimental value was two-fold higher , with C = 100 nm . However , as with the dsDNA measurements , the Moroz-Nelson formula fit these experimental measurements relatively poorly ( Lipfert et al . unpublished data ) , suggesting that some basic assumption of the BPLM approach is violated ( see Discussion below ) . The first measurements of helix mean end-to-end distance versus mean linking number for dsDNA highlighted gaps in theories of DNA elasticity [14] , [15] . We thus expected that our final blind challenge , to predict analogous experiments for dsRNA , would provide a highly stringent test for HelixMC and the BPLM approach . Before presenting the blind comparison , we describe simulation-based tests of assumptions made in the experimental inference of the link-extension coupling g ( also described as twist-stretch coupling ) . In previous work , the coupling has been estimated from two different kinds of experiments: ( 1 ) stretching the polymer at different forces and observing how the linking number changes in the process [14] , [69] , and ( 2 ) setting up a constant stretching force and observing the polymer's extension as increasing numbers of turns are introduced [14] , [15] . In both cases , bending fluctuations at low force ( <15 pN ) should , in principle , cause deviations from the linear relationships assumed to fit the experimental data ( Supplementary Results , Fig . S3 , S4 ) . Nevertheless , linear relationships have been empirically observed for link and force ( in experiment type 1 ) and of link and extension ( in experiment type 2 , but not in experiment type 1 ) for experiments on dsDNA . Furthermore , linear fits from these independent types of experiments gave consistent results ( g = −90±20 pN·nm and −70±20 pN·nm , respectively ) ; due to the convention in use , the negative sign corresponds to over-winding of the double helix upon extension ( Table 2 and Fig . 5 ) . This empirical relation was indeed confirmed in our simulations . We discovered linear correspondences between dsDNA link and extension in both types of simulated experiments , despite non-linear relationships of the underlying variables . The simulated dsDNA data gave couplings of g = −130 pN·nm and −150 pN·nm , respectively , for the two types of experiments , with systematic errors of ±30 pN·nm , based on alternative BPLM parameterizations ( Table 2 ) . The dsDNA calculations were therefore in agreement with experimental values within the estimated errors . For dsRNA , the HelixMC-predicted g value was −120 pN·nm ( from simulations of both types of experiments ) , with errors of ±40 pN·nm based on alternative BPLM parameterizations ( Table 2 and Fig . 5 ) . This predicted dsRNA value is the same , within error , as the dsDNA simulations . Nevertheless , separation of the link into twist and writhe components in the simulation suggested a different physical picture of link-extension coupling to dsRNA than for dsDNA . The simulated writhe vs . force slope is negative for dsRNA but nearly zero for dsDNA . This effect can be again attributed to the “springiness” of dsRNA axis curve , which carries an intrinsic writhe . Stretching dsRNA unwinds this writhe , while stretching dsDNA has little impact on its already straight axis curve . This behavior would result in a positive link-extension coupling g value , opposite in sign to dsDNA . However in the HelixMC dsRNA simulations , the helix twist , the other component of link , rises with extension and overpowers the writhe decrease to produce a net negative link-extension slope , matching the sign of dsDNA simulations . The dsRNA tweezers experiments gave a value of g = +47±14 pN·nm , different from the value given by blind prediction ( −120 pN·nm ) . This discrepancy is well beyond the error associated with different BPLM parameterizations , providing strong evidence against the current BPLM framework for modeling the torsional flexibility of dsRNA . Since the link-extension slope for RNA is a result of cancellation between a positive twist-extension correlation and a negative writhe-extension correlation , the predicted slope is quite sensitive to changes of many of the parameters of the underlying Gaussian potential ( Supplementary Results , Table S8 , S9 ) . Indeed , by modification of the parameters , we were able to recapitulate the experimentally measured link-extension coupling , as discussed extensively in the experimental paper associated with this work ( Lipfert et al . , unpublished data ) . However we note here that this reparameterization is not unique , because the number of parameters ( 15 , for a 6D covariance matrix ) is far greater than the number of experimental measurements ( four , i . e . bending persistence , stretch modulus , torsional persistence and link-extension coupling ) . To understand the sequence-dependence of the mechanical properties being studied , and to propose future tests of the BPLM approach , we performed additional simulations of poly ( A ) /poly ( T ) and poly ( G ) /poly ( C ) for both DNA and RNA ( which has U instead of T ) . Stretches of these homopolymer sequences play critical roles in accessibility of chromatin to RNA polymerase and transcription factors [70] , [71] . We also performed simulations on Z-form DNA , which has been hypothesized to occur during DNA transcription to absorb torsional stress [72] . The results are listed in Table 2 . For sequence-dependent simulations , we found that for poly ( A ) /poly ( T ) DNA , using the default dataset , all the measured mechanical properties increased by 1 . 5- to 3- fold compared to the random-sequence simulations . However if we used BPLM parameters from the 2 . 8_all dataset , which includes protein-binding DNA structures , the poly ( A ) /poly ( T ) results were not significantly different from the random-sequence results . The difference of predicted stiffness can be explained by the different underlying base-pair step parameters ( Supplementary Results , Table S10 ) . We also found smaller but measurable differences between other sequence-specified and random-sequence simulations , and between sequence-specified simulations performed with different base-pair step parameter sets . Further experimental comparisons between sequence-specific and random-sequence DNA/RNA will provide stringent tests of these predictions and to help discriminate which dataset ( if any ) is more accurate in modeling the sequence-dependence of the mechanical properties . Simulations of Z-DNA gave dramatically higher bending and torsional persistence lengths ( 175 nm and 125 nm , respectively ) compared to random B-DNA ( 55 nm and 29 nm , respectively ) . Again , this higher stiffness is encoded in the underlying step parameters ( Supplementary Results , Table S10 ) . Furthermore , the link-extension coupling is estimated to be near zero; this value arises from a complicated cancellation of twist and writhe , and is difficult to explain with simple arguments . Our simulation results agree with data obtained by Thomas and Bloomfield [73] indicating Z-DNA to be much stiffer than B-DNA , with a bending persistence length of 200 nm . However , previous studies on Z-DNA using light scattering , electron microscopy and fluorescence anisotropy have led to inconsistent results , with bending persistence length ranging from 21 to 200 nm and an extremely low torsional persistence length of 7 nm [73]–[75] . These studies did not agree on whether Z-DNA is stiffer then B-DNA . Additional single-molecule tweezers experiments on Z-DNA appear necessary to resolve these issues , and would provide stringent tests of the BPLM approach .
We have presented a set of fundamental tests of how well base-pair level models predict the flexibility of double-stranded nucleic acids , motivated by a desire for improved rigor in this field and by recent single-molecule measurements of dsRNA helices that were blinded to the modelers . A new software package HelixMC that integrates rigorous treatment of twist , writhe , and link allowed direct simulations of dsDNA and dsRNA tweezers experiments with base-pair level models . By fitting the simulated observables with the same analytical models used in experimental measurements , we were able to make direct comparisons of simulation and theory for properties including the bending persistence length , stretch modulus , torsional persistence length and link-extension coupling . We obtained predictions that match some experimental observations , particularly in the ratios of dsRNA to dsDNA values for mechanical properties like bending persistence length . However , we observed quantitative discrepancies for torsional persistence length at high force and the incorrect sign of the link-extension coupling constant for dsRNA . An extensive set of simulations checked that assumptions such as the effects of tethering , the Moroz-Nelson model of torsional persistence length , the curation of the database used to parameterize the BPLM , and the fitted relation of force and link could not account for these discrepancies . The discrepancies between the BPLM model and tweezers measurements could be due to at least five reasons . First , electrostatic repulsion may account for some discrepancies , but it is difficult to see how corrections needed to increase the torsional stiffness of simulations by three-fold would not also substantially increase the simulated bending stiffness beyond the current values , which agree well with experiments . Experiments with different ionic conditions ( particularly highly screening conditions ) would help bound these effects . A second possibility is that the base-pair step distributions observed in crystallized nucleic acids do not reflect the fluctuations of nucleic acids in solution [47] . In this case , however , neither a simple overall scaling nor the parsimonious adjustment of a few parameters suffices to bring simulated data into agreement with experiments . Large changes in multiple BPLM parameters are required , in different directions for dsDNA vs . dsRNA and beyond the systematic deviations seen in different curated crystallographic databases , especially to account for a sign change in dsRNA link-extension coupling while retaining the experimental value for dsDNA link-extension coupling ( Supplementary Results and Table S8 , S9 ) . A third explanation might involve thermal fluctuations involving bulges or non-Watson-Crick pairs , as have been resolved recently albeit with rare population [76]; the population of these alternative structures could be potentially enhanced during torsional stress . Due to the energetic cost of such fluctuations , we would predict that they would lead to a strong temperature dependence of torsional properties . Fourth , the conformation of each base-pair step may affect neighboring base-pair steps . Recent Au-SAXS scattering experiments and crystallographic analyses have suggested the importance of such correlations [47] , [77] . Preliminary tests with multi-base-pair fragments in HelixMC indicate that such correlations may have up to 2-fold effects on predicted tweezers-measured properties ( Supplementary Results and Fig . S5 , S6 ) . A final explanation for the discrepancy involves the applied tension in single molecule tweezers experiments . On one hand , the tweezers data at low force ( <5 pN ) are used to infer the bending persistence length A and low-force effective torsional persistence lengths Ceff . These parameters are sensitive to both bending as well as intrinsic torsional persistence length via fluctuations captured by the Moroz-Nelson model . In this low force regime , BPLM gives predictions for both parameters with less-than-two-fold discrepancies , for both dsDNA and dsRNA . On the other hand , forces higher than 4 pN are required to suppress bending fluctuations and thereby to isolate stretch modulus S , intrinsic torsion persistence length C , and link-extension coupling g . For these values , the BPLM predictions do not agree with dsDNA or dsRNA measurements . Indeed , there is a more fundamental discrepancy: while the Moroz-Nelson model accounts for the predicted torsional persistence length vs . force from BPLM calculations over a wide range of model parameters , the experimental measurements of Ceff at forces >2 pN cannot be fit by this analytical model . These high-force discrepancies could be rationalized by a model in which tensions greater than 1 pN favor structural states that are more pliant to stretching but torsionally stiffer than the ensemble of conformations seen in crystallized dsRNA and dsDNA . Nucleic acids in solution under constant tension or strong torque , as might be provided by solution-based tweezers [78] or circularization , may enable bulk experimental methods like NMR or Au-SAXS to test this model . It is also possible that single-molecule tweezers experiments on alternative polymers such as poly ( A ) /poly ( T ) or Z-form DNA ( simulated above ) will agree well at all forces with BPLM predictions and thereby offer a baseline for comparison to the mixed sequence dsDNA and dsRNA cases . Alternatively if atomic-level computational methods could predict the structure of the putative weakly stretched state and design sequences or atomic modifications that favor it , the HelixMC toolkit should be able to integrate predictions for long helices that can then be precisely tested through future tweezers experiments .
The BPLM framework has been described in detail in previous studies [33] . Briefly , each base pair in the nucleic acid is represented by a vector representing the base-pair center and by a coordinate frame representing the orientation of the base-pair [42] . The degrees of freedom of the system are the base-pair steps , defined by the transformation of coordinates from one base-pair to the next base-pair . Each step is described by six parameters ( shift , slide , rise , tilt , roll and twist ) [79] . The transformation of the step parameters to Cartesian coordinates follows the Calladine and El Hassan Scheme ( the CEHS definition ) [80] , which is also the convention used in the 3DNA package [81] , [82] . The ‘technical details’ section of the 3DNA manual offers comprehensive examples of this scheme . In HelixMC , the origin and the frame of the first base-pair is placed at the origin of the global coordinate system . That is , the base-pair center is placed at the coordinate origin; the normal vector of the base-pair is aligned with the z-axis; and the long-axis of the base-pair lies on y-axis . In terms of experimental setup , this placement is analogous to fixing one end of the nucleic acid to a surface ( i . e . the xy-plane in our simulation ) , an approach routinely employed in magnetic and optical tweezers studies . Once the origin and the frame of the first base-pair are set , the coordinates of the entire helix can be computed from the six base-pair step parameters . In HelixMC , the conformation of helix is stored and updated in this space of the step parameters , instead of in the Cartesian space . This is similar to describing protein conformations with the internal torsion angles instead of using the Cartesian coordinates of the atoms . For each base-pair step , we assumed the six step parameters form a multivariate normal distribution , of which the parameters were derived by surveying the existing RNA crystal structures ( see below ) . This assumption is equivalent to assuming that positions and orientations of adjacent base-pairs are constrained by a six-dimensional harmonic potential [33] . In this work , the BPLM system was simulated using the Monte Carlo ( MC ) algorithm . A typical MC run consists of tens of thousands of cycles . A sample , which includes the current extension and linking number of the helix , was extracted at the end of each cycle ( i . e . number of cycles equals to number of samples in the simulation ) . For each cycle , the base-pair steps of the entire helix was updated sequentially starting from the first base-pair step . For each update , a proposed move was generated by modifying only the conformation of the target base-pair step , while keeping the conformation of the rest of the helix intact . Note that the term “conformation” here refers to the six step parameters of each base-pair step , not the Cartesian coordinates of the base-pairs . Because we assumed the step parameters follow a multivariate normal distribution , this proposed conformational move can be efficiently achieved by drawing a random sample from the distribution . The standard Metropolis criterion [83] was then used to whether to accept the proposed MC move: ( 1 ) Here ΔE equals the energy after the proposed move minus the energy of the initial conformation , T is the temperature and kB is the Boltzmann constant . Because the internal interactions between the base-pair steps are included in the multivariate Gaussian sampling , the ΔE in Eq . ( 1 ) only reflects the applied torque and force , as described next . For cases where external forces and torques are absent ( free helix ) , the ΔE is always zero and the acceptance rate is 100% . For cases with external forces and torques , since each update is applied to one base-pair step only , the new proposed conformation is usually similar to the previous conformation . Therefore the acceptance rates are reasonable in the force and torque range used in this work ( 8% ( 40 pN ) to 55% ( 1 pN ) for dsDNA , Table S11 ) . We performed two types of simulations . In the first type of simulation , a stretching force along the z-direction was applied to the free end of the nucleic acid ( the other end was fixed to the origin ) , and no torsional constraint was applied to the system . The energy of the system due to the applied force was ( 2 ) Here F is the applied stretching force , and z is the helix extension . This simulation was equivalent to the measurement of force-extension curves in typical single-molecule magnetic tweezers or constant-force optical tweezers experiments [8] , [13] , [62] , [84]–[86] . In the second type of simulation , the nucleic acid was subjected to a fixed stretching force and was required to maintain a link ( which is equivalent to the bead rotation ) close to a target value through a harmonic potential . The energy of the system was: ( 3 ) Here krot is the stiffness of the torsional trap ( 200 pN·nm by default ) , Lk is the helix link , and Lkt is the target link of the trap . This type of simulation corresponded to torsion-trapped tweezers experiments [14]–[17] . In both types of simulations , we computed the base-pair center and the coordinate frame of the terminal base-pair as well as the overall link of the helix after each full-helix MC update . The number of base pairs in the simulated double helices was set to 3 , 000 ( 3 kbp ) in this work unless stated otherwise . At the beginning of the simulation , we initialized the helix by assuming that all base-pair steps have step parameters equal to their average values in the input parameter database . We then performed by default 120 cycles of full-helix MC updates to relax the helix under the specified stretching force ( but no link-constraint ) . For link-constrained simulations , we performed further relaxation steps analogous to the torsional trap experiments , which involve slowly rotating magnets of the torsional traps to bring the helix from zero-turn state to a highly twisted state . We first turned on the link constraint , but set initial target link equal to the current link of the helix . Then we performed the following cycles: After this “trap-ramping” step , we further relaxed the helix under the specified force and link constraint for 50 cycles . These relaxation steps ensured that the state of the helix at the beginning of the simulation was random and representative of the specified force and link constraint , without memory of the initial conformation . In the HelixMC package , all the parameters discussed above , including the number of base-pairs and the applied external forces and link constraint , can be modified by user inputs . The details of the setup of the HelixMC calculations reported in this work are given in Supplementary Methods . We set the number of samples collected during our simulations to ensure that the standard errors of the average extensions and links were below 0 . 2% ( Table S12 ) . Computing torsional properties and modeling torque in HelixMC required the integration of mathematical formulae developed in a number of separate papers by different authors . To document our final approach , we describe these equations and their connections here in some detail . The observed bead rotation in a single-molecule tweezers experiment is mathematically described by the link ( also known as the linking number ) . The original definition for the link of circular dsDNA is based on a closed continuous ribbon model [87]–[91] . A ribbon is defined by two mathematical objects: an axis curve , which is a smooth non-self-intersecting closed curve following the axis of the polymer; and a set of ribbon vectors , which are unit normal vectors everywhere along the axis curve that are perpendicular to the axis curve and pointing to reference points on the polymer [91] . To compute the link , we followed previous work by Britton et al . [56] to convert the BPLM to a ribbon model ( Fig . 6A ) . Here we defined the axis curve to be the line connecting the base-pair centers ( black vectors , also known as the base-pair centerline ) , and the ribbon vectors to be the long-axis of the base-pair ( red vectors ) . This discretization scheme leads to a polygonal axis curve where multiple straight lines are joined by sharp bends ( at the base-pair centers ) , and the ribbon vectors are defined only at each bend . While this discretization is simple and easy to manipulate numerically , it leads to two problems that forbid direct applications of the formulations for the closed continuous ribbon model to the BPLM . First , the discretization leads to an axis curve with discontinuous first derivatives at each bend . Therefore the tangent vectors at these bends are ill-defined , and the corresponding ribbon vector is in general not perpendicular to both the axis curve segments connected to the bend . This behavior invalidates the original assumption that the axis curve is smooth and the ribbon vectors are always perpendicular to the axis curve . Second , the BPLM we studied here is for open duplexes , different from the closed curve assumption in the conventional treatment . By the Călugăreanu theorem ( also known as the White's formula , or the Călugăreanu-White-Fuller theorem ) , link equals the sum of writhe and twist [87]–[90] . Intuitively , writhe represents the degree of coiling of the ribbon axis curve , and twist represents the amount of internal twist stored in the ribbon due to the local rotations of ribbon vectors . The sum of coiling and internal twist gives the overall bead rotation of the ribbon . In the following sections , we discuss separately how to compute the writhe and twist for such an open , polygonal ribbon . Before discussing the writhe calculations for the BPLM , we first review the original definition of writhe , which described the coiling of the axis curve . The writhe of a smooth closed ribbon can be computed using the Gauss linking integral: ( 4 ) Here r1 and r2 are the Cartesian coordinates of the axis curve , r12 = r1−r2 is a vector connecting points r1 and r2 , and we compute writhe ( and , below , link and twist ) in units of radians . Note that writhe only depends on the axis curve of the ribbon . Fuller proposed a simplified version of this integral [91]: ( 5 ) Here ez is a unit vector aligned with z-axis , and t is the tangent vector of the axis curve . The Fuller writhe simplifies the original double integral into a single integral but is only correct modulo 4π . ( 6 ) Here the expression “a≡b ( mod n ) ” means ( 7 ) Mathematically speaking , a and b are said to be congruent modulo n . The calculation of writhe of BPLM in this work is based on previous studies on polygonal open curves [53]–[55] . In the section below , we will derive the formulas for computing writhe in BPLM , mainly following the approach developed by Rossetto and Maggs [54] . The twist for a smooth ribbon can be computed as ( 21 ) Here t is the tangent vector of the axis curve , and l is the normalized ribbon vector . Unlike writhe , twist is a local identity , well defined on a curve segment of arbitrary length . Therefore twist is well defined for a smooth open curve . In addition , twist is additive . For our polygonal ribbon , the overall twist of the ribbon equals the sum of the twists of all the line segments . As an example , consider a straight line segment parallel to z-axis of length L ( Fig . 6C ) . The ribbon vector starts as l0 , varies smoothly and ends as l1 . Using the fact that the tangent vector t = ez and the ribbon vectors are perpendicular to t , Eq . ( 21 ) can be evaluated as ( 22 ) Here we used the property that l×dl is parallel to ez . Geometrically , this integral is twice the area on unit circle swept by l throughout the integration . Therefore the twist of a straight line segment is just the angle ( in radians ) between the vectors l0 and l1 . This result is consistent with the conventional definition of twist parameter in a base-pair step . However , applying the above result for straight line segments to our polygonal ribbon is nontrivial , because here the ribbon vectors are not necessarily perpendicular to the straight line segments . A naïve strategy would be to simply sum the twist parameters of all base-pair steps in the helix to obtain the overall twist , but this sum turns out to be inconsistent with the ribbon twist considered in the Călugăreanu theorem . It thus cannot be added with writhe to produce a link that corresponds to the actual experimental observable of , e . g . , bead rotation in a magnetic tweezers experiment . As pointed out by Britton and colleagues [56] , the ribbon twist of dsDNA ( ‘twist’ discussed below refers to the ribbon twist , unless stated otherwise ) is different from the conventional definition of twist parameter for a base-pair step , necessitating a new procedure to calculate twist for base-pair steps . The main challenge in computing twist for the discrete chains of the nucleic acid helix is that the ribbon vector at each base pair , li , is not in general , normal to the continuous axis curve traced by base pair centers ri , as is assumed in the mathematical treatment of ribbons . Our strategy therefore is to first define at each base pair a ‘reference’ ribbon vector bi that obeys this mathematical convention , and to compute a reference twist . We will then compute additional twist contributions by li using its angle with bi . Fig . 6A illustrates the polygonal ribbon model . The choice of , where ti−1 and ti are unit vectors pointing into and out of ri , guarantees normality of bi to the axis curve . Then we can compute the reference twist based on the above result for straight line segments: ( 23 ) Here N is the total number base-pairs in the model , Tw1 and TwN−1 is the twist contribution of the first and the last base-pair steps ( N−1 base-pair steps in total ) , where b's are not defined . βi is the signed angle between the reference ribbon vector bi and bi+1 . Note that because both bi and bi+1 are orthogonal to ti , βi is also the dihedral angle bi - ti - bi+1 ( Fig . 6A , inset ) . The use of alternative reference ribbon vectors to compute the twist can be justified with the following thought experiment . Imagine holding the two ends of a continuous ribbon , and then change the ribbon vectors by rotating the ribbon in the middle . As long as the two ends stay fixed , such changes of ribbon vectors do not affect the overall number of turns of the ribbon ( i . e . the link ) . In addition , the writhe stays constant because it only depends on the axis curve , which is unmodified in this process . By the Călugăreanu theorem , we can conclude that the twist , which equals the link minus writhe , remains unchanged . Therefore in a continuous ribbon we may modify any ribbon vector except the two ends without affecting the overall twist . However for a discretized ribbon ( as in our BPLM ) , such modifications of ribbon vectors may change the twist by 2nπ , where n is an integer ( Fig . 6D ) . In general , we have the following modulo congruence relation between the true twist and reference twist ( see Eq . ( 7 ) for definition of modulo congruence ) : ( 24 ) To address the modulo 2π ambiguity , we must take into account whether the original ribbon vectors li sweep out additional turns around the axis curve relative to the reference ribbon vectors bi . Here we calculate the local twist of each base-pair step as: ( 25 ) Here αi is a signed angle between li and bi; Ti is folded into the range [−π , π ) upon the modulo 2π operation . For the terminal base-pair steps , we first attach virtual segments to both ends , pointing towards −z and +z respectively , to obtain the corresponding bi , then Eq . ( 25 ) can be employed to compute T1 and TN−1 ( illustrated in Fig . 6D ) . The overall twist can then be calculated by summing all the Ti: ( 26 ) As an additional consistency check , Eq . ( 26 ) satisfies Eq . ( 24 ) , as shown below . ( 27 ) The factors α1 and αN correspond to the twist contribution from two ends of the helix; all internal factors cancel . Our twist definition is similar to the definition proposed by Britton and colleagues [56] . The main difference is in the definition of α angle . The previous proposal defined the tangent vector at base-pair i as , then projected the original ribbon vector li to the plane defined by , to obtain the new ribbon vector di . Then α is defined as the angle between di and bi: ( 28 ) While this definition is mathematically correct and gives results equivalent to our definition , the expression can become ill-defined when there is a sharp bend in the ribbon . Fig . 6E illustrates such cases . In the first example , the original ribbon vector l1 is parallel to the tangent vector , therefore the projection gives a null vector d1 , making α ill-defined . In the second example , we take l1′ as the ribbon vector , then the projection gives d1′ parallel to b1 , leading to a zero α even though l1′ and b1 are quite distinct from each other . In both cases , our definition just sets α equals to the angle between l1 and b1 and the angle between l1′ and b1 , leading to a well-defined result . While this type of sharp bend does not occur in natural dsDNA , in our system the line segment of the last base-pair step and the added virtual segment can form such sharp bends , making the previous definition unsuitable for HelixMC . The definition in Eq . ( 25 ) has two additional convenient properties . First , if the axis curve of the dsDNA/dsRNA is perfectly straight and pointing towards +z , and the base-pairs are all parallel to the xy-plane , the calculated ribbon twist equals to the sum of the base-step twist parameters [56] . Second , in our system setup , if the normal vector of the last base-pair aligns along +z , the computed link corresponds exactly to the bead rotation observed in single molecule tweezers experiments . The multivariate Gaussian distributions for sampling are constructed using the base-pair step parameters from crystallographic models in the PDB . To ensure the quality of the data in the default parameter sets , we used models derived from data with resolutions better or equal to 2 . 8 Å . Protein-binding DNA/RNA structures were excluded from the dataset since protein binding may affect the deformability of the nucleic acids . We also tested several other selection schemes to estimate the systematic error , including using higher resolution cutoff ( 2 . 0 Å ) or including protein-binding structures . We then used the 3DNA software [81] , [82] to extract the base-pair step parameters for the canonical Watson-Crick base-pairs ( i . e . not including G-U wobble base-pairs and other non-canonical base-pairs ) . Parameter sets with twist ≤5° ( due to Z-DNA conformations ) , with rise ≥5 . 5 Å ( due to ligand intercalation ) , or with any value more than four standard deviations away from the mean were discarded as outliers . For the dsDNA datasets , we noticed that there were two major clusters in the data , corresponding to the A-form and B-form dsDNA ( except for the ‘DNA_2 . 8_all’ dataset , where the protein binding rendered A-DNA and B-DNA inseparable by clustering ) . We used the k-means algorithm to separate the two clusters and only used the B-DNA parameters in sampling ( Table 1 , Supplementary Methods and Fig . S7 ) . For Z-DNA , the dataset is composed of two distinct base-pair step distribution for the GC steps and the CG steps . The population distribution for the Z-DNA dataset is shown in Fig . S8 . Detailed statistics and population distribution of the curated dataset are shown in Table 1 , S13 and Fig . 7 . Fig . S9 shows the correlation plots between each base-pair step parameter for the default dataset . For each type of base-pair step ( 16 in total , e . g . 5′-AT-3′/5′-AT-3′ , 5′-CA-3′/5′-TG-3′ , etc . ) , a multivariate Gaussian was fitted based on the corresponding six base-pair step parameters , enabling sequence-dependent simulations . We note here that one can also categorize the base-pair parameters into 10 independent sequence-specific categories using the symmetry of the base-pair steps [40] , [80] . This symmetrization is not the default option in HelixMC; symmetrization gives minor changes in the predicted mechanical properties ( Supplementary Methods and Table S2 ) . For simulations with random sequence , in each update we randomly picked a distribution from the 16 types of step parameters and drew samples from it . In this sampling scheme , we effectively averaged the 16 types of parameters , so all base-pair steps follow the same parameter distribution [40] . The distribution can be further approximated with a single multivariate Gaussian ( Supplementary Results ) . The approximation leads to a reduced number of parameters in the model , and therefore facilitates the understanding the effect of each parameter on the observed mechanical properties . However , we note that this sampling scheme may lead to unrealistic base-pair step combinations ( for example , 5′-AT-3′/5′-AT-3′ followed by 5′-GC-3′/5′-GC-3′ ) , therefore the sequence of the RNA is not always well defined in each simulation snapshot . To justify that our sampling scheme indeed gave reasonable estimates of the mechanical properties of a random RNA , we also performed simulations with a single randomly generated RNA sequence ( Table S14 ) . The obtained mechanical properties using a single random sequence agreed within simulation error to our default random sequence simulation . In addition , we observed that some of the population distributions in our dataset did not appear Gaussian ( Fig . 7 ) . To test the validity of the Gaussian approximation , we also tested a different sampling scheme , by randomly picking parameter sets existing in the database without assuming Gaussianity , and obtained nearly undistinguishable results ( see Results ) . All the curated parameter sets and sampling schemes used in this work are available and further documented in the HelixMC package . The bottleneck steps of HelixMC have been optimized in C ( using Cython ) ; a typical single-point HelixMC calculation for a DNA/RNA helix of experimental length ( few kilo-base-pairs ) takes minutes to hours on a standard desktop computer ( Table S15 ) . HelixMC is coded in Python in an object-oriented fashion that allows easy modification and extension , is free and open-source ( http://github . com/fcchou/helixmc ) , and enables fast and accurate predictions with available computational power . We numerically tested the validity of the link calculations above by comparing simulated link values to the bead rotations ( Fig . 8A ) . Here the “bead rotation” is defined as the angle between the y-axis of the global coordinate and the projection of the ribbon vector of the last base-pair to the xy-plane . This is equivalent to attaching a virtual bead along the ribbon vector of the last base-pair and observing its rotation , analogous to recent single-molecule tweezers experiments [17] . For better comparison , in Fig . 8A we folded the computed link into the range of [−π , π ) , and found that the experimentally observed bead-rotation indeed corresponds to the link . The match between the link and the bead rotation was close but not exact ( RMSD of 4 . 5° ) , because the normal vector of the last base-pair did not point exactly to +z during the simulation; this discrepancy induces negligible error in computed helix mechanical properties . As discussed above , the Fuller formula is faster but gives exact writhes only in certain conditions . The formula breaks down if the helix path fluctuates so that segments point away from the applied force ( towards −z ) . Fig . 8B shows a plot of exact writhe vs . Fuller writhe in a simulation of 3 kbp dsDNA at 0 . 1 pN stretching force . It is apparent that in this setting the Fuller formula is only correct modulo 4π ( two turns; spacing between parallel lines ) . To test under which conditions the Fuller formula was accurate , we computed its RMSD error to exact writhe across simulations . For force-extension simulations , Fuller writhe is effectively exact if the force is larger than 0 . 4 pN for dsDNA and 1 pN for dsRNA ( Fig . 8C ) . For link-constrained simulations , the Fuller formula holds in the current simulated link-range , but breaks down when the target link exceeds ±40 turns for DNA and ±20 turns for dsRNA ( corresponding to supercoiling densities of 0 . 022 and 0 . 012; Fig . 8D ) . As with experimental measurements , the simulated data were summarized through fits to the elastic rod model , which assume that the total energy of the helix without external force and torque can be expressed using the above parameters by an integral along the helix axis curve s: ( 29 ) Here L is the helix contour length , kB is the Boltzmann constant and T is the temperature . The constants are bending persistence length A , B = S/kBT the stretching stiffness ( where S is the stretch modulus ) , torsional persistence length C , and D = g/kBT is the unit-less link-extension coupling ( here we use the convention in ref . [14] , where g has units of pN·nm ) . The three quantities β , z and θ describe the deformations per unit length of a short rod segment . β is the bending deformation that measures how the tangent vector changes along the rod , z is the extensional deformation that measures the change in the length of the segment , and θ is the torsional deformation that determines how the each segment is rotated around the rod axis with respect to adjacent segment . The analytical equations used to fit experimental measurements , derived from this model , are compiled in the Supplementary Methods .
|
DNA and RNA are fundamental molecules in the central dogma of molecular biology . Many biological behaviors of double-stranded DNA and RNA – including transcription/translation by proteins and packaging into compact structures – depend on their ability to flex and twist . Single-molecule tweezers now provide accurate mechanical measurements of DNA and RNA helices under force and torque but have not been used to rigorously falsify and thereby advance computational models . Here we present the first such blind challenge , involving recent dsRNA tweezers data that were kept hidden from modelers and a new HelixMC toolkit that resolves challenges in simulating long double helices from base-pair level models . The predictions gave excellent agreement with bending and stretching measurements of dsRNA but failed to recover twisting properties , pinpointing a critical area of future investigation . HelixMC also predicted that poly ( A ) /poly ( T ) and Z-DNA–biologically important variants whose elastic responses have not been studied with tweezers–will have distinct mechanical properties . These results open a route to iteratively falsifying and refining computational models of long nucleic acid helices , as is necessary for attaining a predictive understanding of their biological behaviors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"biochemistry",
"rna",
"nucleic",
"acids",
"biology",
"and",
"life",
"sciences",
"dna",
"biophysics",
"biophysical",
"simulations"
] |
2014
|
Blind Predictions of DNA and RNA Tweezers Experiments with Force and Torque
|
α-defensins are abundant antimicrobial peptides with broad , potent antibacterial , antifungal , and antiviral activities in vitro . Although their contribution to host defense against bacteria in vivo has been demonstrated , comparable studies of their antiviral activity in vivo are lacking . Using a mouse model deficient in activated α-defensins in the small intestine , we show that Paneth cell α-defensins protect mice from oral infection by a pathogenic virus , mouse adenovirus 1 ( MAdV-1 ) . Survival differences between mouse genotypes are lost upon parenteral MAdV-1 infection , strongly implicating a role for intestinal defenses in attenuating pathogenesis . Although differences in α-defensin expression impact the composition of the ileal commensal bacterial population , depletion studies using broad-spectrum antibiotics revealed no effect of the microbiota on α-defensin-dependent viral pathogenesis . Moreover , despite the sensitivity of MAdV-1 infection to α-defensin neutralization in cell culture , we observed no barrier effect due to Paneth cell α-defensin activation on the kinetics and magnitude of MAdV-1 dissemination to the brain . Rather , a protective neutralizing antibody response was delayed in the absence of α-defensins . This effect was specific to oral viral infection , because antibody responses to parenteral or mucosal ovalbumin exposure were not affected by α-defensin deficiency . Thus , α-defensins play an important role as adjuvants in antiviral immunity in vivo that is distinct from their direct antiviral activity observed in cell culture .
In addition to a sophisticated adaptive immune system , mammals retain more primitive immune effectors , such as antimicrobial peptides , as components of the innate response to microbial infection . In humans , one of the most abundant classes of antimicrobial peptides is α-defensins [1 , 2] . α-defensins are subdivided into myeloid α-defensins [e . g . , human neutrophil peptides ( HNP ) 1–4] , expressed primarily in neutrophils and certain other immune cells , and enteric α-defensins [e . g . , human defensins ( HD ) 5 and 6] , expressed by specialized Paneth cells in the small intestinal epithelium and by epithelial cells in the genitourinary tract . α-defensins have potent antiviral and antibacterial activities in vitro and in cell culture against a wide range of organisms . Although the multifaceted contribution of α-defensins to shaping the composition of the ileal bacterial commensal microbiota and to defense against multiple enteric bacterial pathogens in vivo has been described , comparable studies of α-defensin antiviral activity in vivo are lacking [3] . Moreover , clinical correlations between defensin abundance and viral transmission or disease are not clear [2] . To address this gap in knowledge , we investigated mouse adenovirus type 1 ( MAdV-1 ) pathogenesis in mice lacking functional enteric α-defensin processing , the matrix metalloproteinase-7 knockout ( Mmp7-/- ) mouse , as a system in which to study a viral pathogen in its natural host . The Mmp7-/- model provides an elegant solution to overcome the complexity of creating a genetic α-defensin knockout mouse . Mice lack myeloid α-defensins , as all putative myeloid α-defensin genes in the genome have been converted to pseudogenes; however , there has been an expansion of the locus encoding enteric α-defensins , also known as cryptdins [4] . The Defa gene cluster spans ~ 0 . 8 Mbp and is interspersed with non-defensin genes [4 , 5] . Although there are many cryptdin isoforms , they all share the requirement for a common proteolytic processing enzyme to convert inactive pro-α-defensin forms to active , mature forms within Paneth cells [6 , 7] . This step is mediated by MMP7 . Accordingly , Mmp7-/- mice lack functional α-defensins in ileal Paneth cells , and they are α-defensin deficient in the ileal lumen [8] . Because Mmp7 is not expressed in the intact epithelium of any tissue in unchallenged mice other than Paneth cells and efferent ducts of the adult male reproductive tract [9–12] , the effect of the Mmp7 deletion is functionally Paneth cell-specific in the gut in the naïve mouse . Thus , Mmp7-/- mice provide a rational model for dissecting the role of α-defensins in enteric defense of bacterial [6 , 8 , 13] and viral pathogenesis . MAdV-1 pathogenesis has been studied in some detail [14] . Upon parenteral infection , the virus disseminates in the mouse , with particular tropism for macrophages and endothelial cells and high viral loads in the brain and spleen . Mice lacking B cells are much more sensitive to acute infection , establishing a protective role for neutralizing antibodies ( NAbs ) [15] . T cells contribute to immunopathology of acute infection but are instrumental in controlling and ultimately clearing infection [16] . MAdV-1 pathogenesis in adult mice is typified by encephalitis , as the virus is able to cross the blood-brain barrier and stimulate inflammation [17 , 18] . In this study , we compared oral MAdV-1 infection in wild type and Mmp7-/- mice and observed increased sensitivity of Mmp7-/- mice to viral disease . This is consistent with the ability of both human and mouse α-defensins to neutralize infection by MAdV-1 in cell culture . Nonetheless , the kinetics of viral dissemination out of Mmp7-/- and wild type mouse intestine were similar , inconsistent with a direct antiviral barrier due to the presence of α-defensins in wild type but not Mmp7-/- mouse intestine . Rather , we observed decreased viral loads in multiple organs of wild type mice compared to Mmp7-/- mice only at late times post-infection ( p . i . ) that were coincident with the elaboration of a robust NAb response in wild type but not Mmp7-/- mice . Thus , our data support an antiviral role for α-defensins through potentiating a NAb response . This mechanism is distinct from direct α-defensin antiviral activity at the site of initial infection .
To establish a model to assess the role of α-defensins in adenoviral pathogenesis , we first determined the sensitivity of MAdV-1 infection in cell culture to neutralization by mouse and human α-defensins . Since mice lack neutrophil α-defensins but express abundant enteric α-defensins [19] , we focused our studies on the human enteric α-defensin HD5 and three specific isoforms of mouse α-defensins , cryptdins 2 , 4 , and 23 ( Crp2 , Crp4 , and Crp23 ) . We used a replication-competent MAdV-1 construct expressing and encapsidating a fusion protein between eGFP and the minor capsid protein IX ( MAdV-1 . IXeGFP ) to facilitate quantification of viral infection . Upon incubation of purified MAdV-1 . IXeGFP with physiologic concentrations of purified α-defensins , we observed dose-dependent neutralization by HD5 , Crp2 , and Crp23 ( Fig 1 ) . Although the 50% inhibitory concentrations ( IC50s ) for all of these α-defensins were similar ( ~2 . 5 μM ) , HD5 was the most potent peptide and capable of nearly complete inhibition ( Fig 1A ) . Crp2 and Crp23 , which differ by a single amino acid , had almost identical activities and inhibited infection ~4-fold ( Fig 1C ) . In contrast , Crp4 had no detectable antiviral activity even at the highest concentrations tested ( Fig 1C ) . Although Crp4 is potently anti-bacterial in vitro , it has an unusual internal 3 amino acid deletion that may contribute to its inability to inhibit MAdV-1 [20] . In addition , we observed no antiviral activity for HD5Abu , which lacks a regular structure due to the absence of disulfide bonds ( Fig 1A ) . We then measured viral aggregation as a function of α-defensin concentration . We have previously shown that aggregation is directly correlated with the ability of α-defensins to bind to virus but is not sufficient for neutralization [21] . We found a dose-responsive increase in the mean particle size ( z-average diameter ) of MAdV-1 with increasing concentrations of HD5 and Crp2 , consistent with viral aggregation ( Fig 1B and 1D ) . This was not observed for Crp4 or HD5Abu . Thus , human and mouse α-defensins potently block MAdV-1 infection by binding to the virus in a manner dependent upon their disulfide-stabilized structures . All mouse α-defensin precursors ( pro-α-defensins ) are processed to mature forms within Paneth cells by MMP7 proteolysis . Accordingly , Mmp7-/- mice lack activated α-defensins , and their Paneth cells secrete pro-α-defensins [6 , 8 , 13] . Pro-α-defensins , exemplified by proHD5 , did not bind to MAdV-1 or block infection in vitro ( Fig 1A and 1B ) . To directly test the impact of α-defensin antiviral activity on viral pathogenesis in vivo , we infected wild type C57BL/6 mice and isogenic Mmp7-/- mice with MAdV-1 by oral gavage . Consistent with the sensitivity of MAdV-1 to inhibition by α-defensins in cell culture , Mmp7-/- mice succumbed in greater numbers to MAdV-1 infection ( Fig 2A and 2B ) . This phenotype was dose-dependent . Yellow discolorations of the small intestine were noted at necropsy for both wild type ( 5 of 6 ) and Mmp7-/- ( 20 of 24 ) mice euthanized due to illness ( Fig 2E ) . The affected areas were often discontinuous , although in some cases almost the entire small intestine was involved , and typified by diffuse villus blunting ( Fig 2G ) , while the caecum and colon appeared normal . Overall , the phenotypes of sick wild type and Mmp7-/- mice were similar , with noticeable bowel discoloration; however , the frequency of sick mice was substantially higher among Mmp7-/- mice , and most wild type mice at a comparable time p . i . had no obvious lesions ( Fig 2F ) . In unchallenged wild type mice , Paneth cells are the only cells in the intestine that produce MMP7 [22] . Thus , we challenged mice intraperitoneally ( i . p . ) to determine whether the effect of the MMP7 deletion is localized to the intestine . Unlike oral infection , survival from parenteral challenge was equivalent for both mouse genotypes at two different MAdV-1 doses ( Fig 2C and 2D ) . These findings support the interpretation that a major role for MMP7 in protection from oral viral infection is at the initial site of infection in the small bowel . In addition to removing a potential direct antiviral effect of α-defensins through deletion of Mmp7 , the absence of functional α-defensins in these mice could impact MAdV-1 infection indirectly by altering the intestinal commensal microbiota [23] . Although the overall abundance of commensals in Mmp7-/- mice does not differ from that of wild type mice , the composition of the ileal microbiota is changed [23] . Commensal bacteria influence infection and pathogenesis of poliovirus , reovirus , mouse mammary tumor virus , and norovirus in mouse models [24–27] . We confirmed that the abundance of culturable bacteria in feces does not differ between wild type and Mmp7-/- mice ( Fig 3A ) . To determine if the abundance of commensal bacteria affects MAdV-1 infection , we treated mice with a combination of ampicillin , neomycin , vancomycin , and metronidazole to deplete the intestinal microbiota of wild type and Mmp7-/- mice , using a previously described protocol [24 , 28] . As in prior studies , treatment with antibiotics was effective , since we were unable to culture anaerobic bacteria from the feces of these mice when periodically evaluated over 39 d of continuous antibiotic treatment ( Fig 3A ) . We then assessed survival and weight change for both antibiotic-treated and mock-treated control mice upon challenge with MAdV-1 by oral gavage . As in our previous experiments ( Fig 2 ) , Mmp7-/- mice were more susceptible to MAdV-1 infection ( 30% survival ) compared to wild type mice in the absence of antibiotic treatment ( 100% survival , Fig 3B ) . Antibiotic treatment resulted in transient weight loss in mice of both genotypes and greater variability in daily weight change compared to untreated mice ( S1 Fig ) . Nonetheless , survival differences within genotypes between the antibiotic-treated and control mice were not significant , but a significant survival difference between the antibiotic-treated Mmp7-/- ( 11% survival ) and wild type ( 70% survival ) mice was still observed ( Fig 3B ) . Thus , unlike the case for some other viral pathogens , the abundance of commensals did not appear to impact MAdV-1 pathogenesis upon oral infection . In addition , yellowish discoloration of the small bowel was also observed in sick untreated and antibiotic treated mice ( 7 of 8 Mmp7-/- and 2 of 3 wild type ) ( Fig 3C ) . From these experiments , we conclude that alterations in the composition of the commensal microbiota due to differential expression of Mmp7 are unlikely to explain the survival difference between wild type and Mmp7-/- mice . To examine the systemic dissemination of MAdV-1 and the development of pathology in orally infected mice in more detail , we undertook a time course study . Wild type and Mmp7-/- mice were infected with MAdV-1 ( 1x105 i . u . ) and monitored for weight loss , and cohorts of six mice were euthanized every other day up to day 11 p . i . , the last time point before extensive mortality in Mmp7-/- mice would preclude further comparisons with wild type . As in previous experiments , all of the Mmp7-/- mice lost weight between 9 and 11 d p . i . , and four of the six mice in the day 11 cohort were overtly ill , including two mice that were euthanized on day 10 ( Fig 4B ) . In contrast , only one wild type mouse lost weight , and no wild type mice exhibited outward signs of illness ( Fig 4A ) . We quantified copies of the MAdV-1 genome in feces to monitor viral shedding ( Fig 4C ) and MAdV-1 genome copies per cellular genome in brain ( Fig 4D ) , spleen ( Fig 4E ) , and ileum ( Fig 4F ) to monitor viral replication and spread . In each case , we observed virtually identical viral shedding and viral loads in tissues of wild type and Mmp7-/- mice up to day 7 p . i . , including passage of the inoculum in feces on day 1 . On day 9 , there was a significant increase in viral loads in Mmp7-/- spleens compared to wild type . On day 11 , viral shedding in feces and viral loads in all organs examined were higher in Mmp7-/- mice than wild type . Histopathologic analysis of intestine revealed a significant difference for duodenal enteritis between genotypes on day 11 ( Fig 5A and 5C ) . Due to a general trend towards more severe ileal enteritis in Mmp7-/- mice on day 11 ( Fig 5B and 5C ) , we more closely examined the ileal crypts . Crypts with abnormal Paneth cell phenotypes , either fusion ( Fig 5E , right panel ) or complete depletion of the cytoplasmic secretory granules ( Fig 5E , center panel ) , were significantly more prevalent in Mmp7-/- mice than wild type on day 11 but not on day 9 ( Fig 5D ) . Thus , reduced survival of Mmp7-/- mice correlated with increased viral loads in multiple tissues and more severe lesions and more prevalent Paneth cell defects in the intestine; however , the early kinetics of viral dissemination were independent of Mmp7 expression . If direct antiviral activity of mature α-defensins was responsible for the reduced survival of Mmp7-/- mice , we would expect to see delayed or reduced early kinetics of viral dissemination in wild type mice compared to Mmp7-/- mice . Since this was not observed , we hypothesized that α-defensins were modulating pathogenesis indirectly . One clue to the mechanism arose from our analysis of the spleens and mesenteric lymph nodes ( MLNs ) of wild type and Mmp7-/- mice . MLN follicular hyperplasia and splenic marginal zone hyperplasia were significantly increased in wild type mice compared to Mmp7-/- mice on day 11 p . i . ( Fig 6A and 6B ) and were the most striking histologic differences between infected mice of the two genotypes . Moreover , germinal centers were readily apparent even at low magnification ( 4x ) by H&E ( pale staining regions in the follicles ) and peanut agglutinin ( PNA ) staining ( brown staining regions in the follicles ) in the spleens of wild type mice , whereas they were not distinct even at high magnification in splenic sections from Mmp7-/- mice ( Fig 6C ) . Collectively , these histologic findings suggest a robust adaptive immune response to MAdV-1 in wild type mice that is absent or delayed in Mmp7-/- mice . To test this directly , we determined the NAb titers of sera from mice of both genotypes . All wild type mice developed detectable serum NAbs by day 9 p . i . , which were increased in titer on day 11 ( Fig 7A ) . In contrast , little neutralizing activity was observed in sera from Mmp7-/- mice on day 9 , and on day 11 only two of five Mmp7-/- mice had measureable NAb titers . From these data , we conclude that the inability of Mmp7-/- mice to mount a timely protective NAb response to MAdV-1 infection explains the reduced survival in these mice after viral challenge . To determine whether the humoral response in Mmp7-/- mice is globally compromised , we exposed mice of both genotypes systemically ( via i . p . injection ) or mucosally ( via intranasal instillation ) with ovalbumin ( OVA ) using lipopolysaccharide ( LPS ) as an adjuvant . Since oral administration of OVA may be confounded by the development of oral tolerance , we chose intranasal instillation as an established approach to stimulate a mucosal immune response [29 , 30] . In contrast to responses to MAdV-1 infection , all mice of both genotypes mounted humoral responses to both systemic and mucosal antigen and with identical kinetics ( Fig 7C and 7D ) . Also , serum NAb responses of the few Mmp7-/- mice that survived MAdV-1 infection in all of our experiments were similar to those of wild type mice when assayed 28 d p . i . ( Fig 7B ) . Thus , the humoral response in Mmp7-/- mice is not globally compromised; rather the specific NAb response to enteric MAdV-1 infection is delayed .
The impact of α-defensins on viral transmission or pathogenesis has not been previously examined experimentally; however , correlates from clinical samples suggest that high α-defensin levels are associated with reduced transmission of HIV-1 and slower disease progression [2] . In studies of β- and θ-defensins in viral pathogenesis , the expression or administration of defensin reduced viral immunopathology without impacting viral titers [31 , 32] . These studies suggest a more profound effect of defensins on limiting innate immunopathology than as direct antivirals . Similarly , despite their direct antiviral activity in cell culture , the effect of α-defensins on MAdV-1 pathogenesis appears to be indirect . Taken together , our data support a mechanism in which functional processing of enteric α-defensins at the initial site of viral infection in the small intestine is a critical modulator of the protective NAb response , which is required for survival from acute MAdV-1 infection [15] . Myeloid α-defensins ( HNPs ) function as adjuvants [30 , 33] . When mixed with OVA and administered intranasally , HNPs increased anti-OVA serum IgG but not IgA [30] . HNPs also enhanced CD4+ T cell cytokine secretion and proliferation following stimulation either in vivo or in vitro , suggesting an ability of HNPs to stimulate T cell-dependent cellular and humoral immunity . This was substantiated in intraperitoneal immunization studies with other model antigens and tumor-specific antigens [33] . Enteric α-defensins , either mouse or human , have not been previously reported as adjuvants . Moreover , this is the first example of a direct role for an α-defensin in engendering an adaptive response to a pathogen . There are several possibilities for how α-defensins could enhance the adaptive immune response to MAdV-1 . First , given the capacity of α-defensins and HD5 to bind to and aggregate MAdV-1 , one possibility is that an immunogenic complex of virus and defensin forms in the intestine . The aggregate could be taken up by antigen presenting cells differently than free virus , perhaps through unidentified defensin-specific receptors , in the same way that bacteria opsonized by rabbit myeloid α-defensins are more readily taken up by macrophages [34] . Alternatively , the virus-defensin complex could be potently chemotactic for immune cells , a property of some α-defensins , including HD5 , which has not been reported for mouse enteric α-defensins [35 , 36] . Although we found no clear evidence for a direct antiviral effect of α-defensins in vivo , if our model for human AdV neutralization by α-defensins also holds true for MAdV [37 , 38] , prolonged dwelling in the endosomal pathway during virus entry into the cell could alter exposure of the virus to innate immune sensors ( e . g . , toll-like receptor 9 ) with downstream effects on the development of adaptive immunity . Finally , there may be a direct effect of enteric α-defensins on B cell or T helper cell function . Extrapolating from our analysis of proHD5 , proCrps found in Mmp7-/- mice are unlikely to bind to MAdV-1 to mediate any of these functions . One limitation of the Mmp7-/- model is that it is a complete null mutation in all tissues; therefore , we cannot formally exclude the contribution of MMP7 functions other than activation of Paneth cell pro-α-defensins to survival differences in MAdV-1 infected mice . However , there are numerous critical observations in diverse model systems as well as from our study that support the prior use of this model specifically for studies of enteric α-defensin functions [6 , 8 , 13] and our interpretation that the survival phenotype in these mice is due to pro-α-defensin processing by MMP7 rather than other potential MMP7-dependent functions . First and foremost , other than Paneth cells and efferent ducts of the adult male reproductive tract , MMP7 is not expressed in the intact epithelium of other tissues in naïve mice [9–12 , 22] . In addition , other than a complete lack of α-defensin processing , no alteration in cell behavior or gene expression has been observed in these mice , although global gene expression analysis has not been performed [39] . Similarly , we have found no differences in Paneth or goblet cell numbers or the morphology of small intestinal organoids from wild type and Mmp7-/- mice [13] . Thus , MMP7 deficiency is functionally Paneth cell-restricted in the intestines of unchallenged mice , and the mice are otherwise normal . Second , MMP7 is induced in epithelial cells in response to toxic or mechanical injury , bacterial infection , or oncogenic transformation , but its expression is not affected by viral infection [40–54] . When induced in response to colon or lung injury or to airway bacterial infection , Mmp7-/- mice consistently show marked protection against mortality and reduced inflammation compared to wild type mice [40–42 , 55] . Here , we see just the opposite: more death ( Figs 1 and 2 ) and greater immunopathology in the small intestine ( Fig 5 ) of Mmp7-/- mice infected with MAdV-1 compared to wild type mice . Third , MMP7 is expressed in mice only by activated mucosal and glandular epithelia [22 , 46] . Thus , direct functions of MMP7 could not account for differences in lymphoid organs observed in our experiments , as it is not expressed in lymphoid organs . Finally , upon parenteral MAdV-1 infection , we observed no survival difference between genotypes ( Fig 2C and 2D ) , strongly implicating MMP7-dependent functions in the gut epithelium ( pro-α-defensin processing in Paneth cells ) rather than MMP7-dependent functions elsewhere as the primary determinant of survival differences upon MAdV-1 oral infection . Although MMP7 deficiency affects neutrophil and CD103-positive dendritic cell efflux in the lung and pro-inflammatory cytokine ( TNF-α ) activation on macrophages , MMP7 deficiency has not been reported to globally attenuate adaptive immune responses [40 , 42 , 56] . Rather , in a model of experimental autoimmune encephalomyelitis ( EAE ) induced by myelin oligodendrocyte glycoprotein ( MOG ) exposure , splenocytes and lymphocytes from Mmp7-/- mice were able to respond to MOG and induce EAE in wild type mice [57] . This is consistent with our finding that MMP7 deficiency does not globally abrogate a humoral immune response , as we observe normal humoral responses to ovalbumin exposure in the nasal mucosa and upon intraperitoneal injection . Furthermore , Mmp7-/- mice that survive MAdV-1 infection have normal antibody responses when assayed 28 day p . i . , ( Fig 7B ) indicating that a delayed NAb response rather than the absolute failure to mount a humoral response explains the survival difference . In summary , our studies of a natural viral pathogen reveal a profound effect of functional α-defensins in the ileum on survival from oral infection . Although there may be some contribution of direct α-defensin antiviral activity to modulating infection , the delayed NAb response appears to be primarily responsible for the survival difference between the wild type and Mmp7-/- mice . Our data strongly support a role for α-defensins as adjuvants , specifically in the context of enteric viral infection; however , the exact mechanism remains to be determined . Moreover , additional studies in alternative models , ideally an α-defensin genetic knockout , are needed to formally exclude MMP7 activities beyond α-defensin maturation in engendering the antiviral adaptive immune response .
Wild type MAdV-1 was originally obtained from S . Larsen ( Indiana University Medical Center ) . MAdV-1 . IXeGFP ( MAV-1 inp903 ) was created by fusing an eGFP open reading frame ( ORF ) in frame 3’ to the ORF encoding capsid protein IX , using recombineering with a bacterial artificial chromosome containing the genome of MAdV-1 ( pKBS2 MAV-1 , a kind gift of Silvio Hemmi , University of Zurich ) [58] . Wild type MAdV-1 and MAdV-1 . IXeGFP were both propagated on CMT-93 mouse rectal carcinoma cells ( ATCC CCL-223 , a kind gift from Susan Compton , Yale University School of Medicine ) [59] . CMT-93 cells were cultured in DMEM supplemented with 10% fetal bovine serum ( Sigma-Aldrich ) , 4 mM L-glutamine , 100 units/ml penicillin , 100 μg/ml streptomycin , and 0 . 1 mM nonessential amino acids ( complete DMEM ) . For in vivo studies , cleared tissue culture supernatant containing virus was used . For antiviral assays in cell culture and dynamic light scattering , viruses were concentrated from supernatant by polyethylene glycol precipitation and purified by CsCl gradient ultracentrifugation as described [60] . The particle concentration of purified virus was determined using a Bio-Rad Protein Assay ( Bio-Rad , Hercules , CA ) with a bovine serum albumin standard . The infectious titers of wild type MAdV-1 stocks were determined by infecting CMT-93 cells with serial dilutions of virus . The cells were then fixed in 2% paraformaldehyde , permeabilized with 20 mM glycine and 0 . 5% Triton X-100 in phosphate buffered saline ( PBS ) , and stained with anti-hexon antibody 8C4 ( Fitzgerald Industries International ) and an Alexa Fluor 488-conjugated secondary antibody ( Invitrogen ) . Hexon positive cells were enumerated by flow cytometry , and the infectious titer of the viral stock was calculated using the Poisson distribution . Mature HD5 and Crp23 were obtained by oxidative refolding of partially purified linear peptides and purifying the correctly folded species by reverse-phase high-pressure liquid chromatography ( RP-HPLC ) [21] . ProHD5 and HD5Abu were chemically synthesized and purified as described [61 , 62] . Crp2 was synthesized , refolded , and purified using the same method as for HD5 [61] . Purified Crp4 was produced in E . coli and purified by RP-HPLC [63] . For antiviral assays , serial dilutions of MAdV-1 . IXeGFP were used to infect CMT-93 cells in black wall , clear bottom 96-well plates ( Perkin-Elmer ) . Total well fluorescence was quantified with a Typhoon 9400 variable mode imager ( GE Healthcare ) 2 d p . i . Antiviral assays used a virus concentration producing 50–80% maximal signal . To measure their effects on infectivity , increasing concentrations of α-defensins were incubated with purified MAdV-1 . IXeGFP for 45 min on ice in serum-free DMEM ( SFM ) . The mixture was then added to a confluent monolayer of CMT-93 cells in 96-well plates that had been washed twice in SFM . Cells were then incubated at 37°C for 2 h with rocking , washed , and cultured with complete DMEM for 2 d . Total well fluorescence was measured by Typhoon , and background-subtracted fluorescence was quantified using ImageJ software [64] . Fluorescence values were compared to control wells infected in the absence of inhibitor . α-defensins were serially diluted in 10 mM Tris , 150 mM NaCl , pH 7 . 5 and mixed with 6 . 5 x 108 particles of purified wild type MAdV-1 in a total volume of 50 μl . Control samples of MAdV-1 or α-defensin only were diluted in the same buffer . Samples were incubated for 45 min on ice and then equilibrated for 3 min at 37°C prior to analysis . The z-average particle size was obtained by cumulant analysis with a Malvern Zetasizer Nano ZS and manufacturer’s software ( Malvern Instruments ) . All mouse experiments were performed in strict accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and following the International Guiding Principles for Biomedical Research Involving Animals . Experiments were approved by the Institutional Animal Care and Use Committee of the University of Washington under University of Washington Protocol Number 4245–01 . Wild type C57BL/6NHsd ( Harlan Laboratories ) and isogenic Mmp7-/- mice [65] were mated to generate progeny mice heterozygous for Mmp7 . These mice were then intercrossed to generate wild type and Mmp7-/- lines , which were used to breed mice for all experiments over three generations of progeny . Mice were housed under specific pathogen-free conditions and were infected for experiments under ABSL2 conditions between 5 and 9 weeks of age . Mice were infected via oral gavage with 100 μl of sterile tissue culture supernatant containing virus diluted in sterile PBS , except for experiments in Fig 2C and 2D for which virus was administered intraperitoneally in a volume of 100 μl . For experiments in Figs 2A–2D , 4A , and 4B , mice were housed individually beginning 3 or 4 d prior to infection and for the duration of the experiment . For the experiment in Fig 3B , mice from both genotypes were co-housed at weaning and for the duration of the experiment , and the genotypes of individual mice were blinded during the study . Mice were humanely euthanized by CO2 inhalation if moribund , if partially paralyzed , if seizing , if weight loss from maximal recorded weight exceeded 20% , or at the end of the experiment ( 28 d p . i . ) . Food ( PicoLab Rodent Diet 5053 , LabDiet ) and water were provided ad libitum . Mice were weighed on the day of infection and every 1–2 d thereafter , as indicated . Percent weight change between measurements was calculated by subtracting the previous weight from the current weight , dividing by the previous weight , and multiplying by 100 . Mice were treated with 100 μl of a mixture containing pharmaceutical grade ampicillin ( 100 mg/ml , Sandoz ) , neomycin sulfate ( 100 mg/ml , PCCA ) , metronidazole benzoate ( 100 mg/ml , PCCA ) , and vancomycin HCl ( 50 mg/ml , Mylan ) in Ora-Sweet Syrup Vehicle ( Paddock Laboratories ) and peanut butter flavoring ( PCCA ) by oral gavage for 5 d . Control mice were gavaged with peanut butter-flavored vehicle without antibiotics . Upon initiation of antibiotic treatment and for the duration of the experiment , mice were also given a 100-fold lower concentration of the same antibiotic mixture or vehicle control ad libitum in drinking water . Fecal samples ( 1 pellet/mouse ) were collected on day 5 of treatment and periodically thereafter directly from the mouse into sterile PBS and homogenized . The fecal material was serially diluted and cultured on brain heart infusion ( Sigma-Aldrich ) agar plates supplemented with 10% sheep’s blood ( Colorado Serum Company ) . Plates were cultured anaerobically for 72 h using the GasPak EZ Anaerobe Puch System ( BD ) . Colonies were counted to obtain culturable CFUs with a limit of detection of 50 CFU . After 23 d of continuous antibiotic treatment , mice were infected with MAdV-1 by oral gavage as above . Mice were exsanguinated by cardiac puncture under Avertin anesthesia . To minimize autolysis , organs ( MLN , duodenum , ileum , and spleen ) were immediately subjected to immersion fixation in 10% neutral buffered formalin for at least 24 h , embedded in paraffin , sectioned at 5 μm , and stained with hematoxylin and eosin ( H&E ) . A board-certified pathologist scored blinded and randomized samples using a previously established scoring system [66] . Selected mice from the experiments in Fig 2 were subjected to a complete diagnostic necropsy , which was not blinded . To classify and count ileal crypts , H&E stained slides were digitized using a Nanozoomer C9600 Whole Slide Scanner ( Hamamatsu ) and annotated using NDP2 . view2 software ( Hamamatsu ) based on Paneth cell phenotypes visualized by bright field microscopy at higher magnification ( 400–600x ) . To visualize germinal centers , serial sections were stained with biotin conjugated peanut agglutinin ( PNA ) ( Vector Labs ) after antigen retrieval in citrate buffer pH 6 . Bound lectin was detected using the ABC Elite kit ( Vector Labs ) and visualized with 3 , 3-diaminobenzidine ( DAB ) . Sections were counterstained with hematoxylin . At the time of necropsy , small ( ~10 mm3 ) samples of hindbrain ( by pinch biopsy ) , ileum , and spleen were snap frozen in liquid N2 and stored at -80°C . DNA was extracted using the DNeasy Blood and Tissue Kit ( Qiagen ) . MAdV-1 genomes were quantified by quantitative PCR ( qPCR ) using the SsoFast EvaGreen Supermix ( Bio-Rad ) against a standard curve of pKBS2-MAdV1 using primers M1FF2 ( 5’-ATTCCATGATACCCGCCTAA-3’ ) and M1FR2 ( 5’-TCCAACCAATTCCAGCATAA-3’ ) . Cellular genomes were quantified against a standard curve of C57BL/6 liver DNA ( 286 genome copies/ng of DNA ) using primers MmGRO1F and MmGRO1R [67] . For both reactions , conditions consisted of 40 cycles of PCR with 55°C annealing temperatures using an iCycler ( Bio-Rad ) . Limits of detection were defined by the ratio of viral to cellular gene copies detected in samples from each tissue of uninfected mice . To obtain a representative sample of the feces produced by each mouse and to minimalize sampling error , fecal samples consisted of ten fecal pellets that accumulated in the cages of single-housed mice since the previous collection . Accordingly , on the day of infection and after every fecal collection , mice were transferred to new cages with clean bedding . Mice from the cohorts to be euthanized on days 9 and 11 p . i . were analyzed . DNA was extracted from fecal samples using the QIAamp DNA Stool Mini Kit ( Qiagen ) into a total volume of 200 μl . Viral genome copies in 1 μl of this sample were quantified as above and the values ( without normalization ) were plotted in Fig 4C . The limit of detection was defined by the number of viral copies detected in feces from uninfected mice . Serial dilutions of MAdV-1 were used to infect CMT-93 cells in black wall , clear bottom 96-well plates . Cells were fixed and stained with anti-hexon antibody 8C4 and an Alexa Fluor 488-conjugated secondary antibody , and total well fluorescence was quantified with a Typhoon 2 d p . i . , as above . NAb assays used a virus concentration producing 50–80% maximal signal . Two-fold serial dilutions of heat-inactivated ( 56°C for 1 h ) serum were incubated with MAdV-1 for 45 min at RT and added to a confluent monolayer of CMT-93 cells in 96-well plates . After 2 d , cells were stained for hexon , scanned , and background-subtracted fluorescence was quantified using ImageJ software . Fluorescence values were compared to control wells infected in the absence of serum . The greatest dilution of serum that inhibited virus by at least 50% was considered the neutralizing activity of each sample . For Fig 7A , serum from each mouse was analyzed in three independent experiments , and the mean titer for each mouse was calculated by averaging the log transformed value of each replicate . Samples for which no inhibition was observed were imputed with the minimum dilution used for the assay ( 10 in one replicate and 20 in two replicates ) . For Fig 7B , samples were tested once at three serum dilutions . Wild type and Mmp7-/- mice were immunized with a sterile mixture of OVA ( 50 μg/mouse , Sigma-Aldrich ) and E . coli 0111:B4 LPS ( 5 μg/mouse , Sigma-Aldrich ) in PBS via i . p . injection ( 100 μl/mouse ) or intranasal instillation ( 20 μl/mouse ) . Intranasal instillation was performed under light isoflurane anesthesia [68] . Each treatment group of 10 animals was divided into two cohorts of 5 mice , which were sampled by submandibular bleed on alternating days . The cohorts were also euthanized on different days to obtain cardiac blood . Thus , serum samples were obtained from each treatment group on days 4 , 7 , 9 , 11 , 13 , and 15 post-inoculation . An additional wild type mouse was inoculated i . p . , boosted i . p . on days 14 and 21 , and euthanized on day 28 post-inoculation as a positive control for ELISA . Anti-OVA antibodies were quantified by ELISA using microtest plates ( BD Biosciences ) incubated overnight with 50 μl/well of 125 μg/mL OVA in coating buffer ( 0 . 1 M Na2CO3 , 0 . 1M NaHCO3 , pH 9 . 6 ) or coating buffer alone . Wells were blocked with ELISA block ( 5% nonfat dry milk , 0 . 05% Tween-20 , 0 . 025% NaN3 , in PBS ) for 1 h at 37°C , and heat inactivated ( 56°C for 30 min ) serum diluted 20-fold into ELISA block was incubated overnight at 4°C in OVA-coated and control uncoated wells . Antibodies were detected with a peroxidase-conjugated pan-IgA , IgM , and IgG anti-mouse antibody ( Sigma-Aldrich ) and developed with TMB Substrate ( Pierce ) ; and OVA-specific signal for each mouse ( OVA signal minus uncoated control ) was normalized to the OVA-specific signal from the serum of the positive control mouse . Each sample was quantified in two independent assays , and the average of the two values for each mouse was plotted in Fig 7C and 7D . Experiments were analyzed using Prism 5 . 0d , and for all tests P<0 . 05 was considered significant . Survival curves were compared by log-rank ( Mantel-Cox ) test , using the Bonferroni method for multiple comparisons in Fig 3B . For Figs 4 , 5 , 6 and 7 data were compared using one-way ANOVA with Bonferroni post-tests to compare wild type and Mmp7-/- mice at each time point . For Figs 4D–4F and 7A , data were log transformed prior to this analysis . Cryptdin 2 ( Gene: Defa2 , Species: Mus musculus , UniProtKB: P28309 ) Cryptdin 4 ( Gene: Defa4 , Species: Mus musculus , UniProtKB: P28311 ) Cryptdin 23 ( Gene: Defa23 , Species: Mus musculus , UniProtKB: Q5G866 ) Human defensin 5 ( Gene: DEFA5 , Species: Homo sapiens , UniProtKB: Q01523 ) Matrix metalloproteinase-7 ( Gene: Mmp7 , Species: Mus musculus , UniProtKB: Q10738 )
|
Mammals express abundant antimicrobial peptides , including α-defensins , to protect their epithelial surfaces from microbes . α-defensins are potently antibacterial and antiviral ex vivo; however , their contribution to host defense from viral infection in vivo has not been demonstrated . We show that mice lacking functional α-defensins in their small intestines are more susceptible to disease caused by oral viral infection . Although the virus is sensitive to α-defensin antiviral activity in cell culture , the protective effect of α-defensins in vivo is due to a neutralizing antibody response to the virus that is delayed when α-defensins are absent . Thus , α-defensins play an important role as adjuvants in antiviral immunity in vivo that is distinct from their direct antiviral activity observed in cell culture .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"paneth",
"cells",
"antimicrobials",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"viral",
"transmission",
"and",
"infection",
"drugs",
"immunology",
"microbiology",
"vertebrates",
"mice",
"animals",
"mammals",
"epithelial",
"cells",
"animal",
"models",
"model",
"organisms",
"antibiotics",
"pharmacology",
"antibodies",
"antibody",
"response",
"digestive",
"system",
"research",
"and",
"analysis",
"methods",
"immune",
"system",
"proteins",
"animal",
"cells",
"proteins",
"biological",
"tissue",
"mouse",
"models",
"immune",
"response",
"gastrointestinal",
"tract",
"biochemistry",
"rodents",
"cell",
"biology",
"anatomy",
"virology",
"physiology",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"microbial",
"control",
"organisms"
] |
2016
|
Defensins Potentiate a Neutralizing Antibody Response to Enteric Viral Infection
|
Synchronized neuronal activity is vital for complex processes like behavior . Circadian pacemaker neurons offer an unusual opportunity to study synchrony as their molecular clocks oscillate in phase over an extended timeframe ( 24 h ) . To identify where , when , and how synchronizing signals are perceived , we first studied the minimal clock neural circuit in Drosophila larvae , manipulating either the four master pacemaker neurons ( LNvs ) or two dorsal clock neurons ( DN1s ) . Unexpectedly , we found that the PDF Receptor ( PdfR ) is required in both LNvs and DN1s to maintain synchronized LNv clocks . We also found that glutamate is a second synchronizing signal that is released from DN1s and perceived in LNvs via the metabotropic glutamate receptor ( mGluRA ) . Because simultaneously reducing Pdfr and mGluRA expression in LNvs severely dampened Timeless clock protein oscillations , we conclude that the master pacemaker LNvs require extracellular signals to function normally . These two synchronizing signals are released at opposite times of day and drive cAMP oscillations in LNvs . Finally we found that PdfR and mGluRA also help synchronize Timeless oscillations in adult s-LNvs . We propose that differentially timed signals that drive cAMP oscillations and synchronize pacemaker neurons in circadian neural circuits will be conserved across species .
Coordinated neuronal activity is vital for neural networks to regulate complex processes such as behavior . Synchrony can be studied at the microsecond level by measuring neuronal activity , with synchronous activity often achieved via gap junctions that electrically couple neurons [1] . The circadian system offers an unusual opportunity to study synchrony over a much longer timeframe as circadian pacemaker neurons have molecular clocks that oscillate with 24 hour periods . These endogenous clocks drive daily rhythms in pacemaker neuron electrical activity and allow organisms to anticipate environmental transitions such as sunrise and sunset [2] . Although the molecular basis of the circadian clock is well established , how individual clock neurons remain synchronized is much less well understood . Synchrony is essential in the circadian system as the accuracy of individual clocks would be meaningless if they were desynchronized . Coordinated molecular clocks presumably ensure that an animal has a single internal representation of time . In mammals , the primary circadian pacemaker in the suprachiasmatic nucleus ( SCN ) consists of ventral “core” and dorsal “shell” regions of clock neurons . Although SCN clock neurons exhibit 24 hour oscillations of clock proteins , anatomically distinct neurons oscillate with different phases ( reviewed by [3] ) . Oscillations within different SCN neurons are coupled through cyclic AMP ( cAMP ) and Ca2+-dependent mechanisms , promoting synchrony and increasing the amplitude of individual oscillators compared to non-SCN clock neurons ( reviewed by [4] ) . Synchronizing the different phases of SCN oscillations requires RGS16 , which is rhythmically expressed and inactivates the G-protein Gαi to increase cAMP levels in the SCN in a time-dependent manner [5] . The Drosophila clock circuit also contains distinct groups of neurons including the small ventral Lateral Neurons ( s-LNvs ) that communicate with a subset of dorsal Lateral Neurons ( LNds ) and Dorsal clock neurons ( DNs ) to generate bimodal locomotor activity rhythms in light∶dark ( LD ) cycles [6] , [7] . s-LNvs are often called master pacemaker neurons as they set the period for most of the clock network in constant darkness ( DD ) [8] . However , robust behavioral rhythms in DD require LNv and non-LNv neurons to signal at different times of day [9] . Different groups of clock neurons also respond differently to environmental stimuli , such as day length or temperature [10] , [11] , leading to a network view of the clock where different clock neuron groups process information and communicate to keep time for an individual animal [12] . The mammalian neuropeptide VIP and the Drosophila neuropeptide PDF are found in subsets of clock neurons: ventral core SCN neurons in mice and LNvs in flies [3] , [13] . VIP and PDF are both required for robust behavioral rhythms , the maintenance of stable phase relationships between different groups of clock neurons , and synchronized molecular clock oscillations within individual groups of clock neurons [8] , [14]–[17] . The PDF receptor ( PdfR ) and VIP receptor VPAC2R are also required for robust rhythms of behavior , and they both activate Gαs to increase cAMP levels , indicating a conserved mode of action [18]–[21] . However , the precise mechanisms by which signaling across the clock circuit promotes synchronous clock oscillations remain unclear . We used Drosophila to understand how circadian networks are synchronized , taking advantage of the exquisite precision with which individual groups of clock neurons can be manipulated in flies and the variety of genetic tools available . We made extensive use of the minimal larval clock circuit , which has only nine clock neurons per brain lobe , including four PDF-expressing LNvs that display synchronous clock protein oscillations in constant darkness ( DD ) . These rhythms require the transcription factors Clock ( CLK ) and Cycle ( CYC ) that activate period ( per ) and timeless ( tim ) transcription . PER and TIM proteins dimerize , enter the nucleus , and then inhibit CLK/CYC activity . This represses expression of per , tim , and other CLK/CYC targets , including vrille and Par Domain Protein 1 ( Pdp1 ) , that in turn feed back to regulate Clk expression ( reviewed by [22] ) . One entire cycle takes 24 hours . Synchronized LNv oscillations in adult flies require PDF , as s-LNv clocks become desynchronized in Pdf01 null mutants after 6–9 days in constant darkness [17] . Here we show that LNv synchrony in DD is a very active process , as desynchrony can be detected as early as 3 hours into the first subjective morning in Pdf01 mutant larvae . We show that synchronized LNv clocks require two distinct signals: a neuropeptide signal ( PDF ) received around dawn via PdfR and a neurotransmitter signal ( glutamate ) received from DN1s around dusk via the metabotropic glutamate receptor ( mGluRA ) . Surprisingly , simultaneously reducing expression of Pdfr and mGluRA in LNvs severely dampened TIM protein oscillations and blocked larval behavioral rhythms . Thus , oscillations of core clock proteins within pacemaker neurons require signals from other clock neurons . PdfR and mGluRA are GPCRs , and we show that daily oscillations in LNv cAMP levels depend on their receiving PDF and glutamate . Because cAMP has previously been shown to be a molecular clock component in mammals [23] , our data provide a mechanism for how extracellular signals impact molecular oscillations and neuronal synchrony . We extend these findings to adult flies and show that PdfR and mGluRA are required to maintain synchronized high-amplitude TIM oscillations in s-LNvs . In adults , desynchronized s-LNv molecular clocks are associated with noisy behavioral rhythms , including delayed onset of sleep and increased nighttime activity . Our data reveal a surprising degree of conservation in the mechanisms promoting synchronous clock oscillations in the mammalian SCN and Drosophila LNvs . This mirrors the conserved molecular basis of mammalian and Drosophila clocks and indicates that studying the simple Drosophila circadian neural circuit will help understand the more complex mammalian circadian system .
The four PDF-expressing LNvs in each larval brain lobe are precursors of adult s-LNvs . Molecular clock oscillations in larval LNvs are normally tightly synchronized , oscillating in phase with each other so that TIM and PDP1 clock proteins are detectable in all four LNvs at CT21 and undetectable in all four LNvs 6 hours later at CT3 ( Figure 1A ) ( CT , Circadian time , hours in constant darkness ) . Because PER protein rhythms in adult s-LNvs become desynchronized in Pdf01 null mutants in DD [17] , we first tested whether PDF is required to synchronize larval LNv molecular clocks . We measured TIM protein levels instead of PER with the rationale that TIM's shorter half-life [24] , [25] would allow us to detect desymchrony earlier in DD . To visualize LNvs in Pdf01 mutants , we used the Gal4/UAS system [26] to express GFP in LNvs using the Pdf-Gal4 driver . We measured TIM levels in LNvs isolated at CT9 , CT15 , and CT21 on the second day in DD and at CT3 on day 3 . TIM continues to oscillate in Pdf01 mutants , indicating that the molecular clocks in their LNvs are functional ( Figure 1A–B ) . However , the amplitude of TIM rhythms in Pdf01 mutants was reduced compared to controls ( Figure 1B ) , as expected from the reduced amplitude tim RNA oscillations in Pdf01 adult flies [27] . Closer inspection identified a mixture of TIM-positive and TIM-negative LNvs in a single brain lobe at CT15 , 21 , and 3 in Pdf01 mutants ( Figures 1D and S1A; see Materials and Methods ) , which we term desynchronized . Elevated desynchrony likely accounts for the significantly lower average TIM levels in Pdf01 LNvs at CT15 and 21 than in controls ( Figure 1B ) , in agreement with previous reports [17] , [27] . We also quantified the variability within individual LNv clusters by calculating the standard deviation in TIM levels across a single cluster . Figure 1F shows the distribution of standard deviations in TIM levels for each control or Pdf01 LNv cluster at CT3 and CT9 . We chose CT3 because desynchronized LNv clusters were only rarely found at this timepoint in control larvae . In contrast , TIM was detected in one , two , or three of the four LNvs in 50% of Pdf01 LNv clusters at CT3 ( n = 20; Figures 1D and S1A and Table S1 ) . In subsequent experiments we used the presence of TIM in a subset of LNvs at CT3 to indicate that an LNv cluster had lost its normal coherent phase relationship and had become desynchronized , even if we did not observe desynchrony at other time points . Our data show significantly more variability in TIM levels within an LNv cluster in Pdf01 mutants than in control larvae at CT3 ( Figure 1F ) , reflecting desynchronized molecular clocks in Pdf01 LNvs . No significant increase in standard deviation was observed in Pdf01 mutants compared to controls at CT9 ( Figure 1F ) . Indeed , the low TIM levels at CT9 indicate that Pdf01 LNv molecular clocks still oscillate as shown previously [17] , [27] . Because PDF signals via PdfR , we next tested whether the synchrony of larval LNv molecular clocks is also altered in Pdfr mutants . Although overall TIM oscillations were similar between control and Pdfrhan5304 ( Pdfrhan ) hypomorphs , we observed higher TIM levels at CT3 in Pdfrhan than in control larvae ( Figure 1A–B ) . As with Pdf01 null mutants , this is because TIM was detected in 1–3 of the four LNvs in 48% of Pdfrhan mutant LNv clusters at CT3 ( Figures 1D and S1A ) . We found similar results for PDP1 ( Figures 1A , C , E and S1B ) . In contrast , TIM or PDP1 expression was detected in <5% of control LNvs at CT3 ( n = 21; Figure 1D–E and Table S1 ) . The standard deviations in TIM and PDP1 levels are significantly elevated at CT3 in Pdfrhan mutants compared to controls ( Figure 1F–G ) . Thus PdfR , like PDF , is required for LNvs to stay synchronized . In contrast to Pdf01 LNvs , Pdfrhan mutants did not show many desynchronized LNv clusters at CT15 or CT21 , and there was no corresponding reduction in the amplitude of TIM oscillations in Pdfrhan mutants compared to control LNvs . This could be because Pdfrhan is a hypomorph rather than a null allele and/or because type II GPCRs tend to be promiscuous , so receptors other than PdfR may also respond to PDF [28] . We also tested whether LNv molecular clocks required PDF to maintain synchrony under LD cycles . We measured TIM and PDP1 levels in control larvae and in Pdf01 and Pdfrhan mutants at ZT3 , but detected no TIM or PDP1 expression in LNvs ( Figure S1C ) . Thus , light overrides desynchrony in Pdf01 and Pdfrhan5304 mutants , with PDF signaling required for synchronous LNv clock oscillations only in DD . Because adult and larval LNvs express Pdfr ( [29] , [30] and Figure S2C ) , the simplest model to explain how PDF promotes LNv synchrony would be that the four larval LNvs signal to synchronize each other via PDF and PdfR . However , Pdfr is also expressed in many non-LNv adult clock neurons [29] and in larval DN1s ( Figure S2A , B ) . Thus PDF signaling to non-LNvs could also be required for LNv synchronization . We therefore used a PdfrRNAi transgene [31] to reduce Pdfr levels in subsets of clock neurons to determine where PDF signaling is required for LNv synchronization . Expressing PdfrRNAi in LNvs significantly reduced the cAMP response of LNvs to PDF , indicating that PdfrRNAi likely reduces Pdfr expression ( Figure S2C ) . UAS-Dicer-2 ( UAS-Dcr-2 ) was co-expressed to increase RNAi efficacy in this and in all subsequent RNAi experiments unless otherwise stated , but is omitted from written genotypes in the text for simplicity . We first targeted PdfrRNAi to LNvs using Pdf-Gal4 ( denoted as Pdf> ) . At CT3 on day 3 of DD , TIM staining revealed that 44% of Pdf>PdfrRNAi larvae had desynchronized LNvs , whereas >93% of control LNvs were synchronized ( Figures 2A and S3A and Table S1 ) . The standard deviation in TIM levels was also significantly increased in Pdf>PdfrRNAi larvae compared to controls at CT3 ( Figure 2B ) . Similar results were observed for PDP1 at CT3 ( Figure S3B and Table S1 ) . Next , Pdfr expression was reduced in all non-LNv clock neurons using the tim-Gal4; Pdf-Gal80 driver combination ( tim; Pdf-Gal80> ) . We found that 44% of LNvs were desynchronized in tim; Pdf-Gal80>PdfrRNAi larvae ( Figure S3A and Table S1 ) . This probably underestimates the level of defective TIM oscillations , as 16% of tim; Pdf-Gal80>PdfrRNAi LNv clusters showed four LNvs expressing TIM at CT3 , compared to only 6% of controls ( Table S1 ) . There is a corresponding increase in the standard deviation in TIM levels in tim; Pdf-Gal80>PdfrRNAi LNv clusters compared to control LNvs ( Figure 2B ) . Similar results were observed for PDP1 ( Figure S3A–B ) . These data indicate that LNv synchrony depends on PdfR activity in both LNv and non-LNv clock neurons . The non-LNv clock neurons releasing the synchronizing signal could be the larval DN1s , the DN2s , or the fifth LNv . DN1s are the best candidates , as they project to LNv axonal termini and modulate LNv outputs by releasing glutamate to generate circadian rhythms in larval light avoidance [9] . Larval DN1s also respond directly to PDF ( Figure S2A ) . We therefore used cry-Gal4 and Pdf-Gal80 ( DN1> ) to target transgene expression exclusively to DN1s [9] . We first tested whether DN1 ablation affected LNv synchrony by expressing Diptheria Toxin in DN1s ( DN1>Dti ) . We found that TIM rhythms persisted in LNvs after DN1 ablation ( Figure S3D ) , indicating that LNvs do not require DN1s for oscillations per se . However , TIM levels at CT3 on both days 2 and 3 in DD were elevated in DN1-ablated larvae ( Figure S3D ) . Examining TIM staining in DN1-ablated brains in DD revealed that 50% of LNv clusters were desynchronized at CT3 on days 2 and 3 in DD , a significant increase compared to controls ( Figures 2C , D and S3A and Table S1 ) . We observed similar increases in desynchrony of PDP1 expression when DN1s were ablated ( Figures S3A , C , E and S6C–D and Table S1 ) with significantly higher levels at CT3 on day 3 . We did not observe desynchrony in LD cycles ( Figure 2D ) or at CT9 , just like Pdf01 and Pdfrhan mutants . We conclude that PDF signaling ( Figure 1 ) and DN1s ( Figure 2 ) normally maintain larval LNv molecular clock synchrony in constant darkness . To test this model further , we sought to identify the DN1 signal and the relevant receptor in LNvs . Because larval DN1s are glutamatergic [32] , we tested whether reducing DN1 glutamate levels alters LNv molecular clock synchrony . Glutamate decarboxylase 1 ( Gad1 ) was mis-expressed in DN1s , to convert glutamate into GABA [9] , [33] , which cannot be released as DN1s do not produce the vesicular GABA transporter . Thus misexpression of Gad1 reduces presynaptic glutamate . This manipulation does not affect DN1 viability , and their molecular clocks still oscillate [9] . We found that overall TIM oscillations were relatively normal in DN1>Gad1 LNvs ( Figure S4A ) . However , TIM levels were significantly elevated at CT3 in DN1>Gad1 larvae ( Figure S4A ) . This is because DN1>Gad1 significantly increased LNv desynchrony , determined by comparing the standard deviation in TIM and PDP1 expression with control LNvs ( Figures 3A , C , D and S4C and Table S1 ) . Therefore , we conclude that DN1s release glutamate to synchronize LNv molecular clocks . Larval LNvs express two glutamate receptors: a metabotropic glutamate receptor ( mGluRA , [32] ) and a glutamate-gated Chloride channel ( GluCl , [9] ) . To determine whether one of these receptors transduces the glutamate signal to synchronize LNvs , we used RNAi transgenes previously shown to reduce expression of mGluRA or GluCl [9] , [32] . We found that reducing GluCl expression in LNvs had no effect on TIM and PDP1 oscillations or LNv synchrony ( Figures 3B–D and S4B–C ) . In contrast , expressing mGluRARNAi in LNvs produced similar molecular phenotypes to DN1 ablation , with elevated TIM levels at CT3 and 75% of LNvs desynchronized ( Figure 3B–D and Table S1 ) . As an independent way to manipulate mGluRA expression , we measured TIM levels at CT3 in LNvs of mGluRA112b null mutant larvae ( Figures 3B–C and S4D and Table S1 ) . We found desynchronized LNvs in homozygous mGluRA112b mutant larvae but not in heterozygous controls . We saw similar levels of desynchronization when measuring PDP1 levels in Pdf>mGluRARNAi and mGluRA112b mutant LNvs ( Figures 3C and S4C–D ) . Taking these data together with our manipulations of DN1 glutamate levels , we conclude that glutamate released by DN1s helps synchronize LNv oscillations via mGluRA . We previously showed that LNvs require GluCl rather than mGluRA for circadian rhythms in the rapid light avoidance of larvae [9] . Thus , a single neurotransmitter , glutamate , released by DN1s has two distinct functions depending on the receptor in LNvs that perceives the signal . Presumably the rapid action of the ionotropic receptor on LNv excitability [9] is best suited to regulate light avoidance behavior , whereas mGluRA acts on a slower timescale to regulate clock oscillations . LNvs require two different signals to maintain synchrony , as reducing expression of either Pdfr or mGluRA desynchronized LNv molecular clocks . However , we only observed an increase in desynchronized LNv clusters at CT3 in Pdf>PdfrRNAi or Pdf>mGluRARNAi larval brains compared to controls , with most LNv clusters remaining synchronized at CT21 . This suggested that the second signal—glutamate in Pdf>PdfrRNAi and PDF in Pdf>mGluRARNAi larvae—maintains some degree of LNv synchrony and we hypothesized that simultaneously reducing Pdfr and mGluRA expression would more strongly affect LNv clock synchrony . We measured TIM and PDP1 oscillations in LNvs expressing transgenes targeting both Pdfr and mGluRA expression ( Pdf>PdfrRNAi+mGluRARNAi ) . We found that 88% of LNv clusters showed desynchrony in TIM protein levels at CT3 ( Figures 4A–B and S5B and Table S1 ) and 75% for PDP1 ( Figure S5A , C and Table S1 ) . Pdf>PdfrRNAi+mGluRARNAi larvae also had significantly more desynchronized LNv clusters at CT21 and CT3 than control larvae ( Figure S5A ) . Thus simultaneously reducing expression of both receptors dramatically increased the percentage of desynchronized LNvs compared to reducing Pdfr or mGluRA expression alone , indicating that PDF and glutamate signals work together to promote synchrony . Although we observed a few individual LNvs with high TIM levels in Pdf>PdfrRNAi+mGluRARNAi larvae , overall TIM oscillations were almost completely lost ( Figure 4C ) . This contrasts with the robust TIM oscillations of Pdf>PdfrRNAi and Pdf>mGluRARNAi single knock-down larvae ( Figure S5E ) . High-amplitude TIM protein oscillations in LNvs thus depend on external signals , including PDF and glutamate , and are not fully cell-autonomous . Although PDP1 showed elevated desynchrony in Pdf>PdfrRNAi+mGluRARNAi LNvs ( Figure S5A , C ) , overall PDP1 oscillations were relatively unaffected ( Figure S5D ) . Thus Pdf>PdfrRNAi+mGluRARNAi LNvs are still partly functional . These data suggest that TIM is a more direct target than PDP1 in LNvs for the signaling pathways that transduce glutamate and PDF signals . Do the reduced amplitude TIM rhythms in Pdf>PdfrRNAi+mGluRARNAi double mutant larvae affect behavioral rhythms ? We had previously found that light avoidance rhythms require glutamate release from DN1s and transduction via GluCl in LNvs [9] . Because TIM oscillations in LNvs remained intact in Pdf>GluClRNAi larval brains ( Figure S4B ) , we concluded that glutamate received by GluCl modulates LNv outputs rather than LNv molecular clocks [9] . Knocking down either mGluRA or Pdfr individually in LNvs does not block TIM or PDP1 protein oscillations ( Figure S5E–F ) and larval light avoidance is still rhythmic , with peak levels at dawn ( Figure 4D and [9] ) . However , we found that larvae with mGluRA and Pdfr expression simultaneously reduced in LNvs lose light avoidance rhythms ( Figure 4D ) . This result suggests that TIM oscillations in LNvs are essential for light avoidance rhythms and that PDP1 rhythms alone cannot support larval rhythms . Overall , these data indicate the importance of extracellular signals for LNvs to oscillate normally and promote rhythmic behavior . Adult s-LNvs are most excitable at dawn [34] , [35] and drive the morning peak of locomotor activity [6] , [7] . We previously showed that the same is likely true for the larval LNvs that control the dawn peak in light avoidance , whereas larval DN1s most likely signal at dusk [9] . To test whether DN1s signal at dawn or dusk to promote LNv synchrony , we used a temperature-sensitive Shibire transgene ( UAS-Shits [36] ) to temporally block synaptic transmission . Shits was expressed specifically in DN1s ( DN1>Shits ) , and larvae were maintained at the permissive temperature of 25°C for 4 days in LD and 1 day in DD . On the second day in DD , the temperature was elevated to the nonpermissive temperature of 31°C for 6 hours from either CT9 to CT15 ( “CT12 shift” ) or CT21 to CT3 ( “CT24 shift” ) to block DN1 signaling around dusk or dawn , respectively ( Figure 5A ) . Larval brains were dissected at CT3 on day 3 of DD ( i . e . , 12 hours after the end of a CT12 temperature shift or immediately after the end of a CT24 temperature shift ) . We found that 57% of LNv clusters showed desynchronized TIM levels when DN1 synaptic transmission was blocked at dusk ( DN1>Shits , 31°C at CT12 ) compared to 7% of control LNvs ( UAS-Shits/+; Figure 5A–C and Table S1 ) . Similarly , 36% of LNvs in DN1>Shits larvae shifted to 31°C at CT12 had desynchronized PDP1 levels compared to 0% of control LNvs ( Figure S6A–B and Table S1 ) . In contrast , blocking synaptic transmission from DN1s around dawn had no effect on LNv synchrony ( DN1>Shits , 0% desynchronized for TIM or PDP1 with a CT24 heat pulse; Figures 5A–C and S6A–B and Table S1 ) . We therefore conclude that DN1 signaling around dusk is required to synchronize LNvs . To further test the idea that PDF and glutamate promote synchrony at different times of day , we took advantage of the synchronizing effect of LD cycles on Pdf01 and DN1>Dti LNvs ( Figures 2D , S1C , and S3B and Table S1 ) . Based on the likely timing of LNv and DN1 signals , wild-type LNv clocks should have received the PDF signal at subjective dawn by CT3 on day 1 in DD , but not yet received the glutamatergic signal at subjective dusk . Thus we predicted that Pdf01 mutants would show desynchrony at this time point , whereas DN1>Dti LNvs , which still receive the PDF signal , would not . We measured TIM and PDP1 levels in LNvs at CT3 on the first day of DD and found higher TIM and PDP1 levels and an increase in the variability of clock protein levels between LNvs in the same cluster in Pdf01 mutants , indicating that LNvs are already desynchronized just 3 hours into DD ( Figures 5D–E and S6C–D ) . In contrast , the LNv clocks in larvae with DN1s ablated remained synchronized at CT3 on the first day in DD , and desynchrony was first detected on day 2 ( Figures 5D–E and S6C–D ) . We interpret these data to mean that desynchrony in DN1-ablated larvae requires larvae to traverse subjective dusk when the DN1 signal is released . Because desynchrony appears on different days in Pdf01 and DN1-ablated larvae , this supports the model where LNv synchrony depends on PDF received at dawn and glutamate received at dusk . This is consistent with the previously reported timing of LNv excitability [34] , [35] and of the larval LNv and DN1 signals that regulate light avoidance [9] . PdfR and mGluRA are both G-protein coupled receptors . PdfR signals via Gαs [18] , [19] , [21] , [37] and mGluRA can also alter cAMP levels [38] . Because cAMP is a clock component in mammals [23] and likely also in flies [39] , [40] , regulation of LNv cAMP levels by extracellular signals could maintain LNv synchrony and promote robust TIM oscillations . We used the FRET-based Epac1-camps sensor [19] to measure basal cAMP levels on day 2 in DD . We first assayed control LNvs , focusing on their axonal termini near DN1 projections [9] . We found that cAMP levels , measured by the ratio of CFP/YFP , were highest at CT24 , indicating that cAMP levels normally oscillate in LNv projections ( Figure 6A ) . Strikingly , cAMP ( CFP/YFP ) oscillations were lost in the projections of both Pdf>PdfrRNAi and Pdf>mGluRARNAi larval LNvs ( Figure 6A ) . We noticed that Pdf>mGluRARNAi LNv cAMP levels were significantly higher than controls at dusk ( CT12 ) , when DN1s signal for synchrony . This is consistent with data showing that mGluRA reduces cAMP levels by signaling via Gαi [38] , thereby opposing PdfR activity [18] , [37] . To test this idea , we measured the responsiveness of LNvs to PDF with reduced mGluRA activity . We first generated a PDF response curve to determine the minimal PDF concentration that elicits an Epac1-camps response ( Figure S7A–C ) . We then tested whether expressing mGluRARNAi in Pdf>Epac1-camps LNvs altered this response ( Figure 6B–C ) using GluClRNAi as a control . We found that mGluRARNAi , but not GluClRNAi , significantly increased LNv responsiveness to PDF ( Figure 6C ) . Therefore , we propose that mGluRA acts in an opposite manner to PdfR and reduces intracellular cAMP . To test if cAMP links to synchronized clock protein oscillations , we built on the recent identification of Adenylate cyclase 3 ( AC3 ) as the specific Adenylate cyclase downstream of PdfR in LNvs [21] . We tested whether AC3 is required for LNv synchronization by reducing expression of AC3 using two independent RNAi lines ( Pdf>AC3TRiP RNAi and Pdf>AC3Vienna RNAi ) that reduce PDF responses in LNvs [21] . We found that expressing each RNAi line in LNvs desynchronized TIM expression in 35%–40% of LNv clusters and PDP1 expression in 28%–35% of LNv clusters ( Figure S8A–B and Table S1 ) . Reducing AC3 expression in LNvs also significantly increased desynchrony measured by standard deviation in TIM and PDP1 expression ( Figure S8C–D ) . We conclude that PdfR and mGluRA regulate LNv cAMP levels at different times of day , presumably by regulating AC3 activity . This leads to a model in which LNv cAMP rhythms are generated by extracellular signals , with PDF/PdfR increasing cAMP via AC3 around dawn , whereas glutamate inhibits the response of LNvs to PDF via mGluRA by inhibiting AC3 around dusk ( Figure 6D ) . cAMP oscillations then feed into the molecular clock , affecting TIM oscillations through an unknown mechanism , which will be a topic of future research . We next tested whether our findings from larvae held true for the more complicated adult circadian system . Because we observed the most dramatic effects on larval LNv synchrony by simultaneously reducing Pdfr and mGluRA in LNvs , we measured the synchrony of s-LNv molecular clocks in Pdf>PdfrRNAi+mGluRARNAi adult flies . We found that many more Pdf>PdfrRNAi+mGluRARNAi s-LNv clusters were desynchronized than control s-LNvs ( Figure 7A–C and Table S1 ) , with extensive desynchrony detected at CT15 and CT21 on day 2 in DD and CT3 on day 3 . TIM oscillations within Pdf>PdfrRNAi+mGluRARNAi s-LNvs also displayed a reduced amplitude compared to control s-LNvs , although the effect was less pronounced than in larvae ( Figure 7D ) . We also observed significant desynchrony at CT3 when either Pdfr or mGluRA expression was reduced in LNvs ( Figure S9A ) . We conclude that PDF and glutamate contribute to the robustness and synchrony of LNv oscillations in adult flies as well as in larvae . We next tested whether Pdf>PdfrRNAi+mGluRARNAi flies displayed behavioral defects . We compared the locomotor activity of Pdf>PdfrRNAi+mGluRARNAi flies to parental flies and to Pdf>GluClRNAi flies to control for nonspecific effects of RNAi in LNvs , as GluClRNAi does not affect larval LNv synchrony ( Figure 3 ) . Because Pdf>PdfrRNAi+mGluRARNAi flies have ∼24 h locomotor activity rhythms in DD , we conclude that s-LNv desynchrony does not affect period length ( Figure 8A and Table S2 ) . However , we noticed that the activity of Pdf>PdfrRNAi+mGluRARNAi flies was much less consolidated than control or Pdf>GluClRNAi flies , with bursts of activity visible in the subjective night when control flies are inactive ( Figure 8A ) . We calculated the average locomotor activity on the first 5 days in DD for each genotype . Pdf>PdfrRNAi , Pdf>mGluRARNAi , and Pdf>PdfrRNAi+mGluRARNAi flies displayed elevated levels of activity towards the end of subjective day and the beginning of subjective night ( ∼CT6–18 ) compared to control and Pdf>GluClRNAi flies ( Figure 8B ) . Thus altering PDF and/or glutamate inputs to LNvs increases nighttime activity . To further quantify these differences in nighttime activity , we used standard measures of sleep . We found decreased overall sleep levels when mGluRA expression was reduced either alone or with Pdfr ( Figure S9B ) . In contrast , reducing Pdfr expression alone had no significant effect on overall levels of sleep ( Figure S9B ) . Thus , we conclude that glutamate signals to LNvs regulate sleep levels , whereas PDF signals between LNvs do not regulate sleep . Next , we quantified the transition between wakefulness and sleep in the evening by measuring how quickly flies fell asleep after CT12 ( sleep latency ) . To ensure that any effects on the timing of sleep onset did not result from subtle period length differences between genotypes ( Table S2 ) , we measured sleep latency only on day 1 in DD when the phase of locomotor activity between genotypes is minimally affected by small period differences . We found that Pdf>PdfrRNAi+mGluRARNAi flies showed a significant increase in sleep latency compared to all other genotypes ( Figure 8C ) . Their average sleep latency of 213 min compared to 113 min for UAS-PdfrRNAi+UAS-mGluRARNAi/+ control flies exceeds the 30 min period length difference between these genotypes ( Figure 8C and Table S2 ) . We observed no significant effects when either mGluRA or Pdfr expression was reduced singly ( Figure 8C ) . Thus , we conclude that blocking PDF and glutamate inputs to LNvs increases evening activity and delays sleep onset timing . We did not observe a significant effect of reducing mGluRA or Pdfr expression on sleep latency under LD cycles ( Figure S9C ) , consistent with LD cycles synchronizing larval LNv clock oscillations ( Figures 2D , 5D–E , S1C , and S3C ) . Although increased LNv desynchrony may not cause the sleep latency defects observed , it is clear that normal Pdfr and mGluRA activity in LNvs is required for normal sleep in DD . However , it is possible that desynchrony and sleep latency defects are separate phenotypes resulting from abrogated intercellular communication between clock neurons .
Feedback is an essential component in the molecular clocks that drive circadian behavior in animals [22] . Here we demonstrate the importance of feedback across the circadian neural network to synchronize individual clock neurons . We showed that larval LNvs require two signals that cooperate to synchronize their clocks: PDF released at dawn from LNvs themselves and glutamate released by DN1s at dusk . The PDF signal received by PdfR in DN1s presumably also sets the phase of the DN1 clock ( Figure S2D ) [8] to correctly time glutamate release that is then perceived by mGluRA in LNvs . Thus a feedback loop seems to exist at the circuit level , maintaining synchronized LNv clocks in DD . Our experiments also reveal that synchronization of larval pacemaker neurons is a very active process , as LNv clocks were desynchronized 3 hours into the first subjective morning if they miss the dawn PDF signal . Consistent with the dual-synchronizer model , we see increased desynchrony when mGluRA and Pdfr expression is simultaneously reduced in LNvs ( Figures 4B and S5A–C and Table S1 ) . A DN1 glutamate signal released around dusk is required for circadian rhythms of light avoidance when received by the ionotropic glutamate receptor GluCl in LNvs [9] . We now show that DN1 glutamate also promotes LNv synchrony when received by the metabotropic glutamate receptor mGluRA in LNvs . Thus a single neurotransmitter plays two distinct roles in the Drosophila circadian circuit depending on the receptor that receives the signal in LNvs: a rapid behavioral response to light mediated via GluCl and longer-term regulation of the 24 hour molecular clock via mGluRA . Although mGluRA is not required for light avoidance [9] , we found that larvae with reduced expression of both Pdfr and mGluRA lose larval light avoidance rhythms . This is consistent with the loss of strong TIM protein oscillations in the LNvs of Pdf>PdfrRNAi+mGluRARNAi larvae . These defects in the LNv molecular clock probably alter the timing of signals from LNvs and/or the phases of other clock neurons within the circuit . This contrasts with the role of GluCl , where glutamate received by GluCl directly regulates light avoidance by inhibiting the response of LNvs to ACh , independent of the LNv molecular clock [9] . Synchronization of adult s-LNvs also depends on signaling via PdfR and mGluRA as >50% of s-LNv clusters were desynchronized at three of the four timepoints measured when expression of both Pdfr and mGluRA was reduced in LNvs . However , TIM oscillations in adult s-LNvs were not as severely impaired as in larval LNvs . The increased complexity of the adult clock neural circuit probably adds signals from neurons not present in larvae that promote synchronized and robust clock protein oscillations in adult s-LNvs . Because the molecular clock in adult Pdf>mGluRARNAi+PdfrRNAi flies still oscillates , it is not surprising that locomotor activity is also still largely rhythmic . However , the desynchrony and reduced amplitude of TIM oscillations in Pdf>mGluRARNAi+PdfrRNAi LNvs correlates with increased nighttime activity and sleep latency . Desynchrony and increased activity could be independent consequences of reduced glutamate and PDF receptivity in LNvs . An alternative possibility is that because the molecular clock regulates daily firing rhythms of clock neurons [34] , , individual LNvs remain active at the wrong time of day in a desynchronized LNv cluster , preventing sleep . Indeed , if LNvs are electrically coupled like SCN neurons [43] , then firing of a single LNv may cause the remaining LNvs in that cluster to fire earlier and/or later than programmed by their molecular clock . In addition , mistimed LNv signals in Pdf>PdfrRNAi+mGluRARNAi flies will affect the phases of other clock neurons in the circuit , which could also disrupt sleep timing . The loss of strong TIM protein oscillations in Pdf>PdfrRNAi+mGluRARNAi larval LNvs is surprising , as molecular clock oscillations in pacemaker neurons are often regarded as cell-autonomous . Our data extend conclusions from the SCN showing that the VIP receptor , VPAC2R , is required for synchronized molecular clocks [14] . By removing a second receptor simultaneously and by restricting our analyses to a defined subset of pacemaker neurons , we demonstrate that specifically blocking input signals to master pacemaker neurons has a dramatic effect on core clock protein oscillations . We observed a much stronger effect on TIM than on PDP1 oscillations in Pdf>mGluRARNAi+PdfrRNAi larval LNvs . It may be that TIM oscillations are relatively easily modified , allowing information from outside the cell to be integrated into the molecular clock , whereas a more robust PDP1 oscillation prevents LNvs overreacting to external stimuli . It is well-documented that TIM can be regulated at the posttranslational level in addition to transcriptional control [22] , whereas PDP1 protein levels closely follow Pdp1 RNA levels [44] . We propose that external signals mediated via PdfR and mGluRA mainly regulate the clock posttranslationally , and this is supported by recent findings [45]–[47] . Testing this idea will require developing a combination of transcriptional and translational reporter genes . VIP and VPAC2R synchronize the mammalian SCN in a cAMP/Ca2+-dependent manner [14] , [16] , [23] . VPAC2R is expressed more broadly than VIP , and some SCN neurons express both VIP and VPAC2R [48] . This is highly reminiscent of Drosophila , where Pdfr is found in both PDF+ and PDF– clock neurons [29] . Because VIP/VPAC2R and PDF/PdfR are functionally similar and because both mediate synchronization of pacemaker neurons , discoveries about the roles of PDF/PdfR in Drosophila should be relevant to understand how synchrony is maintained across circadian neural circuits in general . In both flies and mammals , a reciprocal relationship between synchrony and clock protein amplitude seems to allow pacemaker neurons to be more precise and robust timekeepers than individual neurons . Our data reveal a remarkable degree of conservation of clock circuit properties between mammals and Drosophila , echoing the conserved molecular basis of the circadian clock . The mechanisms promoting LNv synchrony in flies mirror the signaling pathways that make the SCN a more robust oscillator than other mammalian clock cells ( reviewed in [4] ) . VIP and PDF are both required to synchronize the molecular clocks in different neurons , both promote robust oscillations of clock proteins within clock neurons , and they both likely signal through Gαs and Adenylate cyclase [4] . We have not yet determined the signaling pathways downstream of cAMP that link to clock protein oscillations , but they likely include PKA and/or Epac , which affect circadian rhythms in flies and mammals [23] , [49] , [50] . Recent data show that PKA lies downstream of PDF and cAMP in Drosophila clock neurons ( see Figure 9 ) . In addition , Epac can regulate MAP kinase signaling , which is interesting because MAP kinase has also been proposed to lie downstream of PDF [51] . Our data provide evidence that the external signals that drive cAMP oscillations are received at different times of day . In the SCN , the amplitude of the cAMP rhythm is amplified by increased VIP signaling at dawn . cAMP levels decrease at dusk via falling VIP release and a release of the inhibition of Gαi/o by RGS16 [5] . However , the behavioral phenotypes of Rgs16−/− mice are modest , suggesting that additional signaling mechanisms operate . Based on the similarity of the mammalian and Drosophila systems , we predict that a second signal released around dusk is also required for normal SCN function . Two possible signals are GABA [52] and glutamate perceived via its metabotropic receptor [53] . In Drosophila , different clock neuron groups respond to specific environmental inputs such as light or temperature [10] , [11] . Thus the true function of the cAMP oscillator in flies and mammals may be to integrate information from diverse clock neurons into the molecular clocks of all clock neurons , generating a single time of day for an animal .
The following stocks used in this article have been described previously: Pdf01 [15] , Pdfrhan5304 [37] , cry13-Gal4 [54] , cry39-Gal4 [55] , Pdf-Gal80 [6] , tim ( UAS ) -Gal4 ( referred to in the text as tim-Gal4 for simplicity [56] ) , Pdfr-Gal4GMR18F07 [57] , UAS-Dti [58] , Pdf0 . 5-Gal4 [59] , UAS-CD8::GFP [60] , UAS-Epac1-camps [19] , UAS-mGluRARNAi [32] , UAS-Dicer-2 [61] , UAS-Gad1 [33] , UAS-GluClRNAi ( v105724 ) [9] , [62] , UAS-PdfrRNAi ( v42724 ) [61] , UAS-Shits [36] , mGluRA112b [63] , UAS-AC3Vienna RNAi ( v33217 ) , and UAS-AC3TRiP RNAi ( JF03041 ) [21] . UAS-mGluRARNAi , UAS-PdfrRNAi , Pdfrhan , mGluRA112b , Pdf01 , [Pdf-Gal4; Dcr-2] , [Pdf-Gal80; cry-Gal4] , UAS-Dti , and UAS-Shits stocks all carry the ls-tim allele [64]; thus , differences in TIM expression observed are not due to an inability of flies to express specific tim isoforms . All immunocytochemistry was carried out as in [44] . We used the following antibodies: rat αTIM ( from Amita Sehgal ) , rabbit αPDP1 [44] , mouse αPDF [65] , and rabbit αGFP ( Sigma , St . Louis , MO ) . Images were scanned on a Leica SP2 , SP6 , or SP8 confocal microscope , with the same microscope used for a single experiment . The beginning and end of TIM staining was used to establish the limits of confocal stacks . The mean staining intensity for each channel for each neuron in every confocal stack was quantified using FIJI ( http://pacific . mpi-cbg . de/wiki/index . php/Main_Page ) , with background levels of staining for each channel subtracted to control for variation in staining between brains . For each time course , the mean staining intensities for all LNvs in each brain lobe were averaged to give a single value for an LNv cluster . The average staining intensities per brain were then averaged to generate the time courses shown . We used two methods to measure LNv synchrony . In a simple binary method , we used a cutoff of 20 arbitrary units ( au ) above background to determine if a cell produced TIM or PDP1 or not . We chose 20 au as it is the lowest number where protein levels are convincingly visible above background . An LNv cluster was then scored as “desynchronized” if they contained a mixture of LNvs with and without detectable TIM or PDP1 , or “synchronized” if all four LNvs were the same . To more precisely quantify desynchrony , we also calculated the standard deviation in TIM or PDP1 mean staining intensities between individual LNvs within a single LNv cluster , producing a standard deviation in TIM or PDP1 staining intensity to use as a proxy for the level of desynchrony , allowing statistical comparisons of the data . Larval light avoidance assays were carried out as in [9] . For adult locomotor activity experiments , adults were entrained to 12∶12 LD cycles at 25°C for at least 3 days before transfer to DD . Locomotor activity was recorded using the DAM system ( TriKinetics , Waltham , MA ) . Basal levels of Epac1-camps FRET were used to measure cAMP levels . Larval brains were dissected and mounted in hemolymph-like saline . To minimize the time from dissection to imaging ( ∼1 hour ) , different genotypes were removed from DD , dissected , and scanned in the same order . LNv projections were imaged for CFP ( 460–490 nm ) and YFP ( 528–603 nm ) on an SP5 Leica confocal using a TD 458/514/594 dichroic 63× lens and 3× digital zoom at 100 Hz and 1024×1024 resolution , after excitation with a 458 nm laser . CFP and YFP levels were quantified using the Leica software . Background measurements of CFP and YFP were subtracted from raw CFP and YFP measurements and an average CFP to YFP ratio calculated for each image . For LNv projections , five boutons in each image were quantified for CFP and YFP as above and averaged to give a value per projection . Live cAMP imaging was performed on larval LNvs as described in [66] . Briefly , larval brains were dissected in hemolymph-like saline and mounted to the bottom of a 35-mm Falcon culture dish lid ( Becton Dickenson Labware , Franklin Lakes , NJ ) , fitted with a Petri Dish Insert ( PDI , Bioscience Tools , San Diego , CA ) . Brains were allowed to settle for 5–10 min to reduce movement during imaging . Images were acquired on an Olympus FV1000 laser-scanning microscope ( Olympus , Center Valley , PA ) through a 60× ( 1 . 1N/A W , FUMFL N ) Objective ( Olympus , Center Valley , PA ) using Fluoview software ( Olympus ) . The Epac1-camps FRET sensor was imaged by scanning frames at 1 Hz with a 440-nm laser . An SDM510 dichroic mirror was used to separate CFP and YFP emission . Regions of interest were drawn around single LNv cell bodies in Fluoview . Peptides were bath applied using a micropipette after 30 s of baseline imaging . PDF was dissolved in 0 . 01% DMSO and vehicle controls consisted of 0 . 01% DMSO delivered at the same volume as peptide applications ( 45 µL bath application into 405 µL hemolymph-like saline ) . The lowest PDF dose that evoked a consistent response ( 100 nM ) was used to assay differential responses of LNvs in which PDF or glutamate receptors had been knocked down in Figure 6 . PDF was used at 100 µM in Figure S2 . For each assay , no less than five larvae were imaged . Only one hemisphere was imaged per brain , and 1–4 LNv were imaged per brain . Processing and analysis of Epac1-camps data was as described [67] . Fly locomotor activity was recorded in 5 min bins , using the DAM system ( TriKinetics ) . Data analysis was performed using custom-written scripts in IgorPro ( Wavemetrix ) . Sleep was defined as periods of immobility >5 min . Sleep latency was calculated for each fly on each day as the time from CT12 until the first sleep bout . Locomotor activity was calculated as the average number of beam crossings per 30 min bins and averaged for each genotype over the first 5 days in DD .
|
Circadian molecular clocks are essential for daily cycles in animal behavior and we have a good understanding of how these clocks work in individual pacemaker neurons . However , the accuracy of these individual clocks is meaningless unless they are synchronized with one another . In this study we show that synchronizing the principal pacemaker LNv neurons in Drosophila larvae require two extracellular signals that are received at opposite times of day: namely , the neuropeptide PDF released from LNvs themselves at dawn and glutamate released from dorsal clock neurons at dusk . LNvs perceive both PDF and glutamate via G-protein coupled receptors that increase or decrease intracellular cAMP , respectively . The alternating phases of PDF and glutamate release generate oscillations in intracellular cyclic AMP . In addition to maintaining synchrony between LNvs , this rhythm is also required for molecular clock oscillations in individual larval LNvs . We show that disruption of PDF and glutamate signaling also reduces synchrony in adult LNvs . This impairs the oscillations of clock proteins and flies have delayed onset of sleep . Our data highlight the importance of intercellular signaling in ensuring synchrony between clock neurons within the circadian network . Our findings help extend the conservation of clock properties between Drosophila and mammals beyond clock genes to include clock circuitry .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"circadian",
"rhythms",
"biochemistry",
"molecular",
"neuroscience",
"behavioral",
"neuroscience",
"animal",
"genetics",
"cellular",
"neuroscience",
"genetics",
"chronobiology",
"biology",
"and",
"life",
"sciences",
"neuroscience",
"circadian",
"oscillators"
] |
2014
|
Differentially Timed Extracellular Signals Synchronize Pacemaker Neuron Clocks
|
Two platyhelminths of biomedical and commercial significance are Schistosoma mansoni ( blood fluke ) and Fasciola hepatica ( liver fluke ) . These related trematodes are responsible for the chronic neglected tropical diseases schistosomiasis and fascioliasis , respectively . As no vaccine is currently available for anti-flukicidal immunoprophylaxis , current treatment is mediated by mono-chemical chemotherapy in the form of mass drug administration ( MDA ) ( praziquantel for schistosomiasis ) or drenching ( triclabendazole for fascioliasis ) programmes . This overreliance on single chemotherapeutic classes has dramatically limited the number of novel chemical entities entering anthelmintic drug discovery pipelines , raising significant concerns for the future of sustainable blood and liver fluke control . Here we demonstrate that 7-keto-sempervirol , a diterpenoid isolated from Lycium chinense , has dual anthelmintic activity against related S . mansoni and F . hepatica trematodes . Using a microtiter plate-based helminth fluorescent bioassay ( HFB ) , this activity is specific ( Therapeutic index = 4 . 2 , when compared to HepG2 cell lines ) and moderately potent ( LD50 = 19 . 1 μM ) against S . mansoni schistosomula cultured in vitro . This anti-schistosomula effect translates into activity against both adult male and female schistosomes cultured in vitro where 7-keto-sempervirol negatively affects motility/behaviour , surface architecture ( inducing tegumental holes , tubercle swelling and spine loss/shortening ) , oviposition rates and egg morphology . As assessed by the HFB and microscopic phenotypic scoring matrices , 7-keto-sempervirol also effectively kills in vitro cultured F . hepatica newly excysted juveniles ( NEJs , LD50 = 17 . 7 μM ) . Scanning electron microscopy ( SEM ) evaluation of adult F . hepatica liver flukes co-cultured in vitro with 7-keto-sempervirol additionally demonstrates phenotypic abnormalities including breaches in tegumental integrity and spine loss . 7-keto-sempervirol negatively affects the viability and phenotype of two related pathogenic trematodes responsible for significant human and animal infectious diseases . This plant-derived , natural product is also active against both larval and adult developmental forms . As such , the data collectively indicate that 7-keto-sempervirol is an important starting point for anthelmintic drug development . Medicinal chemistry optimisation of more potent 7-keto-sempervirol analogues could lead to the identification of novel chemical entities useful for future combinatorial or replacement anthelmintic control .
Schistosomiasis and fascioliasis are Neglected Tropical Diseases ( NTDs ) caused by related parasitic blood ( Schistosoma sp . including Schistosoma mansoni ) and liver ( Fasciola sp including Fasciola hepatica ) flukes found within the phylum Platyhelminthes . These NTDs are responsible for chronic conditions of biomedical and veterinary significance and collectively affect a considerable proportion of the world’s human and animal populations . Globally , approximately 200 million people are currently afflicted by schistosomiasis , with this chronic disease being most prominent in tropical/subtropical regions of poverty-stricken , rural areas [1] . While fascioliasis is one of the most important parasitic diseases of ruminant livestock animals , it also negatively impacts 2 . 4 to 17 million humans worldwide by inducing chronic liver pathologies in infected individuals [2] . Control of these two NTDs remains largely centred on the use of large-scale chemotherapy administered to infected individuals in high prevalence areas . Praziquantel ( PZQ ) is presently the gold standard drug of choice for schistosomiasis control due to its safety , low cost , and activity towards the adult life stage of the three major , human-infective species ( S . mansoni , Schistosoma haematobium and Schistosoma japonicum ) [3] . For fascioliasis chemotherapy , triclabendazole ( TCBZ ) remains the drug of choice and is the only available compound effective against both adult and juvenile liver fluke lifecycle stages [4] . In both cases , the over-reliance on a single drug class for maintaining the future of blood and liver fluke control has generated significant concerns that drug resistant flukes could eventually develop ( Schistosoma ) or increase in prevalence ( Fasciola ) . Although potential new replacement or combinatorial anthelmintic compounds have recently been identified [5–11] , concerns have been raised regarding the insufficient activity in this research area due to the perceived sustainability of current options [12 , 13] . Moving forward , it is becoming evident that a relatively untapped source of chemical entities for developing new anthelmintics is plants and their natural products [14–16] . Plants possess a diverse arsenal of chemical reserves that have evolved to aid in plant protection and competition , and this has been exploited for medicinal uses for centuries [17] . For example , while artemisinin ( derived from Artemisia annua ) and derivatives represent the current , frontline anti-malarials [18] , they also display significant activity against Schistosoma and Fasciola [7 , 19] . Further interrogation of the chemical diversity found in plants suggests that terpenoids ( terpenes ) are the most abundant and numerous of the plant secondary products and they possess features , including considerable structural variation [20] , that may be exploitable as next-generation anthelmintics . In clear support of this , previous studies have demonstrated that topical terpenoid application to the skin effectively prevents schistosome penetration , providing an innovative chemoprophylactic method to avert schistosomiasis [21 , 22] . However , it is presently unclear how terpenoids affect schistosome biology or , indeed , if they have activity against other parasitic trematodes , including F . hepatica , due to lack of detailed investigations . In this study a diterpenoid ( 7-keto-sempervirol ) extracted and purified from Lycium chinense ( common name Wolfberry ) , a plant traditionally used in Asian medicine since 1000 AD [23] , was thoroughly investigated for its anthelmintic properties against S . mansoni and F . hepatica larvae and adults . Here , we demonstrate that 7-keto-sempervirol selectively kills larval stages of each trematode and induces tegumental damage , as well as motility disruption , in both hermaphroditic and dioecious adults . Furthermore , 7-keto-sempervirol dramatically inhibits the developmental maturation and oviposition of phenotypically normal S . mansoni eggs . Collectively these findings suggest that diterpenoids , such as 7-keto-sempervirol , have broad activities against related trematodes and should be further investigated as starting points for combating both schistosomiasis and fascioliasis .
All procedures performed on mice adhered to the United Kingdom Home Office Animals ( Scientific Procedures ) Act of 1986 ( project license PPL 40/3700 ) as well as the European Union Animals Directive 2010/63/EU and were approved by Aberystwyth University’s ( AU ) Animal Welfare and Ethical Review Body ( AWERB ) . The diterpenoid 7-keto-sempervirol was isolated from the root of the temperate plant Lycium chinense ( The Organic Herb Trading Company , Milverton , UK ) . Two kilograms of ground material was extracted in CH2Cl2 using a Soxhlet system . Nine fractions were obtained using Biotage 75 flash chromatography ( silica gel , eluted with a step gradient of increasing polarity: n-hexane—ethylacetate—methanol ) . 7-keto-sempervirol was isolated from these fractions via reverse-phase preparative HPLC ( C18 preparative column , eluted with a gradient- water: acetonitrile: 0 . 1% trifluroacetic acid in acetonitrile , with UV detection ) . The compound structure was then determined by UV , 1H NMR , 13C NMR and LC-MS analyses and also through direct comparison with the literature . The compound auranofin ( Sigma-Aldrich , UK ) , a thioredoxin glutathione reductase ( TGR ) inhibitor capable of killing schistosomes , was used at 70 μM ( final concentration ) [24] as a dead control for the Helminth Fluorescent Bioassay ( HFB ) [25] . Both compounds ( auranofin and 7-keto-sempervirol ) were stored at -80°C in DMSO until use . S . mansoni ( NRMI strain ) cercariae were shed from infected Biomphalaria glabrata snails ( NMRI strain ) by exposure to 2 hours of light in an artificially heated room ( 26°C ) . Collected cercariae were mechanically transformed into schistosomula as previously described [26] and resuspended in culture media comprising DMEM ( Dulbecco’s Modified Eagle Medium , Sigma-Aldrich , UK ) lacking phenol red , but containing 4 . 5 g/l glucose , 2 mM L-glutamine , 200 U/ml penicillin and 200 μg/ml streptomycin ( all Sigma-Aldrich , UK ) . Schistosomula were then transferred to a black sided , flat-bottom ( optically clear ) 96-well microtiter plate ( Star Lab , UK ) at a density of 1000 parasites per 100 μl well . The plate was then incubated at 37°C and 5% CO2 for 24 hr to allow parasite equilibration . S . mansoni adult parasites were recovered by hepatic portal perfusion from Tuck Ordinary mice ( Harlan Laboratories , UK ) experimentally infected seven weeks earlier with 200 cercariae . Washed adult worms were cultured in DMEM containing phenol red , 4 . 5 g/l glucose , supplemented with 10% foetal calf serum , 2 mM L-glutamine , 200 U/ml penicillin , 200 μg/ml streptomycin ( all Sigma-Aldrich , UK ) . Schistosome cultures were maintained at 37°C in a humidified atmosphere containing 5% CO2 for 24 hr prior to further manipulations . For egg laying experiments , five adult worm pairs per ml of culture medium ( 48-well tissue culture plates ) were cultivated as above for a total of 72 hr ( in the presence/absence of 7-keto-sempervirol ) . Eggs were counted after 72 hr and classified as normal ( oval and containing a fully-formed lateral spine ) or abnormal ( lacking an oval shape and fully formed lateral spine ) . F . hepatica metacercariae ( Baldwin Aquatic , Inc . , OR , USA ) were transformed into newly excysted juveniles ( NEJs ) and equilibrated for 4 hr in Fasciola saline according to established methodologies [27] . After equilibration , NEJs were distributed into black sided , flat-bottom ( optically clear ) 96-well microtiter plates ( Star Lab ) at a density of 100 parasites per 100 μL well and cultured at 37°C in a humidified atmosphere containing 5% CO2 subject to further treatments . F . hepatica adult flukes were recovered from sheep livers ( Ridgeway Research , UK ) , washed and cultured in media comprising DMEM ( lacking phenol red ) containing 4 . 5 g/l glucose ( Sigma , UK ) . This was supplemented with 2 . 2 mM Ca ( C2H3O2 ) 2 , 2 . 7 mM MgSO4 , 61 mM glucose , 15 mM HEPES , 1 μM serotonin and 5 μg/ml gentamycin ( all Sigma , UK ) . Adult fluke were placed in 12-well tissue culture plates , 1 parasite per well in 1 ml of culture media and cultivated for 48 hr at 37°C in a humidified atmosphere containing 5% CO2 . The human HepG2 cell line purchased from the European collection of cell cultures ( ECACC 85011430 ) was grown to confluency in culture media comprising EMEM ( Eagle’s Minimum Essential Medium ) supplemented with 10% bovine calf serum , 2 mM L-glutamine , 1% non-essential amino acid solution ( all Sigma Aldrich , UK ) and 100 U/ml penicillin and 100 μg/ml streptomycin ( Invitrogen , UK ) . Cells were resuspended by trypsinisation ( 0 . 25% v/v for 5 min ) , rinsed and distributed into black sided , flat-bottom ( optically clear ) 96-well microtiter plates ( StarLab , UK ) at 50 μl/well ( 1x105 cells/ well ) . The plate was equilibrated in a humidified atmosphere containing 5% CO2 at 37°C for 2 hrs and then test compounds were added to relevant wells to create a total well volume of 100 μl/well . Live controls included cells incubated in the same concentration ( 1% v/v ) of DMSO that was used in the experimental ( compound treated ) wells . Dead controls included cells incubated with 1% v/v Triton X-100 ( Sigma-Aldrich ) [28] . Tissue culture plates were returned to a humidified atmosphere containing 5% CO2 at 37°C for a further 24 hrs . The CellTitre-Glo reagents were prepared according to the manufacturer’s instructions ( Promega , UK ) . Tissue culture plates were equilibrated to RT for 30 min before CellTitre-Glo reagents were distributed into each plate well at 100 μl/well ( total volume 200 μl/well ) and mixed on an orbital shaker for 2 min . Cells were then stabilised for 10 min at RT and bubbles that may have formed were removed using a sterilised needle . After this time luminescent signal was read utilising the BMG Labtech Polarstar Omega Plate Reader and exported into Microsoft Excel for further analysis and conversion into percentage viability . The HFB was utilised to objectively determine the viability of schistosomula and NEJ parasites co-cultured in the presence of 7-keto-sempervirol , auranofin ( 70 μM ) [8 , 29] , DMSO ( 1% v/v ) or media only . HFB methodology applied to schistosomula was performed as previously described [25] with a minor alteration . Here , as indicated in the schistosomula culture methods , parasite viability was measured in the absence of foetal calf serum ( FCS ) . A slight modification of the HFB was also applied to NEJs and included assaying only 100 parasites/well ( as opposed to 1000 schistosomula/well ) . A total of 100 μl of test substance ( varying concentrations ) was added to each well containing 100 μl of suspended parasites and cultured for 24 hr in a humidified atmosphere at 37°C before the HFB was performed . The in vitro activity of 7-keto-sempervirol on S . mansoni adult worms and F . hepatica NEJs was assessed by measuring motility disturbances and morphological variations in comparison to an appropriate live control ( DMSO , 1% v/v ) . The scoring matrix used to assess S . mansoni adult worm viability was based on the standard operating procedure for compound screening at the Special Programme for Research and Training in Tropical Diseases , World Health Organization , WHO-TDR as previously described [30] . Motility was numerically scored from 0–4 with 0 being total absence of all motility , 1 indicating absence of motility other than gut peristalsis , 2 representing minimal activity such as occasional head and tail movement , 3 demonstrating slow activity and 4 signifying normal activity . Morphological descriptors of the parasite were also recorded during phenotypic assessment and included ‘knots’ developing along the normally cylindrical body line of the adult worms and the sloughing , blebbing or tubercle swelling of the tegument . Scoring matrix assessment of the worms was performed at 24 , 48 and 72 hr post treatment . NEJ phenotyping ( a supportive metric of the HFB ) was performed using values derived from both movement indices and morphologic formats as previously described [31 , 32] . The movement score ranges from 1–5 with 1 representing good/normal movement and 5 representing a complete absence of movement . The morphologic score ranges from 1–6 with 1 representing a good/normal phenotype and 6 signifying a severely dissolved/granulated parasite . This scoring matrix assessment ( values derived from summation of both movement and morphology metrics ) was conducted at 24 hr post treatment . Prior to scanning electron microscopy ( SEM ) analysis , adult schistosomes and liver flukes were first fixed in 2 . 5% ( w/v ) gluteraldehyde in phosphate buffered saline ( PBS ) for 24 hr at 22–24°C . After fixation , worms were washed 3 times in 1 X PBS , pH 7 . 5 and stored in the same buffer at 4°C until use [33] . Fixed parasite material was subsequently washed twice in double distilled water and dehydrated in ascending acetone percentages ( 30 , 50 , 70 , 80 , 90 , 95 , and 100% ) for 15 minutes each as previously described [33 , 34] . Dehydrated worms were then critically dried ( Polaron Critical Point Dryer E3000 ) for 1 hr in 100% acetone ( critical point of CO2 is 7 . 38 MPa at a temperature of 31°C ) . Critically dried worms were mounted on aluminium stubs , sputter coated with platinum/palladium and observed under a Hitachi S-4700 field emission scanning electron microscope [34] . For laser scanning confocal microscopy ( LSCM ) , adult schistosomes were first fixed in a solution containing 2% ( v/v ) acetic acid , 25% ( v/v ) formalin , 48% ( v/v ) ethanol and 25% ( v/v ) H2O at room temperature for 24 hr . After fixation , worms were stained with Langeron’s Carmine as previously described [35] . Worms were then mounted on glass microscope slides in DPX ( distyrene , plasticiser , xylene ) as previously described [36] and observed using a Leica TCS SP5II laser scanning confocal microscope , equipped with a 40X oil immersion objective and a 488 nm Argon laser and a 561nm DPSS laser . S . mansoni eggs laid by adult females after 72 hr in vitro cultures ( +/- 7-keto-sempervirol ) were fixed in a 10% formaldehyde solution to prepare the biological tissue for microscopy . Fixed eggs were then phenotypically assessed by an ImageXpress micro XL High Content Imager ( Molecular Devices , UK ) . Fluorescent images were obtained using a FITC filter ( 40x magnification ) . Schistosomula subjected to the HFB were visualised on the ImageXpress micro XL High Content Imager using FITC ( to identify fluorescein diacetate positive parasites ) and TRITC ( to identify propidium iodide positive parasites ) filters ( 10X magnification ) . A One Way ANOVA was utilised to identify any significant differences between more than three treatment groups followed by post hoc testing with the Tukey’s test to identify significantly different means ( means that do not share a letter are significantly different ) . Student’s t-test was utilised to determine significant differences between two treatment groups .
Due to the previously described action of select terpenes on schistosome viability [21 , 22] and definitive host penetration [37] , we became interested in assessing the potential anthelmintic activity of 7-keto-sempervirol , a diterpenoid purified from Lycium chinense ( Fig . 1 ) . Employing the HFB [25] , a titration series of 7-keto-sempervirol ( 100 μM–1 . 575 μM ) was first used to assess the ability of this diterpenoid to affect schistosomula viability during in vitro co-culture ( Fig . 2 ) . At high 7-keto-sempervirol concentrations ( 100–25 μM ) , almost all of the schistosomula were killed or severely affected as indicated by low percent viability values ( Fig . 2A ) and fluorescent microscopic images of individual parasites ( PI positive parasites > FDA positive parasites ) ( Fig . 2B ) . Lower 7-keto-sempervirol concentrations ( between 6 . 25 μM–1 . 575 μM ) had little effect on schistosomula viability and phenotype when compared to the DMSO control parasites . Based on these titration experiments , an LD50 of 19 . 1 μM was derived for 7-keto-sempervirol on the schistosomula lifecycle stage . A parallel set of titration experiments was additionally performed with the human HepG2 cell line ( S1 Fig . ) and demonstrated a 7-keto-sempervirol derived LD50 of 80 μM . 7-keto-sempervirol , therefore , demonstrates a therapeutic index of 4 . 2 towards the intra-mammalian schistosomula lifecycle stage . Based on its anti-schistosomula activity , 7-keto-sempervirol’s ability to affect adult schistosome motility , surface-tegument morphology and egg development was subsequently measured using both WHO-adopted indices and microscopic measures . Here , 7-keto-semperivol displayed a significant effect on both male and female worm motility at the highest concentration used in this study ( 100 μM ) at 24 hr and 48 hr post treatment compared to the DMSO ( 24 hr ) control group ( Fig . 3 ) . For both genders , there was no significant difference between 48 hr and 72 hr treatments at this concentration ( Fig . 3 ) nor in DMSO treated worms cultivated for 48 hr or 72 hr ( S2 Fig . ) . Interestingly , female worms ( unlike males ) displayed a 7-keto-semperivol-induced hyperactivity at 24 hr post-treatment ( Fig . 3 ) . There were motility and phenotypic discrepancies observed for some individuals cultured in the presence of 10 μM 7-keto-sempervirol , but these differences were not significantly different compared to the DMSO control group ( S3 Fig . ) . Scanning electron microscopy ( SEM ) of adult male worms co-cultured in the presence of 7-keto-sempervirol ( 100 μM for 72 hr ) further revealed tubercle swelling , spine loss/shortening and surface holes across the tegument ( Fig . 4 ) . Laser scanning confocal microscopy ( LSCM ) of adult females cultured in the presence of 7-keto-sempervirol ( 100 μM for 24 hr ) indicated the presence of irregularly shaped in utero eggs ( Fig . 5 ) . When compared to control eggs ( parasites treated with 1% v/v DMSO , Fig . 5A ) , these abnormal eggs lacked regular autofluorescence as well as fully formed eggshells and were missing the characteristic lateral spines indicative of the species ( Fig . 5B ) . Due to these phenotypic deficiencies in egg development , the effect that 7-keto-sempervirol had on in vitro schistosome oviposition was also assessed ( Fig . 6 ) . Here , 7-keto-sempervirol induced a concentration-dependent ( 100 μM > 10 μM ) ability to inhibit the deposition of phenotypically normal schistosome eggs with a complete lack of oviposition observed in wells containing the highest amount of compound ( 100 μM ) ( Fig . 6A ) . When compared to control wells ( schistosomes co-cultured with 1% v/v DMSO ) , eggs deposited in wells containing 7-keto-sempervirol ( 10 μM ) displayed a range of abnormal phenotypes ( Fig . 6B ) similar to those observed in utero ( Fig . 5B ) . These phenotypes included non-oval shapes , lack of lateral spines and irregular autofluorescence . To assess 7-keto-sempervirol’s activity on F . hepatica NEJs , two complementary methodologies were employed ( Fig . 7 ) . The first methodology , using well-established motility and phenotypic metrics [31 , 32] , indicated that 7-keto-sempervirol induced a negative concentration-dependent effect on NEJ movement and viability ( Fig . 7A ) . This finding was supported by fluorescent microscopy of NEJs co-stained with the discriminatory viability dyes FDA and PI ( Fig . 7A ) . The second methodology , using the HFB as a more objective method for determining NEJ viability [25] , confirmed this concentration-dependent effect and established an LD50 of 17 . 7 μM for 7-keto-sempervirol against F . hepatica NEJs ( Fig . 7B ) . When compared to the mammalian HepG2 cell line ( S1 Fig . ) , 7-keto-sempervirol displayed an anti-NEJ therapeutic index of 4 . 5 . To identify whether 7-keto-sempervirol also affected the surface integrity of F . hepatica adults , similar to S . mansoni ( Fig . 4 ) , SEM analyses were performed on adult liver flukes co-cultured with this diterpenoid ( Fig . 8 ) . Here , in comparison to control flukes incubated with 1% ( v/v ) DMSO ( Fig . 8A-C ) , prolonged ( 48 hr ) exposure to 7-keto-sempervirol ( 50 μM ) induced substantial spine shortening and spine loss that was apparent on both dorsal and ventral sides of the organism ( Fig . 8D-F ) .
Current control efforts aimed at reducing disease prevalence of schistosomiasis and fascioliasis are predominantly based on mono-chemotherapy administration of praziquantel ( PZQ ) and triclabendazole ( TCBZ ) , respectively . Nevertheless , worries about the development of PZQ resistant blood flukes and the spread of TCBZ resistant liver flukes coupled to the slow progression of immunoprophylactic vaccines , have notably increased the number of new anthelmintic drug discovery projects initiated throughout the last decade . While our efforts in this area have successfully leveraged interdisciplinary techniques to identify drug targets [38–42] and to develop high-throughput drug screening methodologies [25] , we have only recently begun characterising the detailed anthelmintic activities of defined chemical entities . Here , we report the findings from one such investigation and demonstrate that a diterpenoid ( 7-keto-sempervirol ) derived from Lycium chinense has key properties useful in the development of a broad-spectrum agent active against both blood and liver flukes . First amongst the examined properties was the ability of 7-keto-sempervirol to kill , in a concentration dependent manner , both S . mansoni schistosomula ( Fig . 2 ) and F . hepatica newly excysted juveniles ( NEJs ) ( Fig . 7 ) during in vitro culture . While this anti-larval effect was not overly potent ( LD50 = 17 . 7 μM for schistosomula and LD50 = 19 . 1 μM for NEJs ) , it was selective ( therapeutic index = 4 . 2–4 . 5 ) and reproducible ( e . g . two independent measurements of NEJ viability provided confirmatory results , Fig . 7 ) . Importantly , this moderate anti-larval effect translated into pronounced phenotypic abnormalities associated with the tegumental surface of both fluke species ( Figs . 4 and 8 ) . Here , and similar to artemisinin’s effect on S . mansoni [7] and artemether/artesunate’s effect on F . hepatica [43] , 7-keto-sempervirol induced tubercle swelling , tegumental breaches and spine loss in both flukes . This degree of surface damage ( also seen in [9 , 33 , 44] ) , if replicated in vivo , would severely compromise the barrier function of this protective layer [45] and negatively affect the ability of both flukes to remain in hostile definitive host environments ( blood and bile ) . Whether 7-keto-sempervirol’s ability to disrupt fluke surface architecture is similar in action to the protonophoric properties of membrane-disruptive , plant-derived quinones ( anti-tumour derived diterpenoids ) [46] is currently unknown , but is consistent with our observations . Further work is required to develop this line of investigation . Another interesting property of 7-keto-sempervirol is its ability to progressively paralyse adult schistosomes during in vitro culture ( Fig . 3 ) . This feature , in addition to tegumental alterations , is similar to the effects induced by praziquantel [47] treatment of adult schistosomes and suggests that this diterpenoid may also perturb calcium homeostasis [3] . Interestingly , the antifungal activities of two related mono-terpenoid phenols ( carvacrol and thymol ) also involve dis-regulation of intracellular calcium balances and suggest a common mechanism of action amongst terpenes [48] and praziquantel . In support of this , treatment of Saccharomyces cerevisiae with either carvacrol or thymol leads to the differential expression of similar gene categories as those found in praziquantel treated schistosomes [47] . These include genes associated with drug transportation across membranes , ribosomal biogenesis , autophagy , heat shock responses , rRNA processing , tRNA processing and pyrimidine metabolism [48] . Whether 7-keto-sempervirol is capable of differentially regulating a similar repertoire of schistosome gene products is currently unknown , but the time-dependent paralysis of adults co-cultured with this diterpenoid in vitro would suggest that this hypothesis is likely . Gender specific transcriptomic responses to 7-keto-sempervirol may also exist , similar to those induced by praziquantel [47] , and could explain the differential sensitivity and hyperactivity observed in females ( but not males ) at 24 hr post treatment ( Fig . 3 ) . While 7-keto-sempervirol/adult F . hepatica co-cultures were not kinetically studied in this investigation , it would be useful to ascertain in future experiments if the diterpenoid-induced , time-dependent paralysis of dioecious blood flukes translates to hermaphroditic liver flukes . A final anthelmintic property examined for 7-keto-sempervirol , based on its established capacity to damage surface membranes ( Figs . 4 & 8 ) and impede schistosome motility ( Fig . 3 ) , was its potential to affect egg production during in vitro cultures ( Figs . 5 & 6 ) . As oviposition is required for schistosome lifecycle transmission and is responsible for chronic definitive host immunopathology , identification and progression of compounds that inhibit this process ( even if they do not kill the parasite ) are likely to benefit schistosomiasis control . Interestingly , the concentration-dependent ( 100 μM > 10 μM ) , 7-keto-sempervirol mediated , inhibition of phenotypically-normal egg maturation ( Fig . 5 & 6B ) and egg production ( Fig . 6A ) observed here are strikingly similar to those findings reported for schistosome pairs co-cultured with kojic acid [41] . Kojic acid exerts its effects on eggshell sclerotisation ( hardening or tanning ) and schistosome oviposition by inhibiting the phenol oxidase activities of S . mansoni tyrosinases 1 and 2 ( SmTYR1/SmTYR2 ) . Whether 7-keto-sempervirol also targets the phenol oxidase activity of SmTYR1/SmTYR2 is currently unknown . However , as egg maturation , sclerotisation and oviposition are complex processes involving cytosine methylation [40] , tyrosine kinase-mediated phosphorylation [49] serine/threonine kinase-mediated phosphorylation [50] , fatty acid oxidation [51] and TGF-beta signalling [52] , 7-keto-sempervirol could theoretically act upon any component of these ( or other ) diverse biological processes . Limitations in obtaining sufficient quantities of adult F . hepatica prohibited the assessment of 7-keto-sempervirol’s effect on liver fluke egg production . Nevertheless , as the process of eggshell formation is thought to occur via similar mechanisms in both schistosomes and liver flukes [53] , it is likely that 7-keto-sempervirol will also inhibit F . hepatica oviposition . Here , we provide complementary evidence that supports the further progression of 7-keto-sempervirol as a dual anthelmintic against S . mansoni and F . hepatica parasites . Although these findings add value to the growing medicinal properties described for diterpenoids [54–56] and expand upon their anti-schistosomal chemoprophylactic characteristics [21 , 22] , improving their selective potency is a necessary next step in their development as wide-acting anthelmintics . Whilst defining a specific mechanism of 7-keto-sempervirol action and experimental animal model verification was beyond the scope of this investigation , our results suggest that membrane biogenesis/maintenance and calcium homeostasis/stress are likely contributing to the diverse in vitro phenotypes observed in both fluke species . Activity against both larvae and adult fluke stages broaden the window of therapeutic opportunity and , if replicable in vivo , would provide a useful alternative to currently used anthelmintics within the biomedical and animal health landscapes .
|
Schistosomiasis and fascioliasis are caused by two related trematodes found within the phylum Platyhelminthes ( flatworms ) , and are classified as neglected diseases of poverty due to their effects on people living in the most underprivileged areas of the world . With no vaccine currently near development , and the existing strategy for global control based on the over-reliance on single-class chemotherapies , there is an urgent requirement for the identification of next generation anthelmintics . Here we demonstrate that 7-keto-sempervirol , a natural product derived from Lycium chinense , displays dual anthelmintic activity towards both Schistosoma mansoni ( causative agent of schistosomiasis ) and Fasciola hepatica ( causative agent of fascioliasis ) . Utilising objective and phenotypic matrices , we show this activity to be selective ( compared to a human cell line ) and moderately potent against S . mansoni and F . hepatica larvae . This anti-larval effect translates into additional activity against both S . mansoni and F . hepatica adults where 7-keto-sempervirol induces phenotypic abnormalities including tegumental damage , motility disruption and oviposition inhibition . Due to 7-keto-sempervirol’s anthelmintic activity against multiple life stages of two parasitic trematodes , we contend that this starting chemical scaffold could be used to develop more effective compounds useful in controlling important parasites of biomedical and commercial relevance .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
The Diterpenoid 7-Keto-Sempervirol, Derived from Lycium chinense, Displays Anthelmintic Activity against both Schistosoma mansoni and Fasciola hepatica
|
We assess how presymptomatic infection affects predictability of infectious disease epidemics . We focus on whether or not a major outbreak ( i . e . an epidemic that will go on to infect a large number of individuals ) can be predicted reliably soon after initial cases of disease have appeared within a population . For emerging epidemics , significant time and effort is spent recording symptomatic cases . Scientific attention has often focused on improving statistical methodologies to estimate disease transmission parameters from these data . Here we show that , even if symptomatic cases are recorded perfectly , and disease spread parameters are estimated exactly , it is impossible to estimate the probability of a major outbreak without ambiguity . Our results therefore provide an upper bound on the accuracy of forecasts of major outbreaks that are constructed using data on symptomatic cases alone . Accurate prediction of whether or not an epidemic will occur requires records of symptomatic individuals to be supplemented with data concerning the true infection status of apparently uninfected individuals . To forecast likely future behavior in the earliest stages of an emerging outbreak , it is therefore vital to develop and deploy accurate diagnostic tests that can determine whether asymptomatic individuals are actually uninfected , or instead are infected but just do not yet show detectable symptoms .
As an example of a disease for which initial cases are frequently not followed by major outbreaks , and with a significant delay between infection and emergence of symptoms , we consider Ebola virus disease . All five strains of the genus Ebolavirus cause severe acute illness , with early non-specific symptoms including asthenia and myalgia typically followed by nausea , vomiting , hemorrhagic symptoms and , in a significant proportion of cases , death [37] . There are reports of cases of Ebola in remote villages in Central and West Africa every few years [38] , hypothesized to be initiated by spillover from reservoirs of infection in wild animal populations , with fruit bats most often implicated as the reservoir host [39] . Often there is no sustained human-to-human transmission , and initial cases do not lead to large outbreaks . However , since 1976 there have been twenty-five distinct reports of primary infection in humans , of which sixteen have led to epidemics causing more than twenty deaths . The largest ever Ebola outbreak started in Guinea in December 2013 and subsequently spread to and caused widespread transmission in Liberia and Sierra Leone , with additional cases in Nigeria , Mali , Senegal , Spain , USA , UK and Italy . This epidemic caused more than 11 , 000 fatalities before it was declared officially over by the World Health Organization on 14th January 2016 , although an additional death was confirmed the following day and additional small flare-ups are still possible [40] . Modeling studies of Ebola have tended to focus on parameter estimation [41–43] and the potential effects of disease control [31 , 32 , 37 , 44] . Here , we instead focus on using an existing epidemiological model fitted to data from the outbreak in Uganda that killed 224 people in 2000 [45] to show how presymptomatic infection affects our ability to predict whether or not reports of initial cases will go on to cause a major outbreak . Ebola is therefore a motivating example for our investigation into how presymptomatic infection affects the predictability of infectious disease epidemics . However , since presymptomatic infection is ubiquitous , our conclusions are applicable to a wide range of pathogens .
We use simulations of stochastic compartmental epidemic models to drive our analyses . The models assume that , at any time , every member of the population belongs to a compartment describing their infection and symptom status . In a single realization of the model , whether or not an individual becomes infected is a random process . If an individual does become infected , then the model generates the time at which the individual is first infected , the time at which symptoms first appear and the time at which the individual either dies or recovers . These times are simply those at which the individual passes into the relevant compartments of the model . We therefore produce a “dataset” for the start of an outbreak by running a simulation model . We “freeze” the outbreak at the time of the fourth death , and calculate two quantities using the model ( Fig 1 ) : the probability of a major outbreak given complete observation of presymptomatic cases ( hereafter referred to as the “true” probability of a major outbreak ) , and the estimate of this probability that only uses data on the timings of symptoms and deaths and not the times at which individuals are initially infected . In this estimated probability , the presymptomatic cases remain hidden and the number of presymptomatic infected individuals is estimated from the data on symptoms and deaths . For an individual outbreak , a confidence interval can be constructed around the point estimate that we consider . For the outbreak in Fig 1 , the distributional estimate of the number of exposed individuals leads to a 95% confidence interval for the current number of infected individuals of [1 , 6] , which corresponds to an extremely wide 95% confidence interval for the probability of a major outbreak of [0 . 24 , 0 . 78] . The point estimate corresponds to a weighted sum over the distributional estimate of the probability of a major outbreak . Our initial analysis considers a simplified , SEIR model with exponential waiting times in each compartment . In the SEIR model , presymptomatic infecteds are confined to the uninfectious , latently infected ( E ) class . We relax this assumption later , and also consider the case where the waiting times follow gamma , rather than exponential distributions . Recent modeling work , focused on the recent outbreak of Ebola , often includes considerable epidemiological detail [31 , 32 , 44] , although there are some exceptions [42 , 43 , 46] . Here we take advantage of a previous parameterization of the SEIR model for Ebola , noting that the SEIR model is widely-used for a number of diseases and captures what we want to investigate here—i . e . the effect of presymptomatic infection on major outbreak forecasting—in the simplest possible way . The true and estimated probabilities of a major epidemic are calculated for many simulated outbreaks , to investigate how presymptomatic infection affects the ability to predict major epidemics early in outbreaks ( Fig 2A ) . The probability of a major outbreak depends on the number of infected individuals at the time of estimation ( S2 Fig ) , and hidden presymptomatic infection therefore frustrates prediction . This is even the case when there are actually no presymptomatic infected individuals in the population , since the distribution that estimates the number of presymptomatic individuals will include values other than zero . In the SEIR model , only discrete values of the true probability of a major outbreak are possible , since the true probability is entirely controlled by the total number of infected individuals at the time of estimation . However , each individual dataset , corresponding to a separate realization of an outbreak , consists of the times that individuals become symptomatic . Slight variations in these times lead to different probability distributions of the number of presymptomatic infecteds . These differences are reflected in the estimated probability of a major outbreak . Consequently , the estimated probability for each true value is effectively a continuously varying quantity . The key qualitative result , i . e . that the estimated and true probabilities of a major outbreak do not match , is robust to performing estimation at different stages of the start of an outbreak , and to different lengths of the incubation period and values of R0 ( S3–S6 Figs ) . Additional uncertainty in the probability of a major outbreak occurs when the parameters for disease spread must also be estimated from the transmission data ( S7 Fig ) . However , no matter how much parameter estimation is improved , for example using data from previous outbreaks to inform estimates , presymptomatic infection still causes significant errors in forecasting major outbreaks . The problem of practical interest for an emerging epidemic is inferring the true probability of a major outbreak . For an individual outbreak , a ( often imprecise ) confidence interval can be constructed around the point estimate as we described above . However , we characterize the implications of presymptomatic infection more generally by examining many simulated outbreaks , inverting our point estimate of the probability of a major outbreak to consider the range of true probabilities that are possible for each estimated value . Similar estimated probabilities of a major outbreak can correspond to a remarkably wide range of true probabilities ( Fig 2B ) . For example , for outbreaks in which the estimated probability is between 0 . 5 and 0 . 6 , the true probability can lie between 0 . 23 and 0 . 83 . We note the extreme values are themselves quite likely: in 13% of these simulated outbreaks , the true probability is in fact either 0 . 23 or 0 . 83 . Estimation of the chance of a major outbreak can be improved by the use of diagnostic tests to determine whether asymptomatic individuals are susceptible or presymptomatic infected . Since the reliability of diagnostic tests affects the extent to which forecasting is improved ( Fig 3 ) , it is not only important to develop diagnostic tests but also to ensure their continued refinement . To illustrate the general principle that diagnostic tests could be used to improve prediction , we simply choose individuals to test at random from the asymptomatic individuals in the population . With random selection , the diagnostic test must be deployed widely to reduce the error in estimates significantly , although of course careful choice of which individuals to test ( e . g . via contact tracing ) would reduce the need for such widespread deployment in practice . The emergence of symptoms and the emergence of infectivity are assumed to coincide in the SEIR model . We relax this assumption by considering two other models . In the first , individuals display symptoms before becoming infectious ( Fig 4A ) . In the second , individuals are infectious before becoming symptomatic ( Fig 4B ) . When symptoms appear before individuals are infectious , the incubation period is reduced , so more infected individuals can be detected . As a result , predictions of major outbreaks become more accurate , although some systematic ambiguity nevertheless remains ( Figs 4A and S8A ) . Conversely , if the incubation period is instead longer than the latent period , as is the case for many human diseases [47] , it becomes more difficult to predict major outbreaks accurately ( Figs 4B and S8B ) . In Fig 4B , the variable heights of adjacent boxplots indicate that the distribution of infected individuals between the asymptomatic and symptomatic infectious classes affects the estimated probability of a major outbreak . For example , the heights of the second and third boxplots from the left can be explained as follows . Consider two outbreaks , each with only a single infected individual at the time that the chance of a major outbreak is being estimated . Suppose that in the first outbreak ( “outbreak one” , say ) , the infected individual is presymptomatic , but in the second outbreak ( “outbreak two” ) the infected individual is symptomatic . In outbreak two , because disease is observed since the infected individual is symptomatic , the estimated probability of a major outbreak will be high compared to outbreak one . However , whilst the estimated probability of a major outbreak is higher for outbreak two , the true probability of a major outbreak is in fact higher for outbreak one . This is because , in Fig 4B , individuals can be infectious both when they are presymptomatic and when they are symptomatic . A presymptomatic individual is therefore likely to be infectious for a longer period in future than a symptomatic individual . A longer time infectious corresponds to ( on average ) more infections , and therefore a higher true probability of a major outbreak . It might also naïvely be thought that prediction would be easiest in outbreaks in which many infected individuals are symptomatic . However , when a large proportion of infected individuals are symptomatic , the total number of infected individuals tends to be overestimated , causing large errors in forecasts ( cf . the boxplots corresponding to a single infected individual at the time of estimation in Fig 4B ) . The default assumption for compartmental models is that incubation and infectious periods are exponentially distributed . We relax this assumption , and draw periods instead from two-parameter gamma distributions to reflect the observed incubation and infectious periods for a number of diseases [49 , 50] ( S9 Fig ) . Recently-infected individuals are more likely to remain infectious for a long period beyond the time of estimation than individuals that have already been infected for a long time . Consequently , outbreaks with many presymptomatic infecteds have a high true probability of a major outbreak . However , because presymptomatic individuals are unobserved , the estimated probability of a major outbreak is low in these outbreaks .
Predicting whether or not a major epidemic is likely , from the limited data typically available during the first few days of an outbreak , has received surprisingly little attention . A notable exception is the paper by Drake [51] , which shows that the exact final size varies significantly between simulated outbreaks under identical conditions . He investigates how this variability scales with the contact rate between individuals and the efficacy and speed of control responses . However an incubation period is not explicitly included in the model used . Craft et al . [7] use a model of rabies in canids to show that the first four death times cannot be used to forecast major outbreaks . However , by assuming that the data consist of death times alone , the factors potentially responsible for this imprecision are confounded . Neither Drake [51] nor Craft et al . [7] quantify the error caused by presymptomatic infection . In addition to quantifying this error , our main message is that presymptomatic infection by itself is sufficient to cause error in predictions of whether or not an outbreak will be major , let alone in predicting the final size exactly . This error is particularly notable when there are no infected individuals in the population at all ( i . e . the outbreak has already faded out ) , since the distribution that estimates the number of presymptomatic infected individuals will include values other than zero . To focus entirely on the uncertainty caused by presymptomatic infection , we worked in an idealized setting in which symptomatic cases and deaths were recorded perfectly and in which the values of disease transmission parameters were known exactly . This allowed us to calculate the exact probability distribution of the current size of the outbreak , i . e . the total number of individuals currently infected , given that presymptomatic infection causes some infected individuals to be unobservable . This distribution drives the estimated probability of a major outbreak . In practice , however , the distribution of possible current outbreak sizes would have to be estimated from incomplete data on symptomatic cases and deaths , without exact knowledge of parameter values and sometimes without even knowing the total population size precisely . One method for doing this is back-calculation , as originally designed by Brookmeyer and Gail for HIV-AIDS [29] , which provides an estimate for the distribution of possible current outbreak sizes . Although , to the best of our knowledge , back-calculation has not been used to estimate the probability of a major outbreak , such a forecast using back-calculation as an input would necessarily be less precise than those used in our analyses here , since we have used the exact distribution of current outbreak sizes given presymptomatic infection . Indeed , by restricting our attention to the case in which there are sufficient data that the number of presymptomatic individuals is the only quantity being estimated , our results provide an upper bound on the ability of any method that seeks to predict major outbreaks from data on symptomatic cases alone . In fact , given the extensive knowledge of the epidemic assumed here , the basic formulation of back-calculation can be extended in a natural fashion to obtain the exact probability distribution of the current size of the outbreak that we use to generate our estimates for the probability of a major outbreak ( S4 Text , S10 Fig ) . Prediction during the recent Ebola outbreak has been criticized for overestimating the total number of cases that actually occurred [52] . Similarly , modeling studies during the 2009 H1N1 outbreak typically overestimated the total number of cases [53] . In contrast with investigations that attempt to predict the final epidemic size , we differentiated only between “minor” and “major” outbreaks . Our focus was prediction during the very early stages of an outbreak , before a major outbreak is underway , rather than forecasting the final extent of a major outbreak once the epidemic has taken off . This very initial phase of outbreaks is particularly important given the recent interest in rapid detection of disease outbreaks [54–57] . We assumed that the parameter values controlling disease spread are unchanged throughout the early stage of the outbreak , whereas in reality these parameters might vary temporally in response to changing contact networks and control interventions [58] , as well as varying environmental conditions [59] . However , any such variations will only exacerbate the uncertainty that we have shown exists . Other sources of uncertainty such as under-reporting , which has posed a challenge to forecasting during the recent Ebola outbreak [60] , will also decrease predictability further , although as we have shown presymptomatic infection alone is sufficient to make precise prediction impossible . A systematic investigation of the errors in forecasting caused by under-reporting in comparison to those due to other features such as presymptomatic infection or epidemiological parameter uncertainty is a possibility for a future study . Our work shows rigorously , for the first time , that no matter how accurately disease transmission parameters are estimated , precise estimates early in outbreaks of whether a major epidemic will occur will remain unavailable without data about presymptomatic infection . This is still the case even if significant resources are devoted to recording symptomatic cases accurately . Consequently , diagnostic tests that can identify presymptomatic infecteds [61 , 62] are extremely important for improving forecasts of epidemic outbreaks . While our simulations consider random testing of asymptomatic individuals , in practice testing is costly [63] , so it is vital that predictability is further improved in a cost-effective way by careful selection of individuals to test . This could be done by contact tracing [64] or using statistical methods to identify individuals with the highest risk of being infected [65] , although of course effective and cheap diagnostics are still required . A systematic investigation into which asymptomatic individuals ought to be tested , accounting for the specificity of the tests as well as the sensitivity , would be a valuable extension to our work . A recent analysis of Ebola [66] has considered testing of individuals already exhibiting symptoms to confirm whether the patients have Ebola or a different disease with similar symptoms . That study shows that using rapid diagnostic tests in combination with slower but more accurate diagnostic tests could have significantly reduced the number of cases in Sierra Leone in the recent outbreak . Our conclusions are robust to various characteristics of the disease , and so apply to all infectious diseases . We chose to use Ebola as a representative case study , but our results are in fact generic . In particular , our key message that presymptomatic infection drives uncertainty in whether an emerging outbreak will become major holds throughout the early stages of the outbreak ( S3 and S4 Figs ) , as well for a number of values of the basic reproduction number of the pathogen ( S6 Fig ) . For Ebola , there is debate as to whether the onset of symptoms and infectiousness coincide [67] or not [68] . However , symptoms and infectiousness are certainly not always concurrent: HIV is a high profile example , for which the time between infection and recognizable symptoms can take years [69] , whereas individuals are infectious within months of acquiring the virus [70] . We have considered different models in which symptoms and infectiousness are not assumed to coincide ( Figs 4 and S8 ) . While we showed prediction is most reliable for diseases for which the incubation period is shorter than the latent period , even very short incubation periods can generate significant uncertainty in the number of presymptomatic infecteds , and therefore the probability of a major outbreak ( S5 Fig ) . This means that our conclusions even hold for diseases such as influenza and norovirus , which have incubation periods of only a few days [4] . The messages we have set out are also robust to different distributions of the incubation and infectious periods , as we showed by considering models for which these periods follow gamma rather than exponential distributions ( S9 Fig ) . Of course , our conclusions are relevant to pathogens of agricultural and wild animals and plants , as well as humans . Xylella fastidiosa is a plant pathogen that is currently invading southern Italy , causing devastating damage to olive groves [71] . Containment and surveillance zones have been set up in an attempt to find the pathogen and subsequently mitigate spread via control interventions . Surveys in the containment zone do include some laboratory testing for presymptomatic infection , with the surveillance zone solely relying on diagnosis from visual inspection [72] . We have shown that consideration of presymptomatic infection is extremely important when forecasting the spread of pathogens , and so it is also likely to be important when planning interventions that attempt to slow or prevent spread . Studies examining the impacts of presymptomatic infection on forecasting and control of specific pathogens would represent valuable applied extensions to this publication . At the time of writing , a point-of-care diagnostic test that can detect Ebola from blood samples has been developed and found to be accurate [73] . In light of our analysis , the continued development , deployment and improvement of this and other diagnostic tests that determine whether asymptomatic individuals are infected is of obvious public health importance , not only for Ebola but also for other infectious diseases .
We perform our analyses using stochastic compartmental models of disease spreading in a small population . Here we outline the three types of model we use: the standard SEIR model , which assumes that symptoms and infectiousness coincide; more complex models that relax this assumption; and a model that assumes that the incubation and infectious periods follow gamma , rather than exponential , distributions . Since our concern is quantifying uncertainty caused by presymptomatic infection alone , we assume that the parameters controlling disease transmission are known , and that complete data are available from the very beginning of the epidemic for changes in the number of symptomatic infected individuals over time . These data can be used to construct the probability distribution for the number of presymptomatic infected individuals at the time of estimation ( S1 Text ) . For the SEIR model , the data on symptomatic cases are used to estimate the probability that an asymptomatic individual is infected , which feeds into a binomial distribution to estimate the number of presymptomatic infected individuals . The approach can readily be adapted for the SEUIR and gamma-distributed incubation and infectious periods cases . In the SEAIR model case , the A class causes the complete time series of infectious individuals to be unobserved , so that the required probability cannot be calculated . Instead reversible jump Markov chain Monte Carlo ( S2 Text ) is used to estimate the probability distribution for the number of currently infected individuals . To illustrate the principle that diagnostic tests can improve forecasts , the sampling of asymptomatic individuals and testing to find presymptomatic infection is modeled by choosing individuals at random out of the S or E classes without replacement . If the individual is susceptible , then infection is not detected ( i . e . the test produces no false positives ) , whereas if the individual is presymptomatic infected , the pathogen is detected with probability pd . The results of the sample can then be integrated into the estimate of the probability distribution of the number of presymptomatic infected individuals , which therefore becomes more precise ( S3 Text ) . We estimate two probabilities using data from individual simulated epidemics at the time of the fourth death: the true probability of a major outbreak , and the best point estimate of this probability consistent with the transmission data . Specifically , we calculate the true probability of an outbreak by “freezing” the infection status of all individuals at the time of four deaths , simulating a very large number of outbreaks ( 100 , 000 ) using these data as initial conditions , and finding the proportion of simulations in which a major outbreak occurs ( defined as more than 10% of the population ever becoming infected , cf . S1 Fig ) . Of course , this calculation is only possible since the number of presymptomatic infected individuals is known . To calculate the estimated probability of a major outbreak , we instead imagine that the exact infection statuses of individuals that are asymptomatic ( i . e . susceptible individuals and presymptomatic infected individuals ) are unknown , as would be the case in practice . We use the data on symptomatic cases up to the time of the fourth death to infer the probability distribution of the number of presymptomatic infecteds . We then calculate the estimated probability of a major outbreak by running an ensemble of simulations that sample initial conditions from this distribution on each forward run .
|
Emerging epidemics pose a significant challenge to human health worldwide . Accurate real-time forecasts of whether or not initial reports will be followed by a major outbreak are necessary for efficient deployment of control . For all infectious diseases , there is a delay between infection and the appearance of symptoms , i . e . an initial period following first infection during which infected individuals remain presymptomatic . We use mathematical modeling to evaluate the effect of presymptomatic infection on predictions of major epidemics . Our results show rigorously , for the first time , that precise estimates of the current number of infected individuals—and consequently the chance of a major outbreak in future—cannot be inferred from data on symptomatic cases alone . This is the case even if the values of epidemiological parameters , such as the average infection and death or recovery rates of individuals in the population , can be estimated accurately . Accurate prediction is in fact impossible without additional data from which the number of currently infected but as yet presymptomatic individuals can be deduced .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"infectious",
"disease",
"epidemiology",
"pathogens",
"tropical",
"diseases",
"ebola",
"hemorrhagic",
"fever",
"probability",
"distribution",
"mathematics",
"forecasting",
"statistics",
"(mathematics)",
"neglected",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"research",
"and",
"analysis",
"methods",
"viral",
"hemorrhagic",
"fevers",
"infectious",
"diseases",
"epidemiology",
"mathematical",
"and",
"statistical",
"techniques",
"probability",
"theory",
"diagnostic",
"medicine",
"physical",
"sciences",
"viral",
"diseases",
"statistical",
"methods"
] |
2016
|
Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks
|
The invasive forms of apicomplexan parasites share a conserved form of gliding motility that powers parasite migration across biological barriers , host cell invasion and egress from infected cells . Previous studies have established that the duration and direction of gliding motility are determined by actin polymerization; however , regulators of actin dynamics in apicomplexans remain poorly characterized . In the absence of a complete ARP2/3 complex , the formin homology 2 domain containing proteins and the accessory protein profilin are presumed to orchestrate actin polymerization during host cell invasion . Here , we have undertaken the biochemical and functional characterization of two Toxoplasma gondii formins and established that they act in concert as actin nucleators during invasion . The importance of TgFRM1 for parasite motility has been assessed by conditional gene disruption . The contribution of each formin individually and jointly was revealed by an approach based upon the expression of dominant mutants with modified FH2 domains impaired in actin binding but still able to dimerize with their respective endogenous formin . These mutated FH2 domains were fused to the ligand-controlled destabilization domain ( DD-FKBP ) to achieve conditional expression . This strategy proved unique in identifying the non-redundant and critical roles of both formins in invasion . These findings provide new insights into how controlled actin polymerization drives the directional movement required for productive penetration of parasites into host cells .
The phylum Apicomplexa encompasses pathogens of significant medical relevance including those responsible for malaria and toxoplasmosis . These parasites cross biological barriers and enter cells by an active process that depends on a unique form of gliding motility [1] . In Toxoplasma gondii , drugs that interfere with actin assembly and dynamics have revealed that gliding critically relies on an intact parasite actin cytoskeleton [2] and requires actin polymerization [3] . Moreover , previous work using reverse genetics highlighted that gliding is powered by the myosin XIV , TgMyoA [4] . Paradoxically , visualisation of actin filaments under physiological conditions , either by electron microscopy or by staining with phalloidin , has proven very difficult in the phylum . Sedimentation experiments suggested that actin is maintained in a globular form ( >98% ) [5] . Work performed in vitro on purified or recombinant actins revealed that preferentially short ( 0 . 1 µm ) actin filaments are assembled [6] , [7] , [8] , hence actin might be tailored to undergo rapid cycles of assembly and disassembly . Among the systems orchestrating actin nucleation , the Arp2/3 complex , generates a network of short , branched filaments , whereas the formin-profilin system catalyzes the processive assembly of unbranched actin filaments [9] . The Apicomplexans lack many actin-regulatory proteins including the Arp2/3 complex [10] . In contrast , they contain at least two formins and a profilin that have been previously associated with parasite motility [11] , [12] , [13] . Formins constitute a large family of proteins involved in many biological processes including cell polarity , cell-cell contact , cell and tissue morphogenesis , cytokinesis , filopodia formation , stress fiber formation , motility and in microtubule-actin cross talk to maintain the cell cytoskeleton [14] . These proteins are composed of multi-domains interacting with other cellular factors to promote actin nucleation and polymerization . The common feature of all formins is the FH2 domain , which nucleates actin assembly and binds the barbed end at nanomolar concentrations allowing the formation of linear and unbranched actin filaments [15] , [16] . The second domain catalyzing the activity of formins is the FH1 for “formin homology 1 domain” . FH1 is typically positioned immediately N-terminal to the FH2 domain and is composed of poly-proline stretches that bind specifically to the profilin-actin complex during barbed end filament elongation [14] . The FH2 domain associates with the barbed end ( fast growing plus end of actin filament ) of actin filaments and in association with the FH1 domain promotes rapid processive barbed end assembly from profilin-actin , increasing the association rate constant of profilin-actin to barbed ends by 2 to 15 fold [14] . Profilin-actin is involved in a rapid delivery step by which FH1-profilin-actin is transferred directly to the FH2-associated barbed end [9] . Formin activity is frequently regulated by autoinhibition , which is maintained by the binding of the C-terminal diaphanous autoregulatory domain ( DAD ) segment to the diaphanous inhibitory domain ( DID ) . Binding of Rho to the GTPase-binding domain ( GBD ) releases the autoinhibition to activate formin [9] . In T . gondii , TgPRF participates in barbed end growth and a conditional knockdown of the gene established its vital role in motility and invasion [13] . P . falciparum formins 1 and 2 ( PfFRM1 and PfFRM2 ) nucleate chicken actin polymerization in vitro [12] and localization of PfFRM1 at the site of contact between invading merozoites and the host cell suggested a role in invasion [12] . Recently , a formin-like protein termed MISFIT was localized to the nucleus of male gametocytes , zygotes and ookinetes of the rodent malaria parasite Plasmodium berghei . This protein was shown to regulate cell cycle progression from ookinete to oocysts but an activity of actin nucleation has not been reported [17] . The T . gondii genome does not contain a gene coding for MISFIT but instead encodes a third putative formin ( TgFRM3 ) that is only found in coccidians and whose function is dispensable and not linked to motility and invasion ( Daher , unpublished ) . In this study , we developed a genetic strategy based upon the expression of dominant negative mutants to establish that both TgFRM1 and TgFRM2 contribute to motility and invasion . This approach was validated by biochemical analyses , which highlighted the distinct properties of the two formins . TgFRM1 and TgFRM2 promote and control growth of the filaments and possibly stabilize their position and directionality to drive parasite entry into host cells .
BLASTP analysis of the ToxoDB database ( http://www . ToxoDB . org ) revealed the presence of three FH2 domain-containing proteins in T . gondii with two of them , TgFRM1 and TgFRM2 being conserved across the Apicomplexa phylum [12] . TgFRM1 and TgFRM2 are large proteins with a predicted molecular weight of 552 kDa and 492 kDa , respectively . The FH2 domain is positioned at the extreme carboxy-terminus in case of TgFRM1 . The presence of a canonical FH1 is not apparent but both formins possess a segment rich in proline residues upstream of the FH2 ( Figure 1A ) . These formins also lack the DAD and DID regulatory domains , however , TgFRM1 possesses four tetratricopeptide repeats also present on PfFRM1 ( Figure 1A ) . Specific antibodies were raised against bacterially produced FH2 domains of both T . gondii formins and western blot analysis confirmed that TgFRM1 and TgFRM2 are expressed in tachyzoites and migrate at their predicted sizes on SDS-PAGE ( Figure 1B ) . Parasite lines expressing the FH2 domains of either TgFRM1 or TgFRM2 were used to confirm the specificity and the absence of cross-reactivity of the two rabbit anti-sera ( Figure S1B , S1C and S1D in Supporting Information S1 ) . To investigate the function and importance of these formins , conditional knockdowns were attempted in the TATi-1 strain , using the tetracycline-based transactivator system previously developed for T . gondii [4] . Given the unmanageable size of the TgFRM1 and TgFRM2 cDNAs , we opted for a promoter exchange approach instead of the two step knockout strategy that requires the integration of a second inducible copy ( Figure S2A in Supporting Information S1 ) . To select for the recombinant lines of interest , a YFP expression cassette was inserted into the knockout vectors allowing FACS sorting of the parasites that underwent double homologous recombination ( YFP negative ) [18] . The TgFRM1 promoter was replaced by the inducible tetO7Sag4 and a myc-epitope tag was inserted at the N-terminus of the TgFRM1 gene . Numerous attempts to replace the promoter of TgFRM2 failed ( 18 transfections ) and resulted only in a single homologous recombination event at the locus ( data not shown ) . In contrast , positive mycTgFRM1-iKO ( Table S2 in Supporting Information S1 ) clones were identified by an indirect immunofluorescence assay ( IFA ) and confirmed by genomic PCR ( Figure S2B in Supporting Information S1 ) . Western blot analysis using anti-FRM1 antibodies revealed that the level of mycTgFRM1i was considerably higher ( ca . 4 fold ) than the endogenous level of TgFRM1 in TATi-1 strain ( Figure 1C ) . In the presence of anhydrotetracycline ( ATc ) , mycTgFRM1i was significantly down-regulated to less than 15% but that still corresponded to ca . 45 to 50% of the endogenous level of TgFRM1 ( Figure 1C; Figure S2C in Supporting Information S1 ) . Both TgFRM1 and mycTgFRM1i localized to the periphery of replicating ( intracellular ) as well as invading ( extracellular ) parasites ( Figure 1D ) , In contrast with a previous report [12] , TgFRM1 was not found to be selectively redistributed to the apical pole , but this discrepancy may be explained by different fixation protocols ( Figure 1D , and Figure S2C in Supporting Information S1 ) . In Baum et al . , a methanol fixation was employed , which may retain only a specific population or may tend to modify the localization during fixation . TgFRM2 showed the same subcellular localization as TgFRM1 ( Figure 1E ) , however , when extracellular parasites were treated with aerolysin , the two formins behaved differently . The pore-forming toxin binds to glycosylphosphatidylinositol ( GPIs ) anchored proteins and induces an osmotic swelling that selectively detaches the plasma membrane ( PM ) from the inner membrane complex ( IMC ) [19] . Upon aerolysin treatment , TgFRM1 remained associated with the PM , whereas TgFRM2 stayed preferentially connected to the IMC ( Figure 1F ) . The nature of the association of TgFRM1 and TgFRM2 with the PM and IMC , respectively , was examined by fractionation at high pH . Complete solubilisation of both formins indicated that electrostatic interactions , either with membrane proteins or with the polar heads of lipids , are responsible for their membrane association ( Figure 1G ) . The presence of TgFRM1 and TgFRM2 at the pellicle is compatible with a contribution of both formins to actin polymerization during invasion . In the malaria parasite , PfFRM2 was detected in trophozoites and its presence in late schizonts could not be established [12] , [20] . To assess the expression of PbFRM1 and PbFRM2 ( Plasmodium berghei formins 1 and 2 ) throughout the erythrocytic stages a myc epitope tag was inserted at their C-termini of both formins via a knock-in strategy based on single crossover recombination ( Figure S3A in Supporting Information S1 ) . The modified locus of the transgenic parasites was confirmed by genomic PCR ( Figure S3B and S3C in Supporting Information S1 ) . Western blot analysis revealed that both formins are present in schizonts , leaving open the possibly that both formins play a role in invasion . However , the low level of expression together with the high background of all myc antibodies tested hampered the assessment of their localization by IFA ( Figure S3B and S3C in Supporting Information S1 ) . The promoter exchange at the TgFRM1 locus leads to a partial depletion in TgFRM1 upon ATc treatment . The phenotypic consequences were first investigated by plaque assays . The lytic cycle of the parasite is a multi-step process that involves invasion , several rounds of replication and egress . The plaque assay corresponds to plaques of lysis formed in a monolayer of human forskin fibroblasts ( HFF ) that recapitulates multiple lytic cycles over several days . When mycTgFRM1-iKO parasites were depleted of mycTgFRM1i after six days of ATc treatment , the plaques formed were significantly reduced in size , compared with the untreated mutant and TATi-1 strain ( Figure 2A and B ) . Further analysis established that depletion in TgFRM1 was not affecting parasite replication ( Figure 2C ) but caused a defect in invasion ( Figure 2D; Table 1 ) . The importance of TgFRM1 for parasite egress was investigated upon addition of the calcium ionophore A23187 and only a modest impairment of 14% in induced egress was recorded ( Figure 2E and Table 1 ) . To monitor if parasites depleted in TgFRM1 could still accomplish the three forms of gliding movement ( helical gliding , circular gliding and twirling ) , trails deposited by moving parasites on coated Poly-L-lysine cover slips were scored by IFA . The mycTgFRM1-iKO strain showed a significant reduction in trail formation after ATc treatment ( Figure 2F , 2G and Table 1 ) . Taken together these results indicated a role for TgFRM1 in invasion , although the phenotype reported here is very modest compared to other invasion factors such as TgMyoA and TgMIC2 investigated before with the Tet-system [4] , [21] . Clearly , this system is not ideally suited to study the function of weakly expressed genes . The partial phenotype observed in the conditional knockdown of TgFRM1 is likely due to the residual amount of mycTgFRM1 ( 13% ) produced in the presence of ATc that corresponds to almost 50% of endogenous levels of TgFRM1 . At this stage , it is not possible to exclude that TgFRM1 and TgFRM2 are functionally redundant . Dimerization via the FH2 domain is essential for the processive function of formins , with one subunit attached on the barbed end of an actin filament while the other adopts an open configuration to recruit the incoming actin subunit [22] . To examine if the two T . gondii formins can nucleate actin filaments , the boundaries of the FH2 domains were delineated ( Figure S1B in Supporting Information S1 ) . Recombinant FH2 domains were produced and purified from E . coli ( Figures S4A , S4C , S5A , and S5B in Supporting Information S1 ) . The FH2 of TgFRM1 ( His-F1L ) and TgFRM2 ( His-F2 ) were analyzed by gel filtration on Superose 6 10/300 GL . F1L ( amino acid positions 4582-5051 ) corresponds to a N-terminal 48 amino acid extension of the FH2 domain , which is enriched in proline residues and might constitute a divergent FH1 domain ( Figure 1A ) . F1L ( 56 kDa ) and F2 ( 100 kDa ) fractionated as both monomers and dimers ( Figure S5C and S5D in Supporting Information S1 ) . A Ni-NTA-Sepharose bead pull-down assay demonstrated that the FH2 domains bind to TgACT1 when incubated with parasite lysates ( Figures S4A , S4C , S5E , and S5F in Supporting Information S1 ) . Although both His-F1L and His-F2 contained a proline rich region , no interaction with myc-TgPRF was observed ( Figure S5E and S5F in Supporting Information S1 ) . These results were confirmed in vivo by immunoprecipitation ( IP ) of TgFRM1 and TgFRM2 under native conditions . While a significant amount of TgACT1 was co-IPed with the two formins ( Figure S5G in Supporting Information S1 ) , no TgPRF was precipitated ( data not shown ) . Finally , beads coated with His-F1L or His-F2 failed to initiate processive actin assembly in the presence of both bovine and Toxoplasma profilin proteins , in contrast to the behaviour observed with mDia1-FH1-FH2-coated beads [23] . However , the results observed regarding the processive actin assembly in the presence of profilin do not exclude the positive effect of Toxoplasma profilin on actin elongation by Toxoplasma formins 1 and 2 , and a new ranges of conditions need to be tested in the future to clarify this point . The effect of F1 and F2 on actin assembly was tested in spontaneous actin polymerization assays . A qualitative estimation of the nucleating efficiency is derived from the formin concentration dependence of the initial rate of polymerization . F1 strongly stimulated actin polymerization at nanomolar concentrations , while higher concentrations of F2 were required to nucleate actin assembly ( Figure 3A , B and C ) . However , the exact number of nuclei generated by formins cannot be determined from these assays , which do not discriminate between effects on filament nucleation and elongation rate constants . These results generated with rabbit muscle actin , are in agreement with the activities reported for PfFRM1 and PfFRM2 with chicken muscle actin [12] . Crystal structure analysis of the Bni1p FH2 in complex with actin has revealed that an FH2 dimer bridges three consecutive actin subunits arranged along a pseudo-filament , with each FH2 arm contacting two actin subunits [24] . In the pseudo-filament the barbed end is blocked by the FH2 arm , and a conformational change from this “closed” to an “open” configuration had to be postulated to accommodate processive filament growth [24] . The actin-FH2 contacts are made by two highly conserved patches on the surface of the hemidimer . These two sites correspond to Ile1431 and Lys1601 and mutation of either of these residues completely abolishes the actin assembly activity of Bni1 FH2 [24] , [25] . The corresponding Lys1601 ( residue R4867 in TgFRM1 and R3709 in TgFRM2 ) were mutated and the resulting FH2 mutants His-F1-R/A and His-F2-R/A were produced and analyzed in actin assembly assays ( Figure S1B in Supporting Information S1 ) . Whereas His-F2-R/A lost at least 90% of its nucleating activity , His-F1-R/A still retained a nucleating activity comparable to that of the wild type protein . A second mutation corresponding to Ile1431 ( residue I4713 in TgFRM1 ) was introduced to create the His-F1-IR/AA double mutant , which showed no nucleating activity up to 500 nM ( Figure 3C and D ) . Unexpectedly , the His-F2-IR/AA double mutant ( residue I3511 in TgFRM2 ) displayed a strong barbed end capping activity in filament barbed end growth assays using spectrin-actin seeds ( Figure 3E ) . A value of 6 nM was derived for the equilibrium dissociation constant of the complex of His-F2-IR/AA with barbed ends ( Figure 3F and G ) . This latter result suggests that the double mutation prevents the postulated conformational change of the FH2-barbed end complex that allows processive elongation . Interaction of these FH2 mutants with barbed ends of filaments was further addressed by monitoring their effect on the initial rate of dilution-induced depolymerization of filaments ( Figure 3H ) . His-F1 totally blocked barbed end depolymerization while His-F2 inhibited the rate of depolymerization by 75% at saturation . Both proteins bound barbed ends with high affinity . His-F1-R/A blocked filament depolymerization from barbed ends as efficiently as His-F1 . His-F1-IR/AA did not affect barbed end depolymerization . The R/A mutation did not change the inhibition of depolymerization of the F2 , while the double mutation IR/AA abolished the blockage of the barbed end depolymerization harboured by F2 ( Figure S5H in Supporting Information S1 ) . In conclusion , the FH2 domains of TgFRM1 and TgFRM2 display the barbed end binding property common to all formins from other species , but each formin exhibits specific activities at barbed ends . F1 is a more efficient nucleator of actin filaments than F2 , but it totally inhibits barbed end depolymerization , which demonstrates its tight binding to the ADP-F-actin terminal subunits that are become exposed during depolymerization . In contrast , F2 only partially inhibits barbed end depolymerization , similar to other formins like mDia1 or Bni1 [23] , [26] . Although a role in invasion could be established , the partial depletion in TgFRM1 hampered a proper assessment of the importance of the gene . Moreover , the lack of success in generating a conditional knockout for TgFRM2 impeded a functional assessment of its contribution . To overcome these technical limitations , we developed an approach based upon the conditional expression of a dominant negative mutant that would be suited to study selectively the function of multiple formins in the same cell . We reasoned that the expression of a FH2 domain should lead to the formation of a defective heterodimer ( FH2-FRM ) . Given that the FH2 WT domain forms homodimers that possess an unregulated capacity to polymerize actin , it was crucial to prevent such activity by introducing mutations in the actin-binding site ( F1-IR/AA and F2-R/A ) . In this scenario both homodimers and heterodimers are predicted to be inactive ( Figure 4A ) . As negative controls , truncated forms of FH2 ( F1-ΔH , and F2-ΔH ) that lack the two helices required for dimerization were generated ( Figures S1B and S4 in Supporting Information S1 ) [12] , [27] . To circumvent the anticipated deleterious effect caused by the expression of these mutants on parasite survival , both the WT and mutated FH2 domains were fused to the destabilization domain ( DD ) of FKBP . This small domain is known to confer instability to proteins in the absence of the folding inducer shield molecule , Shld-1 [28] , [29] . Transgenic parasites expressing WT , deletion mutants unable to dimerize as well as site-specific mutants unable to bind to actin , were generated for both formins in the type I RH laboratory strain of T . gondii ( Figure S4B and S4D in Supporting Information S1; Table S2 in Supporting Information S1 ) . The tight control of DD-FH2 fusions by Shld-1 was assessed by western blot and IFA ( Figure S6A and S6B in Supporting Information S1 ) . Since formins generally form extremely stable dimers ( except maybe mDia2 , [30] ) , the expression of DD-FH2 is not anticipated to form hybrids from preformed endogenous dimers but only to associate with newly synthesized proteins . Given the likely slow turnover of such large proteins , the formation of DD-F1/FRM1 and DD-F2/FRM2 heterodimers was monitored 6 and 48 hours following Shld-1 treatment . IP of DD-F1 and DD-F2 was performed under native conditions and revealed that no heterodimers were formed after 6 hours of Shld-1 treatment . In contrast , significant amounts of heterodimers were detectable at 48 hours ( Figure 4B , and 4C; Figure S6C and S6D in Supporting Information S1 ) . As anticipated , no heterodimers were formed with DD-F1-ΔH and DD-F2-ΔH ( Figure 4D; Figure S6C and S6D in Supporting Information S1 ) . These results highlight the functional conservation of the elements required for the self-association of formins across species [9] . In contrast , DD-F1-IR/AA , and DD-F2-R/A mutants impaired in actin binding and nucleating activity were associated with their corresponding formins with the same efficiency as DD-F1 and DD-F2 respectively ( Figure 4D , 4G , and 4H; Figure S6C and S6D in Supporting Information S1 ) . Importantly , the dimerization is specific for each formin as DD-F1-IR/AA , and DD-F2-R/A were exclusively associated with their corresponding formins , as shown by Western blot analysis of the co-IPs in presence of both anti-FRM1 and anti-FRM2 antibodies ( Figure 4E , and 4F ) . Despite the high level of DD-F1-IR/AA and DD-F2-R/A expression , the coIP experiments revealed that endogenous TgFRM1 and TgFRM2 were not completely sequestered as heterodimers . Even after the second coIPs , when DD-F1-IR/AA and DD-F2-R/A were completely depleted in the second flow through ( FT2 ) , both formins were still present TgFRM1 ( ca . 30% ) and TgFRM2 ( ca . 17% ) compared to the inputs ( Figure 4G , and 4H; lanes corresponding to FT2 ) . Prior to the assessment of their dominant negative effects in T . gondii , the FH2 constructs , expressed as His-fusion and purified from E . coli , were assessed for binding to TgACT1 by pull down assays with parasite lysates . While His-F1 and His-F2 bound efficiently to TgACT1 , no binding was detected with His-F1-ΔH , establishing that dimerization of the FH2 domains is necessary for actin association ( Figure 4I upper panel ) . His-F2-ΔH is very unstable after its purification from bacteria , and was therefore not included in this analysis . In agreement with the polymerization assays , His-F1-IR/AA did not bind to actin , whereas His-F1-R/A showed residual binding ( Figure 4I upper panel ) . His-F2-R/A was considerably impaired in binding to TgACT1 , whereas His-F2-IR/AA showed an increased ability to bind to TgACT1 consistent with the barbed end capper activity detected in polymerization assays ( Figure 4I upper panel ) . To consolidate these results , we verified that all recombinant FH2 proteins bound quantitatively to the nickel column ( Figure 4I lower panel ) . The heterodimers formed between endogenous FRM1 and DD-F1-IR/AA or FRM2 and DD-F2-R/A are predicted to be non-functional and hence to mimic the knockdown of the corresponding gene . In contrast , wild type F1 and F2 form active homodimers that could lead to some pleiotropic effects as a consequence of uncontrolled actin polymerization . Indeed , stabilization of DD-F1 and DD-F2 were severely impaired in plaque formation due to a strong growth defect ( Figure S7A , S7B , and S7C in Supporting Information S1 ) . Stabilization of DD-F1-ΔH and DD-F2-ΔH showed no defect thus ruling out any deleterious effect resulting from the stabilization of DD-FH2 without the ability to form a functional dimer ( Figure S7A in Supporting Information S1 ) . To monitor TgFRM2 function , we excluded DD-F2-IR/AA which exhibits a yet unexplained capping activity ( Figure 3E , F and G ) , and used instead DD-F2-R/A , which is impaired in nucleating activity ( Figures 3E , and 4I ) . Generation of parasite lines expressing both DD-F1-IR/AA and DD-F2-R/A allowed assessment of TgFRM1 and TgFRM2 function simultaneously . Stabilization of DD-F1-IR/AA and DD-F2-R/A highlighted the importance of both formins by plaque assays ( Figure 5A , and B ) . Importantly , when compared to the same strains untreated with Shld-1 , these mutants did not alter intracellular growth , excluding unspecific toxic effect ( Figure S7D in Supporting Information S1 ) . In contrast , less than 50% of parasites expressing DD-F1-IR/AA and 40% of those expressing DD-F2-R/A were able to egress while only 23% of egress was observed for parasites expressing both FH2 mutants ( Figure 5C; Table 1 ) . Invasion efficiency of each mutant was normalized to the invasion efficiency of a YFP strain ( taken as 100% ) . With regards to egress , a partial defect was observed with F1-IR/AA ( 50% ) or F2-R/A ( 50% ) , and an enhanced defect of 68% upon co-expression ( Figure 5D; Table 1 ) . Similarly , parasites expressing DD-F1-IR/AA; DD-F2-R/A and DD-F1-IR/AA+DD-F2-R/A showed a 49% , 62% , and 72% defect in trail formation , respectively ( Figure 5E; Table 1 ) . Gliding defects were examined in more depth by live video microscopy . In presence of Shld-1 , DD-F1-IR/AA and DD-F2-R/A parasites exhibited normal twirling motion but were defective in circular and helical motion . Only 41% of the parasites expressing DD-F1-IR/AA and 13% expressing DD-F2-R/A were able to accomplish a complete multi-circular movement lasting up to one minute ( Figure 5F; Video S1 ) . In contrast , up to 80% of non-treated parasites exhibit normal circular gliding as previously described [31] ( Figure 5F; Video S1 ) . While interference with TgFRM2 function showed a more pronounced impairment on circular gliding ( Figure 5F; Videos S2 and S3 ) , the inhibition of TgFRM1 function caused a preferential defect in helical gliding with 71% of the parasites expressing DD-F1-IR/AA being affected compared to 43% of those expressing DD-F2-R/A ( Figure 5G; compare Video S4 with Video S5 ) .
Gliding motility is a dynamic event involving temporally and spatially controlled actin polymerization . Typically , formins assisted by profilin , bind to an FH1 domain to facilitate a rapid processive assembly of actin filaments [14] , [23] , [32] . Despite the importance of TgPRF in parasite motility and invasion [13] , none of the parasite formins carry a canonical FH1 domain . Since the region right upstream of FH2 domains of both formins is rich in short stretches of proline residues their potential to act as binding site for profilin was investigated but in vitro pull-down experiments and in vivo coIPs failed to show interaction . New subclasses of formins apparently lack FH1 , suggesting that an FH1-independent pathway may mediate actin assembly [9] , [33] . The crystal structure of the Plasmodium PRF in complex with an octa-proline peptide was solved and implicated the N-terminal tyrosine residue ( Tyr5 ) in tethering to the poly-proline [34] . In our hands , all apicomplexan PRFs showed either very low or no affinity for poly-proline ( Plattner F . , unpublished ) . It is plausible that the apicomplexan PRFs bind to a divergent unrecognizable domain on formins and further work is required to unravel how TgPRF contributes to actin filament formation . TgFRM1 and TgFRM2 bind to TgACT1 and share biochemical characteristics with their counterparts in Plasmodium [12] . Both formins are underneath the PM and aerolysin treatment revealed their differential affinity to membranes within the narrow space separating the IMC from the PM . Like TgMyoA , both formins homogenously distribute at the periphery of invading parasites , compatible with the notion that they may nucleate actin at any time and at any point of contact between the parasite and its substrate . Conditional knockout of TgFRM1 established its role in motility and invasion although the effects were modest and impact on egress was minor . Despite multiple attempts , generation of a conditional knockout for TgFRM2 failed . In the course of this study , parasite lines were generated with triple Ty-1 tags inserted at the C-terminus of each formin by single crossing over in the ku-80-ko strain . RT-PCR analysis confirmed in frame integration of the tags but no signal was detectable by IFA or Western blot in these transgenic parasites ( data not shown ) . These results indicate that the endogenous levels of both formins are extremely low and hence explain the weak phenotype observed upon mycFRM1i depletion . In the same context , the lack of success in replacing the endogenous TgFRM2 promoter with an inducible promoter might be due to a deleterious effect of mycFRM2i expression if the level is too high . The function of TgFRM2 and possible redundancy with TgFRM1 was assessed by the expression of FH2 mutants to poison individually or simultaneously the two endogenous formins . This strategy also showed some limitations since the co-IP experiments revealed that 30% of FRM1 and 17% of FRM2 were not sequestered in defective heterodimers . This suggests that the affinity and or the stability of the homodimers ( FRM-FRM and FH2-FH2 ) are higher than the heterodimer ( FRM-FH2 ) . The FH2 WT , F1 and F2 are potent actin nucleators and their overexpression had a severe impact on parasite replication that was not dependent on TgFRM1 and TgFRM2 . Points mutations were introduced in the FH2 domains to disrupt actin nucleation and hence eliminate this non-specific effect . As with Bni1p [25] , a single point mutation in TgFRM2 ( F2-R/A ) was sufficient to abrogate its activity whereas a double mutation ( F1-IR/AA ) was needed to abolish actin nucleation of TgFRM1 . The IR/AA double mutation conferred to F2 an unexpected barbed end capping activity . This mutation may impair the flexibility of the FH2 domain and prevent the switch from the closed to the open configuration during elongation . To understand this phenomenon , the resolution of the FH2 domain structure of TgFRM2 in presence of actin would be necessary . The different effects of the R/A , and IR/AA mutations on the activities of TgFRM1 and TgFRM2 further testify that these two formins have different modes of interaction with actin . The fact that mutations affect differently barbed end growth and depolymerization processes , in which ATP/ADP-Pi-actin and ADP-actin are respectively exposed at barbed ends , suggests that these mutations may affect their interactions with ATP-actin and ADP-actin differently . Similar differences have already been observed with twinfilin , a capping protein that binds preferentially to ADP-bound barbed ends [35] . The R/A mutation does not affect any of the activities of TgFRM1 . In contrast , the R/A mutation of TgFRM2 may weaken its interaction with ATP-actin but not with ADP-actin . The IR/AA double mutation abolishes all activities of TgFRM1 . The same double mutation transforms TgFRM2 into a strong barbed end capper in nucleation and barbed end growth , while leaving the barbed end depolymerization unaffected , which suggests that the double mutation reinforces binding of FRM2 to the ATP-terminal subunits in its “closed” configuration and abolishes its binding to ADP-terminal subunits . Stabilization of DD-F1-IR/AA and DD-F2-R/A did not affect intracellular growth and revealed that both TgFRM1 and TgFRM2 play a role in gliding , invasion and egress . All phenotypes were aggravated when both dominant mutants were expressed in the same parasite ( Table 1 ) . These results give a strong indication that the two formins act in concert . However , since the stabilization of each FH2 mutants failed to sequester all the formins , invasion only dropped to 50% and in consequence it is not possible to completely rule out some level of functional redundancy between the two formins . Nevertheless the results demonstrate that both TgFRM1 and TgFRM2 contribute additively to the three vital aspects of the glideosome function namely gliding motility , host cell invasion and egress from the infected cells . The refined analysis of the gliding motility phenotypes by video microscopy revealed that interfering with TgFRM1 and TgFRM2 preferentially affected helical and circular gliding , respectively , illustrating distinct contributions of the two formins in gliding . This study revealed that TgFRM1 is preferentially positioned at the PM , where fast nucleation occurs in close proximity to the complex formed between actin filaments and the aldolase-MIC2 tail complex . The filaments likely elongate over only a short distance with TgFRM2 potentially serving to stabilize and control the size of the filament close to the IMC . Given the importance of these formins for parasite infection , it will be imperative to elucidate their mode of regulation and interaction with profilin as these unique features might become relevant therapeutic targets .
T . gondii tachyzoites ( RH hxgprt-ko , or TATi-1 ) were grown in human foreskin fibroblasts ( HFF ) . Selections of transgenic parasites were performed with mycophenolic acid ( MPA ) and xanthine for HXGPRT selection [36]; chloramphenicol for CAT selection [37]; anhydrotetracycline ( ATc ) for the inducible system [38]; 1 µM Shld-1 for DD-fusion stabilization [29]; pyrimethamine for DHFR-TS selection [39] . Primers used in this study are listed in the Table S1 in Supporting Information S1 . The ptetO7Sag4mycNtTgFRM1-KO: A genomic fragment of 1513 pbs corresponding to the N-terminal coding sequence of TgFRM1 gene was amplified by PCR subcloned into NsiI and BamHI sites of ptetO7Sag4mycGFP . The 5′ flanking region of TgFRM1 promoter was amplified by genomic PCR and cloned into the ApaI in pTub5CAT . The ptetO7Sag4mycNtTgFRM1 cassette was subcloned into the SacI site of pTub5CAT . The ptetO7Sag4mycNtTgFRM2-KO: A genomic fragment of 2202 pbs corresponding to the N-terminal coding sequence of TgFRM2 gene was amplified by PCR subcloned into NsiI and BamHI sites of ptetO7Sag4mycGFP . The 5′ flanking region of TgFRM2 promoter ( 2633 pbs ) was amplified by genomic PCR and cloned into the ApaI in pTub5CAT . The ptetO7Sag4mycNtTgFRM2 cassette was subcloned into the SacI site of pTub5CAT . The series of pTub8DDFKBPmycFH2 vectors were obtained by cloning of the FH2 cDNAs into NsiI and PacI in pTub8DDFKBPmyc vector . To mutate the Isoleucine ( I ) or the Arginine ( R ) residues , primers described in the Table S1 in Supporting Information S1 were used in a site-directed mutagenesis reaction using the commercial QuikChange II Site-DirectedMutagenesis Kit ( Stratagene ) and according to manufacturer's instructions . All mutated constructs were sequenced along the entire open-reading frame ( ORF ) to confirm the correct sequence . The bacterial expression was achieved by insertion of wild type , truncated and mutated F1 and F2 between NcoI and EcoRI in both pETHTB and pETM30 vectors . F1L ( amino acids numbers 4582-5051 ) , F1 ( amino acids numbers 4630–5051 ) , F1-R/A ( amino acids numbers 4630–5051 , R4867/A ) , F1-IR/AA ( amino acids numbers 4630–5051 , R4867/A and I4713/A ) , F1-ΔH ( amino acids numbers 4684–5051 ) , F2 ( amino acids numbers 3317–4043 ) , F2-R/A ( amino acids numbers 3317–4043 , R3709/A ) , and F2-IR/AA ( amino acids numbers 3317–4043 , R3709/A and I3511/A ) were cloned into pETHTB vector to generate recombinant proteins fused to a His tag . F1L ( amino acids numbers 4582–5051 ) , F1 ( amino acids numbers 4630–5051 ) , F1-R/A ( amino acids numbers 4630–5051 , R4867/A ) , F1-IR/AA ( amino acids numbers 4630–5051 , R4867/A and I4713/A ) , F1-ΔH ( amino acids numbers 4684–5051 ) , F2 ( amino acids numbers 3317–4043 ) , F2-R/A ( amino acids numbers 3317–4043 , R3709/A ) , F2-IR/AA ( amino acids numbers 3317–4043 , R3709/A and I3511/A ) , and F2-ΔH ( amino acids numbers 3480–4043 ) were cloned into pETM30 vector to generate recombinant proteins fused to both His and GST tags . The pET3amycHisF1 and pET3amycHisF2 were used to produce the FH2 for immunization . The knock-in constructs for P . berghei pSD141/CtPbFRM1 and pSD141/CtPbFRM2 were generated by genomic PCR amplification of 1800 bps and 1812 bps corresponding to the C-terminal part of the PbFRM1 and PbFRM2 genes , respectively . The PCR products lacking the stop codon were cloned between KpnI and ApaI of pSD141 vector in fusion with two myc tags [40] . TATi-1 were transformed with 100 µg of 5′flanking tetO7Sag4mycNtTgFRM1-KO vector ( linearized with SfoI ) and subjected to chloramphenicol selection . YFP-negative parasites were recovered using a FACS sorter to collect negative cells . DD-FH2 expressing parasites were obtained in RHhxgprt- and selected for MPA resistance . F1-IR/AA expressing parasites were co-transformed with linearized 90 µg pTUB8-DD-myc-F2-R/A and 10 µg p2854-DHFR . Single crossing over events in PbFRM1 and PbFRM2 loci were obtained as described [41] , [42] . The P . berghei ANKA strain clone 2 . 34 [43] was injected intraperitoneally into CD1 mice . The parasitized erythrocytes were harvested after in vitro maturation . Linearized plasmid DNA was transfected into purified schizonts using Amaxa machine ( Biorad company ) , and pyrimethamine selection was performed [41] . Pools of parasites resistant to pyrimethamine were genotyped and analyzed by Western blot . His tagged proteins were purified on Qiagen Ni-NTA superflow resin ( 30410 ) under native conditions [44] . GST tagged proteins were purified on Amersham Glutathione sepharose 4 Fast flow ( 17-5132-01 ) in Amersham 10/20 Tricorn column ( 18-1163-13 ) . GST-TgPRF was cleaved using the Prescission protease ( Amersham , 27-0843-01 ) . The purified FH2 domains were eluted at the rate of 0 . 4 ml/min with PBS-NaCl 0 . 15 M buffer , with a Superose 6 10/300 GL column using AKTA prime machine ( Amersham Pharmacia biotech ) to determine their oligomeric state . To detect TgFRM1 and TgFRM2 , parasite lysate were fractionated on Tris-Acetate 3–8% precast gels ( Invitrogen ) using the manufacturer's running buffer and electrophoresis was continued until the 71–117 kDa marker reached the bottom of the gel . To compare the amount of mycTgFRM1 protein in presence or in absence of ATc with the endogenous level of expression of FRM1 , a western blot analysis was performed by loading an equal volume from the total protein extracts derived from both TATi-1 and mycTgFRM1i KO strains . The quantification of the bands was processed using ImageJ program . His-F1 and His-F2 were used to immunize rabbits ( Eurogentec ) . Anti-catalase ( CAT ) , anti-TgPRF , anti-SAG1 , anti-TgGAP45 , anti-IMC1 , anti-MLC1 , anti-ACT , anti myc ( 9E10 ) and anti-Ty tag ( BB2 ) were previously described [13] , [45] , [46] . Anti-RON4 was kindly provided by Dr . Dubremetz . Immunoblots were visualized using a chemiluminescent substrate ( Amersham , GE healthcare ) . HFF cells infected with parasites were fixed 15 minutes at room temperature ( RT ) with 4% paraformaldehyde ( PFA ) in PBS or 4%PFA/0 . 05% glutaraldehyde ( PFA/GA ) in PBS depending on the antigen to be labelled . Cells were neutralized 3–5 minutes in 0 . 1 M glycine/PBS , and then permeabilized with 0 . 2% Triton/PBS for 20 minutes . Cells were then incubated with primary antibody ( diluted in 2%BSA/0 . 2% triton/PBS ) for 1 hour at RT on balance , washed 3 times with 0 . 2% Triton/PBS and incubated with secondary antibody as above . Cells were washed 3 times , stained for 5 minutes with DAPI ( 50 µg/ml in PBS ) and washed again . Coverslips were mounted with Fluoromount G ( Southern Biotech 0100-01 ) on glass slides [47] . Parasites expressing mycPRF were used as source of PRF and F1L , F1 , and F2 were fused to GST and shown to polymerize rabbit actin . Freshly egressed parasites ( 3×108 parasites ) were harvested , washed once with buffer G ( CaCl2 0 . 1 mM , Tris 5 mM pH 7 . 8 , ATP 0 . 2 mM , and DTT 1 mM ) , and resuspended in the same buffer containing 0 . 5 mM ATP and protease inhibitors . Successive rounds of freeze/thaw in liquid N2 were performed to break the cells . After ultracentrifugation at 30000 rpm , the supernatant was incubated for 2 hours at 4°C with 75 µg of the bait protein ( His-GST or His-GST-F1 or His-GST-F1-R/A or His-GST-F1-IR/AA or His-GST-F1-ΔH or His-GST-F2 or His-GST-F2-R/A or His-GST-F2-IR/AA ) followed by incubation with 50 µl of Nickel beads ( Qiagen ) for 1 hour at 4°C . Beads were centrifuged and an aliquot of the supernatant was taken ( flow through ) . Beads were washed 3 times with buffer G ( CaCl2 0 . 1 mM , Tris 5 mM pH 7 . 8 , ATP 0 . 2 mM , and DTT 1 mM ) , suspended in protein loading buffer , and analysed by western blot . FRM1-DD-myc-F1 or FRM1-DD-myc-F1-IR/AA and FRM2-DD-myc-F2 or FRM2-DD-myc-F2-R/A heterodimers complexes were immunoprecipitated with monoclonal 9E10 anti-myc antibodies . To achieve this , 3×108 parasites were lysed in PBS/0 . 2% triton-X100 . Incubation for 1 hour at 4°C with an excess of antibodies was followed by incubation with 25 µl of protein A beads for 1 hour at 4°C . Beads were then washed 3 times with washing buffer , suspended in protein loading buffer , and analysed by Western blot using rabbit polyclonal anti-FRM1 and anti-FRM2 antibodies . To quantify how much endogenous formin was sequestered by the corresponding FH2 mutant , two sequential immunoprecipitation experiments were performed . The two immunoprecipitated fractions and the two flow throughs were analysis by Western blot . The quantification of the bands was processed using ImageJ program and nomalized to the % of FRM present in the input ( total lysate ) . Parasites were harvested and extracted in the following buffers: PBS or PBS/Na2CO3 ( 0 . 1 M , pH 11 . 5 ) . Extracts were then centrifuged at 30000× rpm for 1 hour at 4°C . Equivalent amounts of total , supernatant , and pellet were run on Tris-Acetate 3–8% precast gels ( Invitrogen ) for formins 1 and 2 , and on 10% gel for catalase . Cover slips were coated with a solution of Poly-L-Lysine . Prior to use recombinant protoxin was activated for 20 minutes at 37°C in 100 µl of PBS with 2 µl of trypsin diluted at 1 mg/ml into HBS ( 140 mM NaCl , 2 . 7 mM KCl , 20 mM Hepes pH 7 . 4 ) . Freshly harvested parasites were washed with PBS and attached to coverslips coated with Poly-L-Lysine ( incubation at 37°C for 10 minutes ) . The medium was then removed , and parasites were treated with aerolysin at 60 ng/ml for 3 hours at 37°C and then IFA was performed as described . Actin was purified from rabbit muscle acetone powder and isolated in monomeric form by gel filtration on Superdex-200 in G buffer . Spontaneous assembly of actin was monitored using the enhancement of the fluorescence of 5% pyrenyl-labeled actin in a Safas Xenius spectrofluorimeter . Conditions were: 2 . 5 µM actin , 5 mM tris-Cl- pH 7 . 8 , 0 . 2 mM ATP , 1 mM DTT , 0 . 1 mM CaCl2 , 0 . 25 mM EGTA , 1 mM MgCl2 , 0 . 1 M KCl . Seeded actin assembly assays were performed similarly using spectrin-actin seeds and 2 . 5 µM G-actin [48] . Dilution-induced depolymerization assays were performed by diluting 40-fold a solution of 2 . 5 µM F-actin ( 50% pyrenyl-labeled ) in polymerization buffer containing the desired concentrations of formins . The initial rate of fluorescence decrease was measured [23] . Fresh monolayers of HHF on circular coverslips were infected with parasites in the presence or absence of 1 µg/ml ATc and 1 µM Shld-1 for 6 days . Fixation , staining and visualization were performed as previously described [13] . Parasites were pretreated for 96 hours with or without ATc or 63 hours with or without 1 µM Shld-1 , collected promptly after egress and inoculated onto new HHF monolayers . 24 hours later , the culture was fixed with PFA and stained with anti-TgGAP45 . The number of parasites per vacuole was counted for more than 100 vacuoles under each condition . Freshly released parasites were inoculated onto new confluent HHF monolayer and allowed to invade for 1 hour before the cells were fixed . IFA was performed as previously described [13] . Comparison of T . gondii dominant negative mutant strains for invasion efficiency was done in the presence of the RH-2YFP strain as an internal standard as previously described [49] . Parasites were grown for 63 hours ±1 µM Shld-1 . After 33 hours of intracellular growth , most vacuoles contain 16–32 parasites . Media was changed and incubated for 8 minutes at 37°C with DMEM containing 0 . 06% of DMSO or 3 µM of the Ca2+ ionophore A23187 ( from Streptomyces chartreusensis , Calbiochem 100105 ) as previously described [13] . Freshly released tachyzoites were collected by centrifugation , resuspended in 100 µl and deposited onto Poly-L-Lysine coated coverslips ( 1 mg/ml , 2 hrs at RT ) in a wet environment for 15 minutes at 37°C previously . Parasites were fixed with PAF/GA and IFA using the anti-SAG1 antibody was performed to visualize the trails . Freshly released parasites were resuspended in Ringer medium , and allowed to glide on glass-bottom dishes ( MatTek Corp , Ashland , MA ) precoated with poly-L-lysine ( 1 mg/ml ) . Video microscopy was conducted using a spinning disk confocal microscope ( Ultraview ) equipped with Andor Revolution under bright field illumination and in a temperature-controlled stage to maintain 37°C . Images were collected in real time under low-light illumination using an intensified Andor DU-897 E camera with a 60× objective ( Nikon Plan Apo NA 1 . 4 Oil ) . Videos were recorded at 1 . 47 frames per second in a total time of 1 minute 8 seconds with a resolution of 0 . 26 µm/pixel . The video signal was processed using ImageJ program . Toxoplasma gondii Formin 1 , TgFRM1 ( ACY06261 ) ; Toxoplasma gondii Formin 2 , TgFRM2 ( ACY06262 ) .
|
Gliding motility is a unique property of the Apicomplexa . Members of this phylum include important human and animal pathogens . An actomyosin-based machine powers parasite motility and is crucial for parasite migration across biological barriers , host cell invasion and egress from infected cells . The timing , duration and orientation of the gliding motility are tightly regulated to insure successful establishment of infection . Controlled polymerization of actin filaments is a key feature of motility , and we demonstrate here the implication of two formins that catalyse actin nucleation and fast assembly of filaments . Both proteins are essential and act in concert during productive penetration of the parasite into host cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/protozoal",
"infections",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"cell",
"biology/cytoskeleton"
] |
2010
|
Concerted Action of Two Formins in Gliding Motility and Host Cell Invasion by Toxoplasma gondii
|
Fundamental aspects of embryonic and post-natal development , including maintenance of the mammalian female germline , are largely unknown . Here we employ a retrospective , phylogenetic-based method for reconstructing cell lineage trees utilizing somatic mutations accumulated in microsatellites , to study female germline dynamics in mice . Reconstructed cell lineage trees can be used to estimate lineage relationships between different cell types , as well as cell depth ( number of cell divisions since the zygote ) . We show that , in the reconstructed mouse cell lineage trees , oocytes form clusters that are separate from hematopoietic and mesenchymal stem cells , both in young and old mice , indicating that these populations belong to distinct lineages . Furthermore , while cumulus cells sampled from different ovarian follicles are distinctly clustered on the reconstructed trees , oocytes from the left and right ovaries are not , suggesting a mixing of their progenitor pools . We also observed an increase in oocyte depth with mouse age , which can be explained either by depth-guided selection of oocytes for ovulation or by post-natal renewal . Overall , our study sheds light on substantial novel aspects of female germline preservation and development .
Understanding the complex processes of embryonic development and post-natal maintenance in multi-cellular organisms requires advanced methods for cell lineage reconstruction . The mammalian female germline is a prominent example , in which fundamental aspects of these processes remain debatable . Unlike lower metazoans such as C . elegans and Drosophila , in which germ-cell progenitors are set aside during the very first embryonic divisions , in mice , primordial germ cells ( PGCs ) appear at a much later stage [1] , [2] . The late appearance of PGCs , their long-range migration into the gonadal ridges and their co-occurrence with progenitors of hematopoietic and mesenchymal stem cells within the aorta-gonad-mesonephros region of the developing embryo , raised the intriguing possibility that these cell populations may be clonally related [3] , [4]; however this hypothesis as well as the modes of expansion and migration of PGCs to the gonadal ridges [5] has thus far not been experimentally tested . An additional aspect which remains poorly characterized is related to folliculogenesis , the process by which ovarian follicles mature and are selected for ovulation . Folliculogenesis begins with primordial follicles that contain a single layer of squamous pre-granulosa cells that surround an oocyte . Follicles grow through primordial , primary and secondary stages before they develop an antral cavity . The transition from pre-antral to antral follicle occurs only after puberty and is followed by cyclic recruitment of a limited , species-specific number of growing follicles , from which a subset is selected for ovulation [6] . The ‘production-line’ hypothesis suggests that the order by which follicles are selected for growth follows the order at which their oocytes embark on meiosis during embryogenesis , but evidence supporting this notion is sparse [7]–[10] . The ability to reconstruct phylogenies of individual cells and to infer the number of divisions they have undergone since the zygote can address these fundamental questions . While the de-novo generation of oocytes has traditionally been considered to cease during fetal development in most mammals [11] , several recent publications argued for continuous post-natal oocyte renewal in the mouse . These studies were based on several lines of evidence , mainly the discordant proportion between oocyte death and their depletion [12] , [13] , and the detection of primordial germ cell markers in conjunction with proliferative markers in different cell populations in the ovary [12] , [14] and in bone marrow and peripheral blood [15] . These findings were challenged by several publications that failed to reproduce some of the reported observations [16]–[18] . Recently , putative mouse germline stem cells were successfully cultured and transplanted into ovaries of subfertile mice , giving rise to offspring of donor origin [14] , suggesting that cells in the adult mouse retain the capacity for oogenesis . However the existence , source and contribution of germline stem cells during normal development remain unclear . We have previously developed a high-throughput method that uses the information encoded in somatic mutations to reconstruct cell lineage trees [19]–[22] . This phylogenetic method , which was also applied by others [23]–[26] , is based on the notion that the DNA is a molecular clock which effectively counts the number of mitotic divisions a cell has undergone since the zygote ( denoted as “depth” ) and that the pattern of somatic mutations in multiple loci can reveal the lineage relations among individual cells . Our analysis is based on somatic mutations accumulated in microsatellites ( MS ) loci that reside in intergenic regions . Since these mutations do not affect genes , they are not expected to cause phenotypic effects , and can thus serve as neutral developmental molecular clocks . Our method was validated using ex-vivo cell lineage trees [19] and applied to the lineage analysis of cells of a mouse with a tumor [20] , as well as to the estimation of depth of different cell populations [22] , and the study of the development of muscle stem cells . Most recently , we demonstrated the reliability of this method for the detection of stem cells and tissue dynamics in the colon [27] . Here we apply this method to address the lineage relations of oocytes and other cell types . We sampled more than 900 cells from 16 mice spanning a range of ages . Sampled cells included oocytes , bone-marrow derived mesenchymal stem cells and lymphocytes , cumulus cells ( the epithelial cell surrounding the oocytes in the ovarian follicle ) and pancreatic islet cells . We found that in the reconstructed cell lineage trees of mice at all ages , oocytes form clusters that are distinct from other cell populations . Oocytes from the two ovaries , however , do not form two distinct clusters , suggesting a spatially-incoherent mode of expansion and migration of their embryonic progenitors . In the reconstructed cell lineage trees , the depth of oocytes increases with mouse age and this increase is accelerated in mice that have undergone unilateral ovariectomy . Two alternative explanations can possibly account for the age-associated depth increase , one of which is post-natal oocyte renewal from germline stem cells . The alternative interpretation of our results would go along with a depth-guided oocyte selection , posing that in a sexually mature female mouse oocytes are selected to resume meiosis according to the order in which they embarked on meiosis during embryonic life [7] .
In the current application of our method , the cellular genomic signature is derived from a set of MS loci in mismatch-repair ( MMR ) deficient mice ( mlh1−/− ) . The MS mutation rate of these mice is much higher than that of wild type [28] , thus increasing the precision of the cell lineage analysis . These mice are infertile and develop cancer spontaneously , however they display normal ovarian histology [29] , [30] ( Figure S4 ) . Recently , it was shown that on the background of C3H , the oocytes of mlh−/− mice complete the first meiotic division as indicated by the formation of the first polar body in a fraction which is similar to that of oocytes of a wild type mouse [31] . This is unlike a previous report demonstrating that oocytes of mlh−/− mice on a B6 background fail to resume meiosis [28] . Similarly to mice on the C3H background , oocytes of mice used in this study , which are on a dual background of B6 and M129 , complete the first meiotic division , with 100% of the ovulated oocytes extracted from the oviducts displaying a polar body ( Table S3 ) . We sampled oocytes from 17 mice at different age groups ranging from 12 days to one year old ( Table S1 ) . These oocytes were arrested in prophase of the first meiotic division containing a nuclear structure known as germinal vesicle ( GV ) . These oocytes were isolated from Graaffian follicles of sexually mature mice , as well as from pre-antral follicles in younger animals . Few ovulated oocytes , arrested at the second metaphase were recovered from the oviduct . In addition , we isolated other cell types , including mesenchymal stem cells , lymphocytes extracted from the spleen , thymus and lymph nodes , and cumulus cells ( the inner layer of follicular epithelial cells surrounding the oocyte ) . The DNA of all cells was amplified over a panel of 81 microsatellite loci ( Table S2 ) and the size of each allele was determined , thus providing a genomic signature which is the deviation from the putative zygote in the number of microsatellite repeats at each locus . The signatures were used to reconstruct lineage trees using a maximum likelihood Neighbor Joining algorithm ( Materials and Methods ) and the resulting trees were used to estimate depth ( the number of somatic cell divisions since the putative zygote ) . The genomic signature of the putative zygote was taken as the median of the signatures of all sampled cells . Relative depth was converted to absolute depth ( actual number of cell divisions ) by calibrating the system on an ex-vivo tree in which the number of divisions is known [22] ( Text S1 ) . We first examined whether different cell populations form clusters on the lineage tree , by testing whether subtrees are enriched with a given cell population ( Materials and Methods ) . Such a cluster of a cell population would suggest a small number of embryonically distinct progenitors ( Text S2 , Figures S2 , S3 ) . In all reconstructed cell lineage trees of all mice , young and old , oocytes from large antral follicles form a cluster that is distinct from the clusters formed by hematopoietic cells and mesenchymal stem cells ( Figure 1 ) . We found that clustering of cell samples on the reconstructed lineage trees is indicative of the number of progenitors of the cell population studied ( Text S2 , Figure S1 , S2 and S3 ) . The larger the number of progenitors , the less significant clustering observed . The way that subsamples of oocytes form clusters suggests that the number of progenitors of this population is between 3 and 10 ( Figure S3 ) , in line with previous estimates based on measurements of primordial germ cells [1] , [32]–[35] . Our clustering results indicate that the primordial germ cell lineage is a polyclonal population descendant from a few progenitors , which is embryonically distinct from hematopoietic and mesenchymal stem cells and does not significantly contribute to bone-marrow stem cell populations . We next turned to examine the clustering of oocytes sampled from different ovaries with the aim of unveiling the dynamics of primordial germ cell expansion and their migration during fetal life . The progenitors of PGCs are set aside at the pre-gastrulation epiblast stage [1] , [32] and then undergo rapid expansion while migrating to the gonadal ridges [5] , where they separate to the left and right gonads . A cluster of oocytes from one ovary would be indicative of a spatially coherent migration of PGCs , with minimal physical mixing of the progenies of given clones ( Figure 2a1 ) . In contrast , if pairs of cells from the same ovary were not closer on the reconstructed lineage trees than mixed pairs , this would suggest a spatially incoherent mode of expansion-migration . Such a mode entails physical mixing of the progenies of dividing PGCs as they migrate before their allocation to different gonads . In this scenario the populations of both ovaries would form an identical sampling of the progenies of the different founder clones ( Figure 2a2 ) . As a positive control we reconstructed cell lineage trees of cumulus cells extracted from two different follicles , one from each ovary . The population of cumulus cells in a given follicle has been shown to originate from only five progenitors [36] and is thus expected to form a cluster on a reconstructed lineage tree . Our analysis of the reconstructed cell lineage trees revealed that while cumulus cells sampled from different follicles form distinct clusters ( Figure 2b ) , oocytes from large antral follicles from the left and right ovaries never do ( Figure 2b , Figure S5 ) . This result suggests a mode of incoherent clonal expansion during the process of primordial germ cell migration to the gonadal ridges , as depicted in Figure 2a2 . While primordial germ cells undergo several additional rounds of mitotic divisions after having settled in the gonadal ridges thus giving rise to small clones , the lack of lineage clustering between the two ovaries suggests that at this stage the number of such clones is significantly larger than the number of oocyte sampled here . Thus the probability to sample more than one oocyte from a single clone is small ( Figure S2 ) . The topology of reconstructed lineage trees can shed light on the developmental processes of the oocyte lineage . To address the possibility of post-natal oocyte renewal during adulthood we next analyzed the depth of oocytes sampled from large antral follicles of mice at different ages . Analysis of the reconstructed cell lineage trees showed that oocyte depth increases significantly with mouse age ( Figure 3 , R = 0 . 81 , p = 0 . 007 , bootstrap R = 0 . 56 ) . Unlike oocytes , the depth of pancreatic islet cells , which have been shown to have a low turnover rate in adult mice [37] , [38] , does not increase with age ( Figure 3e ) . On the other hand , epithelial intestinal cells display a substantial increase in depth from age 1 to 11 months [27] . The depth of other cell types sampled also increases during adulthood ( Figure S6 ) . To control for the apparent inter-mouse depth variability ( Figure 3d ) , we performed three longitudinal unilateral ovariectomy experiments in which one ovary was removed when the mouse was one month old , whereas the second ovary of this same mouse was removed at the age of four months . Analysis of the reconstructed cell lineage trees revealed that oocytes from large antral follicles harvested from the ‘old’ ovary are significantly deeper than such oocytes harvested from the ‘young’ ovary ( Figure 4 ) . Interestingly , the depth of oocytes from the ‘old’ ovary is significantly higher as compared to oocyte depth in mice at similar age that did not undergo this intervention ( Figure 4d ) . By rejecting the explanation of spontaneous mutations for the increase with age in accumulated somatic mutations , we conclude that oocytes in older mice are deeper ( undergo more mitotic divisions ) than oocytes in young mice . Such divisions can occur at either embryonic development or during adult life . We next consider these two possibilities . The ‘production-line’ hypothesis introduced by Henderson and Edwards [7] , [10] , [42] , posits that the order at which oocytes are ovulated during adult life follows the order at which PGCs enter meiosis during fetal life . This would result in a lower depth of oocytes from large antral follicles in young mice as compared to oocytes recovered from such follicles of old mice ( Figure 6a ) . Indeed , both , the order at which germ cells enter meiosis and the growth of primordial follicles have been suggested to be spatially structured [43] , [44] and thus not completely arbitrary . However , the exact sequence and spatial details of these two processes have not been firmly resolved [42] . While the oocytes sampled in this study were taken from large antral follicles selected for ovulation , a wide distribution of oocyte depth extracted from pre-antral follicles at birth would be suggestive of depth-guided oocyte maturation . To examine this possibility we sampled oocytes from 12 day-old mice , an age at which the entire population of the ovarian follicles is at the pre-antral stage . Thus , our sampling in these mice was not enriched for follicles selected for ovulation . We found that the median depth of two 12 day-old mice was similar to the median depth of young animals ( Figure 6b , Figure S15 ) . To compare the depth distributions in pre-antral follicle oocytes in 12 day old mice to that expected from the ‘production-line’ hypothesis we performed the following simulation – a putative depth distribution was create based on a uniform weighting of the depth values of oocytes measured at different ages . The depth of values sampled from this distribution was compared to that seen in the 12 day old mice , and the fraction of simulated values for which the maximal/median depth was smaller was reported as a p-value . We used Fisher's method for combining the p-values of two 12-day old mice . We found that the distribution of oocyte depths of pre-antral follicles from 12 day old mice is not statistically different from that expected from a pre-existing wide distribution that spans the entire depth range seen for all ages ( Figure 6c ) . As such our results do not conclusively rule out the ‘production-line’ hypothesis indicating that depth-guided maturation could give rise to the observed increase in oocyte depth with age [12] , [13] .
The ability of the germline to generate all somatic cell types , the analogous ability of bone-marrow derived stem cells to differentiate to a wide range of cells [45] , [46] , and the physical proximity of the progenitors of these populations during embryonic development , raised the intriguing possibility that these different cell populations are the progenies of a common precursor [3] , [4] . Here , using cell-lineage trees reconstructed from somatic mutations , we show that the mouse female germline is embryonically distinct from cells derived from bone marrow stem cells , be they mesenchymal or hematopoietic stem cells . Thus , this study indicates that progenies of primordial germ cells do not contribute to bone-marrow stem cell populations . In addition , this study suggests that putative germline stem cells that may contribute to post-natal oocyte renewal are progenies of the primordial germline , rather than being of bone marrow origin [15] . We considered analyzing cells from the ovarian surface epithelium , which were suggested to harbor the germline stem cells . However because germline stem cells are expected to constitute a small fraction of this cell compartment [47] and thus our chances of sampling them are very low , and since we must analyze each mouse separately to obtain an informative lineage tree , we could not pursue this direction in our system . Similar cell-lineage analysis of other cell populations , such as that presented here could shed light on the embryonic development and post-natal dynamics of other tissues and cell populations , providing novel insight into the architecture of multi-cellular organisms . The reconstructed cell lineage trees revealed that oocytes from older mice undergo more mitotic divisions since the zygote as compared to oocytes of young mice . This finding may point towards post-natal oocyte renewal . However , since sampling in our study was directed at oocytes that reside in the large antral follicles , selected for ovulation , the age-associated increase in their depth could also represent depth-guided oocyte selection [7] . The depth distribution of oocytes recovered from from pre-antral follicles of 12-day-old mice does not deny the possible depth-guided oocyte selection , thus leaving the two optional interpretations of our results open . Further cell lineage analysis studies , for example using a mouse model susceptible to post-natal induction of MMR-deficiency , are needed to decide between the two . While previous evidence regarding the ‘production-line’ hypothesis linked the time of entry into meiosis with the time of post-natal oocyte maturation [9] , [10] , [42] , the present study suggests that under the hypothesis of depth-guided oocyte maturation , the number of divisions rather than the embryonic day , determines the order of oocyte maturation during adult life . Similar cell-lineage analysis of other cell populations , such as that presented here , could shed light on the embryonic development and post-natal dynamics of other tissues and cell populations , providing novel insight into the architecture of multi-cellular organisms . The accelerated increase in oocyte depth in the unilaterally ovariectomized mice may be related to the previously described phenomenon of doubling the rate of ovulation from the contralateral remaining ovary that is a subsequent to a systemic elevation of pituitary stimulating gonadotropins [48] . Our study implies that under these conditions the remaining ovary is populated by deeper oocytes . Along this line , it has been shown that hormonally-stimulated super ovulation results in oxidative damage to DNA and mitochondrial DNA mutations [49] . Thus , exposure to high doses of stimulating hormones , as is often practiced to treat infertility , may severely impair oocyte quality by introducing into the oocyte pool deeper oocytes . In summary , we present a comprehensive analysis of the mouse oocyte lineage at the single cell level , addressing open questions regarding both the development and post-natal maintenance of female gametes . Our analysis revealed that oocytes are clustered distinctly from bone-marrow derived cells , that progenitors of oocytes from different ovaries are mixed , that oocyte depth increases significantly with mouse age , and that this increase is accelerated after ovariectomy . Our methodology can be used to infer the early developmental processes and post-natal clonal dynamics of other tissues and cell populations .
C57Bl/6 mice , Mlh1+/− ( kind donation of Prof . Michael Liskay ) [50] and 129SvEv mice , Mlh1+/− ( kindly provided by Prof . Ari Elson from the Weizmann Institute , Israel ) were mated to yield Mlh1−/− progeny of the dual backgrounds , enabling us to distinguish , in all our experiments , between two alleles in the same locus . All animal husbandry and euthanasia procedures were performed in accordance with the Institutional Animal Care and Use Committee at the Weizmann Institute of Science . Mice were not superovulated in order to avoid any effect of external hormones on our analysis . Ovaries were removed and placed in Leibovitz's L-15 tissue culture medium ( Gibco ) , supplemented with 5% fetal bovine serum ( Biolab , Jerusalem , Israel ) , penicillin ( 100 IU/ml ) and streptomycin ( 100 µg/ml , Gibco ) . The follicles were punctured under a stereoscopic microscope in order to release the cumulus–oocyte complexes that were then placed into acidic L-15 medium ( pH 6 . 0 ) to obtain cumulus-free oocytes ( Figure S14 ) . Most of the oocytes were MI-arrested from pre-antral and from develpme , thus retaining all four meiotic products . In few cases ovulated oocytes were isolated from the oviduct and their polar body was seen and it was extracted together with the oocyte . Our oocyte samples in M268 and M26–150 included a few ovulated oocytes that have extruded their polar body ( blue dots , Figure 3c and Figure 4b ) . Excluding them had no effect on median oocyte depths in these mice . Each oocyte was placed in a 0 . 2 mL tube ( ABgene ) in a volume of 2 µL medium . Oocytes were frozen in liquid nitrogen and kept in −80°C until DNA ampification . In the longitudinal experiment , mice were anesthetized and then underwent unilateral ovariectomy . The section was clipped and after additional 4 months the other ovary was removed when the animal was sacrificed . Mesenchymal stem cells , B-cells , NK-cells and T-cells were isolated as described in [21] . Pancreatic islet cells where extracted from 1-month old and 9-month old male mice using single cell laser capture microdissection of Hematoxylin stained tissue , as described in [21] . DNA extracted from individual cells was amplified using whole-genome amplification ( WGA ) with the GenomiPhi DNA amplification kit ( GE Healthcare , UK ) as described in [23] . Aliquots of WGA products were used directly ( without purification ) as templates in subsequent PCRs . PCR repeats and negative controls ( DDW ) were included in every PCR plate ( Figure S10 and Text S3 and Text S4 ) . Loci that exhibited a signal in the negative control were excluded from the analysis of all samples that were run on the corresponding PCR plate . The method achieved high-throughput through the use of a liquid handling robotic system . Microsatellite loci were chosen to have a substantial allele size difference between the two mice strains , so as not to confound allele sizing ( see Figure S8 for representative capillary signals ) . Whole genome amplification introduced very few microsatellite mutations , but resulted in allelic drop-out of 32 . 5+−5% . Capillary signals that displayed more than two alleles per locus were excluded from the analysis . In addition only cells in which more than 25 alleles were amplified were included in the analysis ( 998 cells , Table S1 ) . Trees were reconstructed using the distance-based neighbor joining algorithm [51] , [52] . Pairs of cells were sequentially merged according to a distance matrix of lineage distances . Each entry in the distance matrix is taken as the maximum likelihood estimate of the number of divisions separating the two cells , assuming a symmetric stepwise model and an average mutation rate estimated from the ex-vivo trees , equal to 1/30 divisions ( Text S1 ) . Depth was read off the trees as the branch lengths leading from the root to each terminal leaf . Root signature was taken as the median of the allele size values of all sampled cells . This method was found to be more efficient and robust than estimating depth using a squared distance metric ( Text S5 ) . It is important to note that we estimated the microsatellite mutation rate in this study to be one mutation per 30 cell divisions; this estimation is based on our measurements of cell divisions in ex-vivo trees ( Text S1 ) . Nevertheless , to be sure that our conclusions are not dependent on this specific mutation rate , we reconstructed trees using different mutation rates ranging from 1 mutation per 10 cell divisions to 1 mutation per 200 cell divisions . Our conclusions ( i . e . the depth increase with age , the enrichment of oocytes on the lineage tree and the mixing of oocytes between left and right ovary ) are robust to different mutation rates ( Figure S11 , S12 , and S13 ) . While we have previously estimated a higher oocyte depth [22] our current study used a dramatically increased resolution for depth estimation , based on a larger and more informative panel of high-mutation rate microsatellite loci as well as a larger oocyte sample size . P-value for the Pearson correlation between median depth and age was based on permutations of the median depth values . Bootstrap correlation coefficient of depth vs . age was obtained by generating 1000 median depth values extracted from sampling with replacement of the cell depths for each mouse . P-values for differences in distributions were calculated using Kolmogorov-Smirnov method . Hypergeometric tests were carried out for each internal branch to assess whether subtree leafs are enriched for a cell population . P-values declared as significant were corrected for multiple hypothesis testing using false discovery rate of 0 . 2 . Whenever subtrees were embedded only the subtree with the most significant p-value was retained . Noteworthy , clustering of oocytes is stronger in M37 and M278 relatively to M27 . We examined whether this could be attributed to allelic dropout , however we did not find any significant difference between the average amplified loci per cell in these different mice ( 42+−5 , 51+−9 and 43+−5 in M27 , M37 and M278 , respectively ) . The variability in lineage clustering is strongly dependent on the number of cells sampled from each population and on the number of progenitors . Since oocytes are a polyclonal population , sampling in different mice could give rise to either more cells from fewer clones ( leading to strong lineage clustering , Figure S2 and S3 ) or alternatively cells distributed equally among the progenitor clones ( leading to weaker clustering ) . In addition , the progenitor clones themselves could be distributed differently on the progenitor lineage tree , thus affecting variability of clustering between animals . This issue is further discussed in Text S5 and Figures S2 and S3 .
|
Many aspects of mammalian female germline development during embryogenesis and throughout adulthood are either unknown or under debate . In this study we applied a novel method for the reconstruction of cell lineage trees utilizing microsatellite mutations , accumulated during mouse life , in oocytes and other cells , sampled from young and old mice . Analysis of the reconstructed cell lineage trees shows that oocytes are clustered separately from bone-marrow derived cells , that oocytes from different ovaries share common progenitors , and that oocyte depth ( number of cell divisions since the zygote ) increases significantly with mouse age .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"algorithms",
"systems",
"biology",
"developmental",
"biology",
"computer",
"science",
"genomics",
"mathematics",
"physiology",
"genetics",
"biology",
"anatomy",
"and",
"physiology",
"evolutionary",
"biology",
"population",
"biology",
"computational",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Cell Lineage Analysis of the Mammalian Female Germline
|
Cellular receptors usually contain a designated sensory domain that recognizes the signal . Per/Arnt/Sim ( PAS ) domains are ubiquitous sensors in thousands of species ranging from bacteria to humans . Although PAS domains were described as intracellular sensors , recent structural studies revealed PAS-like domains in extracytoplasmic regions in several transmembrane receptors . However , these structurally defined extracellular PAS-like domains do not match sequence-derived PAS domain models , and thus their distribution across the genomic landscape remains largely unknown . Here we show that structurally defined extracellular PAS-like domains belong to the Cache superfamily , which is homologous to , but distinct from the PAS superfamily . Our newly built computational models enabled identification of Cache domains in tens of thousands of signal transduction proteins including those from important pathogens and model organisms . Furthermore , we show that Cache domains comprise the dominant mode of extracellular sensing in prokaryotes .
Signal transduction is a universal feature of all living cells . It is initiated by specialized receptors that detect various extracellular and/or intracellular signals , such as nutrients , and transmit information to regulators of different cellular functions [1 , 2] . Receptors are usually comprised of several domains and one or more of them are designated sensors that physically interact with the signal . There is a great diversity in the sensory domain repertoire , but a few of these domains appear to be dominant . The most abundant sensory module that is found in tens of thousands of signal transduction proteins throughout the Tree of Life is the Per/Arnt/Sim ( PAS ) domain [3 , 4] . PAS domains are related to another large group of dedicated sensors–cGMP phosphodiesterase/adenylyl cyclase/FhlA ( GAF ) domains [5 , 6]: both superfamilies belong to the profilin-like fold [6 , 7] and are found in similar types of signal transduction proteins in eukaryotes and prokaryotes . PAS and GAF are amongst the largest superfamilies of small molecule-binding domains in general , and the largest among those solely dedicated to signal transduction [8] . Originally , PAS domains were discovered as exclusively intracellular sensors [9 , 10]; however more recent studies have identified several extracytoplasmic PAS domains . Members of this group include quorum- [11] , dicarboxylate- [12 , 13] and osmo-sensing [14] receptor kinases , and chemotaxis receptors [15 , 16] from bacteria as well as the Arabidopsis thaliana cytokinin receptor [17] among others . As commonly accepted in structure-based approaches , these domains were termed PAS ( or PAS-like ) based on expert’s visual inspection of three-dimensional structures . Surprisingly , none of these structurally defined domains matched any sequence-derived PAS domain models . Furthermore , novel structural elements previously unseen in PAS domains have been noticed in some of these structures and a new name , PDC ( acronym of three founding members , PhoQ , DcuS and CitA ) , has been suggested for these extracellular domains [18] . On the other hand , several unappreciable , but independent observations pointed toward a possible link between extracellular PAS-like structures and yet another sensory domain superfamily , Cache [19] . Cache was originally described as a ligand-binding domain common to bacterial chemoreceptors [20] and animal voltage-dependent calcium channel subunits [21] that are targets for antineuropathic drugs [22] . First , the authors of the original Cache publication suggested that three predicted strands in the Cache domain might form a sheet analogous to that present in the core of the PAS domains structure; they also suggested a circular permutation of the Cache domain in extracellular regions of DcuS and CitA [19] , proteins that later became the founding members of the proposed PDC domain [18] . Second , in their structural classification of PAS domains , Henry and Crosson [4] noted that a few sequences corresponding to structures included in their analysis were annotated as Cache in domain databases . Third , Zhang and Hendrickson reported that a conserved domain search detected the presence of a single Cache domain in their two related structures of the double PDC domain , namely 3LIA and 3LIB ( PDB identifiers ) , but not in the other three closely related structures of this domain , 3LIC , 3LID and 3LIF [23] . Nevertheless , these potential relationships with Cache have never been explored further and extracellular PAS-like domains are being referred to as PAS [4] , PAS-like [14] , PDC [18] , PDC-like [24] , and PDC/PAS [25] ( S1 Table ) . Furthermore , there is no agreement between sequence- and structure-based classifications of these domains and associated structures provided by leading databases ( Fig 1 , S2 and S3 Tables ) . The fundamental problem beyond classification issues and semantics is that other than a handful of examples with solved 3D structure , receptors containing these domains cannot be identified by tools implemented in major biological databases , such as the NCBI Conserved Domain database [29] , Pfam [26] , SMART [30] , etc . This , in turn , is a barrier for practical applications , such as a proposed use of bacterial receptors as drug targets [31] . On the other hand , Dunin-Horkawicz and Lupas [32] were able to detect many extracellular PAS-like domains in genomic datasets by using a sensitive profile-profile search tool HHpred [33] and PDB derived profiles , thus laying a foundation for further exploration of these complex sequence-structure relationships . Here we show that extracellular PAS ( PDC ) -like domains belong not to PAS , but to the Cache superfamily . By building new Cache domain models utilizing structural information , we implicated more than 50 , 000 signaling proteins from all three domains of life as new members of this superfamily thus more than doubling the space of its current computational coverage . We also provide evidence that while being a distinct superfamily , Cache is homologous to the PAS superfamily and propose that the Cache domain emerged in bacteria from a simpler intracellular PAS ancestor as a benefit of extracellular sensing . Finally , we show that Cache domains are the dominant mode of extracellular sensing in prokaryotes .
To illustrate the level of ambiguity in classification of extracellular PAS/PDC-like domains ( S2 Table ) we compared it to that of diverse intracellular PAS domains from bacteria , archaea and eukarya ( S3 Table ) . The results show a nearly perfect classification coverage and agreement between sequence- and structure-based definitions for the latter and a state of disarray for the former ( Fig 1 ) . We subjected protein sequences of all twenty-one single and double extracellular PAS-like domains [4] with known 3D structure to similarity searches against the Pfam database ( v . 27 . 0 ) using sequence-to-profile search tool , hmmscan [34] and a more sensitive , profile-to-profile search tool HHpred [33] . None of the sequences had any PAS domain models as the best hit in any type of search . For fourteen of them ( including both single and double domains ) , best hits were to domain models from the Cache superfamily , whereas for the remaining seven structures , best hits are not assigned to any domain superfamily ( S4 Table ) . Mapping regions matched to Cache domains onto corresponding structures revealed the nature of ambiguity between sequence- and structure-based domain definitions . Single domain structures showed better agreement with sequence-based domain models ( S1 Fig ) , although some of them still had substantial discrepancies . For example , the full-length Cache_2 model does not include the last three β-strands of the PAS-like domain ( Fig 2A ) . Dual domain structures showed major disagreements with sequence-based domain models . The Cache_1 model captures the last three strands from the membrane distal PAS-like domain , the first two strands of the membrane proximal domain , and the connecting elements between the two domains ( Fig 2B ) . Some of the most conserved structural elements , such as the long N-terminal helix captured in the Cache_2 model and connecting elements between two globular domains captured in the Cache_1 model , are never seen in proteins that belong to the PAS domain superfamily , which led to a suggestion that these domains are different from PAS [23] . We also confirmed that the long N-terminal helix in some of the double domain structures ( Fig 2B ) matches a Pfam model MCP_N ( S4 Table ) . Cache domains were represented in Pfam 27 . 0 as a clan ( Pfam definition of a superfamily ) comprised of six families: Cache_1 , Cache_2 , Cache_3 , YkuI_C , DUF4153 and DUF4173 . We used newly uncovered relationships between structure and sequence characteristics to construct new Cache domain models . Three key facts about Cache domains were taken into account . First , structural studies revealed that both single and double Cache domains occupy the entire extracellular region between two transmembrane helices [11–17 , 23] . Second , Cache domains have been identified exclusively in proteins that contain output signaling domains . Third , the vast majority of Cache domains are found in prokaryotes . Consequently , in order to identify potential Cache domains , we retrieved a non-redundant set of prokaryotic sequences that contained at least one output signaling domain and a predicted extracellular region flanked by two transmembrane helices ( see Methods for details ) . The final set of predicted extracellular regions ( non-redundant at 90% identity ) was used in the hidden Markov model ( HMM ) construction . Models were built in three stages using sequence-to-sequence and HMM-to-HMM comparisons ( see Methods for details ) . We constructed eight new Cache models ( four double Cache models–dCache_1 , dCache_2 , dCache_3 , Cache_3-Cache_2 and four single Cache models–sCache_2 , sCache_3_1 , sCache_3_2 and sCache_3_3 ) to replace the three models ( Cache_1 , Cache_2 , and Cache_3 ) from Pfam 27 . 0 ( Table 1 ) . The alignments for the eight new models are shown in S1 Data . The fourth Pfam model from the Cache clan , YkuI_C , was found to adequately capture the domain structure and to perform well ( S1B Fig ) . Two other members of the clan , DUF4153 and DUF4173 were found to be unrelated to Cache based on both sequence similarity and secondary structure prediction . Consequently , these models will be removed from the clan . The new models revealed complex relationships between single and double Cache domains . HMM-HMM comparison ( see Methods ) showed that the membrane distal subdomain of dCache_1 was more similar to sCache_3 , whereas the membrane proximal subdomain was more similar to sCache_2 ( S2 Fig ) . On the other hand , dCache_2 and dCache_3 domains appear to be a result of sCache_2 and sCache_3 duplication , respectively . Finally , the Cache_3-Cache_2 domain likely originated as a fusion of sCache_3 and sCache_2 domains . The new models demonstrated dramatically improved sensitivity by identifying more than 50 , 000 Cache domains in the NCBI non-redundant database that escaped detection by Pfam 27 . 0 models ( S2 and S3 Data , S5 Table ) . HMM-HMM comparisons of newly identified Cache domains were carried out against the HHpred PDB70 profile database . 91% of the newly identified Cache domains were found to hit the PDB profile generated from available structures of the extracellular “PAS-like” domains ( S4 Data ) . The results further support that the newly generated models correctly identify Cache domains . A small number of newly identified Cache domains ( ~4% ) overlapped with other non-Cache Pfam domains , such as MCP_N , TarH , VGCC_alpha2 and few others ( S5 Data ) . As already discussed earlier , we consider MCP_N as a part of the Cache domain as it defines a subset of conserved Cache structural elements . Overlap with TarH is caused by inclusion of several Cache-domain containing sequences in the seed alignment for a model depicting an all alpha-helical TarH domain [35] . VGCC_alpha2 is usually present C-terminal to the Cache domain in Calcium channel subunits and in fact is a C-terminal part of the Cache domain missing from a Pfam 27 . 0 seed alignment . After correcting for these artifacts , the overlap of newly defined Cache domains with unrelated Pfam domains is about 0 . 15% . New models also showed a significantly improved average coverage ( Fig 3 , S6 Table ) . The average length of single and double Cache domains of known 3D structures is 140 and 271 amino acid residues , respectively , matching well previously observed bimodal distribution of extracellular ligand-binding regions in chemoreceptors [36] . Occasionally , single Cache domain models match to extracellular regions that are significantly larger than the average length of single Cache domains ( S3 Fig ) . Similarly , double Cache domain models occasionally match to extracellular regions with a size of a single Cache domain . This is likely due to the complex modular nature of these domains ( S2 Fig ) . We used sequences with known 3D structures as controls to visualize the increased specificity and coverage of the newly built Cache models ( S7 Table ) . All new models , further refined according to Pfam standard protocols , are now available in the Pfam 29 . 0 release . When carrying out sensitive profile-to-profile searches initiated with the sequences of extracellular “PAS-like” structures , we noticed statistically significant ( although never the best ) hits with profiles corresponding to several Pfam domains other than members of the current Cache clan . We explored this indication of potential remote homology further by consistently analyzing all statistically significant HHpred matches for all nineteen structures . The results show that statistically significant hits belong either to the PAS and GAF superfamilies or to small families that have not been assigned to any domain superfamily , for example LuxQ-periplasm , CHASE , Diacid_rec , etc ( S6 Data , spreadsheet 1 ) . Nearly the same repertoire of small families and members of PAS and GAF superfamilies were statistically significant hits in HHpred searches initiated with newly constructed Cache models ( S6 Data , spreadsheet 2 ) . Finally , we have performed a reverse search , where queries were models from small families as well as PAS and GAF superfamilies identified as statistically significant hits in the previous two types of searches ( S6 Data , spreadsheet 3 ) . These searches have identified nine additional current Pfam families that lacked any superfamily assignments . We now assign these families to the Cache superfamily ( see Methods , Table 1 , S6 Data , spreadsheet 4 ) . The sequence logos for all the members of the new Cache superfamily are shown in S7 Data . Relationships between all members of the Cache , PAS and GAF superfamilies at profile and sequence levels are shown in Fig 4 . The clustered heat map ( S4 Fig ) generated using HHsearch Prob scores , shows four main clusters , one each for PAS , GAF and Cache superfamily and a fourth cluster comprising of several new Cache family members along with some smaller GAF and PAS families . While being closely related to PAS and GAF , members of the Cache superfamily are more related to each other , thus fully justifying a separate superfamily designation . Satisfactorily , homologous relationships between Cache , PAS , and GAF were also captured in a new database ECOD ( Evolutionary Classification of Protein Domains ) [37] , which also included most of the related “orphan” families described above into the same superfamily . A key unsolved biological problem in signal transduction is linking computationally derived models of sensory domains with their ligands . We have compiled a comprehensive literature survey , which showed that only a handful of Cache domains have known ligands ( S8 Table ) . While it is unlikely that proposed models for individual Cache families capture the ligand-specific information ( see Discussion ) , there seem to be at least some interesting trends . For example , the majority of known ligands for dCache_1 domains are amino acids , whereas many single Cache domains bind organic acids . Interestingly , no sugars were identified so far as ligands for Cache domains . By performing the hmmsearch against the Pfam 27 . 0 associated UniProt database using eighteen domain models from the newly defined Cache superfamily , we have identified 31 , 572 protein sequences containing these domains . Thus , the size of the Cache superfamily is comparable to that of PAS ( 88 , 093 sequences ) and GAF ( 47 , 618 sequences ) superfamilies . Overall phyletic distribution of Cache domains is also similar to that of PAS and GAF ( Fig 5 , S8 Data ) . We have used the TMHMM2 tool to identify transmembrane regions in all 31 , 572 sequences with detectable Cache domains and determined that members of all Cache families are predicted to be principally extracellular , except for two small families , Diacid_rec and YkuI_C that are principally intracellular ( S9 Table ) . Altogether , 78% of all Cache domains were confidently predicted to be extracellular . For comparison , 74% of all PAS domains were confidently predicted to be intracellular . Analysis of the domain architecture of all Cache domain-containing protein sequences revealed known output domains of signal transduction systems , except for the SMP_2 family members ( Table 1 ) . The SMP_2 domain is the closest relative of the DUF2222 domain ( mutual best hits in HHpred searches ) and both are found exclusively in proteobacteria . While DUF2222 is the sensory module of the BarA/GacS/VarA-type histidine kinases that are global regulators of pathogenicity in gamma-proteobacteria [38] , SMP_2 appears to be a sensory module that was cut off from the rest of the protein . The likelihood of this scenario is further supported by the nearly identical phyletic distribution of both domains and the fact that SMP_2 proteins are also implicated in virulence in gamma-proteobacteria [39] . Apart from this neofunctionalization , all other Cache domains appear to serve as extracellular sensory modules for all major modes and brands of signal transduction proteins in prokaryotes , including sensor histidine kinases , cyclic di-GMP cyclases and diesterases , chemotaxis transducers , adenylate and guanylate cyclases , etc . Furthermore , Cache domains are dominant among known extracellular sensory domains in prokaryotes ( Fig 6 , S10 Table ) , significantly outnumbering the best studied such domain , a four-helix bundle [35 , 40] . Among tens of thousands of newly identified Cache domains , many are present in signal transduction proteins from important human pathogens and model systems ( Fig 7 ) . For example , we have confidently detected the Cache domain in the extracellular region of the WalK sensor histidine kinase from low G+C Gram positive bacteria , which plays a critical role in regulating cell division and wall stress responses [41] . WalK is a novel target for antibacterial agents against multidrug-resistant bacteria , including methicillin-resistant Staphylococcus aureus [31 , 42] . We identified the new double Cache domain in the YedQ diguanylate cyclase , which regulates cellulose biosynthesis and biofilm formation in Escherichia coli and Salmonella enterica [43 , 44] . This domain was also identified in the Rv2435c adenylate cyclase in Mycobacterium tuberculosis , which is a part of the cAMP network involved in virulence [45] . Our new dCache_1 model has identified the double Cache domain in the extracellular region of the osmosensing histidine kinase Sln1 from Saccharomyces cerevisiae , which controls activity of the HOG1 pathway [46] . The region , which is now designated as the Cache domain , was shown to be essential for its sensory function [47] . A meaningful phylogenetic tree of Cache domains cannot be produced due to extreme sequence variation between families . Consequently , evolutionary analysis of Cache is limited to less informative options . However , phyletic distribution , relative abundance and protein context all point towards a probability that Cache domain ( s ) evolved from simpler intracellular PAS-like ancestor ( s ) . We have shown that Cache is homologous to PAS and GAF ( Fig 4A ) , which is also independently supported by CATH [28] and ECOD [37] classification . PAS and GAF ( that are homologous to each other ) or their common ancestor originated in the last universal common ancestor [5 , 8 , 48] . Cache has all basic structural elements of PAS , but also contains novel structural elements that are not seen in PAS/GAF [23] including a long N-terminal helix previously mistaken for a separate domain ( MCP_N ) . Thus , PAS and GAF are structurally simpler than Cache . Domains that are structurally simpler are expected to be more ancient and more abundant than their structurally more complex derivatives [49] . In bacteria , PAS , GAF , and Cache domains are nearly equally abundant , whereas in archaea and eukarya Cache is significantly less abundant suggesting that Cache has likely originated in the bacterial lineage after its separation from the archaeal/eukaryotic lineage . Incidences of Cache in archaea and eukaryotes appear to be due to horizontal gene transfer . For example , Cache domains in Metazoa are mostly limited to a single type of protein–a voltage-dependent calcium channel alpha-2-delta subunit [21] ( S8 Data ) , whereas vertically inherited PAS and GAF domains are found in diverse signal transduction proteins [3 , 50] . In plants and fungi , Cache is limited to histidine kinases ( S8 Data ) that are known to be horizontally transferred from bacteria [51 , 52] . In Naegleria , a representative of Excavates , the Cache domain is found in a single protein , a bacterial-type adenylate cyclase ( Fig 7 ) . In a striking contrast , Cache domains in bacteria are found in all major types of signal transduction proteins ( Table 1 ) similarly to PAS and GAF , and their phyletic distribution and abundance in bacteria are similar to that of PAS and GAF . Finally , the Cache-to-PAS ratio in archaea and eukaryotes is nearly five times smaller than that in bacteria ( S8 Data ) . Taken together , these observations suggest that PAS and GAF predate Cache , which is consistent with the previous suggestion that intracellular sensing predates extracellular sensing [53] .
Our findings show that experimentally solved three-dimensional structures of so-called “extracellular PAS domains” belong not to PAS , but to Cache superfamily . Our new sequence profile models for the Cache superfamily dramatically improve computational coverage and enable identification of Cache domains in tens of thousands of signal transduction proteins including those from human pathogens and model systems . Consequently , we demonstrated that Cache is the most abundant extracellular sensory domain in prokaryotes , which probably originated from a simpler intracellular PAS/GAF ancestor as a benefit of extracellular sensing . The key structural innovation in Cache domains , when compared to PAS and GAF , is the long N-terminal alpha helix ( Fig 2 ) , which is a direct extension of the first transmembrane helix . It appears that this simple innovation ( along with a helical extension of the C-terminus to connect it to the second transmembrane helix ) was sufficient to convert an intracellular sensor to an extracellular sensor . However , this also placed significant physical constraints on the ability of the sensor to transmit information . Intracellular PAS and GAF domains have multiple options for interacting with downstream signaling domains , including direct domain-to-domain binding . In contrast , the only option for an extracellular Cache to transmit signals is via its C-terminal transmembrane helix , similarly to the sensory four-helix bundle exemplified by the E . coli aspartate chemoreceptor [54] . It is highly likely that these physical constraints dictated some re-wiring of the PAS/GAF-like core in Cache domains resulting in evolutionary conservation of amino acid positions that are not under such constraints in cytoplasmic PAS and GAF domains . Although our new domain models and expansion of the Cache superfamily helped to newly identify tens of thousands of Cache domain-containing proteins in hundreds of species , the key biological question–what do these Cache domains sense–remains unanswered . At this time , only a handful of Cache domains have known ligands ( S8 Table ) and high sequence variation essentially prohibits the computational identification of function-specific positions for various Cache domains . This is a persistent problem in signal transduction . Changes in just two or three amino acid positions in the ligand-binding site can convert a serine sensor into an aspartate sensor [55] and in case of a covalently bound cofactor a single amino acid residue may define the receptor specificity [56] . On the other hand , certain trends connecting different Cache families to specific ligand classes can be observed . For example , the majority of known ligands for dCache_1 domains are amino acids , whereas organic acids comprise the major known class of ligands for single Cache domains ( S8 Table ) . High-throughput screens , such as the one recently developed for microbial chemoreceptors [57] , should lead to substantial expansion of the known ligand repertoire for Cache domains . Once various ligands are identified for different Cache domains , a computational analysis aiming at linking specific ligands ( or ligand classes ) to conserved sequence features may become productive . Finally , our results demonstrate that solving ambiguous sequence- and structure-based domain definitions can dramatically improve computational models and significantly accelerate computational coverage of the protein sequence space [58] .
The central data source for all analyses was the local MySQL Pfam 27 [26] database based on Uniprot 2012_06 release . The database files for PfamScan were downloaded in December 2014 . The Non-redundant database fasta file was retrieved from NCBI on April 2015 . Uniref90 ( April 2015 ) was used for running Psipred [59 , 60] . The following software packages were used in this study: BLAST 2 . 2 . 28+ [61 , 62] , HHsuite-2 . 0 . 16 [33 , 63 , 64] , CD-HIT 4 . 5 . 7 [65] , Cytoscape 2 . 8 . 3 [66] , BLAST2SimilarityGraph plugin for Cytoscape [67] , Graph-0 . 96_01 ( UnionFind ) Perl library , MAFFT v7 . 154b [68] , Jalview v2 . 7 [69] , TMHMM 2 . 0c [70] , Phobius v1 . 01 [71] , DAS-TMfilter ( December 2012 ) [72] , HMMER 3 . 0 ( March 2010 ) [34] , PfamScan ( October 2013 ) [26] , MEGA 5 . 05 [73] , Circos v0 . 64 [74] and Psipred v3 . 5 . The multiple sequence alignments were built with MAFFT-LINSI using legacygappenalty option . Maximum likelihood trees were constructed to aid in the model building using MEGA with pairwise deletion and the JTT substitution . Domain predictions with PfamScan and hmmsearch were carried out at sequence E-value and domain E-value thresholds of 1E-3 for new Cache models and default thresholds for other Pfam models . Sequence logos were generated using the Skylign web-server [75] . A flow chart showing the model building approach is shown in S5 Fig . More than 1 million sequences containing at least one signal transduction output domain as defined in MiST2 database [76] were retrieved from a local copy of the Pfam database ( S5 Fig ) . Eukaryotic sequences were discarded , because domain boundaries for Cache domains in eukaryotes are unclear . Predicted extracytoplasmic regions that were longer than 50 amino acids were scanned for Pfam domains and redundancy ( at 90% identity ) was removed resulting in 36 , 320 sequences . In the next step , a similarity network was built using the BLAST2similarityGraph Cytoscape plugin . Nodes were connected by edges if the blast alignment resulted in an E-value less than 1E-10 and a query coverage of >95% reciprocally . Each connected component was considered as a distinct cluster . At this threshold the known families of Cache–Cache_1 , Cache_2 , Cache_3 and YkuI_C were separated into distinct clusters . 38 clusters comprising of at least ten members and containing at least one Cache domain ( 7577 sequences in total ) were further chosen for building models . Representative sequences were obtained using a custom script ( S6 Fig ) for each cluster and the sequences in each cluster were aligned using MAFFT-LINSi with the legacygappenalty option [77] . In case of the largest cluster , which was primarily comprised of sequences with the Cache_1 domain , the alignment was improved by dividing the cluster into smaller groups based on a maximum-likelihood tree generated using MEGA [78] . Individual groups were realigned using MAFFT-LINSi . HMM models for each cluster were built using hhmake and all-against-all HMM-HMM comparison was carried out using HHsearch [64] . Based on the probability scores and coverage , the clusters were then merged using mafft-profile . Representatives of each cluster were chosen to construct HMMs using the hmmbuild utility in the HMMER3 package [34] . The sensitivity of the models was improved by incorporating remote homologs that were identified by a more sensitive HMM-HMM comparison using HHblits and HHsearch [63 , 64] . This algorithm outputs representative sequences for a given set of sequences based on all-against-all blast results ( S6 Fig ) . Each query sequence is considered to be a representative of all hits that meet a certain threshold E-value and query coverage . The set of hits for a given query will be referred to as the represented set and the query sequence as the representative sequence . In order to reduce redundant computation , represented sets that were identical or subsets were discarded . The representative sequences were sorted based on the size of the represented set . The sequence with the largest represented set was first added to the list of representative sequences and the represented sequences were added to a new set , which we will refer to as the working set . Iteratively , a representative sequence was added to the list of representatives and the corresponding represented sequences are added to the working set . In each iteration , the representative sequence chosen was the one that results in the largest working set of represented sequences . Sequences were added to the list of representatives until all sequences that were provided as input have been included in the working set . The newly identified Cache sequences that were not detected with Pfam models were used to carry out HMM-HMM comparisons with HHpred PDB70 profile database ( Sep 2015 ) in order to detect similarity to Cache domains with known structures . 638 sequences that were not in NCBI non redundant database ( Feb 2016 ) were excluded . HHblits was first run to generate profiles for newly identified Cache sequences and HHsearch was then used to identify PDB hits for each sequence . The sequences of extracellular PAS-like domains with available PDB structures were used as queries for HHpred search using default parameters against Pfam 27 database . Only hits with a probability score greater than 95 for at least one of the PDB queries were considered . The alignments used for creating the new Cache models were also used as queries for performing profile-profile comparisons using the HHpred web server against Pfam 27 database . All hits with a probability score greater than 70 were considered to be potentially homologous . To further explore the relationship between the families , we retrieved models for these hits along with new Cache models and the PAS and GAF clan . All-against-all HMM-HMM comparison was carried out using standalone hhsearch . A similarity network was created with the domain families as nodes and hits representing reciprocal hhsearch hits with ( i ) E-value less than 1E-3 ( ii ) E-value less than 1E-1 and ( iii ) probability score > 90 . The E-value thresholds of 1E-3 and 1E-1 were used in accordance with the thresholds presently used in Pfam to define members of a clan ( Pfam definition of a superfamily ) . In addition the threshold probability score of 90 was used to detect more remote relationships . The nodes in the network were manually rearranged after using unweighted Force-directed Layout . Families were assigned to the Cache clan when the E-value from HHpred was less than 1E-3 ( LuxQ-periplasm , CHASE4 , Diacid_rec and DUF2222 ) or when Cache was the closest superfamily ( CHASE , Stimulus_sens_1 and 2CSK_N ) . SMP_2 and PhoQ_Sensor were included in Cache clan as they are mutual best hits with DUF2222 and 2CSK_N respectively . A clustered heat map was also constructed using HHsearch Prob scores from HMM-HMM comparison . The Heatmap web server ( http://www . hiv . lanl . gov/content/sequence/HEATMAP/heatmap . html ) was used to carry out hierarchical clustering using threshold Prob score of >20 , Euclidean distance method and Ward clustering . We also performed sequence-sequence comparisons using all-against-all BLAST . The sequences for PAS clan , GAF clan and Cache clan comprising of new families were retrieved . For Cache clan , sequences that have overlapping domain prediction with other sensory Pfam domains were disregarded . 100% redundant sequences were removed using CD-HIT . The similarities between different domains were demonstrated using Circos tool [74] . In order to show the phyletic distribution , only those organisms having more than 1000 proteins in Pfam 27 . 0 database were selected to exclude organisms with relatively incomplete genomes . The Sunburst was created by clustering the main level taxonomic ranks retrieved from NCBI Taxonomy database with the lowest rank used that of species . The domains were considered to be present if any strain of a given organism was found to contain a given domain . The Sunburst was generated using a custom script . PAS and GAF clans include all the families defined in Pfam 27 . 0 . However , the Cache domains indicated comprise of those identified by the eight new models , YkuI_C as well as the other families ( 2CSK_N , CHASE , CHASE4 , Diacid_rec , DUF2222 , LuxQ-periplasm , PhoQ_Sensor , SMP_2 and Stimulus_sens_1 ) that were identified to be a part of the Cache clan in this study . The secondary structure prediction by Psipred was mapped on to the alignment for each model . Only the PAS-like regions comprising of five beta strands were extracted . HMM profiles were built for each alignment using hhmake tool in the HHsuite . All-against-all HMM-HMM comparison was performed using hhsearch . A distance matrix was generated using probability scores from hhsearch . The dendrogram showing similarity between single Cache domains and the membrane-distal and membrane proximal domains of double Cache was generated using the DendroUPGMA web server [79] .
|
Cell-surface receptors control multiple cellular functions and are attractive targets for drug design . These receptors often have dedicated extracellular domains that bind signaling molecules , such as hormones and nutrients . Computational identification of these ligand-binding domains in genomic sequences is a pre-requisite for their further experimental characterization . Using available three-dimensional structures of several bacterial cell-surface receptors , we built computational models that enabled identification of the Cache domain , as the most common extracellular sensor module in prokaryotes , including many important pathogens . We also demonstrated that the Cache domain is homologous to , but sufficiently different from the most common intracellular sensor module , the PAS domain . These findings provide a unified view on molecular principles of signal recognition by extra- and intracellular receptors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"sequencing",
"techniques",
"database",
"searching",
"social",
"sciences",
"neuroscience",
"molecular",
"biology",
"techniques",
"sequence",
"similarity",
"searching",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"sequence",
"alignment",
"proteins",
"prokaryotic",
"cells",
"biological",
"databases",
"sensory",
"receptors",
"molecular",
"biology",
"biochemistry",
"signal",
"transduction",
"psychology",
"sequence",
"databases",
"cell",
"biology",
"database",
"and",
"informatics",
"methods",
"protein",
"domains",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"sensory",
"perception"
] |
2016
|
Cache Domains That are Homologous to, but Different from PAS Domains Comprise the Largest Superfamily of Extracellular Sensors in Prokaryotes
|
It is widely documented that hybridisation occurs between many closely related species , but the importance of introgression in adaptive evolution remains unclear , especially in animals . Here , we have examined the role of introgressive hybridisation in transferring adaptations between mimetic Heliconius butterflies , taking advantage of the recent identification of a gene regulating red wing patterns in this genus . By sequencing regions both linked and unlinked to the red colour locus , we found a region that displays an almost perfect genotype by phenotype association across four species , H . melpomene , H . cydno , H . timareta , and H . heurippa . This particular segment is located 70 kb downstream of the red colour specification gene optix , and coalescent analysis indicates repeated introgression of adaptive alleles from H . melpomene into the H . cydno species clade . Our analytical methods complement recent genome scale data for the same region and suggest adaptive introgression has a crucial role in generating adaptive wing colour diversity in this group of butterflies .
Closely related species often hybridise through incomplete barriers to gene flow , but the evolutionary consequences of such genetic interchange remain a matter of debate [1] , [2] , [3] , [4] , [5] , [6] . This is primarily because hybridisation is considered unlikely to introduce useful genetic variation [1] , [4] , [5] , [7] . Alleles that cross species boundaries may be neutral in their effects [7] or , perhaps most commonly , natural selection will prevent the introgression of foreign genetic material into a genetic background that is already well adapted [8] . However , sometimes , introgression may be favoured if the region gained confers advantages to the recipient species [5] . Although such favourable gene combinations may be produced only rarely , they might still contribute important variation for adaptive change . Importantly , hybridisation is a potential source of novel alleles already tested by natural selection that would be unlikely to arise through mutation alone . In organisms other than bacteria , evidence for adaptive introgression in nature is scarce [9] , [10] . Nonetheless , several remarkable examples in plants have demonstrated adaptive introgression , for example in transferring herbivore resistance in Helianthus [5] , flood tolerance in Iris [11] and the gene controlling rayed flowers in Senecio vulgaris [12] . In animals , examples include adaptive introgression of melanism from domestic dogs into North American wolves [13] and warfarin pesticide resistance in European house mice , gained from the Algerian mouse [14] . Nonetheless , these examples all represent a single instance of transfer of a trait , often in association with environments showing significant levels of human intervention . A more pervasive role for introgression in recent adaptive radiations has been postulated , for example in Darwin's finches and sailfins [15] , [16] , but convincing genetic evidence for introgression of specific adaptive traits is still missing in these systems . Heliconius butterflies display a striking radiation in adaptive wing patterns , facilitated by Müllerian mimicry between distantly related species and coupled with divergence between closely related species [17] . These butterflies frequently hybridise across species boundaries [18] , [19] , and it has been hypothesised that introgression might play an important role in speciation and adaptive radiation . In particular two closely related species groups , Heliconius melpomene and Heliconius cydno are known to hybridise occasionally , and genetic evidence indicates a low level of ongoing gene flow [20] , [21] . H . melpomene has radiated into almost 30 geographical colour pattern races across Central and South America [22] , broadly falling into two main phenotypes , which we here refer to as the red-banded type ( presence of a red band or patch in the forewing controlled by the B allele , regardless of hind wing phenotype ) and the rays type ( orange forewing basal patch and orange rays in the hind wing ) . The sister clade to H . melpomene includes the species Heliconius cydno , H . pachinus , H . timareta and H . heurippa , jointly referred to hereafter as the H . cydno clade [23] . The former two species are typically black with white or yellow elements [22] , while the latter two species exhibit patterns similar to those of H . melpomene [23] , [24] . We have previously suggested that the presence of red phenotype elements in these H . cydno affiliates , that is H . heurippa and H . timareta , could be the result of the acquisition of mimicry colour patterns via adaptive introgression from H . melpomene [3] , [19] , [24] , and in the case of H . heurippa have provided DNA sequence evidence in support of this transfer [25] . However , these phenotypic patterns could also be explained if red variants were either ancestral , with multiple subsequent trait losses in the H . cydno clade , or if they had independent origins in both H . melpomene and the red H . cydno affiliates , specifically H . timareta and H . heurippa [26] . In H . melpomene the HmB locus controls variation in red colour patterns [27] , [28] , a trait under strong natural selection [29] , [30] . Genomic analysis of this region has identified clear peaks of genetic divergence between adjacent races of H . melpomene associated with variation in red phenotypes [25] , [28] , [31] . In H . melpomene , the strongest divergence lies in a non-coding region in between a kinesin gene and the transcription factor optix [31] . The latter is the strongest candidate gene so far for the red locus [32] , and its expression shows a perfect association with red wing colour elements in a wide range of geographical races of H . melpomene and its co-mimics H . erato , prefiguring in both species the forewing red band , the dennis orange patch and the hind wing rays [32] . Having such information provides an excellent opportunity to explicitly test the introgression hypothesis for red wing patterns across the broader H . melpomene/H . cydno species complex . Here , we specifically examine the phylogenetic history of divergent and convergent colour pattern races of H . melpomene , H . cydno , H . timareta and H . heurippa and ask how this history varies between loci linked and unlinked to colour pattern . The data allows us to understand the origins of adaptive colouration and ask whether similar wing patterns have multiple independent origins , or arose once within the complex and crossed species boundaries . Thus , we provide an explicit test of the hypothesis that hybridisation has repeatedly contributed to an adaptive radiation . This study was carried out alongside a genome-wide study of a subset of the taxa included here [33] . The analyses presented here on smaller gene regions , sequenced across a much larger set of taxa , permit a different set of analytical tools to be used to test for the extent and direction of introgression .
We analysed 221 haplotypes from nine loci ( Table S1 ) , sampled from 111 individuals in five species ( Figure 1 ) . Three loci ( the mitochondrial fragment COI and nuclear GAPDH and Hsp90 ) were unlinked to colour pattern , whereas the remaining six loci were sampled across the genomic interval modulating red pattern variation , specifically where the highest genetic divergence peaks associated with variation in red phenotypes have been found in H . melpomene [31] . Analysis of molecular variance in the mitochondrial fragment COI showed population structure largely explained by species relationships ( ∼47% ) and geography ( ∼30% ) but less by colour phenotype ( Table 1 ) . Phylogenetic analysis supports three monophyletic clades: ( i ) H . cydno-H . timareta , ( ii ) H . melpomene from the Pacific and the Atlantic coast , and ( iii ) H . melpomene from the Amazonas and the Andes ( Figure 2 ) . In previous studies , nuclear markers showed varying degrees of clustering by species , with some loci showing mutual monophyly between the H . melpomene and H . cydno clade species , while others showed substantial allele sharing among species [20] , [34] . Here , both unlinked nuclear markers ( GAPDH and Hsp90 ) showed little population structure either by colour phenotype , species or geography ( Table 1 ) with only about 15% of the variation explained by species and much less by colour pattern phenotype ( Table 1 ) . This result was corroborated by phylogenetic analysis ( Figure 2 ) , where similar alleles were spread broadly among species , wing pattern phenotypes , and across major biogeographic boundaries . Even among some loci within the red pattern interval , for instance kinesin and Hm01012 , there was a poor correspondence with either species boundaries , geography or colour pattern , with each factor explaining less than 10% of the molecular variation at these markers ( Table 1 ) . Similarly , phylogenies of these two markers did not exhibit clear clustering by any of these categories ( Figure 3 ) . Other markers across the red locus showed an increasing tendency to partition variation by colour pattern phenotype . Coding sequence of the transcription factor optix clustered most of the red-banded phenotypes of H . melpomene together , but H . melpomene individuals with rayed phenotypes were scattered across the genealogy . Optix also failed to show a clear phenotype association for H . timareta and H . heurippa ( Figure 3 ) . Nonetheless , colour pattern explained 28% of the variation within optix alleles ( Table 1 ) . A similar result was observed at HmB449k and HmB520k , which were 75 kb downstream and 2 kb upstream from optix , respectively , in the red interval ( Table 1 ) . Both loci grouped H . melpomene red-banded phenotypes into a monophyletic lineage ( Figure 3 ) , but failed to show a phenotype association in H . heurippa , H . timareta and rayed H . melpomene . HmB453k was the striking exception to these patterns and showed strong population structure based on colour phenotype when analysed both by Neighbour-Joining and Maximum Likelihood ( Figure 3 , Figure 4 ) . Over 60% of the segregating variation at this locus was explained by colour pattern phenotype ( Table 1 ) . Moreover , the allelic genealogy of this locus clearly defined three major clades , which largely corresponded to three major colour pattern phenotypes ( Figure 4 ) . The first clade contained red-banded type taxa ( H . melpomene , H . timareta subsp . nov from Peru and H . heurippa ) , the second grouped rayed species ( H . melpomene , H . timareta florencia and H . timareta contigua ) , and the third containing the species with no dorsal red wing colouration ( H . cydno , H . timareta subsp . nov from Colombia and H . timareta timareta ) . Strikingly , individuals of the polymorphic population of H . timareta from eastern Ecuador were separated by phenotype , with rayed and non-rayed individuals sampled from the same locality falling into their respective phenotypic clades . There were some exceptions to the complete clustering by phenotype in HmB453k ( Figure 4 ) . For example , the east Andean race , H . m . plesseni possesses white and red spots on the forewing and is typically considered a red-banded pattern . However , here all individuals from this race form a distinct monophyletic group on the HmB453k genealogy ( Figure 4 ) . This perhaps indicates that this phenotype shows an independent origin as compared to other red-banded patterns , consistent with its distinct white and red band phenotype . In addition , six haplotypes from the rayed race H . m . malleti did not cluster in the same clade as other rayed individuals , but similarly formed a separate monophyletic clade nested within the broader genealogy ( Figure 4 ) . This might also represent an independent origin of rayed phenotypes within H . melpomene , but is perhaps more likely a result of recombination between the HmB453k marker and nearby functional sites . In order to address alternative explanations for the strong colour pattern signal within the HmB453k genealogy [26] , we tested three alternative tree topologies for this fragment . The first alternate topology assumed that mtDNA topology correctly reflected the relationship among the three species; the second , a topology that considers independent phenotypic convergence in H . melpomene , H . timareta and H . heurippa and the third , a topology where H . melpomene constitutes a red polymorphic ancestral taxon and H . cydno/H . timareta/H . heurippa are derived with multiple losses of red patterns ( Figure S1 ) . According to the Shimodaira–Hasegawa ( SH ) test , the ML tree was better supported than any of the three alternative topologies ( p<0 . 05 in all cases ) [35] . These same three alternative tree topologies ( Figure S1 ) were also tested against a ‘perfect’ ML HmB453k genealogy where the non-clustering alleles of H . m . malleti and H . m . plesseni were removed . In this case , again the SH test showed that the ML tree was better than any of the three alternatives ( p<0 . 05 in all cases ) . Thus , we can rule out the alternative hypotheses proposed for pattern sharing across this group , namely multiple independent origins of red patterns , or ancestral red patterns subsequently lost multiple times [26] . To determine whether introgression is the cause of the shared DNA sequence variation observed among species , we applied the Isolation with Migration model in H . melpomene , H . timareta and H . heurippa using the program IM [36] . In order to obtain non-recombining blocks of sequence for this analysis , the taxa were separated into rayed and red-banded groups ( see methods ) . In both datasets , IM estimated a population size of H . timareta smaller than that of H . melpomene ( Table S3 ) and a time of divergence between these two species of ∼700 , 000 years . Maximum-likelihood estimates for introgression ( 2Nm ) , in general showed evidence of gene flow between species in the four markers analysed ( Table 2 ) . Models invoking gene flow in both directions were a significantly better fit than any model with no gene flow in any or in both directions ( Table 3 , models ABC0D , ABCD0 , ABC00 ) . We also found evidence for significant asymmetry in gene flow , as the model with unequal gene flow between species was significantly better than the model with similar gene flow in both directions ( Table 3 , model ABCDD ) . When gene flow parameters were estimated for individual genes , nuclear genes Hsp90 and GAPDH , together with the mitochondrial fragment COI , showed evidence for ongoing gene flow between the study species ( Table 2 ) . However , the fragment HmB453k was the only marker consistently showing the strongest unidirectional introgression from H . melpomene to H . timareta in both phenotype datasets , thus suggesting that HmB453k alleles of H . timareta are derived from those of H . melpomene ( Figure 5 ) . Most notably in the rayed data set , this marker showed the highest magnitude of gene flow seen at any of the markers ( Table 2 ) . As the HmB453k fragment is located in the genomic region controlling the red wing phenotypes that is known to be under selection , one of the IM model assumptions is violated . Previous IM analysis on simulated scenarios with divergent selection in early stages of divergence have showed underestimated gene flow rates ( 2Nm ) [37] . It could be argued therefore , that if selection is having an effect on our estimates we might be underestimating migration rates . To further explore and confirm the signatures of introgression between these species we also used a linkage disequilibrium ( LD ) test for gene flow [38] . Briefly , the difference ( x = DSS−DSX ) between the magnitude of LD among all pairs of shared polymorphisms ( DSS; Disequilibrium Shared-Shared ) and that among all pairs of sites for which one member is a shared polymorphism and the other is an exclusive polymorphism ( DSX; Disequilibrium Shared-Exclusive ) , is indicative of whether or not polymorphisms in the populations are the result of gene flow ( positive x value ) or retained ancestral polymorphism ( negative x value ) [38] . This because polymorphisms that are shared due to ancestral polymorphism are expected to be older on average , having more time to recombine and break down associations , than polymorphisms acquired via post-divergence gene flow [38] . We applied this test to the same phenotypic groups analysed with IM , and additionally , to pairs of H . melpomene and H . timareta populations found in sympatry . In general , the LD analysis showed values consistently positive across all comparisons and loci ( Table 4 ) , suggesting onging gene flow between H . melpomene and H . timareta . Notably , HmB453k was the only locus with significant gene flow in both the phenotypic and sympatric datasets , where H . timareta was always the recipient species ( highest positive value ) of H . melpomene alleles ( p<0 . 001 , Table 4 ) . This analysis therefore provides strong confirmation of the IM results .
Adaptive novelty can arise de novo from mutations , from standing variation within populations or through gene flow among related populations or species , and the relative importance of these factors remains an open question in evolutionary biology . In Heliconius butterflies , the recent identification of the optix transcription factor as the locus of selection for red wing phenotypes offers the opportunity to address this question [32] . In a parallel study , we demonstrated that the distantly related H . melpomene and H . erato radiations use independently derived optix alleles to generate mimetic red patterns , implicating de novo mutations at the same locus [39] . Here , in contrast , we show that mimicry between more closely related species has involved multiple instances of allele sharing through adaptive introgression . Thus , the allelic variants that fuel adaptation do not necessarily need to be generated de novo , but can also be derived from introgression , accelerating the evolutionary process . In this and previous studies , putatively neutral markers have shown that H . melpomene and the H . cydno clade are two distinct species assemblages that occasionally exchange genes [20] , [21] , [40] . Despite evidence for gene flow at neutral markers , H . melpomene and the H . cydno clade species often coexist in Central America and the Andes , and are ‘good’ species with distinct ecologies and strong barriers to gene flow , including both strong pre- and post-mating isolation [22] , [41] , [42] , [43] . Here , we also found pervasive gene flow among H . melpomene , H . timareta and H . heurippa , similar to that previously observed in comparisons involving H . melpomene and H . cydno [21] , [40] . The gene flow observed at markers unlinked to the wing pattern locus is bi-directional and not correlated with any obvious phenotypic trait . In contrast , the HmB453k marker , located within the red colour locus in a non-coding region downstream of optix , shows a striking association with wing phenotype and unidirectional introgression from H . melpomene to H . timareta . The functional sites driving phenotypic variation within Heliconius are almost certainly cis-regulatory elements of optix and perhaps other adjacent protein coding regions , which act as a phenotypic switch for red pattern elements [32] . Notably , optix shows no amino acid substitutions between divergent colour pattern forms of the same species or between convergent forms of distantly related species [32] . To date , HmB453k shows the strongest association with wing pattern phenotype , much stronger than kinesin , which showed evidence for adaptive introgression of red phenotypes into H . heurippa [25] , and even stronger than the optix coding region [32] . The strong signal we observe at HmB453k argues that it must be very close to the functional region ( s ) regulating colour pattern variation . Nonetheless , the fact that two races ( H . m . plesseni and H . m . malleti ) do not fall into the expected clades in this marker , might suggest that HmB453k does not itself contain functional sites . It is also likely that multiple functional sites across the genomic region control different aspects of the phenotype . Indeed , linkage disequilibrium analysis shows at least three sites in optix and HmB453k that consistently co-segregate ( Figure S2 ) , and in general there is substantial linkage disequilibrium across the HmB locus . The lack of a strong association at the kinesin locus was surprising given the strong association seen at this locus in our previous study of H . heurippa , albeit with a much more taxonomically restricted sample [25] . However , we have considerably smaller sequence coverage of kinesin here , which might affect the signal we recovered from this gene . We believe that the previous study identified a genuine signal of introgression , but that the functional sites controlling the phenotype , which are likely to be regulatory in nature , are located in the non-coding sequence between kinesin and optix . Unpublished expression data indicate that there is evidence for functional involvement of both genes in wing pattern specification ( Pardo-Diaz , unpublished data ) . Previous criticism of the hypothesis of adaptive introgression in Heliconius and these species in particular , has focused on two alternative hypotheses , that either red variants were ancestral , with multiple subsequent trait losses , or that they have independent origins in these closely related lineages [26] . We have explicitly ruled out these alternatives , both by coalescent analysis using IM and LD analysis that indicate strong and significant evidence for directional gene flow , and by tree topology tests . In addition , it could also be hypothesised that natural selection might drive independent and convergent evolution of the sequence variants seen in the HmB453k region , if these were directly responsible for regulation of optix expression . Under this hypothesis however , one would expect multiple divergent haplotypes to be associated with this region in the surrounding sequence . Instead , we clearly observe a single haplotype at the centre of the associated region , with a decline in association with genetic distance , consistent with a single origin for each phenotype in this clade . Alongside a parallel study involving a complete genomic sampling of the red colour region in a subset of the taxa used here [33] , our data provide the first evidence for adaptive introgression driven by mimicry in Heliconius . The introgression previously documented in H . heurippa established a novel non-mimetic phenotype in eastern Colombia [24] , [25] . In contrast , the additional cases of introgression documented here represent convergence due to mimicry selection , rather than establishment of an entirely novel pattern , albeit with a common genetic origin for the shared patterns . The direction of the asymmetrical gene flow is consistent with mimicry theory . First , H . melpomene is generally more locally abundant in the eastern Andes as compared to H . timareta ( CJ , pers . obs . ) , so mimicry theory would predict that rare species should experience stronger selection to converge onto abundant models . Thus it seems likely that H . timareta adopted the local H . melpomene wing pattern , rather than vice-versa . Second , H . cydno and its co-mimics H . sapho and H . eleuchia are almost entirely restricted to the western side of the Andes [44] . One plausible scenario is therefore that the ancestors of H . timareta migrated along the eastern slope of the Andes and were faced with the absence of a white/yellow co-mimic . It seems likely that this imposed an additional selection pressure to mimic H . melpomene and H . erato instead . This eventually led to the establishment of H . timareta as a replacement of H . cydno distributed along the eastern slopes of the Andes in sympatry with H . melpomene . The data provide evidence for multiple independent introgression events . H . t . florencia shares a rayed pattern with its co-mimic , H . m . malleti , in south-eastern Colombia [45] , while the very different phenotype of the red-banded race H . t . ssp . nov . is mimetic with H . m . amaryllis in the Tarapoto region of Peru . A likely additional case is represented by the polymorphic population of H . timareta in Ecuador . Although the rayed phenotype in this population may share a common origin with that of H . t . florencia in Colombia , their distribution is disjunct , separated by the red banded H . tristero found in Mocoa , Colombia . Thus , the acquisition of red patterns by H . timareta has been driven by natural selection for mimicry , and has occurred multiple times ( at least once for each red colour element ) in the last 700 , 000 years . The introgression of regions controlling red wing colouration from H . melpomene to the H . cydno clade has facilitated mimicry and has also played a role in speciation . In H . heurippa the red/yellow hybrid pattern is used as mating cue , which contributes to reproductive isolation from its closest relatives [24] , [25] , [46] . Although barriers to gene flow within H . timareta have not been investigated , it is possible that similar isolation might be found between red-banded and rayed races of this species , such that these might represent incipient species generated through hybridisation . In this and previous work we are beginning to piece together a more complete picture of the history of this complex adaptive radiation . It seems likely that the red-banded pattern in H . erato spread and diversified early in the history of the radiation , followed by emergence of the H . erato rayed pattern that spread across Amazonia interrupting the geographical continuity of the ancestral red-banded phenotype [39] . In the H . melpomene lineage there was a speciation event in which H . cydno colonised the yellow/white phenotypic niche to mimic the H . eleuchia and H . sapho clade , and H . melpomene diversified to mimic the phylogenetically distant H . erato [39] . Reproductive isolation between the species is partly due to colour pattern mate choice , which arose between closely related taxa such as H . melpomene and H . cydno . Then divergence of the H . timareta/heurippa lineage from the rest of H . cydno , around 700 , 000 years ago , arose as a result of adaptive introgression of wing patterning alleles from H . melpomene in the eastern Andes . In summary , we provide evidence that contributes to resolving the longstanding debate over the evolutionary importance of hybridisation in animals . Our data allow statistical tests for the incidence of introgression based on both coalescent patterns and linkage disequilibrium , with consistent results , and indicate the direction of introgression . The results imply that interspecific hybridisation facilitates adaptability and diversification , not only when the selection pressure is human-mediated , but also allows the colonisation of either empty or under-utilised fitness peaks in animal adaptive radiations . In other adaptive radiations such as Darwin's finches [15] , Daphnia waterfleas [47] and African cichlids [48] , rapid diversification may similarly be mediated by introgression [1] . The evolutionary impact of such transfers might be higher if the traits interchanged are also involved in reproductive isolation , thus contributing to speciation .
Our sample set consisted of 111 individuals from 4 different species , namely H . melpomene , H . cydno , H . timareta and H . heurippa ( Table S1 ) . In total 14 races of H . melpomene , 8 races of H . cydno , 5 races of H . timareta and one of H . heurippa were sampled covering most of the geographic distribution of each species from Central to South America ( Figure 1 ) . H . numata was included as outgroup . DNA was extracted using the QIAGEN DNeasy 96 Blood & Tissue Kit . One mitochondrial and eight nuclear fragments ( Table S2 ) were amplified with QIAGEN Taq DNA Polymerase , purified using ExoSAP and sequenced with ABI Big Dye Terminator . Two of the nuclear markers are unlinked to the HmB red locus whereas the remaining six are all located across the region ( Table S2 ) . From these colour-linked fragments , optix and kinesin have previously been implicated in red wing pattern determination . The remaining four were identified as regions under divergent selection with high levels of population differentiation associated with red colouration [31] . Sequences were aligned and cleaned using Codon Code Aligner . Haplotype inference for heterozygous calls was conducted with the PHASE algorithm implemented in DNAsp v5 . 10 . 01 [49] , with 5000 iterations and allowing for recombination . Inferred haplotypes with a confidence higher than 95% were accepted . In the case of the fragments HmB449k and HmB453k cloning was necessary due to the presence of considerable indel variation . PCR products of these two markers were ligated into the pGEM-T easy vector and five to ten clones per individual were selected and sequenced . Sequences were deposited in GenBank under accession numbers JX003980–JX005837 . For each fragment , phased haplotypes were used to construct phylogenetic trees using the Neighbour-Joining method under the P model of uncorrected distance in PAUP* 4 . 0b10 . Node support in the resulting trees was estimated by 1000 bootstrap replicates using a heuristic search . To confirm the phylogenetic groupings obtained by Neighbour-Joining for HmB453K , a maximum likelihood phylogeny was also constructed with PhyML [50] , using the GTR+I+G substitution model selected by Modeltest [51] and with branch support values obtained by 1000 bootstrap replicates . The stability of the inferred phylogeny for HmB453k was examined using the Shimodaira-Hasegawa test ( SH test ) [35] in PAUP* 4 . 0b10 . For all phylogenetic inferences trees were rooted with H . numata as outgroup . Analysis of molecular variance ( AMOVA ) with 1000 permutations , implemented in ARLEQUIN v . 3 . 5 [52] , was used to assess population structure by species , geography and phenotype . For species , four groups were set , corresponding to H . melpomene , H . cydno , H . timareta and H . heurippa . In the geography analysis , haplotypes were grouped into six geographic regions: ( i ) the Guiana shield , ( ii ) Amazon , ( iii ) Pacific , ( iv ) East Andes foothills , ( v ) Cauca Valley and ( vi ) Magdalena Valley . These geographic clustering matches the biogeographic provinces ( i ) Humid Guyana , ( ii ) Napo+Imeri , ( iii ) Choco+Wester Ecuador+Arid Ecuador , ( iv ) North Andean Paramo , ( v ) Cauca and ( vi ) Magdalena defined by Morrone [53] . When compared by phenotype , haplotypes were grouped in three groups: the red-banded type [presence of red forewing band] , the rayed type [presence of orange rays in the hind wing] and the non-red type [absence of any dorsal red element on the wings] . The outgroup H . numata was excluded from these analyses . In order to estimate the role and direction of historical gene flow between H . melpomene and H . timareta ( H . heurippa was included in H . timareta for the purposes of this analysis ) , we used the Isolation-Migration ( IM ) Bayesian model [36] . IM uses Markov chain Monte Carlo ( MCMC ) sampling to obtain maximum-likelihood estimates of six parameters: current population sizes , ancestral population size , rates of migration between two populations ( m1 and m2 ) and the timing of divergence ( t ) . IM assumes both free recombination between loci and no recombination within them , therefore the software SITES [54] was used to select genetic blocks with no recombination within each locus . To fulfill the assumption of free recombination between loci , only the unlinked colour loci and one of the fragments linked to red colouration [HmB453k] were selected for this analysis . We ran IM on two modified datasets for each species pair: ( i ) H . melpomene rayed type-H . timareta and ( ii ) H . melpomene red-banded -[H . heurippa and H . timareta] . These groups constituted the maximal units in which we could get enough data without recombination , with the rayed dataset being a block of 379 bp and the postman dataset one of 313 bp . Unfortunately , pairwise comparisons involving all the species' alleles were not possible nor were comparisons involving species in parapatry because such groupings contained small non-recombining blocks that lacked enough informative sites . However , since our main interest was to determine the direction and magnitude of introgression ( m ) within phenotypes , these datasets are sufficient for addressing this question . For all datasets , after searching for the parameter range using preliminary runs , 30 million steps were sampled from the primary chain after a 300 , 000 burn-in period under the HKY model with 10 chains per set . Mixing properties of the MCMC were assessed by visual inspection of the parameter trend plots and by examining that the effective sample size ( ESS ) was higher than 50 , as recommended [36] , [37] . To get biologically meaningful units of gene flow , the maximum likelihood estimates and 90% highest posterior density ( HPD ) interval for the migration rates ( m ) were converted into the effective number of gene migrations received by a population per generation ( 2Nm , in Table 2 ) . For this conversion , we used a generation time of 35 days and a mutation rate per gene calculated with the calibration time proposed by Wahlberg et al . for Nymphalidae [55] coupled with the divergence between the melpomene/cydno clade per locus estimated with the software SITES . Our estimates of mutation rate per locus per year were: 6 . 2×10−6 for COI , 7 . 1×10−8 for GAPDH , 1 . 2×10−6 for Hsp90 and 3 . 4×10−5 for HmB453k . We finally compared the model including all six parameters to simpler demographic models in order to statistically test the hypothesis of zero or equal gene flow between populations ( m1 = 0 , m2 = 0 , m1 = m2 = 0 , m1 = m2>0 ) . These analyses were conducted using IMa [36] by running the initial M-mode output with identical settings in the L-mode and sampling 5×105 genealogies . We further tested the presence , significance and direction of gene flow per locus between H . melpomene and H . timareta using a method based on linkage disequilibrium ( LD ) developed by Machado et al in 2002 [38] . In this test , a positive difference between the LD among all pairs of shared polymorphisms ( DSS ) and the LD among all pairs of sites for which one member is a shared polymorphism and the other is an exclusive polymorphism ( DSX ) , is indicative of gene flow . The magnitude of the difference directly measures the direction of the introgression , with the species with the highest positive value being the recipient [38] . The same phenotypic datasets used in the IM analysis and also groups of species in complete sympatry , were subjected to independent runs , each of them with 30000 simulations . The input files were prepared with the program SITES [56] calculating D' as a measure of linkage disequilibrium ( as suggested by Machado et . al [38] ) and analysing linkage disequilibrium among shared polymorphism between groups ( by choosing the -s and -p options in the LD string ) . Linkage disequilibrium across the HmB region was calculated for all populations using the software MIDAS [57] only considering sites with allele frequency higher than 5% , and visualised with the R package LDheatmap [58] .
|
Hybridisation occurs between many animal species , however its evolutionary relevance is still a matter of great debate . While some argue that hybridisation leads to maladaptive gene combinations , and therefore to an evolutionary dead end , others consider interspecific hybridisation as a process with great potential to fuel evolution . We examine this question by exploring the origins of red wing colouration , a trait under natural selection , in the adaptive radiation of closely related species of Heliconius butterflies . By sequencing genetic regions both linked and unlinked to the red wing pattern locus , we found experimental evidence supporting multiple hybridisation events that have mediated the acquisition of colour adaptations from H . melpomene to H . timareta . This introgression has allowed H . timareta to colonise new fitness peaks in the Müllerian mimicry landscape . In this way , our results support the idea that interspecific hybridisation in animals constitutes a source of genetic variation that promotes diversification .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"introgression",
"biology",
"evolutionary",
"biology",
"hybridization",
"evolutionary",
"processes"
] |
2012
|
Adaptive Introgression across Species Boundaries in Heliconius Butterflies
|
Hepatitis C virus ( HCV ) is a blood-borne pathogen and a major cause of liver disease worldwide . Gene expression profiling was used to characterize the transcriptional response to HCV H77c infection . Evidence is presented for activation of innate antiviral signaling pathways as well as induction of lipid metabolism genes , which may contribute to oxidative stress . We also found that infection of chimeric SCID/Alb-uPA mice by HCV led to signs of hepatocyte damage and apoptosis , which in patients plays a role in activation of stellate cells , recruitment of macrophages , and the subsequent development of fibrosis . Infection of chimeric mice with HCV H77c also led an inflammatory response characterized by infiltration of monocytes and macrophages . There was increased apoptosis in HCV-infected human hepatocytes in H77c-infected mice but not in mice inoculated with a replication incompetent H77c mutant . Moreover , TUNEL reactivity was restricted to HCV-infected hepatocytes , but an increase in FAS expression was not . To gain insight into the factors contributing specific apoptosis of HCV infected cells , immunohistological and confocal microscopy using antibodies for key apoptotic mediators was done . We found that the ER chaperone BiP/GRP78 was increased in HCV-infected cells as was activated BAX , but the activator of ER stress–mediated apoptosis CHOP was not . We found that overall levels of NF-κB and BCL-xL were increased by infection; however , within an infected liver , comparison of infected cells to uninfected cells indicated both NF-κB and BCL-xL were decreased in HCV-infected cells . We conclude that HCV contributes to hepatocyte damage and apoptosis by inducing stress and pro-apoptotic BAX while preventing the induction of anti-apoptotic NF-κB and BCL-xL , thus sensitizing hepatocytes to apoptosis .
Hepatitis C virus ( HCV ) is a positive strand RNA virus that belongs to the family Flaviviradae . HCV is a blood borne pathogen which is a major cause of liver disease worldwide , with an estimated 200 million people infected . It is estimated that 30% of chronically infected patients eventually develop progressive liver disease including cirrhosis and end stage liver disease [1] . HCV is now the leading indication for liver transplantation in North America [2] . Due the absence of a proofreading activity in the viral RNA polymerase , HCV has a high mutation rate that contributes to the genetic heterogeneity of the virus . Six different genotypes , and at least 52 subtypes have been described [3] . Chronic infection by HCV results in a highly variable disease course , and despite advances in the molecular virology of HCV , the factors involved in hepatocyte injury and the progression of liver disease remain unclear . The complexity of the host response has been examined by transcriptional profiling of liver biopsy samples from both chimpanzees and humans . These studies show that HCV induces genes involved in the interferon response , lipid metabolism , oxidative stress , and chemokines , as well as markers of inflammation [4] , [5] . Such studies are hampered by the requirement that infected and uninfected hepatocytes come from different patients and also by the presence of an adaptive immune response in the patients . Gene expression profiles of human hepatocytes from SCID/Alb-uPA chimeric mice infected with HCV patient serum , control these variables [6] . Until recently it has been thought that HCV is a non-cytopathic virus and that hepatocyte damage in chronic HCV infection is due to HCV-specific adaptive immune responses [7] , [8] . However , in SCID mice lacking an adaptive immune system , we observed induction of apoptosis in HCV infected mouse livers , similar to that seen in liver biopsies from HCV infected patients [6] . During HCV infection , hepatocyte apoptosis could be induced by immune attack on infected cells or directly by viral infection . It has been shown that hepatocyte damage can lead to apoptosis , which plays a role in the recruitment and activation of stellate cells and macrophages and the subsequent development of fibrosis [9] , [10] . HCV infected patients have higher levels of immune related death ligands; TRAIL , TNF-α , FAS , and FASL are all elevated in HCV infected patients [11]–[13] . Increased expression of stimulators of apoptosis in HCV infected patients is tempered by hepatocyte insensitivity to death ligand mediated apoptosis . In hepatocytes death receptor mediated caspase-8 activation is weak , and thus they are inherently resistant to TNF-α and TRAIL killing [14] , [15] . Hepatocytes are likely type II cells and can be sensitized to death ligand mediated apoptosis by caspase-8 induction with IFN-α ( interferon ) or toxins [16]–[18] . In addition , hepatocytes can be sensitized to both TRAIL and TNF-α induced apoptosis by inhibition of NF-κB activity [19]–[21] . Conversely , induction of NF-κB has been shown to inhibit TRAIL , TNF-α , and FAS mediated killing [22]–[24] . The induction of apoptosis directly by HCV remains controversial . Several HCV proteins have been proposed to have both pro- and anti-apoptotic effects [25]–[28] . It has been shown that expression of either the HCV genome or individual HCV structural proteins induces endoplasmic reticulum ( ER ) stress [27] , [29] and the unfolded protein response ( UPR ) , which can lead to apoptosis . However , HCV proteins have also been shown to modulate the UPR [30] , [31] . It has also been proposed that HCV infection induces oxidative stress , which can enhance apoptosis [6] , [32] . Expression of HCV core induces oxidative stress and expression of antioxidant genes [33] , [34] . In addition , HCV patients have more DNA lesions produced by oxidative damage [35] . Oxidative stress leads indirectly from DNA damage to p53 induction , which can lead to activation of BAX and apoptosis [36] , [37] . However , there are also reports of inhibition of p53 induced apoptosis by NS5A [38]–[41] . In this study , we used the mouse model for HCV infection in which severe combined immunodeficiency disorder ( SCID ) mice transgenic for an array of urokinase plasminogen activator ( uPA ) under control of the albumin ( Alb ) promoter are transplanted with human hepatocytes and then infected with HCV [6] , [42]–[44] . We have previously compared HCV induced gene expression in chimeric mice infected with genotype 1a patient serum to uninfected controls containing human hepatocytes from the same donor [6] . There was evidence of activation of innate antiviral signaling pathways , induction of lipid metabolism genes , as well as signs of hepatocyte damage and an inflammatory response . To further reduce variation from HCV quasispecies present in the mice , in this study we have infected mice with the infectious clone HCV H77c [45] , [46] . We confirmed the results of the previous study that used infectious patient serum , and to determine the cause of hepatocyte damage , we examined the expression HCV antigens and key proteins involved in the stress response and apoptosis using immunohistochemical and fluorescent confocal microscopy . We found that HCV infection correlated with increased levels of the ER chaperone GPR78/BiP , a key regulator of the unfolded protein response . In addition , levels of pro-apoptotic BAX were increased , while anti-apoptotic NF-κB and BCL-xL were decreased in HCV infected cells . Taken together these results indicate that ER stress induced by HCV combined with lower NF-κB and BCL-xL levels sensitizes hepatocytes to apoptosis .
Previous studies indicated HCV infection in chimeric mouse livers was restricted to the human hepatocytes [44] . We confirmed this by performing immunofluorescent confocal microscopy on uninfected and HCV H77c infected mouse livers with antibodies specific for the HCV NS3 protease and human albumin ( Figure 1 and S1A–B ) . Only liver sections infected with HCV H77c stained with HCV specific antibodies , and this was restricted to hepatocytes that also were also stained with antibodies specific for human albumin . Since liver consists of a mix a hepatocytes and adventitial cells , and albumin only stains hepatocytes , there was the possibility that some human cells other than hepatocytes also colonized the mouse liver . Therefore we wished to examine whether any of the adventitial cells were also human . We performed in situ hybridization using probes specific for human Alu repeats on chimeric mouse livers ( Figure 2 ) . Only the hepatocytes were stained , indicating all of the adventitial cells in chimeric mouse livers were of mouse origin . This , and the elimination of a small percentage of mouse sequences that cross-hybridized to the human arrays , ensured that the transcriptional profiling reflects only the processes occurring in human hepatocytes . Transcriptional profiling was performed on mRNA samples isolated from three HCV-infected animals and from uninfected controls . All animals contained hepatocytes from a single donor . The serum HCV titers , liver viral loads , and the length of time infected are given in Table 1 . The experiments included three liver samples from an animal infected with H77c ( + ) serum ( 990 ) , two samples from an animal inoculated intrahepatically with H77c RNA ( 975 ) , and a single experiment with liver tissue from an animal inoculated intrahepatically with H77c RNA containing a mutation in the active site of the NS5B polymerase ( 986 ) . Four samples from three separate animals were pooled to serve as the uninfected control . Because each pair of mice contained hepatocytes from the same donor , changes in gene expression should mainly be induced by the HCV infection and be independent of host variation . Consistent with previous studies , the effect on host gene expression by HCV infection was not extreme , 766 genes showed a 2-fold or higher change in expression ( P value≤0 . 05 ) in at least one experiment ( Figure 3 ) . The grouping of experiments by the clustering algorithm suggested that the effect on host gene expression was very similar among individual pieces of liver from the same animal . Importantly , the global gene expression profiles in the animals infected with H77c ( + ) serum ( 990 ) and H77c RNA ( 975 ) were also very similar . This suggests that the source of HCV inoculum does not significantly impact the host transcriptional response to infection . Interestingly , the animal inoculated with H77c RNA encoding an inactive NS5B polymerase also showed a similar host response . While it was expected that the mouse inoculated with the replication defective HCV RNA might show activation of dsRNA and RIG-I signaling pathways similar to replicating virus [47] , [48] , we expected substantial differences in the overall host response 47 days after RNA administration . This mouse showed no detectable HCV RNA in the serum at the time of sacrifice and no HCV RNA was detected in the sample used for microarray analysis . Infection with HCV H77c activated innate antiviral signaling pathways , as indicated by the induction of interferon-stimulated genes ( ISGs ) ( Figure 4A ) . In general , the induction was similar among all three infected animals . However , there does seem to be a slightly higher induction of ISGs in the animal ( 975 ) inoculated intrahepatically with wild-type H77c RNA relative to the animal ( 990 ) inoculated with H77c ( + ) serum , which is likely due to the high level of naked RNA injected directly into the liver of this animal ( 100 µg , 2×1013 copies ) . This increased response relative the animal inoculated with serum ( 990 ) was not observed in the animal ( 986 ) injected with H77c RNA containing inactive NS5B , indicating that part of the increased response in animal 975 might be due to replication of the inoculated HCV RNA . In our previous study of mice infected with HCV-positive patient sera the magnitude of induction of ISGs varies among mice containing hepatocytes from different donors . Comparison of the gene expression data from HCV H77c-infected mice with that from the initial study indicate the induction of ISGs in the H77c-infected mice is relatively weak . Consistent with what was observed in animals with a weak IFN response in the initial study , regulation of numerous genes associated with lipid metabolism were observed in the HCV H77c-infected mice ( Figure 4B ) . These included genes involved in cholesterol and fatty acid biosynthesis , β-oxidation and peroxisome proliferation . There did not appear to be any significant differences due to inoculum source . Interestingly , the animal that received replication incompetent H77c RNA also showed some regulation of these genes , although at a lower magnitude . The fact that this mouse shows any changes at all , in the absence of viral replication , may be because injection of viral RNA from a positive-sense RNA virus likely results in the synthesis of viral proteins . Consistent with the up regulation of genes involved in oxidative stress seen in this and previous expression array studies , histological analysis revealed signs of hepatocyte damage in the human hepatocytes of HCV infected chimeric mice ( Table 2 ) . Steatosis was apparent in the majority of the human hepatocytes regardless of infection . However , there were significant differences in the histology between the animals inoculated with HCV RNA and naive animals . Increased hepatocyte ballooning and lobular inflammation were associated with HCV-infection . Staining of sections with the antibody F4-80 ( anti CD68 ) revealed that the lobular inflammation was due to infiltration of monocytes and macrophages ( not shown ) . A particularly intriguing observation was the presence of apoptotic hepatocytes in HCV-infected animals originally detected as caspase-3 activation and quantitation of apoptotic bodies [6] . We also observed apoptosis in H77c-infected mice by TUNNEL assay ( Figure 5D–F ) . Apoptosis was absent in the animal inoculated with the replication defective H77c-AAA mutant . This suggests that apoptosis observed in HCV-infected mice is dependent upon active HCV replication . The expression of genes associated with cell death was analyzed to gain further insight into possible mechanisms of apoptosis . While there was regulation of cell-death related genes in HCV H77c infected animals , the number of genes affected is small ( data not shown ) . This is perhaps not surprising given the low percentage of hepatocytes that are actually undergoing apoptosis . Quantitation of the TUNEL data in Table 2 ( average 723 cells/field ) revealed on average 5% of cells undergoing apoptosis in infected mice . To further investigate the mechanism of increased apoptosis associated with HCV infection , we examined FAS expression on liver sections from infected and uninfected mice and compared this to liver sections subjected to TUNEL analysis . Similar to what has been seen in mice infected with patient serum and in patient biopsies [6] , there was increased FAS staining in infected compared to donor matched uninfected mice ( Figure 5A–C ) . As can be seen in Figure 5 and at higher magnification in Figure S2 , there was strong FAS reactivity in a majority of the human cells in infected mice , however TUNEL positive nuclei were seen only in a small proportion of human cells ( Figure 5D–F ) . Interestingly , although staining was not as intense , there was an increase in FAS staining in the mouse inoculated with the H77c-AAA mutant , without a correlative increase in the TUNEL positive nuclei ( Table 2 ) . This suggests that increased FAS expression is not the only factor required for induction of apoptosis and TUNEL reactivity . We next investigated the correlation between HCV infection , and either FAS expression , or TUNEL reactivity . When we stained liver sections with FAS- and HCV-specific antibodies ( Figure 6A ) , we found that expression of FAS does not depend on the presence of HCV in the cells , but is a host reaction to infection of neighbouring cells . However , we cannot rule out the possibility that only a portion of cells in the liver express enough HCV antigen to be detected using this antibody . When we subjected liver sections to a fluorescent TUNEL assay and then stained them with HCV specific antibodies , we found that all of the TUNEL positive cells in areas populated by human hepatocytes also stained with HCV specific antibodies ( Figure 6B ) . The exception was that some murine Kupffer cells which also contained multiple TUNEL positive nuclei . Thus , HCV replication seems to be required for hepatocyte apoptosis . This is unlike FAS expression , which could be induced by HCV replication in neighbouring cells . To further investigate the relationship between HCV infected cells and apoptosis we performed immunohistochemistry and fluorescent confocal microscopy using antibodies for key proteins involved in apoptosis . It has been proposed that HCV induces oxidative stress [6] , [32] . Oxidative stress can lead to the induction of p53 and BAX , both of which can translocate to the mitochondria and induce apoptosis [36] , [37] . We performed immunohistological staining for p53 on infected and uninfected liver sections and found no evidence for p53 induction in either the cytoplasm or nucleus or its translocation to the mitochondria except in one infected animal where very few cells appeared to have up-regulated p53 ( data not shown ) . Expression of HCV structural proteins has been shown to induce ER stress [27] , [49] , which can induce the oligomerization and translocation of BAX/BAK to the mitochondria . To examine the role of ER stress in HCV induced apoptosis , we compared the expression of the ER chaperone GRP78 ( BiP ) in uninfected and H77c infected mice by immunohistochemistry ( Table 3 and Figure 7A–C ) and by fluorescent confocal microscopy ( Figure 7D , E ) . We found higher levels of BiP in infected mice , and that BiP expression correlated with HCV infection . Additionally , BiP and HCV seemed to co-localize , consistent with replication of HCV on the ER . The ER chaperone BiP is a key sensor in the unfolded protein response ( UPR ) ; it maintains ER membrane signal proteins in inactive states . It has been shown that BAX and BAK interact directly with one of these membrane signal proteins , IRE1 , and are essential for IRE1 activation [50] . Extensive or prolonged ER stress initiates apoptosis through activation of BAX/BAK . We therefore examined the expression of BAX in liver sections ( Table 3 and Figure 8 ) and found BAX was also overexpressed in HCV infected livers . BAX is normally diffusely expressed throughout the cell , however when activated it translocates to the mitochondria and appears as a granular staining pattern . We found that both patterns of staining were elevated in HCV infected livers ( Figure 8A–D ) . Figure 8D shows both the intense granular staining pattern as indicated by the black arrows and the less intense cytoplasmic staining indicated by the red arrows . The large granular staining pattern correlated with HCV infection ( Figure 8F ) . It is worth noting that not all cells that stained positive for HCV also showed activated BAX . The number of cells staining for activated BAX approximately correlated with the number of TUNEL positive nuclei . The elevation of both BiP and BAX in the absence of increased levels of p53 suggests that ER rather than oxidative stress leads to BAX activation , however additional mechanisms of BAX activation by oxidative stress cannot be eliminated . In cells under ER stress , BiP preferentially binds to malfolded proteins , releasing IRE1 , PERK , and ATF6 , activating downstream effectors which induce transcription of alarm and adaptation genes including BiP itself and GADD153 ( CHOP ) [51] . When the UPR is overwhelmed , apoptosis is induced by a number of molecules including CHOP , which translocates to the nucleus and blocks transcription of BCL-2 , [52] an inhibitor of BAX/BAK . To examine whether the ER stress found in infected livers overwhelmed the unfolded protein response , indicated by translocation of CHOP to the nucleus , we performed immunofluorescent confocal microscopy with anti-CHOP and anti-HCV antibodies ( Figure 9 ) . Because we found very few nuclei that stained positively for CHOP and these did not correlate with the staining by HCV specific antibodies , in Figure 9 both panels are from infected mice; panel A shows a predominately infected area , and panel B shows a predominantly uninfected area with only a few infected cells . CHOP can be elevated in both infected and uninfected cells indicating that HCV infection does not overwhelm the UPR . This may explain why we do not see expression of ER stress genes in the microarray analysis . In addition to pro-apoptotic proteins , we also examined key inhibitors of apoptosis . Both CHOP and NF-κB are both activated by ER stress [53] , [54] , but have opposing effects; CHOP is pro-apoptotic while NF-κB is anti-apoptotic . NF-κB is activated in response to a myriad of other stimuli , at least one of which is inhibited by HCV [55] . BCL-xL inhibits the apoptosis induced by BAX , and its transcription is activated by NF-κB [56] . Overall , when HCV infected livers were compared with uninfected livers , the levels of both NF-κB and BCL-xL appeared to be elevated in the infected liver , consistent with expression analysis , which indicated that NF-κB levels are elevated by HCV infection . However , when HCV infected cells are compared to uninfected cells within HCV infected livers , we found that total levels of NF-κB p65 expression was lower in HCV infected cells than in surrounding uninfected cells ( Figure 10A–B and E and S4 ) . Quantitation of total p65 fluorescence from uninfected and infected cells in 6 fields from infected livers revealed that p65 levels in infected cells were approximately half that in uninfected cells . The average fluorescence of uninfected cells in a field was arbitrarily set to 1 . Consistent with reduced expression of NF-κB , the expression of BCL-xL was also lower in cells that stained with HCV specific antibodies ( Figure 10C–D and F ) . Quantitation of total BCL-xL levels in infected livers also revealed that levels of BCL-xL in infected cells were approximately half of that in infected cells .
The course of HCV pathophysiology is extremely variable , as a result of complex interactions between viral variants and the host's innate and adaptive immune systems . We have used SCID/Alb-uPA mice that have chimeric human and mouse livers to examine the processes that occur in hepatocytes in response to HCV infection . Previous studies have shown that the transcriptional response HCV infection in mice is similar to that of humans and chimpanzees , with the exception of immune cell markers , which are absent in SCID mice . To further simplify the host response to infection , we infected mice with RNA from the HCV clone H77c [45] , [46] . We found the same induction of interferon response genes and changes in expression of genes involved in lipid metabolism seen in earlier studies . This has been postulated to lead to the generation of oxidative stress [6] , [57] , and it has been shown that both oxidative stress and ER stress can lead to apoptosis [27] , [29] , [58] , [59] . As well , apoptosis is one of the factors in the induction of fibrosis , which can culminate in cirrhosis [9] , [10] , [60] , [61] . Interestingly , in the absence of an adaptive immune system , there was evidence for the induction of apoptosis in HCV infected mice . This was restricted to HCV infected cells despite increased FAS expression on both infected and uninfected human hepatocytes of infected animals . This generalized FAS expression may be a consequence of the interferon response occurring throughout the liver [6] since FAS/FASL are among those mediators of apoptosis that are also interferon response genes [62] . We therefore examined processes leading to apoptosis that we thought were likely to be affected by HCV in infected cells . Oxidative stress generated by HCV induced lipid metabolism , and ER stress generated by HCV replication and protein translation in and on the ER were both potential candidates . Hepatocytes are type II cells that contain low levels of caspase 8 and therefore require activation of the mitochondrial apoptosis amplification pathway to initiate apoptosis . This can be blocked by over expression of BCL-2 or BCL-xL . Mitochondria seem to be the site where the antiviral interferon response and apoptotic signals are integrated; recently it has been shown that the mitochondrial signaling molecule interferon promoter stimulating factor-1 ( IPS-1 ) is cleaved during apoptosis and cleavage can be blocked by overexpression of BCL-xL [63] . In addition , both the response to oxidative stress and the response to ER stress converge at the mitochondrion; p53 activated by oxidative stress stimulates the oligomerization of BAX [36] , and BiP responding to ER stress releases IRE-1 to which BAX is bound . BAX has been shown to be required for both IRE-1 activation and for apoptosis initiated by ER stress [50] , [51] . Since oxidative stress can lead to p53 induction , Bax activation and apoptosis [36] , [37] , we examined p53 localization and levels and found that they were not affected by HCV infection . This may be due to NS5A mediated inhibition of the mitochondrial translocation , apoptosis inducing , and DNA binding activities of p53 [38]–[41] . ER stress can lead to induction of the UPR , and activation of BiP , CHOP , BAX and apoptosis . Consistent with the generation of ER stress by HCV we found that induction of the ER chaperone BiP and pro-apoptotic BAX correlated with HCV expression , but there was little translocation of CHOP/GADD153 to the nucleus , which indicated that the UPR was not overwhelmed . In hepatocytes , it appears that NF-κB is one of the key determinants of whether apoptosis is induced in response to death ligands [19]–[24] , [64] . In evading of the interferon response , HCV inhibits the activation of NF-κB; inhibition of the TLR-3 and RIG I pathways by cleavage of TRIF and IPS-1/MAVS/VISA/Cardif by the HCV NS3/4A protease , inhibits NF-κB and IRF-3 phosphorylation preventing nuclear translocation in response to RIG-1 activation by viral RNA [65]–[68] . In addition , there are a number of other reports that HCV modulates NF-κB activity [69]–[72] . Consistent with the reports of inhibition of NF-κB we found that total levels of NF-κB p65 were lower in HCV infected cells . Furthermore consistent with the transcriptional regulation of BCL-xL by NF-κB , we found that total levels of BCL-xL were lower in HCV infected cells . In conclusion , we propose a model ( Figure 11 ) where HCV induces both ER stress and oxidative stress in infected cells , and activates pro-apoptotic Bax while it prevents induction of anti-apoptotic BCL-xL thus sensitizing HCV infected cells to apoptosis which may be mediated by death receptors and ligands , for example FAS and TRAIL ( TNFSF10-Figure 4B ) . A combination of induction of pro-inflammatory chemokines ( Figure 4A ) and cross talk between human and mouse chemokines and their receptors may lead to a situation similar to that in patients; inflammation , which in turn stimulates release of pro-inflammatory cytokines and effector molecules such as TNF-α and FasL ( which in these mice may be released by macrophages and NK cells ) , creating the circle of hepatocyte damage and repair that is a hallmark of HCV infection .
All mice were housed VAF and treated according to Canadian Council on Animal Care guidelines . Experimental approval came from the University of Alberta Animal Welfare Committee , and human hepatocytes were obtained following informed consent of all donors with ethics approval from the University of Alberta Faculty of Medicine Research Ethics Board . Animals were transplanted with freshly isolated human hepatocytes [42] , [44] , [73] . Eight weeks after transplantation mice with human α-1antitrypsin ( hAAT ) levels [42] greater than 100 µg/mL were injected intrahepatically ( ih ) , with 50 µg of in vitro transcribed H77c RNA [45] into each of 2 red liver nodules ( presumed to be human hepatocytes ) . As a negative control , mice were injected ih with non-replicative H77c RNA in which NS5B polymerase active site residues GDD ( amino acids 2736–2738 ) have been changed to AAA ( H77c-AAA ) . Passage of H77c virus was done by ih inoculation of naive mice with 50 µL of serum obtained from mice infected by H77c RNA . One mouse was infected by ih inoculation with patient serum for histochemical comparison . Serum samples were taken at various time points after inoculation and HCV RNA was quantified . Animals were infected for 25 or 47 days and dissection of mouse livers , isolation of RNA , genomic DNA , and ratio of human to mouse cells in each sample was performed as previously described [43] . The serum HCV titers , liver viral loads and the length of time infected are given in Table 1 . The plasmid for in vitro transcription was pCV H77c and was a gift from Dr . Jens Bukh . The purity of human hepatocytes was greater than 70% in all samples used for microarrays . Microarray format , protocols for probe labeling , and array hybridization are described at http://expression . microslu . washington . edu . Briefly , a single experiment comparing two mRNA samples was done with four replicate Human 1A ( V2 ) 22K oligonucleotide expression arrays ( Agilent Technologies ) using the dye label reverse technique . This allows for the calculation of mean ratios between expression levels of each gene in the analyzed sample pair , standard deviation and P values for each experiment . Spot quantitation , normalization and application of a platform-specific error model was performed using Agilent's Feature Extractor software and all data was then entered into a custom-designed database , Expression Array Manager , and then uploaded into Rosetta Resolver System 4 . 0 . 1 . 0 . 10 ( Rosetta Biosoftware , Kirkland , WA ) and Spotfire Decision Suite 7 . 1 . 1 ( Spotfire , Somerville , MA ) . Data normalization and the Resolver Error Model are described on the website http://expression . microslu . washington . edu . This website is also used to publish all primary data in accordance with the proposed MIAME standards . Selection of genes for data analysis was based on a greater than 95% probability of being differentially expressed ( P≤0 . 05 ) and a fold change of 2 or greater . The resultant false positive discovery rate was estimated to be less than 0 . 1% ( Walters , unpublished data ) . We have previously assessed the degree of cross hybridization in chimeric samples and eliminated the small percentage of genes that did cross react from subsequent analysis [43] . In situ hybridization using FITC labeled Alu DNA probes ( InnoGenex , San Ramon , CA , USA ) was performed according to the manufacturer's specifications , and developed using the supersensitive polymer HRP-ISH system ( BioGenex ) . TUNEL was performed using the Apoptag Plus Peroxidase In Situ Apoptosis Detection kit ( Chemicon International , Temecula , CA , USA ) according to the manufacturer's specifications . The number of Tunel positive nuclei is an average of 15 fields at 200× magnification . Haematoxylin and eosin , reticulin , Mason's trichrome , and periodic acid/Shiffs staining were performed according to standard procedures [74] . The sections were graded for inflammatory activity and staged for fibrosis according to the modified Batts and Ludwig scoring system [75] . The degree of fatty change was scored as 0 ( <5% ) , 1 ( 6–33% ) , 2 ( 34–66% ) or 3 ( >66% ) . Hepatocyte ballooning and macrophages were scored on a scale of 0–4 where 0 is none and 4 is many . The lobular apoptotic body count is an average of 5 fields counted at 100× magnification . Immunohistochemical and immunofluorescent analysis was performed on 4 µm formaldehyde fixed paraffin embedded sections that were deparaffinized by incubation in xylene for 5 min , followed by sequential rehydration by incubating twice for 3 min in each of 100% , 95% , and 70% ethanol , followed by a 5 min incubation in distilled water . Antigen retrieval was then performed by boiling in pH 6 . 0 10 mM citrate buffer for 15 min followed by cooling for an additional 15 min . For immunohistochemical staining with rabbit anti-FAS antibodies ( 1∶50 , Santa Cruz ) , or rabbit anti-BAX ( 1∶50 , Cell Signaling Technologies ) , or purified rabbit IgG isotype control , slides were blocked in normal goat serum , washed , incubated with the primary antibodies , washed , incubated with 3% peroxide , and incubated with secondary goat anti rabbit poly-HRP antibodies ( Dako Cytomation ) . The peroxidase was developed using the DAB Plus liquid substrate chromogen system ( Dako Cytomation ) . For staining with goat anti-GRP78/BiP antibodies ( 1∶50 Santa Cruz ) or purified goat IgG isotype control , slides were blocked with normal donkey serum , incubated with primary antibody , endogenous biotin was blocked using the avidin/biotin blocking kit ( Vector laboratories ) , and the signal was amplified using the ABC method ( Vector laboratories ) . The peroxidase was developed as before . Caspase staining was performed as previously described [6] . FAS staining was scored semi-quantitatively where 0 is no staining , 1 ( 1–25% ) , 2 ( 26–50% ) , 3 ( 51–75% ) and 4 ( 76–100% ) . Caspase staining was scored semi-quantitatively as follows: 0 = none , 1 = focal weak , 2 = diffuse weak; 3 = diffuse weak and focal strong; 4 = diffuse strong . GRP78/BiP staining was scored semi-quantitatively where 0 is no staining , 1 ( 1–15% ) , 2 ( 16–30% ) , and 3 ( 31–50% ) . The intensity of the stain was also scored on a scale of 0–3 , where 0 is no staining and 3 is intense staining . Since activated activated BAX has a distinct punctate staining that can be easily distinguished form inactive BAX , active and inactive BAX was scored on separate semi-quantitative scales . Inactive Bax was scored in the same manner as BiP , and the scale for activated Bax was 0 is no staining , 1 ( 1–5% ) , 2 ( 5–10% ) , and 3 ( 10–15% ) . For immunofluorescent confocal microscopy , the slides were deparaffinized , the antigens retrieved as before , and blocked as before . Additional blocking using mouse IgG ( 0 . 1 mg/ml ) for 1 hour , followed by incubation with goat anti mouse-IgG ( 1 mg/ml ) overnight at 4°C was done prior to incubation with mouse anti HCV NS3/4 diluted 1∶50 ( TORDJI-22 , Abcam ) , or its isotype control , and one of rabbit anti-FAS , BAX , GADD ( Santa Cruz ) , NF-κB p65 ( C-20 Santa Cruz ) , BCL-xL ( Cell Signaling Technologies ) , or rabbit IgG all diluted 1∶50 , or rabbit anti human Albumin diluted 1∶1000 ( Dako Cytomation ) . Slides were blocked with 3% peroxide prior to incubation with goat anti mouse poly HRP and goat anti rabbit Alexa 488 ( diluted 1∶100 , Molecular Probes , Eugene , OR , USA ) . The peroxidase was developed using the TSA Plus fluorescence system with tyramide-tetramethyl red ( Perkin Elmer ) . Mounting media ( Vectastain-Vector laboratories ) contained 1 µg/ml 4 , 6-diamidino-2-phenylindole ( DAPI ) . For BiP/GRP78 , the staining procedure was essentially the same , except slides were blocked with normal donkey serum and avidin/biotin block ( Vector laboratories ) , the primary antibodies were goat anti-GRP78/Bip with the mouse anti-HCV , and the secondary antibodies were donkey anti-goat alexa 488 ( Molecular probes ) , and donkey anti-mouse biotin , followed by avidin-HRP ( Vector laboratories ) . The peroxidase was developed as before . For co-localization of HCV antigens and TUNEL reactivity , the In Situ Cell Death Detection kit ( fluorescein ) ( Roche ) was used , according to the manufacturer's specifications . The incubation with terminal deoxynucleotidyl transferase was carried out prior to incubation with the primary TORDJI-22 antibody . All subsequent steps were carried out as before . Nuclei were stained using DAPI . Confocal microscopy was carried out using a Zeiss scanning LSM510 microscope with the 351 nm , 488 , and 543 nm excitation lines , and digital images were collected with a 1 µm optical slice . The accession numbers for the genes/proteins discussed in this manuscript are the following: HCV-H77c AF011751 , TNF-α X20910 , FAS M67454 , FASL U11821 , BiP/GRP78 NM_005347 , p53 AF307851 , CHOP/GADD153 BC003637 , BAX NM138763 , BCL-Xl Z23115 , BCL-2 M14745 , NF-κB p65 Z22751 , Caspase-8 U60520 , Caspase-3 BC016926 , CD68 S57235 , RIG-I AF038963 , IPS-1-Q7Z434 , TLR-3 U88879 , TRIF AB086380 , IL28RA AY129153 , CMKOR1 BC008459 , IFITM1 J04164 , HLA-DRB5 NM_002125 , IFIT1 M24594 , IFIT2 M14660 , B2M AB021288 , HLA-A D3219 , HLA-B M15470 , HLA-F AY253269 , HLA-G NM_002127 , GBP1 BC002666 , BIRC4BP X99699 , CXCL11 U66096 , CXCL10 X02530 , CXCL9 X72755 , PSMB9 NM_002800 , OAS3 AF063613 , OAS1 X04371 , OASL AF063611 , STAT1 NM_007315 , G1P3 BC15603 , G1P2 BC009507 , IFI44 D28915 , IFI27 X67325 , ANGPTL4 AF202636 , NR4A1 L13740 , BDKRB2 S56772 , EPO X02157 , PPARGC1A AF106698 , PCK1 NM_002591 , ARG2 D86724 , APOA5 AF202889 , AVP M25647 , CPT1A L39211 , MT1A BC029475 , FABP5 M94856 , RXRA X52773 , SREBF1 BC057388 , PLIN AB005293 , C11orf11 AB014559 , APOF L27050 , HPX J03048 , CYP1A1 BC023019 , SAA2 M26152 , THRSP Y0809 , SCD AF097514 , PBP NM_04139 , ACSS2 AF263614 , GCK AF041014 , FDPS J05262 , HMGCR NM_000859 , CYP51A1 U51685 , SQLE D78130 , HSD17B6 AF016509 , C5 M57729 , FTFD1 X69141 , RDH16 NM_003708 , TNFSF10 U37518 , SC4MOL U93162 , HMGCS1 NM_002130 , IRE1 AF059198 , PERK AF110146 , ATF6 AB015856 , hAAT X01683 .
|
Hepatitis C virus is a common cause of liver disease worldwide . The details of how HCV causes liver disease are not well understood . It has been thought that HCV infection does not kill liver cells directly , but indirectly by stimulating the immune system to kill HCV-infected liver cells . In this study we have used a mouse model that supports HCV infection and replication . These mice do not have an adaptive immune system . Despite the lack of an adaptive immune system , we have shown that HCV infection still leads to the death of infected liver cells . This study provides new insight into how HCV damages the liver in chronic HCV carriers .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"virology/animal",
"models",
"of",
"infection",
"infectious",
"diseases/viral",
"infections",
"virology/effects",
"of",
"virus",
"infection",
"on",
"host",
"gene",
"expression",
"virology/host",
"antiviral",
"responses"
] |
2009
|
HCV Induces Oxidative and ER Stress, and Sensitizes Infected Cells to Apoptosis in SCID/Alb-uPA Mice
|
Controlled human malaria infection ( CHMI ) in healthy human volunteers is an important and powerful tool in clinical malaria vaccine development . However , power calculations are essential to obtain meaningful estimates of protective efficacy , while minimizing the risk of adverse events . To optimize power calculations for CHMI-based malaria vaccine trials , we developed a novel non-linear statistical model for parasite kinetics as measured by qPCR , using data from mosquito-based CHMI experiments in 57 individuals . We robustly account for important sources of variation between and within individuals using a Bayesian framework . Study power is most dependent on the number of individuals in each treatment arm; inter-individual variation in vaccine efficacy and the number of blood samples taken per day matter relatively little . Due to high inter-individual variation in the number of first-generation parasites , hepatic vaccine trials required significantly more study subjects than erythrocytic vaccine trials . We provide power calculations for hypothetical malaria vaccine trials of various designs and conclude that so far , power calculations have been overly optimistic . We further illustrate how upcoming techniques like needle-injected CHMI may reduce required sample sizes .
In 2015 , malaria caused an estimated 438 , 000 deaths ( 236 , 000–635 , 000 ) [1] , most of which were associated with Plasmodium falciparum ( Pf ) infections . Hypothetically , a malaria vaccine targeting the sporozoite parasite stage ( pre-hepatic vaccine ) , hepatic parasite stage ( hepatic vaccine ) , and/or blood stage parasites ( erythrocytic vaccine ) could prevent many such deaths . In the past ten years , over 40 malaria vaccines have reached the clinical trial stage . So far , only the RTS , S vaccine has shown promising results ( 30%–65% protection against clinical malaria ) [2–6] , with a recent large , multi-center phase three trial showing 45 . 7% protection against clinical malaria in infants and children aged 5–17 months over a period of 18 months after three vaccine doses [7] . In response , the World Health Organization has recommended RTS , S for pilot implementation studies in Africa [8] . However , before any malaria vaccine can be tested in the field , its efficacy and safety need to be evaluated in controlled settings , which is most often done by means of controlled human malaria infection ( CHMI ) [9 , 10] . CHMI is currently considered to be a powerful tool in clinical vaccine development , as it allows researcher to control the otherwise highly variable infection rates . It is traditionally conducted by exposing a limited number of volunteers to laboratory-reared Anopheles spp . mosquitoes carrying Pf sporozoites [10 , 11] , and more recently , also through needle injection of a defined number of aseptic cryopreserved sporozoites [12 , 13] . Parasitaemia can be monitored by means of blood smear microscopy but increasingly by sensitive quantitative real-time polymerase chain reaction ( qPCR ) [14–17] . The traditional study endpoint is detection of blood stage parasites by microscopy , at which point a study subject is treated with a curative regimen of an anti-malarial drug [10] . Under these conditions and with some additional precautionary measures , CHMI studies are considered to be safe , though the risk of severe adverse effects and the burden of venipuncture up to three times per day have to be weighed against the benefits of the information to be gained [18–20] . Power calculations for CHMI-based vaccine trials are therefore critical to ensure an appropriate number of included participants to obtain meaningful estimates of protective efficacy . Here we provide an optimized method for power calculations for CHMI-based hepatic and erythrocytic vaccine trials in malaria-naive individuals , using qPCR and bloodsmear data from mosquito-based CHMI experiments in 57 non-vaccinated individuals . We developed a novel non-linear model for parasite kinetics during the first two weeks of an experimental infection , based on an earlier statistical model for cyclical patterns in CHMI [21] . We implemented the model in a Bayesian hierarchical framework to capture important sources of variation both within and between individuals ( i . e . by means of random effects ) , and extended the model to explicitly capture the processes leading to measurements below the qPCR detection limit ( censoring ) and termination of the experiment due to positive blood microscopy . With this model , we performed power calculations for hypothetical hepatic and erythrocytic malaria vaccine trials under varying assumptions about blood sampling schemes and vaccine impact on parasite growth .
In Fig 1 we provide an example of the model fit to the data for a subset of individuals , and illustrates the cyclical pattern in both the data and model predictions . Note how censored observations ( black triangles ) coincide with model predictions near or under the qPCR detection limit ( dashed line ) . Similar plots for all individuals can be found in S1 Fig . For a minority of subjects no clear cyclical pattern in parasitaemia levels was visible , resulting in relatively wide credible intervals for predicted parasitaemia levels . Data from one individual were excluded ( ZONMW1 . 1270 ) because they were prohibitively divergent from the rest to effectively fit the model ( first observation above detection limit at day ten and no clear cyclical pattern while this individual–like all others–had no vaccine protection ) . S2 Fig depicts the association between parasite concentration in the blood and probability of positive blood microscopy in comparison to the data , illustrating good agreement between the model and the data . We estimated that the average number of first generation parasites among the 56 included study subjects was 635 Pf/ml ( 95%-BCI 406–945; Table 1 ) . Inter-individual variation in the number of first generation parasites was estimated at a standard deviation ( SD ) of 1 . 40 on the natural logarithmic scale , which is equivalent to a relative standard deviation ( RSD ) of exp ( 1 . 402 ) −1=247% . Assuming that infected hepatocytes release on average 6 . 0 merozoites per mL blood ( based on 30 , 000 merozoites per hepatocyte and an average of 5 litres of blood per person , as assumed before [21] ) , we can deduce from the average concentration of first generation parasite that on average about 107 hepatocytes were infected and that each experimentally infected mosquito transmitted on average about 21 sporozoites ( that successfully infected hepatocytes ) . The average time of appearance of first-generation parasites was at day 6 . 87 ( 95%-BCI 6 . 76–6 . 99 ) , with little variation between individuals ( SD 0 . 036 , RSD 3 . 6% ) . The average multiplication rate of P . falciparum parasites within individuals was estimated at 11 . 8 per parasite cycle ( 95%-BCI 9 . 0–15 . 3 ) , with inter-individual variation estimated at a SD of 0 . 47 on the natural logarithmic scale ( RSD 50% ) . In Fig 2 , we illustrate the correlation between time of appearance of first generation blood parasites , peak concentration in blood of first generation parasites , the parasite multiplication rate , and the relative odds of an individual having positive blood microscopy . As there was no clear correlation pattern between these four individual-level parameters , we did not further explicitly model their joint distribution ( i . e . we assume they are independently distributed when simulating data for power calculations ) . Next , we used the model to simulate ten thousand repeated data sets for each of various hypothetical vaccine trial designs . In each simulated vaccine trial , we define vaccine efficacy as the relative reduction in total number of released merozoites induced by a hepatic vaccine , or the relative reduction in parasite growth rate induced by an erythrocytic vaccine . To explicitly account for previously excluding one out of 57 original study subjects , we allowed each individual in the simulated vaccine trials to be dropped with a 1/57 probability ( which turned out to be of little consequence for power estimates ) . Fig 3 illustrates how trial power increases with the number of individuals per trial arm , allowing one to deduce the required number of individuals for a desired power level . Study power is most dependent on the number of individuals in each treatment arm; in contrast , inter-individual variation in vaccine efficacy and the number of blood samples taken per day matter relatively little for study power . Due to high inter-individual variation in the number first-generation parasites , hepatic vaccine trials required significantly more study subjects than erythrocytic vaccine trials . For instance , over 30 individuals per trial arm are needed to achieve 80% study power when hepatic vaccine efficacy is 70% or lower . S2 File provides a graphical user interface to look up power of all simulated vaccine trial settings . The effective probability of a Type 1 error when using T-tests ( assuming unequal variances and setting α = 0 . 05 ) was approximately 5% and even somewhat lower for small sample sizes ( S3 Fig ) , which confirmed the validity of T-tests for identifying a difference between trial arms . S4 Fig also depicts the association between trial power and number of individuals per treatment arm ( like Fig 3 ) , but under the assumption that inter-individual variation in peak first-generation parasite density in terms of SD is halved ( e . g . by using needle-injected instead of mosquito-based CHMI ) . This figure illustrates how such a reduction in inter-individual variation may reduce the number of individuals required for hepatic vaccine trials . For instance , only 11 to 15 individuals per trial arm ( depending on number samples per day ) would be needed to achieve 80% study power when hepatic vaccine efficacy is 70% , instead of around 30 or more when using mosquito-based CHMI ( Fig 3 ) . For comparison , we further repeated the power analysis using the log-linear sine model [22] , which we also improved to explicitly capture censored observations and inter-individual variation in slope , intercept , and timing of the log-linear sine curve . Table S2 in S1 Text summarizes the assumed prior distributions and posterior parameter estimates for the sine model; S5 Fig depicts the model fit to the data . The log-linear sine model resulted in a higher estimate of the parasite growth rate than our non-linear model: 4 . 80 per day , or 17 . 9 per parasite cycle ( vs . 11 . 8 per cycle ) , assuming a parasite cycle duration of 1 . 84 days ( as estimated by the non-linear model ) . Further , the sine model resulted in a higher estimate of qPCR measurement error ( 1 . 22 vs . 0 . 98 on the natural logarithmic scale , or RSD of 155% vs . 129% ) , which is an indication of the sine model providing an inferior fit to the data compared to the non-linear model , as it attributed some of the temporal variation in the data to ( random ) measurement error . As a result , the sine model produced more optimistic estimates of study power than our non-linear model , especially for hepatic vaccines ( S6 Fig ) .
We present robust power analyses for malaria vaccine trials based on controlled human malaria infection ( CHMI ) experiments , using a Bayesian non-linear model for blood parasite kinetics . Our model adequately accounts for the cyclical nature of P . falciparum concentrations in the host blood , based on robust estimates of biological parameters underlying CHMI . Our study supersedes earlier models and power calculations: we prevent overly optimistic estimates of trial power by directly modeling the biological processes behind the cyclical patterns in CHMI to best capture temporal patterns in the data , and by jointly and robustly estimating all model parameters in a Bayesian hierarchical model framework . Furthermore , we avoid excessive weighing of outliers in data simulation , appropriately model the impact of detection limits , and account for five important sources of variation between individuals: time of first parasite appearance , first cycle amplitude , parasite multiplication rates , probability of positive blood microscopy ( and consequent termination of the experiment ) , and vaccine efficacy . This makes our model a more suitable tool for power calculations of CHMI-based vaccine trials than previous approaches to date . Our study confirms that for vaccine trial power , inter-individual variation in vaccine efficacy and the number of blood samples taken per day matter relatively little , and that erythrocytic vaccine trials require significantly fewer study subjects than hepatic vaccine trials [23] . Importantly , our analyses suggest that loss of only a few study subjects ( e . g . due to experimental failure ) may lead to drastic loss of trial power , unless the trial power is already close to 100% , which is important to consider during trial design . For instance , if the anticipated efficacy of a hepatic vaccine is 80% ( reduction in number of first-generation parasites ) , 15 people per treatment arm will provide just over 80% power to detect this level of efficacy , but drop out of even one study subject per treatment arm may diminish the trial power below 80% . Further , our results suggest that a 50% reduction in the inter-individual variation of the number of first-generation parasites ( e . g . by using needle-injected instead of mosquito-based CHMI ) may result in a substantial reduction in the number of individuals required per trial arm , especially so for hepatic vaccine trials ( reductions up to 50% ) . These estimates will be further refined based on forthcoming data from needle-injected CHMI experiments . The average multiplication rate of P . falciparum parasites estimated by our non-linear model is very similar to earlier estimates [22 , 24 , 25] or somewhat higher [21 , 23 , 26] . However , our results do not confirm an earlier report by Sheehy et al . of negative correlation between the parasite multiplication rate and the peak concentration of first generation parasites in mosquito-based CHMI [13] , and suggest that this previous finding should be revisited . Possibly , Sheehy et al . overestimated the multiplication rate in study subjects with low initial parasite loads ( two parasites per mL ) as typically , parasite loads below the detection limit for such individuals are set to some arbitrary low value ( e . g . half the value of the detection limit[26] ) while ignoring measurement error ( i . e . true levels may be above the detection limit ) . If Sheehy et al . had left out observations with low initial parasite loads ( <10 Pf/mL ) from their analysis , very little correlation would have remained . The parameter estimates from our study are intended for power calculations for phase 1/2 hepatic and erythrocytic vaccine trials using CHMI of malaria-naive individuals with NF54 or similar strains ( i . e . first-time infections only ) , and using qPCR to monitor parasitaemia levels . To avoid variation due to differences between labs and strains , future vaccine trials using CHMI should either be performed in a single lab using a single strain ( as was the case for the data used in this study ) , or in case of multi-center trials , each lab should cover each trial arm to allow within-lab comparisons . Of course , the validity of power estimates based on our model relies on understanding one’s potential vaccine efficacy in terms of its impact on the number of first generation blood parasites and the parasite replication rate at the time of challenge ( in contrast to impact on clinical outcomes such as time until positive blood microscopy or duration of sterile protection ) . The potential vaccine efficacy in a power calculation is ideally based on a target product profile ( TPP ) defined as part of a vaccine development process . We therefore recommend that TPPs are defined not just in terms of clinical outcomes but also in terms of impact of a vaccine on parasite growth . The link between impact on parasite growth and clinical outcomes in healthy volunteers and inhabitants of endemic areas is a topic of ongoing research . Based on our study in health volunteers , however , we can say that to achieve sterile protection in healthy volunteers , an erythrocytic vaccine would have to reduce the parasite multiplication rate by at least 92% ( 1 − 1/β2 , where β2 is the parasite multiplication rate per cycle ) . Further , our estimates for biological parameters can be used as prior information ( in a Bayesian framework ) in studies that aim to estimate biological and/or vaccine efficacy parameters . If there is reason to believe that the study population is somehow different from the population covered by the current study ( e . g . a different parasite replication rate is expected ) , it is important that a sensitivity analysis be performed by incrementally diluting the relevant prior information ( i . e . increasing prior variance on the population mean and variance of e . g . replication rates ) so that the prior information becomes weaker and the model is relatively more informed by the data at hand . Here , we predict that mosquito-based CHMI vaccine trials may require several tens of volunteers per trial arm , especially if the expected vaccine efficacy is under 70% ( in terms of impact on patterns in parasite growth ) and/or if a hepatic vaccine is being tested . In our experience and as reported in literature [27] , CHMI experiments are typically executed with 5–8 volunteers at a time , or in several batches of that size . Working with larger groups is impractical and expensive because many volunteers will turn positive for malaria infection on the same day , requiring more attending physicians and lab personnel . However , future vaccine candidates should preferably have an impact on parasite growth that is much higher than 70% , which for erythrocytic vaccines can be demonstrated with 80% power using only 5 to 15 volunteers per trial arm . Achieving similar study power for hepatic vaccine trials with the same number of volunteers might be made possible by the use of needle-based CHMI . It has been argued that the non-linear model that we build on here [21] is unnecessarily complex as simpler models may yield very similar estimates of relevant biological parameters , and requires the estimation of vaccine-irrelevant parameters and the assumption that some of those parameters are fixed [26] . We argue that log-linear models ( a straight line through the log-transformed parasite concentrations [24] ) and sine models ( log-linear model in which the slope is multiplied with a sine function [22 , 28] ) are too simple and require assumptions that clearly contradict the data ( linearity of data; data before first parasite generation are ignored; parasite cycles follow a symmetric , sinusoid pattern ) , making them less fit for power calculations . In power analyses , it is imperative to consider uncertainty in all relevant parameters ( like we do here ) [29] , including the processes leading to censoring of observations below the detection limit and termination of an experiment in case of positive blood microscopy . In this study , we demonstrate that the log-linear sine model ( even after properly accounting for censoring and variation between individuals ) results in higher power estimates than our non-linear model , which we attribute to the fact that the sine model does not capture temporal patterns in CHMI data as well as our non-linear model , as indicated by the sine model’s higher estimate for qPCR measurement error . The bootstrapping approach to power calculation used by Roestenberg et al . [23] is a major improvement over log-linear and sine models , but it is based on the assumption that the empirical distribution of data observed so far is representative of the distribution in the population , which may cause it to assign excessive weight to outliers in power calculations , especially when based on small datasets . Rather , our model , with its hierarchical design , shrinks such outliers towards the population mean when estimating population-level parameter values , while retaining the ability to simulate such outliers by chance . Because we more robustly quantify parameter uncertainty , our power estimates are more realistic and dictate higher required sample sizes compared to those by Roestenberg et al . [23] Further , in the current study , we relaxed the assumption of fixing parameters by jointly estimating all parameters in a Bayesian framework . A possible limitation in the application of our model for mosquito-based CHMI data , is the assumption that first-generation malaria parasites appear in the blood in a single wave ( an assumption shared with the sine model [22 , 28] ) . Obviously , this was not the case for subjects in whom no clear cyclical pattern in parasitaemia levels was visible . In this respect , CHMI data based on needle-infected PfSPZ may provide less noisy data than mosquito-based CHMI [12 , 13] , and may even result in fewer asynchronous parasite cycles , although this remains to be evaluated . In short , like all other statistical models [22–24 , 28] , our model relies on assumptions that are not always fulfilled; nevertheless , our model best approximates and quantifies uncertainty in the biological mechanisms behind CHMI and is therefore the most suitable for performing power calculations . As already mentioned , only the RTS , S vaccine has shown promising results so far ( 30%–65% protection against clinical malaria ) [2–6] , with a recent large , multi-center phase three trial showing 45 . 7% protection against clinical malaria in infants and children aged 5–17 months over a period of 18 months after three vaccine doses [7] . Unfortunately , RTS , S vaccine trials have only used blood microscopy to evaluate infection levels in trial participants so far , which does not allow evaluation of vaccine impact in terms of the effect on parasite growth patterns ( and thus a link with our power analyses ) . This obstacle may be readily overcome by the use of qPCR to monitor infection levels in Phase 1/2 studies , which may also help to reduce study sample sizes . In conclusion , to maximize the probability of identifying effective candidate malaria vaccines , and to keep the risk of severe adverse events and the number of invasive procedures to a minimum , it is important to perform power analyses . With this study , we provide robust power estimates for malaria vaccine trials using mosquito-based CMHI , superseding previous models and power analyses . Given the simulation-based nature of our approach , it is straightforward to implement more complicated assumptions for future vaccine trials . Last , our model suggests that using needle-injected instead of mosquito-based CHMI may improve the power of hepatic vaccine trials .
We used data from 57 volunteers participating in 8 different sporozoite challenge trials at Radboud University Medical Center ( RUMC , Nijmegen , The Netherlands ) from 1999 to 2011 ( Table 2 ) [14 , 21 , 30–35] . The data set included subjects from immunological studies ( n = 20 ) , infectivity controls from immunization trials ( n = 18 ) , and non-protected subjects from a malaria vaccine trial ( n = 19 ) . Volunteers were challenged by bites of 4–7 ( n = 20 ) or 5 infected mosquitoes ( n = 37 ) for 10 minutes; the number of bites was unknown as exposure to mosquito bites took place in the dark ( under a cloth ) , and not all individuals developed a clearly visible skin reaction to every bite . Mosquitoes were laboratory-reared and infected with the NF54 strain of Pf . Presence of sporozoites in mosquitoes was confirmed by salivary gland dissection . Trial subjects were followed 2–3 times daily from day 5 after challenge until 3 days after antimalarial curative treatment . At every visit , blood samples were collected and assessed for presence of parasites by microscopy ( threshold 4 parasites/μL ) and quantified by qPCR ( detection limit 20 or 200 parasites/mL ) [14] . Ethical approval was obtained for each trial separately from the RUMC institutional review board and/or for some trials from the Dutch Central Committee on Research Involving Human Subjects ( 0004–00900 , 0011–0262 , 2001/203 , 2002/170 , NCT00442377 , NCT00757887 , NCT00509158 , NCT01002833 , NCT01236612 ) . We modeled parasite kinetics in a novel Bayesian non-linear statistical model , which is based on a previous , simpler model for cyclical patterns in CHMI [21] . The model predicts parasite concentration in the blood as measured by qPCR as a function of days since infection , mimicking successive cycles of appearance and disappearance ( sequestration ) of blood parasite generations . The model parameters capture the following biological processes: the total number of first generation blood parasites per mL blood ( β1 ) ; the blood parasite multiplication rate , i . e . the number of next generation parasites per current generation parasite ( β2 ) ; the average time from inoculation to appearance of first generation blood parasites ( μ1 ) ; the average duration of the blood parasite stage ( μ2 ) ; the average time from parasite sequestration ( i . e . when a parasite cannot be detected in the blood ) to appearance of next generation parasites ( μ3 ) ; and the standard deviation of time of appearance and disappearance of individual blood parasites from a given generation , which represents the rate at which parasite concentrations change ( σ1 , lower values mean higher rate ) . We assumed that qPCR measurement error follows a lognormal distribution . We extended the original model [21] with regard to the following points . We allowed parasite kinetics to vary between individuals by including random effects for model parameters β1 , β2 , and μ1 . Given that the nine CHMI experiments ( Table 2 ) were performed at the same lab , using the same strain , and were performed by the same person ( CCH ) on the same PCR machine , we assumed that inter-study variation is negligible relative to inter-individual variation ( i . e . no random intercept for study ) . Observations below the detection limit of the qPCR ( i . e . censored observations ) were explicitly modeled , rather than assuming a value equal to half the detection limit [21 , 26] ( which would introduce artificial information ) or leaving out such observations altogether [28] ( which would ignore information in the data ) . We further jointly modeled the probability of detecting parasites through blood microscopy as a function of the predicted parasite load ( i . e . before measurement error or censoring ) , using a standard hierarchical logistic regression model ( random intercept for individuals ) . Model parameters were jointly estimated in a Bayesian framework , giving several advantages over previously applied classic ( frequentist ) approaches [21 , 25 , 26] . The Bayesian approach allowed simultaneous estimation of all model parameters and the associated uncertainty ( including the model parameters for positive blood microscopy ) , without the need to fix a subset of parameters . Furthermore , the Bayesian framework allowed for exact rather than approximate inferences based on normality assumptions . See S1 Text for a detailed description of the statistical model and the parameter estimation procedure . Model parameter estimates are summarized in terms of posterior mean and a 95%-Bayesian credible interval ( 95%-BCI ) , which we defined as the 2 . 5th and 97 . 5th percentiles of the posterior samples for each parameter . We performed power calculations for erythrocytic and hepatic vaccine trials , using combinations of presumed mean vaccine efficacy ( 30% , 40% , 50% , 60% , 70% , 80% , 90% , or 95% reduction in number of first-generation parasites or parasite multiplication rate ) , inter-individual variation in vaccine effect ( beta distribution with standard deviation 0 . 05 or 0 . 10 ) , number of study participants in each group , and the blood sampling frequency: one ( 8am ) , two ( 8am , 4pm ) , or three ( 8am , 4pm , 10pm ) per day , or once every two days ( even days ) , either in the morning ( 8am ) or afternoon ( 4pm ) . For each combination of assumptions , we simulated ten thousand repeated vaccine trials , using one posterior draw of model parameters to generate one set of trial data . We explicitly simulated the probability of blood microscopy turning positive ( and consequent termination of the experiment ) as a function of predicted blood parasite concentrations to arrive at the most realistic individual time series possible . For each repeated vaccine trial , all individual-level random effects were drawn independently from each other ( i . e . we did not re-use the random effects estimated from the data ) . The qPCR detection limit was set to 20 parasites per mL blood ( i . e . the current practice at Radboud University Medical Center , Nijmegen , The Netherlands ) . Because the Bayesian non-linear model used in this study requires a substantial amount of data ( i . e . at least two , preferably three samples per day ) , each simulated vaccine trial was analyzed using simple frequentist statistical tests as previously described [23] . First , censored observations were set to half the value of the detection limit . Next , we categorized data by parasite cycle ( days 6 . 5–8 . 5 , 8 . 5–10 . 5 , 10 . 5–12 . 5 ) and calculated the mean log-transformed blood parasite concentration per parasite cycle and individual . For hepatic vaccine trials , we compared first-cycle log-blood concentrations between vaccine and control arms with two-sided t-tests ( assuming unequal variances due to potential censoring ) . For asexual erythrocytic vaccine trials , we calculated the differences in average log-blood concentrations between consecutive cycles for each individual , and then averaged these differences over cycles within individuals . The average differences were compared between groups , again with two-sided T-tests ( assuming unequal variances due to potential censoring ) . The predicted power of a vaccine trial was expressed as the proportion of repeatedly simulated trials that resulted in a p-value equal to or lower than 0 . 05 . The validity of using the T-test ( i . e . the effective probability of a Type 1 error ) was checked in a similar fashion , but by simulating vaccine trials with zero effect in both treatment arms . To explore the potential impact of using needle-injected CHMI on vaccine trial power , we repeated the power calculations assuming that the inter-individual variation in the number of first-generation parasites is half that of mosquito-based CHMI ( i . e . σβ1 , needle=σβ1/2 , based on data digitally extracted from Fig 4A in Sheehy et al . 2013 [13] ) . We further repeated the power calculation based on an analysis of the data using the simpler log-linear sine model [22] , assuming that the parasite generation time is 1 . 84 days ( as estimated by the main model ) . For the sake of comparison , we improved the log-linear sine model by adding random effects for starting parasitaemia levels , parasite growth rate , and timing of the parasite cycle in each individual , and explicitly modeled observations under the qPCR detection limit ( see S1 Text for model details ) . For the purpose of the power analysis we assumed that two full parasite cycles would be observed in each individual ( the log-linear sine model does not provide a prediction for termination of an experiment ) . Other than that , this power analysis was executed in exactly the same fashion as that based on the main analysis . None of the funders were involved in the writing of the manuscript or the decision to submit it for publication . The authors have not been paid to write this article by a pharmaceutical company or other agency . The corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication .
|
Controlled human malaria infection ( CHMI ) in healthy human volunteers is an important and powerful tool in clinical malaria vaccine development . However , to obtain meaningful estimates of protective efficacy , it is important to include an appropriate minimum number of participants , while minimizing the risks and burden for volunteers . Existing power calculations have limited value due to important influential assumptions . To optimize power calculations for malaria vaccine trials , we developed a non-linear , Bayesian statistical model for parasite kinetics as measured by quantitative real-time polymerase chain reaction , using existing data from mosquito-based CHMI experiments . Using our model , we provide improved , robust power calculations for various hypothetical malaria vaccine trials , taking account of important sources of variation between and within individuals . We conclude that so far , power calculations for malaria vaccine trials have been overly optimistic . We further illustrate how upcoming techniques like needle-injected CHMI may reduce required sample sizes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"parasite",
"replication",
"immunology",
"tropical",
"diseases",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"parasitology",
"vaccines",
"preventive",
"medicine",
"protozoans",
"mathematics",
"statistics",
"(mathematics)",
"infectious",
"disease",
"control",
"vaccination",
"and",
"immunization",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"malarial",
"parasites",
"hematology",
"statistical",
"models",
"blood",
"anatomy",
"physiology",
"biology",
"and",
"life",
"sciences",
"malaria",
"physical",
"sciences",
"organisms"
] |
2017
|
The Power of Malaria Vaccine Trials Using Controlled Human Malaria Infection
|
Ductal carcinoma is one of the most common cancers among women , and the main cause of death is the formation of metastases . The development of metastases is caused by cancer cells that migrate from the primary tumour site ( the mammary duct ) through the blood vessels and extravasating they initiate metastasis . Here , we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct , in the circulatory system and in the bone . Through a branching process model , we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer ( EPCAM , CD47 , CD44 and MET ) . In particular , the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability . We also include the administration of drugs as bisphosphonates , which reduce the formation of circulating tumour cells and their survival in the blood vessels , in order to analyse the dynamic changes induced by the therapy . We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue . Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics .
Breast cancer is characterised by multi-year survival from the first diagnosis of bone metastases . It is a leading cause of cancer death among women , and if detected at an early stage , its prognosis is favourable , with 5-year survival—for death from the cancer—in more than 90% of the patients . However , when initial diagnosis is of advanced metastatic disease , the 5-year survivals decrease to around 30% . The survival and prognosis of cancer patients with metastatic skeletal disease vary widely and depend on many factors including features of the primary tumour ( histological type and grade ) , presence of extraosseus metastatic disease , patient’s characteristic ( performance status and age ) , level of tumour markers and extension of skeletal disease . In fact , every cancer is different; as cancer grows , a mixture of cells builds up over time and becomes more and more complex . Cancer cells often detach from the primary tumour , become circulating tumour cells ( CTC ) and invade blood vessels . Once in the bloodstream , they reach the skeleton and adhering to the endosteal surface , they colonize the bone , subverting the cellular processes of normal remodelling and causing bone pathology [1] . Cancer phenotypic heterogeneity may be due to progressive , but asynchronous changes in tumour–bone interactions ( i . e . progressive accumulation of driver and non driver mutations ) . In particular , the Transforming Growth Factor-β ( TGF-β ) pathway mutations are determinant in generating cancer heterogeneity and in the formation of CTCs causing bone metastasis . TGF-β is among the most abundant growth factors in bone , and its role in skeletal metastases is established . It is deposited in the bone matrix by osteoblasts , released and activated during osteoclastic resorption , and it regulates bone development and remodelling [2] . Advanced cancers frequently escape growth inhibition by TGF-β , which also activates epithelial-mesenchymal transition ( EMT ) and invasion , promoting metastases . TGF-β also increases angiogenesis and suppresses immune surveillance . It specifically stimulates bone metastases by inducing pro-osteolytic gene expression in cancer cells , such as parathyroid hormone related protein ( PTHrP ) [3] . Moreover , therapies acting on the TGF-β pathway seem effective at all levels and compartments where TGF-β is involved , generating a retroaction effects on the primary tumour , the circulatory system and the bone [4] . Recently , Baccelli et al . [5] have identified a set of genetic markers in CTCs which are key players in establishing bone metastasis ( metastasis-initiating cells ) and largely influencing outcomes and patient’s survival . The overexpression of EPCAM , CD44 , CD47 and MET cell proteins in a subset of CTCs correlates with lower overall survival . These four markers are known to be involved in tumourigenesis [6–8] and are co-regulated with the TGF-β signalling pathway [9] . Because of the complexly structured and heterogeneous process as well as the paucity of experimental data , it is important to model how the dynamics of TGF-β-driven CTCs couples with the primary ( mammary duct ) and secondary ( bone niche ) cancers . Indeed , cancer mathematical models play an important role in assisting biologists in the interpretation of results and in experimental design ( see Maini [10] , Bellomo [11] and Chaplain [12] for breast cancer and bone cancer modelling , among others ) with a growing interest in combining epidemiological ( e . g . survival information ) , clinical and molecular data . In a recent work [2] , we have modeled how TGF-β drives the formation of early neoplastic signature in breast and perturbs the bone remodelling process . Here , we present a multi-compartment mathematical model that aims at elucidating the effects of the TGF-β and the concomitant therapies in the three microenvironments ( mammary duct , circulatory system and bone niche ) . Fig 1 summarises the structure of the model . Starting from statistical data ( including molecular and clinical data ) , we develop a model able to predict the survival probability by using the gene expression profile of CTCs . We aim at a quantitative understanding of the relationship between gene expression levels in breast cancer and formation of bone metastasis with respect to the survival statistics . Indeed , we propose a mathematical model linking the amount of CTCs to the survival times , in order to predict the patient-specific survival . By using a branching process technique [13] , we compute the probability of developing EPCAM+ CD44+ MET+ CD47+ CTCs . Through the model it is also possible to predict bisphosphonates-therapy outcomes based on the patient’s specific markers . Bisphosphonates are drugs commonly used as treatment for several bone diseases in order to reduce osteoporosis and recent works have shown the anti-tumour effectiveness of bisphosphonates administered in a biological window therapy in naive bone-only metastatic and locally advanced breast cancer [14] ( see also [15] ) . This work is organised in the following way: in the first two subsections of the Results section , we discuss the roles of TGF-β and CTCs in metastatic breast cancer . In the third subsection , we present the system of ODEs for each compartment ( mammary duct and circulatory system ) . The equations including the treatment are reported in forth subsection . The fifth subsection shows how we used the model to simulate the disease evolution so to produce survival curves . In the sixth subsection , we present the results obtained by numerical simulations and we discuss the cases of higher number of driver mutations and the case of immune response delay . Information relative to the analysis of gene expression data is given in Methods section . Finally , the conclusions give a brief summary and critique of the findings .
The earliest stage of breast cancer is revealed by abnormal cells inside a ductal lobular unit in the breast . In these cells , the TGF-β is highly expressed and induces cells to undergo apoptosis . Recent studies about TGF-β activation have highlighted the important role of integrins , an adhesion molecule that mediates the attachment between a cell and its surroundings [16] . In particular , the binding of integrins to the latent TGF-β promotes the production of active TGF-β . The invasive ductal carcinoma is characterised by the loss of epithelial cadherin ( E-cad ) function via epigenetic silencing , or via genetic inactivation by TGF-β [17] . E-cad is a hallmark of well differentiated epithelium , and it maintains the junction between cells preventing the cancer cell proliferation and migration . Indeed , the E-cad downregulation by TGF-β is proved to prevent mammary cell differentiation and produces more spherical cells which promote metastatic growth [18] . Cells with driver mutations causing TGF-β resistance , when located close to sites of elevated activation of the transforming growth factor , diminish their E-cad induced adhesion and reduce the probability of incurring death [18] . Amongst all the mutated cells , only those freed from the cell-to-cell junctions have a higher possibility of migrating through the TGF-β altered tissue and reaching the near blood capillaries . Therefore , CTCs are cells originated from the primary tumour site ( and/or secondary metastatic sites ) and sharing the same characteristics and the same phenotype heterogeneity of the primary tumour cells . In breast cancer , the epithelial cells in the mammary ducts affected by the tumour represent the main sources of CTCs [19] . Moreover , the size of the tumour is a sort of power source factor that contributes to the quantity of CTCs in the blood stream , so that bigger tumour sizes correspond to higher numbers of CTCs . Different types of breast cancer affect the power source as a result of their different velocity in evolving and growing throughout the tissue . On the other hand , we assume that in tumours of the same size and with the same rate of E-cad unbinding , CTC sources behave equally ( i . e . they release the same amount of CTCs per unit of time ) . The explanation is given by the fact that only when a cell is completely separated from the surrounding neighbour cells and the extracellular matrix , it can be part of the amount of CTCs . CTCs is associated with large quantities of TGF-β as well as their progenitors [20] . The synthesised TGF-β , which depends on the cell phenotypes , might not suffice the cancer cells’ need because of the more dispersive space and less cramped geometry which facilitate the dispersion of TGF-β . Nevertheless , TGF-β production serves also as an alerting inflammatory signal helping the immune system to detect and attack the CTCs . In part for their instabilities caused by mutations and in part for the immune system response , generally CTCs do not survive long in the blood stream . This is true especially when the concentration of CTCs is low while , at higher concentration , CTCs cluttering and overwhelming of the immune system might extend the life of the same cancer cells [21] . Furthermore , CTCs seem to have very low proliferation rate when flowing in the blood stream . Using different technological platforms , clusters of CTCs has been detected within the circulating system of patients with cancers of different origin [21] . While most clusters are relatively small , ranging from 2 to 50 cancer cells , they have from 23- to 50-fold increased metastatic potential . This property of CTC clusters , together with the adverse prognosis of breast cancer patients with abundant CTCs clusters , support an important role for these cells in the blood-bone spread of cancer . It has been experimentally shown that CTCs overexpressing EPCAM , CD47 , CD44 and MET have a high probability of succeeding in generating bone metastasis [5] . Overexpression of EPCAM is a phenotypical characteristic inherited since the tumour cell was in the lobular duct and remained present during the epithelial-mesenchymal transition ( EMT ) process . In the extravasation process , EPCAM helps cancer cells in exiting the circulatory system , by inducing the anchorage between CTCs and the vascular endothelium [7 , 8 , 22] . CD47 is a protein expressed on all the cell membranes , and it interacts with integrins and immunogenic complexes on the cells . It is involved in several processes , including the spreading and aggregation of platelets [23] , and modulation of T-cell activation [24 , 25] . CD47 operates as a “self” marker on red blood cells in order to prevent their clearance by macrophages [26] . Elevated expression of CD47 helps CTCs to evade the immune system . CD44 is a receptor principally present on lymphocytes . This protein is implicated in a variety of immunological functions , such as vascular extravasation and T-cell co-stimulation [27] . CD44 is prevalently upregulated at various stages of the cancer evolution , and the protein also mediates adhesion between stroma cells and bone marrow progenitor cells . Promotion of CTCs extravasation across endothelial vessels and homing into peripheral organs makes CD44 responsible for metastasis formation in the bone tissue [28–31] . MET is a receptor involved in embryonic development and organ regeneration . It contributes to establish the normal tissue patterning by orchestrating cell proliferation , disrupting the cell-to-cell junctions , facilitating the migration through the extracellular matrix and inhibiting apoptosis [6] . MET deregulation induces cancer cells to leave the primary tumour , move towards different organs and give rise to metastasis [32] . In the long run , the heterogeneity of CTCs will increase reflecting the phenotypic cellular diversity in the primary tumour source . At the same time , a small component of the whole CTC population capable of evading the immune system ( EPCAM+ CD47+ CD44+ MET+ CTCs ) , extravasating and seeding in the bone will branch from the rest of the CTCs and initiate the process of development of metastasis . CTCs and bone metastasis formation . The CTC populations have different phenotypes reflecting the heterogeneity of the primary tumour source . Among different cancer cells , those with a phenotype more sensitive to the TGF-β chemoattraction reach the bone niche . Furthermore , during the bone remodelling process , TGF-β and cytokines attract the near blood vessels toward the portion of lesioned bone matrix . The reduced distance between the peripheral blood stream and the source of TGF-β increases the probability of few CTCs exiting the capillary and entering the bone tissue . CTCs in the fractured bone rarely begin a fast invasion of the tissue , on the other hand , they change the remodelling process of the bone by strongly interfering with the quantity of TGF-β involved in the differentiation and maturation of the osteoblasts and cause a prolonged osteolytic activity . Cancer cells provoke a reduction of bone re-mineralization which results in a weaker bone , hence higher probability of re-occurrence of new fracture-remodelling cycles . Meanwhile , the number of CTCs slowly increases taking advantage of the extra TGF-β released and extra space left in the bone multicellular unit ( BMU ) at each cycle . In order to describe the early stages of breast cancer and the formation of metastasis , we develop a model that includes three compartments representing three distinct body systems and involves different regions of the body: 1 ) the epithelial tissue in the mammary duct , 2 ) the circulatory system and 3 ) the bone . Our approach , even though it encompases extense and distinct body parts , allows us to semiquantitatively reproduce the progression of the disease . The first and the third compartments are geometrically connected through the circulatory system which plays a fundamental role in the migration of cancer cells from the primary tumour site in the mammary duct to the secondary sites in the bone tissue . Hence , the three compartments are kinematically and dynamically interconnected . The model shows the evolution of the tumour in terms of invasion of the three compartments by cancer cells . The different fitness landscapes of cancer cells surviving in each compartment ( cancer cells intravasation , extravasation and metastasis formation ) represent the interactions between the cancer cells and the environment . We constrained ourselves to the early stages of breast cancer for the sake of simplicity . During the early stages , the concentration of cancer cells is limited , and tissue irregularities are negligible as well as the volumes of tissues interested by the disease; therefore , hypoxia effect can be disregarded , and cancer cell dynamics can be considered as a perturbative effect on the normal dynamics or on the homeostasis of the compartments . Under these constraints , also a self-seeding phenomenon causing a feedback signal from the bone niche toward the breast lobular duct is negligible . In our model , we also address the case of cancer progression when medical treatments are provided . More precisely , we focus our attention on the effects of bisphosphonates on cancer cells . Drugs represent a further form of coupling between the model compartments affecting the dynamics of the microenvironment and the cancer cells fitness landscapes ( see [33 , 34] for a description of fitness landscapes ) . Under the assumption that the mean field approximation holds true ( i . e . average over all the cell populations ) , the system dynamics is described in terms of ordinary differential equations ( ODE ) for molecule concentrations and cell population densities . Below , we present and discuss the system of ODEs for each compartment , we show the results obtained by numerical simulations and how we used the model in order to simulate different trajectories representing the disease evolution so to produce the respective survival probability curves . Branching processes and heterogeneity . The possibility that cancer cells , developed in the breast , form metastasis in the bone tissue is due to the occurrence of driver mutations causing overexpression of specific proteins which help the cells to accomplish such process . The numerosity of these populations with improved pro-metastatic behaviour depends on their capability of surviving in a given environment . Indeed , the development of a mutation occurs during asymmetric proliferation , a rare process in which a cell divides in two daughter cells where one of the two is equal to the parent , while the other presents a mutation . In our model , we focus on the number of cells that develop one given mutation or present simultaneously all the pro-metastatic mutations . Such multi-mutation path can be obtained through several paths , where a path is a sequence of mutations leading from the initial profile j = ( 0 , 0 , … , 0 ) ( no mutation ) to the final profile j′ = ( 1 , 1 , … , 1 ) ( all the genes in the path are mutated ) . When the considered system is characterized by various cell groups , each of which is different from another due to specific properties , the branching process is the process used to dynamically link these cells together , as well as , to describe the relation between groups in terms of parents and offspring . As a consequence , in the present case , the acquiring of new genetic mutations by cancer cells can be described in terms of a branching process [13] . Let xj ( t ) be the expected number of cells at time t with j = ( j1 , j2 , … , jm ) driver mutations . Each component ji of j , corresponding to the specific driver mutation i , can have value 1 if the mutation occurred , or zero otherwise . The length m of j is the minimum number of driver mutations necessary to perform an action ( i . e . forming metastasis in the bone ) . We assume that each driver mutation with integer index i ∈ [1 , m] can be caused by the variation of the state of a single gene . A mutational path , 𝓟 = j → j′ , corresponds to an ordered set of driver mutations s = ( i1 , … , ik , … , im ) . We can choose another path by rearranging the driver mutations in a different order . The path 𝓟 describes the passage from the cell population xj , where ji = 0 for all the elements i ∈ s , to the population xj′ , where ji = 1 for all the elements i ∈ s , through the sequence of acquired mutational steps: i1 , … , ik , … im . If we consider only the cases in which each driver mutation gives the cells a single specific pro-metastatic capability ( i . e . overexpression of a protein ) , and we also neglect the possibility for a cell and its progenies to loose such a capability due to future random mutations , then , for a specific path 𝓟 , the evolution of the sub-population xk ( t ) of CTCs being at the k-th mutational step is given by: ∂ t x k ( t ) = r b ( 1 − u 0 ) x k ( t ) ︷ symmetric proliferation + ( r b u 0 C k x k − 1 ( t ) ︷ k − th mutation − r b u 0 C k + 1 x k ( t ) ︷ ( k + 1 ) − th mutation ) − r d x k ( t ) ︷ apoptosis , ( 1 ) where rb is the cell proliferation rate , rd is the cell death rate , u0 is the punctual probability of mutation per unit of RNA expression level , Ck is the k-th gene expression level and u0 = um+1 = 0 . In Eq ( 1 ) , the integer index k ∈ [0 , m] , and the initial conditions are x0 ( 0 ) = 1 and xl ( 0 ) = 0 for any l ≠ 0 . In the RHS of Eq ( 1 ) , the first term takes into account the proliferation of cells when none of the m driver mutations occur . The second term in the parenthesis describes the asymmetric proliferation of both the sub-populations xk−1 and xk involved in k-th and ( k+1 ) -th driver mutation of the path 𝓟 , respectively . The last term represents the apoptotic process . It is important to notice that on the one hand , when a cell mutates , it branches , and on the other hand , heterogeneity in the gene expression of a cell sub-population influences the probability of branching , or more precisely , it affects the time rate at which similar cells mutate . In order to take into account the genetic cell heterogeneity , we could have introduced a second index in the cell sub-population so to discriminate them in sub-populations of sub-populations . Nevertheless , due to the lack of specific datasets ( at least to our knowledge ) for gene expression on single CTCs derived from breast cancer , it would be difficult to determine the corresponding probabilities of mutation . In this work , we focus on the tumour cells characterised by CD44 , CD47 and MET mutations [5] . Hence , we apply Eq ( 1 ) to describe all the possible paths leading from the initial profile ( 0 , 0 , 0 ) to the final profile ( 1 , 1 , 1 ) with all the three genes mutated . By solving the corresponding system of equations , the number of cells with a profile j⋆ corresponding to a single mutation at the ji-th position is given by: x j ⋆ ( t ) = x 0 ( 0 ) e r t ( 1 − e − r b u 0 C i t ) , where r = rb−rd . It is very convenient to rewrite the solutions independently from the specific traversed path and order of mutations in terms of sub-populations x 𝓓 ¯ ( t ) = ∑ { j ∣ j k = 1 ∀ k ∈ 𝓓 } x j ( t ) of cells which have acquired at least a specific sub-group of pro-metastatic behaviours 𝓓 . Rearranging the solution of Eq ( 1 ) , we have: x 𝓓 ¯ ( t ) = x 0 ¯ ( t ) ∏ k ∈ 𝓓 [ 1 − e − r b u 0 C k t ] , ( 2 ) where x 0 ¯ ( t ) = x 0 ( t ) = x 0 ( 0 ) e r t is the sum of all the sub-populations ( see Supplementary Information S1 Text for the mathematical derivation ) . From Eq ( 2 ) , the ratio x 𝓓 ¯ ( t ) x 0 ¯ ( t ) is a number in [0 , 1] representing the portion of cells with 𝓓 mutations . Identifying this ratio with the joint probability of a cell having those pro-metastatic properties derived by the 𝓓 mutations and under the condition that each driver-mutation occurs independently from the others , it follows that x 𝓓 ¯ ( t ) x 0 ¯ ( t ) = ∏ k ∈ 𝓓 Γ ( C k ) , where each Γ ( Ck ) corresponds to the probability of acquiring the pro-metastatic behaviour k . In order to describe the effects of EPCAM on CTCs , we consider a first part of the branching process , strictly related to EPCAM , which occurs on breast cancer cells and identifies the cells that can intravasate . Considering all the tumour cells that are about to enter the near blood vessels , they will have a small probability of proliferating as CTCs; hence , all their pro-metastatic behaviours are due to previous cell divisions and mutations . Consequently , the second part of the branching process ( related to CD47 , CD44 and MET ) occurs while tumour cells are still in the mammary duct . In the blood vessels , cells with low values of CD47 are attacked by the immune system and eliminated; therefore , only cells with sufficiently high CD47 proteins on their surfaces can evade the immune system . More precisely , the higher the concentration of CD47 , the longer the survival probability of CTCs is . The proteins CD44 and MET are involved in the extravasation process of circulating cells . Hence , their absence contributes to the permanence of the tumour cells in the circulatory system , and their presence contributes to characterise the component of CTCs population able to reach the bone and seed . The branching process strictly divides the CTCs population in sub-populations of circulating cells labelled by specific driver mutations which follow specific cell behaviours . Nevertheless , in the blood stream CTCs follow trajectories which are much less distinct . The causes are due to the interactions with the microenvironment which are responsible for the selection on the basis of the four proteins concentrations and give rise to variability and further heterogeneity . Based on the results in [5] , we consider only the four proteins overexpressed in CTCs with a high potential of generating bone metastasis: EPCAM , CD44 , CD47 and MET . Nevertheless , the method can be extended , or modified to include other proteins for other type of cancers . In Simulations subsection , we discuss what happens increasing or decreasing the minimum number of proteins necessary for creating metastasis . Mammary duct compartment . In order to describe the tissue dynamics as populations of healthy and mutated cells , we introduce a branching process based on the tissue scale model proposed in [2] where the cell populations are ρ ( ϕ , t ) and the index ϕ ∈ [0 , Φ] represents the cell state which is identified with the cell phenotype . We perform an order parameters reduction of that model neglecting the intra/extra-cell scale equations since the reactions involved are much faster than those at the tissue level . Hence , the TGF-β synthesised , activated and bounded with the receptors on the cells membrane R ec ⋆ ( ϕ ) , which are internalised so to generate the signalling inside the epithelial cells of the mammary duct , can be considered constant without significantly affecting the dynamics at larger scales . The TGF-β values are set equal to those at the equilibrium reached during the dynamical evolution of the tissue sub-system . We neglect also asymmetric proliferation and we constrain the cells to change their phenotype only in sequential steps . Using the same terminology in [2] , healthy cells have phenotype ϕ = 0 , pre-neoplastic cells are indexed as ϕ = 1 , tumoural cells correspond to ϕ = 2 and cells with aggressive tumoural behaviour and strong resistance to TGF-β inhibiting signalling have phenotype ϕ = Φ = 3 . We associate the cell phenotype to the TGF-β which is one of the proteins involved in the reduction of cell-to-cell E-cad connection . Hence , the activated TGF-β , when internalized , induces morphological changes on the cells which become more round and unconstrained . The TGF-β synthesised by cancer cells with index ϕ > 0 are more elevated than the quantity produced by healthy cells; therefore , the higher is the index ϕ , the higher is the chance it moves and/or positions itself unrespective of the morphological structure of the tissue . It is important to remark that the index ϕ is not related to the expression of proteins involved in the metastasis formation processes . The equation governing the cell sub-populations density ρ of the mammary duct epithelium tissue in a unit volume containing a cell and its nearest neighbour cells at time t and having phenotype ϕ is: ∂ t ρ ( ϕ , t ) = r p ( 1 − δ ϕ , 0 ρ 0 ˜ C ϕ − ∑ 0 ≤ η ≤ Φ η ≠ ϕ ρ ( η , t ) C η ) ( 1 − ∑ 0 ≤ η ≤ Φ ρ ( η , t ) C η ) ρ ( ϕ , t ) R ec ⋆ ( ϕ ) g ( ϕ ) ︷ symmetric cell proliferation + − ∑ ϕ = 0 ϕ ρ ( ϕ , t ) C ϕ r a R ec ⋆ ( ϕ ) g ( ϕ ) ρ ( ϕ , t ) ︷ TGF − β induced apoptosis + ∑ ϕ = 0 ϕ ρ ( ϕ , t ) C ϕ r m [ ( 1 − δ ϕ , 0 ) ρ ( ϕ − 1 , t ) − ( 1 − δ ϕ , Φ ) ρ ( ϕ , t ) ] ︷ cell mutation ∑ 2 1 2 + − ∑ ϕ = 1 ϕ − 1 ρ ( ϕ , t ) C ϕ r i n t ( 1 − δ ϕ , 0 ) ρ ( ϕ , t ) Γ ( C E P C ) ︷ cells entering the blood stream ∑ 2 1 2 . ( 3 ) The first term on the RHS of Eq ( 3 ) represents the proliferation process of cells . The factors in the parentheses take into account the maximum volumetric capacity Cϕ and the minimum capacity ρ 0 ˜ left to healthy cells by cell populations with ϕ > 0 , respectively . Cell proliferation is regulated by the TGF-β entering the cell ( R ec ⋆ ) , and the effect of this protein depends on the phenotype sensing exponent g ( ϕ ) . For non-tumoral cells ( ϕ < 2 ) , the capacity Cϕ expresses the average maximum number which can lay on the surface of the mammary duct , and for tumoral cells ( ϕ ≥ 2 ) , it represents the average maximum number of cells which can be hosted above , below and on the surface of the mammary duct . The second term describes the apoptosis induced by the TGF-β and depends on the phenotype sensing exponent g ( ϕ ) . For sub-population with phenotypes ϕ < Φ , the exponent g ( ϕ ) are non-negative and decreasing with ϕ; consequently , higher quantity of TGF-β inhibits proliferation and increases the apopotosis rate of these sub-populations . On the contrary , g is negative when ϕ = Φ . Therefore , TGF-β enhances the proliferation and reduces the apoptosis of the aggressive population highlighting the anti-oncogenic and pro-oncogenic role of TGF-β on different cell populations . The third terms expresses the mutation transition of a cell from a state ϕ to the state ϕ+1 , and the delta of Kronecker δα , β , which is 1 for α = β and 0 otherwise , implies that there are no cell which mutate to healthy cells and no further mutation occurs on cells in the state Φ . The last term on the RHS of Eq ( 3 ) is the first step of the branching process relative to the expression of cell membrane proteins favouring the formation of metastasis , and it describes the intravasation of cancer cells in the nearest blood vessels occurring at rate rint with probability Γ ( CEPC ) , where CEPC indicates the EPCAM gene expression level ( see Methods section and Branching process and survival probability prediction subsection ) . Overexpression of EPCAM increases the probability that a cell per unit of time passes through the nearest cells and reaches the circulatory system . Hence , because of driver mutations and over-production of TGF-β , cancer cells with EPCAM overexpression will easily unbind from the neighbour cells increasing their chance of reaching the local blood vessels and becoming CTCs; therefore , only cells with ϕ > 0 contributes in generating CTCs . It is worthy to notice that there is an obvious relationship between the cell density phenotypes ρϕ and the frequencies of the branching process populations xk . The index ϕ refers to mutations inducing TGF-β resistance , and the index k refers to mutations affecting the expression of the three specific markers on the membrane of bone metastasising cancer cells . The former mutations are related to the behaviours of the source of the CTCs ( the epithelial cells in the breast ) , while the latter are related to the behaviours of the CTCs . Hence , mutations altering the normal TGF-β signalling will propagate their effects on the concentration of the populations xk . Furthermore , in a complex biological process as the breast cancer cells metastasising in the bone , the order and the times at which all these mutations occur might play a relevant role . Nevertheless , for the sake of simplicity , we divided the two type of mutations ( indexed ϕ and k , respectively ) into two independent groups . Therefore , the two types of mutations occur in parallel introducing only a partial complexity in the system and disregarding further time interdependent causalities . CTCs in the bloodstream compartment . After the first step of the branching process depending on the expression of EPCAM , the remaning branching of cancer cells discriminates groups of CTC sub-populations with different genetic characteristics . All the sub-populations of cancer cells with overexpressed EPCAM , by definition , will intravasate , but not all of them will survive to the immune system control and not all of them will be able to extravasate and seed . The outcomes and the time of permanence of CTCs in the blood system depends on the properties of the CTCs themselves . Indeed , only a small component of all the CTCs with sufficient high pro-metastatic behaviours have high chances in forming metastasis . For example , CTCs with low CD47 are eliminated by the immune system control at rate rimm , and even though they might have high CD44 or MET , they will have a small chance of surviving and a short lapse of time to attempt extravasation . Similarly , cells with high CD47 will have a high chance of surviving the immune system attacks , but if they express low quantity of CD44 or MET proteins , they do not have a high probability of forming metastases . Nevertheless , since these CTCs remain longer in the circulatory system , they can attempt to extravasate more times with rate rext . The time evolution of the CTCs population is described by the following equation: ∂ t C T C ( t ) = r i n t V R O I ∑ ϕ = 1 Φ ρ ( ϕ , t ) Γ ( C E P C ) ︷ intravasating CTCs − ∑ a = 1 Φ 1 r i m m C T C ( t ) Γ ( C 47 ¯ − C 47 ) ︷ CTCs do not escape the immune system + − ∑ a a a r e x t C T C ( t ) Γ ( C 44 ) Γ ( C 47 ) Γ ( C M E T ) ︷ extravasating CTCs . ( 4 ) and the equation for the extravasating CTCs , CTCe , is: ∂ t C T C e ( t ) = ∑ a a a r e x t C T C ( t ) Γ ( C 44 ) Γ ( C 47 ) Γ ( C M E T ) ︷ extravasating CTCs − ∑ a a a r s e e d C T C e ( t ) ︷ seeding CTCs . ( 5 ) The first term on the RHS of Eq ( 4 ) represents the outward flux of epithelial cells escaping from the mammary duct into the bloodstream . The parameter VROI is used to relate the amount of CTC produced in the breast unit of volume , which encloses a cell and the neighbour ones , with the unit of volume of blood . We suppose that healthy cells ( ϕ = 0 ) do not generate CTCs , and the total amount of epithelial cells entering the blood stream VROI rint Γ ( CEPC ) increases with the synthesis of EPCAM , where Γ ( CEPC ) is the probability of having EPCAM overexpressed ( see Methods section and Branching process and survival probability prediction subsection ) . Moreover , by increasing the value of rint , it would be possible to describe the flux of clusters of CTCs presented in [21] . The second term in Eq ( 4 ) describes the CTCs incapacity of surviving against the attacks of the immune system . Therefore , it expresses the amount of CTCs eliminated by the immune defense . The probability that a cell does not survive depends on the expression level of CD47 . Indicating with C 47 ¯ the maximum value of CD47 overexpression ( see Methods section ) , higher is the difference C 47 ¯ − C 47 , lower is the probability of escaping the immune system attack . The third term deals with the effects of CD44 , CD47 and MET , which are the genes contributing to the invasiveness of CTCs and to the increased capability of CTCs in generating metastasis ( C44 , C47 and CMET correspond to the gene expression levels of CD44 , CD47 and MET respectively , see Methods section and Branching process and survival probability prediction subsection ) . Hence , the third term is the flux of CTCs exiting the bloodstream , and it appears with opposite sign on the RHS of Eq ( 5 ) . The second term in Eq ( 5 ) represents the cell seeding in the bone tissue at rate rseed . Activation and maturation of osteoclasts during bone repairing are highly reduced by bisphosphonates which strongly bind to hydroxyapatite and prevalently impregnates sites characterised by elevated bone remodelling activity [2] . Various works have confirmed the important role of bisphosphonates on cell types not directly involved with the osteoclastogenesis , but which are related to metastasis formations in the bone tissue highlighting the antioncogenic effects of the drug . Clinical trials have shown that breast cancer patients ( with or without bone metastasis ) increase their disease free survival when treated with bisphosponates [14] . Recently , studies on breast cancer have shed light on some of the underlying mechanisms of the interaction between bisphosphonates and different breast cancer cell lines [35–37] . The epithelial-mesenchymal transition ( EMT ) activated by the TGF-β induces the epithelial cells of the lobular duct to undergo cellular changes which cause the reduction of cell-to-cell contacts , the loss of cell polarity and the weakening of the cytoskeletrical structure [38] . The resulting effects are single cells detached from the surrounding tissue forming elongated laminopodia-like or filopodia-like structures , with increased capability of migrating and proliferating [39 , 40] . Cancer cells treated with bisphosphonates reduce the formation of protrusions , and promotes the expression of epithelial markers to the detriment of mesenchymal markers . In fact , bisphosphonates revert the EMT before cancer cells unbind from the nearest cells reducing the aggressiveness of unbound cells . In benign cancer breast tissue , epithelial cells in the lumen express low levels of the epithelial marker EPCAM , but in different tumour tissues , including breast cancer , cell adhesion molecules are overexpressed . This is in accordance with the statement in [15] that reversing of the EMT occurs prior to the detachment of luminal cells , and it is in agreement with the experiment in [5] . Bisphosphonates have two other major direct effects on cancer cells . First , they slow down the proliferation rate of breast cancer cells by arresting both the G1 and the S phase of the cell cycle [15] [41] . Second , the treatment is also responsible for increasing the apoptosis signalling of epithelial cells [37] . As reported by the authors in [42] , the apoptotic signalling induced by bisphosphonates is more enunciated in aggressive breast cancer cells while this effect is mitigated in low and non-tumourigenic cells; hence , this differentiated tumoural action causes bisphosphonates to contrast the effects due to overproduction of TGF-β and , at the same time , it supports the suitability of the bisphosphonates for a pharmacological intervention on patients with breast cancer [14] . The use of bisphosphonates in breast cancer therapy has been the target of intensive studies that have elucidated several aspects of the interaction between the drugs , cancer subtypes and pathways ( [14] , [15] , [35] , [36] , [37] , [43] , [44] , [45] , [46] ) . International clinical trials have found a striking differences of bisphosphonates treatment effects between pre-menopausal and post-menopausal women ( see [42] , [47] , [48] , [49] , [50] , [51] among others; Ingunn Holen ( Sheffield ) , Daniele Parenti ( Parma ) , Ignacio Tusquets Trias de Bes ( Barcelona ) and Andreas Trumpp ( Heidelberg ) personal communications ) . The time of permanence of CTCs in the circulatory system depends on their own characteristics allowing them to survive or extravasate . Indeed , while in the blood vessels , cancer cells are attacked by the immune system . In this environment , the TGF-β released by the tumoural cells activate the immune surveillance and help immune cells to detect the CTCs . At the same time , TGF-β signalling modulates platelet aggregation around the CTCs [52] [53] . Platelet aggregations shield CTCs and protect them against immune mediated clearance [54] . Furthermore , platelets create a micro-environment favouring the EMT of CTCs [53] [55] . It has been shown that high concentration of bisphosphonates in the circulatory system , for example by intravenous administration , strongly affect the survival of CTCs . Bisphosphonates reduce the activation and aggregation of platelets [56] [57] , and diminish the probability of platelets of both preferentially intercepting CTCs and fostering agglomeration around CTCs . Therefore , bishosphonates make CTCs more susceptible to immune cell attacks and direct bisphoshonates apoptotic signalling . Bisphophonates: treatment in the compartments . Bisphosphonates as zoledronate and alendronate have been adopted against osteoporotic diseases due to the inhibitory interaction of the drug against the mature osteoclasts in the resorption of the bone mineral matrix . Only recently , bisphosphonates have been used as an anti-cancer drug . In the work [14] , the authors studied the effects of zoledronic acid on a cohort of 33 patients with breast cancer and on a cohort of 20 patients with breast cancer and bone metastasis . The patients were treated by intravenous administration of 4 mg of bisphosphonate diluted in 100 ml of saline solution to study the variations of the CTCs during the following 14 days . In order to describe the time evolution of the concentration of bisphosphonate per 7 . 5 ml of blood ( BP ) , we introduce the following equation: ∂ t B P ( t ) = ∑ a a a H ( t − t 1 ) H ( t 2 − t ) B P ¯ t 2 − t 1 ︷ drug administration − ∑ a a a d B P B P ( t ) ︷ drug decay + − ∑ a a a r B P B P ( t ) [ σ 0 ∑ ϕ = 1 Φ ρ ( ϕ , t ) + C T C ( t ) + C T C e ( t ) ] ︷ drug absorption . ( 6 ) where B P ¯ is the total amount of bisphosphonate administered , [t1 , t2] is the dispense time interval and H ( t ) is the heaviside step function . The first term expresses the drug administration while the second one describes the drug decay at rate dBP . The last term takes into account the absorption of the drugs in all the compartments ( mammary duct—∑ ϕ = 1 Φ ρ ( ϕ , t ) , CTCs and bone—CTCe ( t ) ) . The terms of interaction do not present any time delay between the concentration of the drug and the population of cancer cells in each compartment . The reason is that the intravenous bisphosphonate rapidly diffuses in the circulatory system distributing homogeneously all over the systems while slowly relaxes toward the equilibrium; therefore , the drug reaches the compartments approximatively at the same time , and the concentration will remain equal among the compartments at any time . The interaction introduced by the bisphosphonates represents a further form of coupling between the compartments; indeed , if we could exclude one of the compartments , we would have less dispersion of bisphosphonates and an increase of drug absorbed by the remaining compartments . In order to reproduce the effects of the bisphosphonates on the CTCs in the blood system , in the ductal lobular unit ( DLU ) and in the bone multicellular unit ( BMU ) , we added the interaction terms between the administered bisphosphonates and the cancer cells in each compartment . These terms of interactions work as sink of cancer cells and they are linear in the concentration of the drug and the interacting cells . Therefore , we modified the ODEs system Eqs ( 3–5 ) by adding the following terms describing the therapy effect on the right hand side of: the equation for the cell populations in the mammary duct , Eq ( 3 ) − r B P B P ( t ) ρ ( ϕ , t ) , ( 7 ) the equation for the CTC in the circulatory system , Eq ( 4 ) − r B P B P ( t ) C T C ( t ) , ( 8 ) the equation for the CTCe in the bone niche , Eq ( 5 ) − r B P B P ( t ) C T C e ( t ) ( 9 ) For the full set of model equations see Supporting Information S2 Text . In [14] , the two cohort of patients ( immediately after they were diagnosed with breast cancer and matched the baseline characteristics for the study ) were treated with bisphosphonates and observed during the biological window of 14 days in which no chemotherapy , radiotherapy or other types of drugs were administered . During this period , the absence of further treatments highlighted the effect of bisphosphonates on the patients . The counting of CTCs in the blood during the biological window provided us with suitable measurements for tuning some parameters of our model in order to reproduce the CTCs enumeration . It is important to stress that we address the early stages of breast cancer; therefore , we describe cases which have the initial conditions corresponding to a healthy state , and we run the system until the cancer in the breast reaches a detectable size . Comparing the cases with and without bisphosphonates , in the former case , CTCs increase monotonically; while in the latter , the curve of CTCs abruptly decreases immediately after the drug administration ( see Simulations subsection ) . We used the model in order to identify the regions of time when the administration of bisphosphonates has the most effect . From our simulations , we have seen there is a specific time before which it holds true that the sooner the bisphosphonates are injected , the more the number of CTCs decreases . However , the time at which breast cancer is diagnosed represents the minimum time limiting when the bisphosphonates can be administered . In our multi-compartment model , the treatment allows us to investigate the response of the systems to the external perturbation . In the next subsections , we discuss how the drug permits us to investigate the connections between the cancer cell concentrations in the compartments and the survival probability of patients , as well as , the effects of further necessary mutations in the branching process and the delay in the immune system response . We consider two types of survival probabilities: the overall survival and the progression free ( PF ) survival . The former is the probability curve given by the measurement of times which goes from the moment of diagnosis of the disease to the occurrence of the event “death” . The latter is the curve describing the probability given by the measurement of times which goes from the administration of the treatment to the worsening of the disease . In order to create a survival distribution , we define two groups of parameters: the fixed parameters which describe the baseline characteristics common to all the elements in the sample set ( patients in the cohort ) and the stratification parameters identifying the properties of specific sub-groups of patients . The fixed parameters are set to the same constant values in all the simulations , while the stratification parameters are chosen randomly for each simulated patients . The time of the event “death” used in generating the overall survival is defined as the first occurrence time of the following relation: ρ ( 2 , t ) +ρ ( 3 , t ) +σ1 CTC ( t ) > ρ ( 0 , t ) +ρ ( 1 , t ) , which compares the sum of the most aggressive cell phenotypes ( ρ ( 2 , t ) and ρ ( 3 , t ) ) and CTCs with the sum of the populations of healthy and pre-neoplastic cells ( ρ ( 0 , t ) and ρ ( 1 , t ) respectively ) . The event of change of patient’s condition identifying the disease worsening in the progression free survival is set as the first occurrence time at which C T C e > C T C e ¯ holds true , where C T C e ¯ is the mean value of extravasating CTCs over all the sample , see Methods section , Tables 1 and 2 . All the times defined above are larger than the time t⋆ when the tumour reaches a detectable size which for medical and gene expression data corresponds to the time when patients are diagnosed with breast cancer . In the simulated trajectories , the time of detection corresponds to t⋆ ∼ 2 years of evolution of the system starting from a healthy initial condition and developing breast cancer . To highlight the role of the overexpression of the four proteins ( EPCAM , CD47 , CD44 and MET ) in the development of breast cancer and understand the relationship between CTCs ( with and without pro-metastatic behaviours ) and survival times , we compare simulated overall survival probabilities in the case of CD44 , CD47 and MET overexpressions versus the case in which the three CTCs’ markers are underexpressed . In the simulations , we focused only on CD44 , CD47 and MET overexpressions and underexpressions ( excluding EPCAM ) because we are considering the cancer cells that are already in the blood vessels . Hence , we are assuming that in all these cells EPCAM is already overexpressed . In particular , we randomly generated a first group of 12 patients with overexpressed gene markers and a second group of 12 patients with underexpressed values of the proteins . We defined a marker overexpressed ( underexpressed ) if its gene expression level is higher ( lower ) than the average value ( C44 , C47 and CMET ) extracted from the mRNA datasets , see Methods section and Table 1 . Fig 2a shows that patients with all the three markers overexpressed ( “Triple-positive high” patients [58] ) have a survival probability lower than patients with underexpressed markers ( “Triple-positive low” patients ) , in accordance with [5] . Adopting the same conditions used in the previous cases , in Fig 2b , we present the survival curves obtained with the administration of a single dose of 4 mg/100 ml of bisphosphonate for 15 minutes starting at time t⋆ . Even after the bisphosphonates administration , the “Triple-positive high” patients present a lower survival probability than “Triple-positive low” patients . However , including the drug administration the median overall survival times increase by about 2 years in both the cases ( from 3 . 36 to 5 . 77 years in “Triple-positive high” patients and from 3 . 82 to 5 . 91 years in “Triple-positive low” patients ) . In order to analyse the effects of high concentration of CTCs in the blood , we compare the overall survival probability obtained from the 24 patients simulated in the previous case . However , in this case , we divided the 24 patients into two different groups by separating those which at time 1 . 7 × 108 seconds ( time at which the amount of CTCs are sufficiently elevated ) have more than 5 CTCs per 7 . 5 ml of blood ( “CTC high” patients ) from those which have less or equal to 5 CTCs per 7 . 5 ml of blood ( “CTC low” patients ) . We predict the overall survival probability without treatment in Fig 3a and with a single dose of bisphosphonates in Fig 3b . The method of selection does not guarantee that the group with high number of CTCs has also an high values of extravasating CTCs and vice versa . Indeed , in both the figures , the survival curves for the two groups are very close one another . In Fig 4a and 4b , we compare the PF survival curves with and without administration of bisphosphonates in “Triple-positive high” and “Triple-positive low” patients , simulated as in the first case . As before , patients with all the three markers overexpressed have median PF survival time lower than “Triple-positive low” patients ( 3 . 11 and 4 . 01 years respectively ) . After treatment , the median PF survival times increase in both the case reaching 5 . 34 years for “Triple-positive high” patients and 5 . 58 years for “Triple-positive low” patients . In order to compare the survival prediction of the model with existing cancer data , we ran our model by using the data in GSE2034 . The dataset provides gene-expression profiles and survival times for a cohort of 286 primary breast cancer patients with distant metastasis . By using the normalised gene-expression profiles of EPCAM , CD44 , CD47 and MET , we computed the overall survival probability , as shown above , for each patient in the cohort . Fig 5 shows the comparison between the real survival curve provided by the analysed dataset and the survival curve predicted by the model . It is important to note that the predicted survival curve reported in Fig 5 has been shifted back of 2 years . The shifting was necessary because the starting time of the model corresponds to the first-mutation time , while the follow up period presented in the real data starts when the cancer has been diagnosed . Hence , the time computed by the model includes the time necessary to the tumour to reach a detectable size and to be diagnosed . Further analysis on the shifting-back time could be done to analyse the time when the first driver mutations took place . Simulations consist in numerically solving with the explicit Runge-Kutta method the set of ODEs Eqs ( 3–5 ) , which represent the general case , and the equations relative to the special cases Eqs ( 6–9 ) . The initial conditions adopted in all the simulations represent an individual in healthy state . More precisely , this means that in the DLU which is going to develop the disease , all the cells are healthy epithelial cells with no driver mutations , ρ ( 0 , 0 ) = 6 and ρ ( ϕ > 0 , 0 ) = 0 [2]; furthermore , the CTCs are zero and consequently also the extravasating CTCs are null , see Table 1 . In each case , the parameters are divided into fixed parameters and stratifying parameters . The former are set constant and are derived from the literature , while the latter are drawn from the distributions derived from mRNA datasets , or they are chosen equal to the average values depending on the type of simulation . All the averaged parameter values are listed in Table 2 . The Sensitivity Analysis of the parameters is shown in S3 Text and S1–S12 Figs . Assuming tumours of spherical shapes , we set the ductal carcinoma detectable size equal to a volume with an average diameter of 6 mm corresponding to 35% mammography screening test sensitivity [59] . The time t⋆ at which the tumour ( simulated with nominal values of the parameters , Table 2 ) reaches the detectable size is 7 × 107 seconds ( ∼ 2 years ) , and the ratio of tumour cells and healthy cells , ∑ ϕ > 0 Φ ρ ( ϕ , t ⋆ ) ρ ( 0 , t ⋆ ) , is 0 . 002 . For breast cancer cells with an averaged diameter of 50 μm , the total amount of cancer cells at detection time is of the order 106 [60] . Using the above estimated values , the maximum size of the simulated tumour is 5 cm . The solutions of the system of Eqs ( 3–5 ) are shown in Fig 6 . The parameters are all set to the averaged values as listed in Table 2 . We observe that before the time of detection the concentration of cancer cells ( ρ ( 1 , t ) , ρ ( 2 , t ) and ρ ( 3 , t ) ) in the breast is very small , and also the growth of the tumour is slow . Around the detection time , there is a change in the concentration of cancer cells which begins to increase rapidly . Aggressive cancer cells with ϕ = 3 have a concentration characterised by an initial plateau , corresponding to the time necessary to develop a TGF-β resistant phenotype , and a final plateau where the number of cancer cells are limited by a maximum capacity both in volume and resources . The CTCs generated by cancer cells in the breast begin to increase after tumoural and aggressive cancer cells develop; nevertheless , the growth of CTCs slows down due to the extravasation process and the response of the immune system against the CTCs . The decrease of pre-neoplastic cancer cells is due to the competition with more aggressive cancer cells ρ ( 2 , t ) and ρ ( 3 , t ) . As already discussed , TGF-β has a key role in the tumour evolution when aggressive cancer cells are present . To better understand this aspect , in Fig 6 , we show the differences in the dynamics of the cancer when we neglected the effects of the TGF-β . We simulated the system of Eqs ( 3–5 ) under the condition that the TGF-β produced by all the cell sub-populations is set to the minimum value R ec ⋆ ( ϕ ) = R ec ⋆ ( 1 ) , and we imposed all cells to respond to the TGF-β signaling in the same way R ec ⋆ ( ϕ ) g ( ϕ ) = R ec ⋆ ( 1 ) g ( 1 ) . Constraining all cells to produce the same minimal amount of TGF-β causes all four cell phenotypes to reach similar asymptotic steady values ( ∼ 1 . 7 for ρ ( 1 ) and ρ ( 2 ) and ∼ 2 . 3 for ρ ( 3 ) and ρ ( 4 ) based on their respective capacities , see Table 2 ) at times bigger than the simulated time Tf = 8*108 s . To have a complete picture of the multi-compartment model , we compare the system of ODEs Eqs ( 7–9 ) ( bisphosphonates treatment ) to the general case ( no treatment ) in order to understand how the drug affects the response . In the previous subsection , we have already seen how the administration of bisphosphonates affects the survival probability . In terms of mean field cell populations , we observe that healthy cells in the DLU ( ρ ( 0 , t ) ) are minimally affected by the bisphosphonates as indicated in [42]; on the contrary , healthy cells increase as a consequence of the reduction of competition between them and cancer cells . Pre-neoplastic and cancer cells in the mammary duct ( ρ ( 1 , t ) and ρ ( 2 , t ) ) drop immediately after the administration at time t1 forming a step . As shown in Fig 6 , the development of new cancer cells , after the administration of bisphosphonate , is delayed , and the effect is more enhanced in the aggressive sub-population ρ ( Φ , t1 ) . The intensity of the step on the sub-populations ρ ( ϕ > 0 , t1 ) depends both on the quantity of drug administered per unit of time and on time t1 . The bisphosphonates also delays the formation of CTCs . After the transition period following the treatment , the slope of the CTCs increases until it becomes parallel to the slope of the curve without drugs , while on the contrary , CTCe recovering is slower than in the case without bisphosphonates . In Fig 7 , we show the relative variations of CTCs and extravasating CTCs ( CTCe ) when we add the bisphosphonates compared with the case without treatment . The drug reduces the CTCs by more than 40% , and the extravasating CTCs are reduced by more than 60%; immediately after t1 , both types of CTCs have a rapid drop of 90% . Considering that CTCe are the only cells that metastasise , we can see that bisphosphonates have an effective role in contrasting the development of metastasis and delaying the death of the patients . In the previous cases ( with and without treatments ) , we have considered that metastases in the bone are originated only by breast cancer cells with four driver mutations relative to four proteins EPCAM , CD44 , CD47 and MET , and they correspond to four branching of the CTCs departing from the DLU . An important question to address is why is it necessary to have only four driver mutations to develop metastasis ? Is this a constraint related to the evolutionary process of cancer ? There is no easy answer for these questions , but with our model , we can answer the following questions . What happens if , for some reason , there is a further driver mutation in the CTCs metastasisation process which has been neglected or has not yet been included ? What happen if , instead , we overestimate the number of important driver mutations necessary to generate CTCs capable of forming metastasis in the bone niche ? In order to answer these questions , let us consider the first special case where we modify Eqs ( 4–5 ) , and we include a further factor x j x 0 in the extravasating flux corresponding to a generic driver mutation necessary to develop pro-metastatic behaviour as follows: ∂ t C T C ( t ) = ∑ a a a r i n t V R O I Γ ( C E P C ) ∑ ϕ = 1 Φ ρ ( ϕ , t ) ︷ intravasating CTCs − ∑ a a a r i m m C T C ( t ) Γ ( C 47 ¯ − C 47 ) ︷ CTCs do not escape the immune system + − ∑ a a a r e x t C T C ( t ) Γ ( C 44 ) Γ ( C 47 ) Γ ( C M E T ) Γ ( C G E N ) ︷ extravasating CTCs , ( 10 ) ∂ t C T C e ( t ) = ∑ a a a r e x t C T C ( t ) Γ ( C 44 ) Γ ( C 47 ) Γ ( C M E T ) Γ ( C G E N ) ︷ extravasating CTCs − ∑ a a a r s e e d C T C e ( t ) ︷ seeding CTCs . ( 11 ) where CGEN is the average gene expression of a necessary protein . In our simulation , we set CGEN equals to CMET to give a possible example . Solving the ODEs and comparing the results of the general case versus the special case with the modified ODEs Eqs ( 10–11 ) ) in Fig 8 , we see that after the time t⋆ , the number of CTCs remaining in the circulatory system is higher in the case with five driver mutations than in the case with four driver mutations . However , the further mutation causes a decrease of the number of CTCe which remains lower than 10 units per 7 . 5 ml of blood . This means the development of metastasis with a further branching related to the protein GEN is much less probable , and in average , the time needed to obtain a sufficient number of CTCs for the formation of metastasis before the occurrence of death increases drastically . Also the overall survival probability ( Fig 9 ) , reflects the previous stated behaviours showing that the slope of this special case ( “Quadruple-positive high” ) is larger than in the general case . Similarly , if we include only three driver mutations and , for example , we consider the effects of the MET protein unnecessary , than setting Γ ( CMET ) = 1 into Eqs ( 4 ) and ( 5 ) , we see that the extravasating CTCs increase much faster than in the previous cases reaching 10 units per 7 . 5 ml of blood few months after the time of detection , see Fig 8; hence , the formation metastasis occurs too prematurely resulting in inconsistency with clinical survival times , see Fig 9 . Considering the experimental difficulties and costs , the mathematical multi-compartment model proposed here represents a predictive tool that helps to identify the minimum number of driver mutations involved in the formation of metastasis by linking it with the survival probability . The attack of the immune system on the CTCs might change from person to person . The diversity , or the delay of the immune response given the same quantity of CTCs might result in metastasis formation even by cells with moderate or low CD47 protein on their surface . Hence , in the second special case , we address the delay of the immune system response by setting to zero the number of cells killed by the immune cells for the period of time [ti1 , ti2]; therefore , defining the function Δi ( t ) = [1−H ( t−ti1 ) H ( ti2−t ) ] , Eqs ( 4 ) and ( 5 ) become: ∂ t C T C ( t ) = ∑ a a a r i n t V R O I Γ ( C E P C ) ∑ ϕ = 1 Φ ρ ( ϕ , t ) ︷ intravasating CTCs − ∑ a a a r i m m C T C ( t ) Γ [ ( C 47 ¯ − C 47 ) Δ i ( t ) ] ︷ CTCs do not escape the immune system + − ∑ a a a r e x t C T C ( t ) Γ ( C 44 ) Γ ( C 47 ) ︷ extravasating CTCs , ( 12 ) ∂ t C T C e ( t ) = ∑ a a a r e x t C T C ( t ) Γ ( C 44 ) Γ ( C 47 ) ︷ extravasating CTCs − ∑ a a a r s e e d C T C e ( t ) ︷ seeding CTCs . ( 13 ) In this case , we aim to propose a variation of the model to describe those sub-groups of patients which develop metastases at very early stages . In Fig 10 , we see that the CTCs decrease occurs later in respect to the general case resulting in a higher amount of CTCs . The effects due to the reduced efficacy of the immune response in the interval of time [ti1 , ti2] is even more evident in the increase of the extravasating CTCs . Delay in the immune response might depend on many factors , for example , a reduced sensitivity of the immune system to detect CTCs and distinguish the “self” from the “non-self” . Presence of other diseases or depression are factors that can overwhelm the response of the immune system and its promptness . The sensitivity of the immune system is independent from the amount of CD47 , but depends on its threshold of detecting variations . This is why we included the model of this phenomenon separately as a special case .
We presented a mathematical model able to predict survival outcomes in metastatic breast cancer patients by using the gene expression profile of circulating cancer cells . The proposed model emphasises the strong relationship between CTCs and survival probability in metastatic breast cancer . In particular , the model integrates different aspects of physiology ( compartments ) , epidemiology ( survival ) and molecular information ( gene expression data ) ; this integration represents a semiquantitative but meaningful approach inching towards disease outcome predictability . The mathematical model contains novelties in all its parts such as the application of the branching models for molecular biomarkers in a dynamic model . TGF-β pathway and therapy couple several compartments; even drugs with a limited action on each compartment could have a larger effect on the whole system . This suggests that the administration of a cycling or multiple therapy could have even larger impact . Hence , this model could help understanding drug effectiveness in the breast-blood-bone system . Other aspects such as drug effectiveness for different therapy cycling administration or simultaneous administration of concurrent therapies could be incorporated . An important development of the model is the possibility to use molecular data and survival analysis to estimate how many unknown markers or how much the known markers should be overexpressed to give productive metastasis . In other words , if the number of productive markers are experimentally known , we can estimate the overexpression levels requires to match the CTCs heterogeneity . If they are not known we could estimate their number or their nature . Therefore , given molecular and survival data the model could be integrate with inferential approaches based on Cox network regression . We believe that the introduction of mathematical modelling of tumour microenvironments will help in bridging molecular and clinical evidences for bone metastasis derived from breast cancer . The model also provides a useful tool to predict survival probability under different conditions ( CTCs expression levels and different immune responses ) . Recent studies have highlight controversial results of the use of mammography for cancer survival ( known as “The Great Mammography Debate” , see [61–65] ) . As shown in this work , measuring CTCs in the blood stream can be an effective complementation to mammography . Therefore , we felt that the huge interest in CTC-based monitoring and therapies would benefit from this predictive mathematical model to analyse the probability of formation of CTCs and their interaction with different microenvironments . Moreover , the model and the software are effective in supporting hypothesis generation , mode of action understanding for candidate drugs , as well as supporting the construction of disease pathway interactions for different types of cancer and mathematical modelling for drug development projects .
In order to set the gene expression values for the parameters CEPC , C44 , C47 and CMET , we used four breast cancer microarray datasets downloaded from the Gene Expression Omnibus as raw . CEL files ( accession numbers: GSE4525 , GSE3494 , GSE2034 and GSE6532 ) . All the four datasets were generated by the Affymetrix HG-U133A platform . We averaged expression values for probes which map to the same gene and normalised each dataset with respect to one sample . Finally , we set CEPC , C44 , C47 and CMET equal to the average gene expression value ( 1 . 0828 , 1 . 0363 , 1 . 0971 and 1 . 0828 respectively ) . Moreover , we set C 47 ¯ ( the CD47 alert threshold ) as the maximum value of CD47 gene expression level among the four datasets ( C 47 ¯ = 3 . 2976 ) . This threshold is useful to regulate the effect of the immune system in Eq ( 4 ) , Eq ( 8 ) , Eq ( 10 ) and Eq ( 12 ) . Indeed , we need an upper-bound for the C47 expression level so that the difference C 47 ¯ − C 47 provides a measure of the CD47 expression level . Higher is the difference C 47 ¯ − C 47 , lower is the probability of evading the immune control . Further statistical analysis to characterise the overexpression of these markers ( statistical significance tests ) are reported in the Supporting Information ( S4 Text and S1 Table ) and in [66–75] . For the estimation of the parameters relative to the synthesis of TGF-β , the expression of their receptors and the amount of internalised TGF-β , we have used the model in [2] and analysed the following datasets ( accession numbers ) : GSE14548 , GSE33450 and GSE8977 . These datasets originate from experimental design on early stages breast cancer progression and tumour microenvironment . The raw files were processed and normalised individually by RMA package and library files provided by the Bioconductor project [76] . The Bioconductor package limma was also used to calculate average expression levels . We have used gene expression averaged quantities to better unveil the functions of the TGF-β in the cancer dynamics .
|
Breast cancer is caused by genetic mutations leading to uncontrollable cell reproduction . During successive proliferations , the progenies of tumour cells acquire further mutations increasing their heterogeneity . Among the tumoural mutated cells , there are some which present specific markers of increased aggressiveness and resistance . Sufficiently skilled cancer cells detach from the mammary epithelial cells , enter the blood vessels becoming circulating tumour cells , and reach the bone tissue where they seed . Breast cancer survival probability is the statistical representation of clinical data describing the times patients will survive after the diagnosis of the disease . Breast cancer survival is strongly correlated to genetic markers which increase the resistance and the invading skills of cancer cells but , it is poorly correlated to the amount of circulating tumour cells . To improve the understanding of the dynamic progression of the disease and assisting biologists in the interpretation of results and in experimental design , we developed a mathematical model encompassing the evolution of cancer cells originated in the breast , passing through the circulatory system , and invading the bone tissue based on survival probabilities of patients with different genetic expressions . The model allows us to strongly correlate the gene expression data of cancer cells with the survival probability by identifying the circulating tumour cells responsible for the formation of metastasis . Survival probabilities generated with the model are a useful tool to identify the presence of hidden markers not yet taken into consideration and study the effects of drugs’ administration .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
Modelling Circulating Tumour Cells for Personalised Survival Prediction in Metastatic Breast Cancer
|
ADP-ribosylation is a ubiquitous post-translational addition of either monomers or polymers of ADP-ribose to target proteins by ADP-ribosyltransferases , usually by interferon-inducible diphtheria toxin-like enzymes known as PARPs . While several PARPs have known antiviral activities , these activities are mostly independent of ADP-ribosylation . Consequently , less is known about the antiviral effects of ADP-ribosylation . Several viral families , including Coronaviridae , Togaviridae , and Hepeviridae , encode for macrodomain proteins that bind to and hydrolyze ADP-ribose from proteins and are critical for optimal replication and virulence . These results suggest that macrodomains counter cellular ADP-ribosylation , but whether PARPs or , alternatively , other ADP-ribosyltransferases cause this modification is not clear . Here we show that pan-PARP inhibition enhanced replication and inhibited interferon production in primary macrophages infected with macrodomain-mutant but not wild-type coronavirus . Specifically , knockdown of two abundantly expressed PARPs , PARP12 and PARP14 , led to increased replication of mutant but did not significantly affect wild-type virus . PARP14 was also important for the induction of interferon in mouse and human cells , indicating a critical role for this PARP in the regulation of innate immunity . In summary , these data demonstrate that the macrodomain is required to prevent PARP-mediated inhibition of coronavirus replication and enhancement of interferon production .
ADP-ribosylation is the post-translational covalent addition of a single ( mono-ADP-ribosylation or MARylation ) or multiple ( poly-ADP-ribosylation or PARylation ) subunits of ADP-ribose from NAD+ to a protein . This process is catalyzed by intracellular poly ( ADP-ribose ) polymerases ( PARPs ) also known as diphtheria toxin-like ADP-ribosyltransferases ( ARTDs ) , although extracellular cholera toxin-like ADP-ribosyltransferases ( ARTCs ) and some sirtuins also catalyze ADP-ribosylation [1] . Humans encode 17 PARPs , while mice encode 16 . Four ( PARP1 , PARP2 , PARP5a , and PARP5b ) are PARylating , while the rest are MARylating or nonenzymatic [2] . Like many post-translational modifications , ADP-ribosylation is reversible by enzymes such as poly ( ADP-ribose ) glycohydrolase ( PARG ) , ADP-ribosylhydrolases ( ARHs ) , and macrodomains [3–7] . ADP-ribosylation alters the structure and function of the substrate protein and has been implicated in several processes including DNA damage repair , cellular stress response , and virus infection [8] . For instance , PARylating PARPs , such as PARP1/2 and PARP5a/b regulate several nuclear processes such as DNA repair , transcription , and Wnt pathway activation [9 , 10] . Mono-ADP-ribosylating PARPs also play a variety of roles in cell biology . For example , PARP16 is required for activation of ER-stress pathways [11] , PARP14 binds to STAT-6 and enhances IL-4-dependent gene expression [12–15] , PARP9 augments IFNγ-dependent gene expression in macrophages [15] , and an unknown ADP-ribosylating enzyme inhibits RNAi following stress responses or poly ( I:C ) treatment [16–18] . In addition , two sirtuins , SIRT4 and SIRT6 , use ADP-ribosylation to inhibit glutamate dehydrogenase and promote DNA repair respectively [19 , 20] . PARPs have evolved rapidly , which may reflect their involvement in virus infections [21 , 22] . Consistent with this , several PARPs are known ISGs ( interferon stimulated genes ) , and many PARPs have been shown to be antiviral . PARP13 , also called zinc antiviral protein ( ZAP ) , inhibits replication of multiple classes of viruses , including retroviruses [23 , 24] , alphaviruses [22 , 25 , 26] , and filoviruses [27] by binding to viral RNA and recruiting the RNA-degrading exosome complex [28] . ZAP was also found to be required for the ADP-ribosylation and subsequent degradation of influenza A virus proteins PA and PB2 despite being catalytically inactive [29] . Atasheva et al . demonstrated that exogenous expression of PARPs 7 , 10 , and 12 had inhibitory effects on protein translation and on Venezuelan equine encephalitis virus ( VEEV ) virus replication using ADP-ribosylation-dependent and -independent mechanisms [30] . This group and others demonstrated that PARP12 could also inhibit vesicular stomatitis virus ( VSV ) , Rift Valley fever virus ( RVFV ) , and encephalomyocarditis virus ( EMCV ) [31 , 32] . PARP12 has been further shown to restrict Zika virus replication by promoting the degradation of viral proteins in an ADP-ribosylation-dependent manner [33] . PARP9 has been shown to complex with the DTX3L ubiquitin ligase to ubiquitinate the host histone H2BJ to enhance IFN signaling , resulting in the inhibition of RNA virus replication [34] . This complex also targets the EMCV 3C protease for ubiquitination and degradation [34] . While PARP9 was important for these activities , whether its ADP-ribosylating activity is required is unclear . In other cases , ADP-ribosylation is important for efficient virus replication as PARP1 inhibitors restrict the replication of several viruses such as herpesviruses , adenoviruses , and HIV [35–37] . PARP7 has both anti- and pro-viral activities as it binds to and induces degradation of Sindbis virus RNA [30 , 38] but also promotes influenza A virus infection by ADP-ribosylating TBK1 , which inhibits type I IFN ( IFN-I ) production [39] . Finally , sirtuins 1–7 , including the ADP-ribosylating sirtuins SIRT4 and SIRT6 , were shown to inhibit the replication of a wide variety of DNA and RNA viruses in MRC-5 cells [18] . However , the mechanism of viral inhibition by sirtuins and whether ADP-ribosylation is involved remains unknown . All Togaviridae , Coronaviridae , and Hepeviridae encode for a macrodomain protein that can remove ADP-ribose from proteins in vitro [40–42] . Several residues have been identified to be important for macrodomain activity , most of which fall in the ADP-ribose binding pocket [43] . Recombinant alphaviruses and hepatitis E virus ( HEV ) with mutations in these residues generally do not replicate well , while macrodomain-mutant coronaviruses generally replicate normally in tissue culture cells but are highly attenuated in vivo [40 , 41 , 44–50] . Collectively , these results suggest that viral macrodomains counter cellular ADP-ribosylation , but whether PARPs , ARTCs , sirtuins , or other unknown ADP-ribosyltransferases mediate ADP-ribosylation leading to the attenuation of macrodomain-mutant viruses is still unknown . Coronaviruses ( CoVs ) are enveloped positive-sense RNA viruses that cause severe disease in several mammalian species . Some , such as porcine epidemic diarrhea virus and porcine delta coronavirus , cause severe disease in agriculturally important animals , while others , such as severe acute respiratory syndrome ( SARS ) -CoV and Middle East respiratory syndrome ( MERS ) -CoV , cause lethal human diseases [51] . Mouse hepatitis virus strain JHMV ( termed MHV herein ) causes acute and chronic demyelinating encephalomyelitis and is the prototypical CoV used in many studies [52] . CoVs maintain several proteins that are important for blocking the innate immune response , including enzymes such as an O-methyltransferase ( nsp- ( nonstructural protein ) 16 ) , a deubiquitinase ( DUB ) ( nsp3 ) , an endoribonuclease ( nsp15 ) , and an ADP-ribosylhydrolase , the aforementioned macrodomain ( nsp3 ) [53–57] . Accordingly , the SARS-CoV macrodomain-mutant virus was shown to induce a robust pro-inflammatory cytokine response following infection both in vitro and in vivo [40] . In addition , SARS-CoV and human CoV 229E macrodomain-mutant viruses had increased sensitivity to IFN-I treatment in cell culture , demonstrating that the CoV macrodomain counters antiviral activities of ISGs [48] . Together , these studies suggest that IFN-stimulated ADP-ribosylation is countered by the conserved CoV macrodomain . Here , we show that PARP inhibitors specifically enhance the replication of MHV and decrease IFN production during macrodomain-mutant virus infection , further implicating the macrodomain in countering IFN-induced PARP-mediated antiviral ADP-ribosylation .
Mice infected with neurovirulent MHV develop lethal encephalitis [52] . To study the role of the viral macrodomain in MHV-induced neurological disease , we previously created a recombinant virus containing an alanine mutation of a highly conserved asparagine residue ( N1347A; herein denotated as N1347A MHV or virus ) . The location of the macrodomain within nsp3 of MHV and the specific location of this mutation have been previously reported [40 , 58] . This asparagine residue is present in all enzymatically active macrodomains , and the asparagine-to-alanine mutation either reduces ( CHIKV , HEV ) or abolishes ( SARS-CoV ) the ADP-ribosyl hydrolase activity of viral macrodomains [40–42] . Structurally , the location of this residue within the protein is highly conserved among CoV macrodomains and appears to coordinate the 2’ OH of the distal ribose to influence ADP-ribose binding , catalysis , or both [59 , 60] . N1347A MHV replicates poorly and does not cause disease in mice , indicating the importance of this residue for macrodomain function [50] . Macrophages play a central role in this protection as infection with N1347A virus caused severe disease if microglia were depleted from the brain [61] . To directly assess replication of the mutant virus in macrophages in vivo , we purified CD11b+ cells ( 80–90% purity , S1A Fig ) from the brains of mice infected with wild-type ( WT ) or N1347A MHV containing eGFP in place of ORF4 [50] . Of note , ORF4 is not required for optimal virus replication in vitro or in vivo [62] . Herein , WT virus refers to the previously described revN1347 virus where the WT macrodomain sequence was reinserted into the N1347A MHV BAC clone [50] . Similar to results found in whole brain [50] , N1347A virus replication , measured by viral genomic RNA ( gRNA ) content , was reduced compared to that of WT virus in isolated brain CD11b+ cells ( S1B Fig ) . Because the macrodomain is predicted to counter PARP-mediated ADP-ribosylation , we also analyzed whether PARP expression changed after infection . Consistent with a role for ADP-ribosylation , several PARPs were highly upregulated in these cells following infection with either WT or N1347A virus ( S1C Fig ) . To date , no cell culture system exists in which a CoV macrodomain-mutant virus has a robust growth defect . Since brain-derived CD11b+ cells are not practical for molecular studies , we next examined whether bone marrow-derived macrophages ( BMDMs ) could recapitulate the replication deficiency of N1347A MHV seen in vivo . To this end , we harvested murine bone marrow cells , differentiated them into macrophages , and infected these BMDMs with WT and N1347A virus at a low multiplicity of infection ( MOI ) ( Fig 1 ) . At 20 hours post infection ( hpi ) , BMDMs infected with N1347A virus had >10-fold lower titers and gRNA levels than those infected with WT virus ( Fig 1A and 1B ) . Furthermore , total viral protein levels were noticeably decreased in N1347A virus-infected cells when measured by immunoblotting for nucleocapsid ( N ) protein ( Fig 1C ) or by visually analyzing virus-encoded GFP expression and syncytia formation by fluorescence microscopy ( Fig 1D ) . Unfortunately , the large syncytia formed by infected cells made quantitative flow cytometric analysis of GFP-expressing cells unfeasible . Previously , we observed a diminished innate immune response in the brains of mice infected with N1347A virus [50] , likely reflecting diminished virus replication . To determine if inactivation of the macrodomain in MHV also inhibits the innate immune response in infected BMDMs , we quantified IFN-I and cytokine production after infection with either WT or N1347A virus . However , in contrast to the results seen in infected mice , both CXCL-10 and IFNβ transcript levels and secreted levels of IFNα and IFNβ protein were significantly increased at 12 hpi in BMDMs infected with N1347A virus compared to levels in WT virus-infected samples ( Fig 1E and 1F ) , suggesting that the CoV macrodomain inhibits the innate immune response in infected BMDMs . To determine if restriction of N1347A MHV replication is due to mechanisms upstream or downstream of IFN-I signaling , we infected BMDMs isolated from WT , MAVS-/- ( mitochondrial antiviral signaling protein ) , and IFNAR-/- ( interferon α/β receptor ) mice with WT and N1347A MHV . Loss of MAVS or IFNAR greatly reduced IFNβ mRNA levels compared to those in WT cells ( Fig 2A ) . Furthermore , the N1347A MHV-mediated increase in IFNβ mRNA seen in WT cells was ablated in MAVS-/- cells ( Fig 2A ) . However , while the replication deficiency of N1347A virus in WT BMDMs was retained in MAVS-/- cells , it was largely rescued in IFNAR-/- cells as measured by genomic RNA levels , viral titers , and visualized by GFP expression ( Fig 2B–2D ) . To determine if these in vitro findings correlate with virulence , we infected WT , MAVS-/- , and IFNAR-/- mice intranasally with WT and N1347A virus ( S2 Fig ) . N1347 virus-infected WT and MAVS-/- mice exhibited 100% survival and minimal differences in weight loss . In contrast , 60% of IFNAR-/- mice infected with N1347A virus succumbed to the infection and exhibited weight loss similar to that induced by WT virus . To further confirm that the factor ( s ) limiting N1347A MHV replication is downstream of IFN-I , we pretreated WT BMDMs with different doses of IFNβ for 8 hours and then infected cells with WT or N1347A virus ( Fig 2E ) . Increasing amounts of IFNβ further reduced N1347A virus titers compared to that of WT , demonstrating that ISGs restrict N1347A MHV replication . The macrodomain is an ADP-ribosylhydrolase , raising the possibility that PARP enzymes are responsible for the attenuation of the N1347A virus . PARPs are known ISGs , and the lack of upregulation of these proteins in IFNAR-/- cells or mice could explain the restoration of N1347A MHV-specific phenotypes . First , we determined whether PARPs were upregulated during infection by measuring PARP mRNA levels in BMDMs infected with WT and N1347A virus ( Fig 3A ) . Several PARP family members , including PARPs 7 and 9–14 , were upregulated in both WT and N1347A virus-infected BMDMs compared to mock-infected cells . Of note , our PARP13 primers were designed to detect all isoforms of PARP13 , and we were unable to detect PARP2 or PARP6 . Furthermore , the expression of these upregulated PARPs were also increased in infected MAVS-/- cells , while the expression of PARPs 9–12 and 14 were not increased in infected IFNAR-/- cells ( Fig 3B ) , suggesting these PARPs are ISGs , consistent with previous reports [31 , 32 , 63 , 64] . We confirmed this by treating WT and IFNAR-/- BMDMs with IFNβ and measuring PARP mRNA levels ( Fig 3C ) . As expected , PARPs 9–12 and 14 , in addition to PARPs 3 , 4 , and 5a , were upregulated following IFNβ stimulation in WT but not in IFNAR-/- BMDMs , consistent with previous studies [31 , 65] . Interestingly , PARP7 and PARP13 were induced by IFNβ but were also induced in infected IFNAR-/- cells , demonstrating that , while these PARPs are ISGs , they are also regulated by additional mechanisms during infection ( Fig 3B and 3C ) . We conclude that most PARPs are ISGs in primary murine macrophages and that PARPs 7 and 9–14 are highly expressed following CoV infection . To directly test whether PARPs inhibit CoV replication and facilitate IFN-I production in the absence of macrodomain ADP-ribosylhydrolase activity , we infected cells with WT and N1347A MHV prior to treatment with PARP inhibitors 3-aminobenzamide ( 3-AB ) and XAV-939 ( Fig 4 ) . 3-AB is a general PARP inhibitor , while XAV-939 was developed as a PARP5a/b inhibitor , but at higher concentrations it inhibits most , if not all , PARPs [66] . These inhibitors did not affect cell growth or metabolism at the concentration used in this study but diminished cellular PARylation , demonstrating efficacy ( S3A & S3C Fig ) . Importantly , both inhibitors significantly increased N1347A virus replication compared to vehicle treatment as visualized by GFP expression or measured by viral titers or genomic RNA levels ( Fig 4A–4C ) . Further , levels of IFNβ transcript produced in N1347A virus-infected cells treated with inhibitors were reduced to levels seen in WT virus-infected cells ( Fig 4D ) . Importantly , neither inhibitor had a significant effect on replication or IFN production in WT virus-infected cells , suggesting that PARPs are potentially counteracted by macrodomain activity during WT infection . As these inhibitors are known to target the PARP catalytic site [66] , these data indicate that PARP-catalyzed ADP-ribosylation is responsible for decreased replication and increased IFNβ production during N1347A MHV infection . To determine which individual PARP ( s ) restricts replication of N1347A MHV , we transfected BMDMs with siRNAs for the most highly expressed PARPs and examined the effects on WT and N1347A virus replication . We were unable to reliably knockdown PARP13 expression , so this PARP was excluded from our analysis . Knockdown of all other tested PARP mRNAs in both WT and N1347A MHV-infected BMDMs was observed ( S4 Fig ) . Knockdown of PARPs 7 , 9 , 10 , and 11 did not significantly increase WT or N1347A virus gRNA levels over that of control siRNA-transfected N1347A virus-infected cells ( Fig 5A , top row ) . In contrast , two independent siRNAs directed toward PARP12 and PARP14 significantly rescued N1347A virus gRNA levels without having a significant effect on WT virus ( Fig 5A , bottom row ) . Viral titers were also increased in cells transfected with siPARP12 . 2 or with siPARP14 . 1 , although the increased replication of N1347A virus in PARP14 knockdown cells did not reach statistical significance ( Fig 5B ) . To further examine the role of PARP14 in N1347A MHV infection , we infected BMDMs harvested from PARP14-/- and PARP14+/- mice ( S5 Fig ) . N1347A virus replication was not significantly different in PARP14-/- and PARP14+/- cells ( Fig 5C ) , suggesting that other PARPs or factors important for restricting replication may have compensated for or were lacking in the congenital absence of PARP14 . In an effort to resolve these differences , we utilized a recently developed PARP14 inhibitor , compound 8K , which targets the MARylating catalytic site of PARP14 ( Fig 5D ) [67] . While compound 8K did not affect cell viability or metabolism or inhibit global cellular PARylation ( S3B & S3C Fig ) , it significantly restored replication of N1347A virus in BMDMs ( Fig 5E ) . In general , these results support a role for PARP12 and PARP14 in blocking N1347A MHV replication . However , whether or not the catalytic domain of PARP12 is required for this role will require further validation . Because PARP14 impacts innate immune signaling pathways [12 , 63] , we also tested whether the PARP14 inhibitor affects IFN production . We found that , in addition to partially rescuing N1347A MHV replication ( Fig 5D ) , the PARP14 inhibitor 8K caused a reduction in IFNβ mRNA levels in both WT and N1347A virus-infected BMDMs ( Fig 6A ) . Consistent with these results , PARP14-/- in contrast to PARP14+/- cells showed no increase in IFN expression following infection with N1347A virus ( Fig 6B ) . Notably , overexpression of PARP14 , but not of GFP , was also sufficient for IFN induction in delayed brain tumor ( DBT ) cells , which normally express very low , if any , IFN ( Figs 6C & S6 ) . Overexpression of a PARP14 mutant with inactivating mutations H1698F , Y1730N , and E1810K in the catalytic triad of the PARP domain ( CM , described in [68] ) also induced IFN expression in DBT cells following transfection ( Fig 6C ) . This result suggests that PARP14 has both ADP-ribosylation-dependent and -independent mechanisms for regulating the IFN response . To determine whether PARP14 is also important for IFN induction in human cells , we used an assay based on IFN-mediated inhibition of VSV ( vesicular stomatitis virus ) replication . We engineered six PARP14 knockout ( KO ) clones of A549 cells and a pool of PARP14 KO cells in normal human dermal fibroblasts ( NHDFs ) using lentiCRISPR/CAS9-v2 ( Figs 6D & S7A ) . These cells were transfected with poly ( I:C ) for 6 hours , at which point the supernatant ( conditioned media ) was collected and transferred to naïve A549 cells . After a 2-hour incubation , we then infected these cells with an eGFP-expressing mutant recombinant VSV ( VSV M51R ) that is unable to suppress the innate immune response [69] and measured virus replication by plaque assay on Vero cells to quantify the amount of antiviral cytokines in the conditioned media ( Fig 6E ) . As expected , treatment with conditioned media from poly ( I:C ) -transfected WT A549 ( Fig 6F and 6G ) or WT NHDF cells ( S7B and S7C Fig ) induced a substantial decrease in VSV M51R replication compared to media from mock-transfected cells . In contrast , conditioned media harvested from stimulated PARP14 KO A549 ( Fig 6F and 6G ) or PARP14 KO NHDF ( S7B and S7C Fig ) cells showed only partial antiviral activity , resulting in ~3-fold enhanced replication of VSV M51R compared to cells that received conditioned media from stimulated WT cells . To confirm the antiviral activity in the conditioned media is due to IFN-I , we engineered cells functionally knocked out for IFNAR1 and tested whether the conditioned media had any effects on VSV M51R replication in these cells . The addition of exogenous IFNα to IFNAR KO A549 cells had no effect on VSV M51R replication , whereas 10 units of IFNα restricted VSV replication in WT cells , confirming that the IFNAR KO cells were defective for IFN-I signaling ( S7D Fig ) . Conditioned media from both WT and PARP14 KO cells treated with poly ( I:C ) still had a mild inhibitory effect on VSV M51R replication in IFNAR KO cells , suggesting that antiviral cytokines in addition to IFN-I were present in the media . However , a smaller difference in VSV replication between WT and PARP14 KO conditioned media-treated cells was observed in IFNAR KO recipient cells compared to WT recipient cells ( Figs 6G and S7C ) , indicating that the primary antiviral factor produced by stimulated cells was IFN-I . Taken together , these data indicate that PARP14 is necessary for efficient IFN-I production during CoV infection and poly ( I:C ) stimulation in both mouse and human cells respectively .
Here we show that PARPs , specifically PARP12 and PARP14 , are required to inhibit the replication of a macrodomain-mutant CoV and that PARP14 is also required for optimal IFN expression . Previous reports have shown that CoVs were unable to cause disease in the absence of viral macrodomain ADP-ribosylhydrolase activity and that this attenuation was associated with reduced viral loads and changes in pro-inflammatory cytokine expression in vivo [40 , 47 , 50] . However , it has been difficult to elucidate the details of CoV macrodomain function due to the lack of a cell culture system that recapitulates these phenotypes . Our results showed that BMDMs are useful for this purpose since N1347A MHV replicated poorly and induced a robust IFN response in these cells ( Fig 1 ) . BMDMs provide several advantages over other primary cells in that they are easily cultured , can be obtained in large numbers , have a fully functional innate immune response , and are productively infected by MHV [70] . PARPs have both antiviral and immunomodulatory roles [8] . Here we found that pan-PARP inhibitors both decreased IFN production and enhanced the replication of MHV lacking ADP-ribosylhydrolase activity but had no significant effect on WT virus ( Fig 4 ) . The antiviral properties of PARPS were dependent upon their ADP-ribosyltransferase activity since these inhibitors target the catalytic site of PARPs . These results suggest that the conserved CoV macrodomain functions to directly counter the activity of cellular PARP enzymes; however , this conclusion will require further validation . In addition , utilizing siRNA knockdown and a PARP14 specific inhibitor , we provide evidence that both PARP12 and PARP14 are necessary to restrict N1347A virus replication ( Fig 5A , 5B and 5E ) . PARP12 , which has high sequence similarity to PARP13 ( ZAP ) , has previously been shown to block both cellular and viral protein translation and inhibit virus replication by both ADP-ribosylation dependent and independent mechanisms [30] . On the other hand , PARP14 was not previously shown to inhibit virus replication but rather was shown to modulate both innate and adaptive immune responses [15 , 63 , 68 , 71] . Both proteins are known components of stress granules where they interact with a variety of proteins , including Argonaute 2 and PARP13 ( ZAP ) [16] . Interestingly , PARP14 was recently shown to ADP-ribosylate PARP13 in human cells [72] . It will be intriguing to determine: i ) if PARP12 and PARP14 localize to stress granules during a CoV infection , ii ) whether PARP13 is involved in restricting N1347A MHV replication , and iii ) how these PARPs specifically impact the CoV lifecycle . Most important will be to identify those proteins that are ADP-ribosylated by PARPs and targeted by the macrodomain during infection to fully understand this important host-virus interaction . Since the macrodomain is a subunit within a large transmembrane viral protein ( nsp3 ) , it is likely that the targets for de-ADP-ribosylation are cellular proteins in close vicinity to nsp3 . These include proteins located in replication-transcription complexes ( RTCs ) or other subcellular structures such as P-bodies or stress granules . Studies with chikungunya virus and Sindbis virus demonstrate that the alphavirus macrodomain also counters cellular ADP-ribosylation as mutants with decreased ADP-ribose binding and enzymatic activity had mild to severe replication defects [44 , 45 , 73] . However , following infection with chikungunya virus , very little PARP induction was observed in infected cells , and a MARylation inhibitor decreased WT virus replication [44] . Thus , while PARPs play an antiviral role during a CoV infection , their role during alphavirus infection may be more nuanced . About half of the PARP family members are induced by MHV infection in BMDMs ( Fig 3 ) , raising the possibility that PARPs in addition to PARP12 and PARP14 contribute to the antiviral response . Consistent with this , pan-PARP inhibitors reduced N1347A MHV-induced IFN-I levels ( Fig 4D ) much more effectively than the PARP14-specific inhibitor ( Fig 6A ) . Notably , the expression of PARP7 and PARP13 following infection were upregulated in infected IFNAR-/- BMDMs ( Fig 3B ) , suggesting that they are part of an IFN-independent cellular response to infection . In addition , knockdown of either PARP7 or PARP10 mRNA mildly reduced gRNA production in cells infected with either WT or N1347A virus ( Fig 5A ) , suggesting that they have pro-viral functions in MHV-infected BMDMs , perhaps analogous to alphavirus-infected cells but in contrast to other virus infection models where they are known to be antiviral [30 , 38] . Finally , using a recently developed PARP14-specific inhibitor and PARP14-/- BMDMs ( Fig 6A and 6B ) as well as human PARP14 KO A549 and NHDF cells ( Fig 6D–6G , S7A–S7C Fig ) , we found that PARP14 was required for robust IFN-I production during CoV infection or poly ( I:C ) stimulation . These data are consistent with a recent report detailing a role for PARP14 in IFN-I induction following LPS stimulation of RAW 264 . 7 cells and BMDMs [63] . In that study , the absence of PARP14 did not affect IRF-3 translocation to the nucleus but rather altered histone modification and reduced pol II recruitment to specific ISG promoters in the nucleus . Consistent with these data , we also showed that PARP14 overexpression induced IFN-I transcription in DBT cells ( Fig 6C ) . PARP14 with three inactivating mutations in the catalytic triad also induced IFN expression ( Fig 6C ) , suggesting that PARP14 uses both ADP-ribose-dependent and independent mechanisms to regulate the IFN response during CoV infection . This result may not be surprising since a PARP14 fragment without the catalytic domain has been shown to activate STAT-6 dependent transcription , but the catalytic domain was required for maximal activation [12] . Many of the signaling proteins involved in the production of IFN-I and ISGs are known to be regulated by post-translational modifications such as phosphorylation and ubiquitination [74] . Recently , several of these proteins have been demonstrated to be regulated by PARPs as well [13 , 15 , 39 , 63 , 75 , 76] . These findings , along with our data , unveil previously unknown mechanisms of innate immune regulation and suggest that PARP-dependent ADP-ribosylation will impact several proteins in these pathways . Clearly , the next step will be to identify the targets of individual PARPs and determine precisely how ADP-ribosylation regulates the innate immune response .
Animal studies were approved by the University of Iowa Institutional Animal Care and Use Committee ( IACUC ) as directed by the Guide for the Care and Use of Laboratory Animals ( Protocol #6071795 ) . Anesthesia or euthanasia were accomplished using isoflurane and ketamine/xylazine or ketamine/xylazine , respectively . Delayed brain tumor ( DBT ) ( propagated in Perlman laboratory since 1983 ) , normal human dermal fibroblasts ( NHDF ) ( ATCC ) , A549 ( ATCC ) , Vero ( a gift provided by Robert Krug , University of Texas at Austin ) , BHK-21 ( a gift provided by Dr . Emin Ulug , University of Texas at Austin ) and HeLa cells expressing the MHV receptor carcinoembryonic antigen-related cell adhesion molecule 1 ( CEACAM1 ) ( HeLa-MHVR ) ( a gift from Dr . Thomas Gallagher , Loyola University Chicago ) were grown in Dulbecco’s Modified Eagle Medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) , 100 U/ml penicillin and 100 μg/ml streptomycin . Bone marrow-derived macrophages ( BMDMs ) sourced from WT , MAVS-/- , IFNAR-/- , PARP14+/-and PARP14-/- C57BL/6 mice were differentiated by incubating cells with 10% L929 cell supernatants and 10% FBS in Roswell Park Memorial Institute ( RPMI ) media for seven days . Cells were washed and replaced with fresh media every day after the 4th day . pcDNA3-GFP was previously described [40] , pcDNA3-FLAG/PARP14 and pcDNA3-FLAG/PARP14-CM were obtained as a generous gift from Dr . Mark Boothby ( Vanderbilt University , Nashville TN ) , and pSport6-FLAG/PARP12 was obtained as a generous gift from Dr . Oberdan Leo ( Universite Libre de Bruxelles , Gosselies , Belgium ) . Mouse IFNβ and human IFNα was purchased from PBL Assay Science ( 12401–1 and 11200–1 ) . 3-aminobenzamide ( 3-AB ) ( A4161 ) and XAV-939 ( A1877 ) were purchased from APExBIO . Compound 8K was previously described [67] . Following differentiation , BMDMs were treated with the indicated compounds for 24 hours . Cell viability was assessed using a Vybrant MTT Cell Proliferation Assay ( Thermo Fisher Scientific ) following manufacturer’s instructions . Pathogen-free C57BL/6 WT and IFNAR-/- mice were purchased from Jackson Laboratories , and MAVS-/- mice were obtained as a generous gift from Dr . Michael Gale ( University of Washington , Seattle , Washington ) . These mice were bred and maintained in the animal care facility at the University of Iowa . PARP14-/- mice were a generous gift from Dr . Mark Boothby ( Vanderbilt University , Nashville , Tennessee ) and were bred and maintained in the animal care facility at Harvard Medical School . Recombinant WT ( rJIA-GFPrevN1347 ) and N1347A ( rJIA-GFP-N1347A ) MHV were previously described [50 , 77] . Both viruses expressed eGFP . Virus stocks were created by infecting ~1 . 5×107 17Cl-1 cells at an MOI of 0 . 1 plaque-forming units ( PFU ) /cell and collecting both the cells and supernatant at 20 hpi . The cells were freeze-thawed , and debris was removed prior to collecting virus stocks . Virus stocks were quantified by plaque assay on Hela-MHVR cells . BMDM cells were infected with MHV at an MOI of 0 . 1 PFU/cell with a 45–60 min adsorption phase . Infected cells were then incubated and collected at the indicated timepoints . Recombinant eGFP-expressing mutant VSV ( rM51R-M-EGFP ) was obtained from Dr . Douglas Lyles , Wake Forest School of Medicine , Winston-Salem , NC [78] . Virus stocks were amplified in BHK-21 cells and quantified by plaque assay on Vero cells . For mouse infections , 5-8-week-old mice were anesthetized with ketamine/xylazine and inoculated intranasally with 3×104 PFU of virus in 12 μL DMEM . Mice were either monitored for weight loss or were sacrificed at 4–6 days post infection ( dpi ) to harvest the brain tissue . Brain tissues were homogenized , and leukocytes were isolated as previously described [79] . CD11b+ leukocytes from brain tissues were purified using CD11b MicroBeads ( Miltenyi Biotec ) as per manufacturer’s instructions . For surface staining , brain leukocytes were treated with Fc block ( CD16/32 , 2 . 4G2 ) and then incubated with specific mAbs or isotype controls . Monoclonal antibodies used for these studies included CD45-PECy7/FITC ( 30-F11 , Biolegend ) and CD11b-e450 ( M1/70 , Thermo Fisher Scientific ) . Cells were analyzed using a FACS Verse flow cytometer ( BD Biosciences ) . All flow cytometry data were analyzed using FlowJo software ( Tree Star , Inc . ) . 5×105 DBT cells were transfected with 0 . 5 μg total plasmid expressing GFP , PARP12 , or WT or CM PARP14 using PolyJet In Vitro Transfection Reagent ( SignaGen Laboratories ) as per manufacturer’s protocol . Media was replaced 8 hours after transfection , and cells were incubated for 16 hours before collection . For siRNA knockdown , DsiRNA oligonucleotides were purchased from Integrated DNA Technologies ( IDT ) . Sequences are listed in S1 Table . Negative control DsiRNA was also purchased from IDT and is listed as a non-specific control . BMDMs were transfected with 50 pmol/ml of siRNA with Viromer BLUE ( Lipocalyx ) following the manufacturer’s protocol . Media was replaced 4 hours after transfection , and cells were further incubated for 24 hours prior to infection . Three independent siRNAs were acquired for each gene , and the one giving the best knockdown was used for viral replication assays , except for PARPs 12 and 14 assays , which utilized 2 independent siRNAs . PARP14 KO A549 and NHDF cells and IFNAR KO A549 cells were generated using the lentiCRISPR/CAS9-v2 system[80] . For both PARP14 and IFNAR1 pLentiCRISPR/CAS9-v2 constructs , a pair of oligos were phosphorylated , annealed , and inserted into pLentiCRISPRv2 ( Addgene , plasmid 52961 ) between BsmBI restriction sites as described[80] . The sequences of the oligos are listed in S2 Table . The packaging of lentiviruses and transduction were performed as described previously [81] . In summary , pooled PARP14 KO NHDF cells or PARP14 KO or IFNAR KO A549 cell lines were generated by transduction with lentiviruses with lentiCRISPR/CAS9-v2-gRNAs . 72 hours post transduction , NHDF and A549 cells were treated with puromycin ( 2μg/ml ) for 3 days . A549 cells were diluted to obtain single cell clones , while NHDF cells were pooled . PARP14 KO clones were screened by immunoblotting for PARP14 . For IFNAR KO cells , ablation was confirmed by assessing infection sensitivity to IFNα . NHDF and A549 cells transduced with irrelevant gRNA ( gRNA-NC ) were used as negative controls . WT or PARP14 KO A549 or NHDF cells were seeded in 12-well plates ( 105 cells/well ) . 16 hours later , cells were mock transfected ( wild-type cells ) or transfected with 2 μl Lipofectamine 2000 reagent ( Invitrogen ) and 500 ng poly ( I:C ) ( HMW , InvivoGen ) in 200 μl of DMEM in duplicate as per manufacturer’s protocol ( for recipient WT and IFNAR KO A549 cells ) . To reduce the effect of the remaining poly ( I:C ) in the conditioned media , cell media were replaced with fresh media 2 h post transfection . At 6 h post transfection , cell media was collected and centrifuged to remove cell debris , and supernatants were collected as conditioned media . Recipient WT or IFNAR KO A549 cells were then incubated with one of the duplicate samples of conditioned media for 2 h prior to infection with eGFP-VSV ( rM51R-M-EGFP ) at an MOI of 1 PFU/cell . VSV titers in the supernatants were determined by counting plaques on Vero cells . WT or IFNAR KO A549 cells were incubated with 1 ml of IFNα-containing media ( 0 , 1 , 10 , 100 , 1000 units/ml ) for 4 h prior to infection with eGFP-VSV ( rM51R-M-EGFP ) at an MOI of 1 PFU/cell . At 16 hpi , infected cells were monitored under an AMG-EVOS FL Digital Inverted Fluorescence Microscope ( software version 15913 ) . GFP fluorescence microscopy was performed with a light cube of GFP ( Ex 470 nm/Em 525 nm ) under a 4X objective lens ( the scale bar on the image represents 1000 μm ) , and images of six different fields were taken for each condition . The quantification of GFP-VSV-infected cells was performed using ImageJ software ( NIH ) to count GFP ( green ) points . The means ± SEM of percentages of infected cells from six different fields ( three each from two different replicate wells of a 12-well-plate ) are shown . RNA was isolated from cells using Trizol ( Thermo Fisher Scientific ) via phase separation or Direct-Zol column purification ( Zymo Research ) as per manufacturer’s instructions . cDNA was prepared using MMLV-reverse transcriptase as per manufacturer’s instructions ( Thermo Fisher Scientific ) . Quantitative PCR ( qPCR ) was performed on a QuantStudio3 real-time PCR system using PowerUp SYBR Green Master Mix ( Thermo Fisher Scientific ) . qPCR primers are listed in S3 Table . Primers were designed to span an exon-exon junction when possible to prevent quantification of any residual genomic DNA . All qPCR reactions were run with a -RT control to confirm the lack of significant DNA contamination . Cycle thresholds were normalized to that of housekeeping gene hypoxanthine-guanine phosphoribosyltransferase ( HPRT ) by the following equation: ΔCT = CT ( gene of interest ) —CT ( HPRT ) . All results are shown as a ratio to HPRT calculated as -2ΔCT . Total cells were lysed in sample buffer containing SDS , β-mercaptoethanol , protease/phosphatase inhibitor cocktails ( Roche ) , PMSF , and universal nuclease ( Thermo Fisher Scientific ) . Proteins were resolved on an SDS polyacrylamide gel , transferred to a polyvinylidene difluoride ( PVDF ) membrane , hybridized with a primary antibody , reacted with an infrared ( IR ) dye-conjugated secondary antibody , visualized using a LI-COR Odyssey Imager , and analyzed using Image Studio software ( LI-COR ) . Primary antibodies used for immunoblotting include anti-FLAG monoclonal antibody ( M2 , 1:500 , Millipore-Sigma ) ; anti-PARP14 polyclonal antibodies ( C-1 , 1:500 , Santa Cruz Biotechnology; HPA012063 , 1:1000 , Millipore-Sigma ) ; anti-PAR monoclonal antibody ( 10H , 1:500 , Trevigen ) ; rabbit anti-MHV polyclonal antibody ( 1:10 , 000 ) [82]; and anti-actin monoclonal antibody ( AC15 , 1:10 , 000 , Abcam ) . Secondary IR antibodies were purchased from LI-COR . Supernatants from infected cells were collected at 12 hpi , and protein levels of IFNα and IFNβ were determined using the Luminex Protein Assay ( Thermo Fisher Scientific ) according to the manufacturer’s instructions . BMDMs plated on glass cover slips were infected with GFP-expressing MHV . At 14 hpi , cells were fixed with 4% paraformaldehyde , and coverslips were transferred to a glass slide . Vectashield Antifade Mounting Media with DAPI ( Vector Laboratories ) was applied , and a second coverslip was overlaid . Slides were visualized on an Olympus IX-81 inverted fluorescence microscope ( Olympus ) , and images were analyzed using SlideBook software ( Meyers Instruments ) . An unpaired two-tailed Student’s t-test was used to assess differences in mean values between groups , and graphs are expressed as mean ± SEM . MHV titers are presented as geometric mean ± SEM . The n value represents the number of biologic replicates for each figure . The n for WT and N1347A virus-infected samples were the same unless otherwise indicated . Significant p values are denoted with *p≤0 . 05 , ** p≤0 . 01 , *** p≤0 . 001 .
|
ADP-ribosylation , an understudied post-translational modification , facilitates the host response to virus infection . Several viruses , including all members of the coronavirus family , encode a macrodomain to reverse ADP-ribosylation and combat this immune response . As such , viruses with mutations in the macrodomain are highly attenuated and cause minimal disease in vivo . Here , using primary macrophages and mice infected with a pathogenic murine coronavirus , we identify PARPs , specifically PARP12 and PARP14 , as host cell ADP-ribosylating enzymes important for the attenuation of these mutant viruses and confirm their importance using inhibitors and siRNAs . These data demonstrate a broad strategy of virus-host interactions and indicate that the macrodomain may be a useful target for antiviral therapy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"vesicular",
"stomatitis",
"virus",
"pathology",
"and",
"laboratory",
"medicine",
"gene",
"regulation",
"pathogens",
"microbiology",
"light",
"microscopy",
"viruses",
"rna",
"viruses",
"microscopy",
"adp-ribosylation",
"research",
"and",
"analysis",
"methods",
"small",
"interfering",
"rnas",
"proteins",
"medical",
"microbiology",
"fluorescence",
"microscopy",
"gene",
"expression",
"microbial",
"pathogens",
"guide",
"rna",
"chemistry",
"viral",
"replication",
"biochemistry",
"rna",
"rhabdoviruses",
"nucleic",
"acids",
"virology",
"viral",
"pathogens",
"genetics",
"interferons",
"biology",
"and",
"life",
"sciences",
"chemical",
"reactions",
"physical",
"sciences",
"non-coding",
"rna",
"organisms"
] |
2019
|
The coronavirus macrodomain is required to prevent PARP-mediated inhibition of virus replication and enhancement of IFN expression
|
The sexual Fus3 MAP kinase module of yeast is highly conserved in eukaryotes and transmits external signals from the plasma membrane to the nucleus . We show here that the module of the filamentous fungus Aspergillus nidulans ( An ) consists of the AnFus3 MAP kinase , the upstream kinases AnSte7 and AnSte11 , and the AnSte50 adaptor . The fungal MAPK module controls the coordination of fungal development and secondary metabolite production . It lacks the membrane docking yeast Ste5 scaffold homolog; but , similar to yeast , the entire MAPK module's proteins interact with each other at the plasma membrane . AnFus3 is the only subunit with the potential to enter the nucleus from the nuclear envelope . AnFus3 interacts with the conserved nuclear transcription factor AnSte12 to initiate sexual development and phosphorylates VeA , which is a major regulatory protein required for sexual development and coordinated secondary metabolite production . Our data suggest that not only Fus3 , but even the entire MAPK module complex of four physically interacting proteins , can migrate from plasma membrane to nuclear envelope .
Eukaryotic organisms communicate between cell surface and nucleus to respond to environmental signals . The mitogen-activated protein kinase ( MAPK ) module consisting of a cascade of three protein kinases represents a highly conserved eukaryotic signal transduction system present from yeast to man . MAP3K phosphorylates a second kinase , MAP2K which itself phosphorylates the MAPK . This final kinase phosphorylates nuclear target proteins to activate appropriate gene expression [1] , [2] . The sexual pathway of the budding yeast Saccharomyces cerevisiae represents a paradigm for signal transduction in eukaryotes [3]–[5] . This MAP kinase pathway responds to pheromones and induces differentiation processes which trigger sexual mating of yeast [4] , [6] . The central complex of MAP3K Ste11 , MAP2K Ste7 and MAPK Fus3 is assembled on the scaffold protein Ste5 as a hub to keep these kinases in a close proximity for enhanced relay of phosphorylation and thereby controls the flow of information [7] . Binding of pheromone to the transmembrane receptors Ste2 or Ste3 , which are coupled to guanine nucleotide binding proteins ( G protein , G protein coupled receptor: GPCR ) , initiates signal transduction . This induces the release of the Gβγ subunit from the trimeric Gαβγ protein . The Ste5 RING domain binds to activated free Gβγ complex and recruits the MAP kinase module Ste11-Ste7-Fus3 to the membrane [8]–[10] in close distance to the p21 activated kinase ( PAK ) Ste20 . Preactivated Ste20 is localized in the membrane and initiates the kinase cascade system by phosphorylating the MAP3K Ste11 [4] . Ste50 represents a second adaptor which binds to the Opy2 membrane anchor and provides membrane association of the entire MAPK module . Ste50 mediated membrane localization is required for Ste11 activation [11] , [12] . The information is transmitted as phosphate signal from Ste11 via Ste7 to the MAPK Fus3 . According to the current model phosphorylated Fus3 is released from the Ste5 scaffold complex and leaves the membrane associated complex [13]–[15] . Phosphorylated Fus3 crosses the cytoplasm and enters the nucleus where it phosphorylates target transcription factors as Ste12 . Ste12 is necessary to activate the sexual pathway and also controls developmental processes [4] , [5] . Pheromone pathway genes have been studied in various fungi and are not only involved in sexual reproduction but also in fungal pathogenicity [16]–[21] . The Fus3 MAPK module is highly conserved in filamentous fungi with the exception that homologs for Ste5 are absent [22] , [23] . In the self-fertile model fungus Aspergillus nidulans , the Ste11 MAP3K homolog SteC ( AnSte11 ) [24] , the Fus3 MAPK homolog MpkB ( AnFus3 ) [25] , and the Ste12 homolog for the transcription factor SteA ( AnSte12 ) [26] are necessary for sexual fruiting body formation , suggesting that there are similarities in the molecular function of the MAPK signal transduction as in yeast . A . nidulans grows vegetatively as a filament . When placed on a surface , after germination of the spores at least 12 hours of growth is required to establish developmental competence in response to external signals [27] . There are two developmental options: light supports the asexual and inhibits the sexual developmental pathway ( Figure 1A ) . AnFus3 is not only required for sexual development but also for the control of secondary metabolism which is a typical feature of many filamentous fungi [28] . Sexual development of A . nidulans is coordinated with the production of secondary metabolites , including mycotoxins . This coordination requires velvet domain proteins which are common for filamentous fungi but absent in yeast [6] . The velvet heterodimers VeA-VelB and VosA-VelB have different developmental functions . VeA-VelB heterodimer promotes sexual development whereas VelB-VosA dimer inhibits asexual differentiation . Association of the putative methyltransferase LaeA [29] with the VelB-VeA heterodimer , which makes the VelB-VeA-LaeA trimeric complex , coordinates development and secondary metabolism [6] , [30] , [31] . Comparison of the intracellular molecular mechanism of signal transduction of Fus3 MAPK of yeast and A . nidulans revealed that AnFus3 MAPK can reach the nuclear envelope in a complex with other proteins of the MAPK module , including the adaptor protein AnSte50 . Only AnFus3 enters the nucleus and phosphorylates VeA , which elucidates a novel link between MAPK and velvet domain proteins that act as control elements at the interface of fungal development and secondary metabolism .
S . cerevisiae Fus3 interacts with transcription factor Ste12 that activates the mating pathway . The A . nidulans MAP kinase AnFus3 [MpkB] also controls sexual development [25] , [28] , [32] . Tagged AnFus3 recruited the transcription factor AnSte12 [SteA] by tandem affinity purification ( TAP ) only when the fungus was induced for sexual development but not during vegetative filamentous growth or asexual development ( Figure 1B , Table S1 ) . Endogenously expressed AnFus3::sGFP was functional ( Figure S1 ) and immunoprecipitation of the fusion protein was able to enrich the SteA protein in a sexually induced culture ( Table S1 ) . The AnFus3-SteA interaction was further verified by bimolecular fluorescence complementation ( BiFC ) and was observed in fungal nuclei ( Figure 1C ) . This corroborates that the interaction between kinase and transcription factor is conserved from yeast to filamentous fungi . Due to their similar roles in development and secondary metabolism [28]–[30] , we examined whether AnFus3 interacts with the velvet domain proteins and LaeA . AnFus3 interacted in vivo in a BiFC assay with LaeA and subsequently with VeA , but not with VelB . In addition , AnFus3 interacted with VosA ( Figure 1D ) . VosA is part of the VosA-VelB heterodimer which represses asexual development [31] , [33] . These results suggest that distinct velvet domain proteins or LaeA may include targets of MAPK phosporylation . AnFus3 was immunoprecipitated from vegetatively grown fungal cells as sGFP fusion protein ( Figure 2A ) to identify direct substrates of AnFus3 in in vitro kinase assays . VeA expressed and purified from E . coli was the only tested protein which could be specifically phosphorylated by AnFus3 , whereas bacterially produced VosA , LaeA or VelB were not phosphorylated . Further phosphorylation experiments performed with phospho-specific serine and threonine antibodies further supported that VeA was phosphorylated by AnFus3 and treatment of phosphorylated samples with lambda protein phosphatase ( λ-PP ) resulted in loss of phosphorylation signal ( Figure 2B ) . VeA bridges VelB and LaeA in the trimeric VelB-VeA-LaeA complex . We addressed whether AnFus3 activity affects complex formation . VeA protein levels ( Figure 2C ) were similar in wild type and mpkB mutant strains . velB RNA was unchanged whereas laeA transcripts were downregulated as previously reported ( Figure S2A ) [28] . TAP purification of natively expressed VeA::cTAP revealed that under conditions where sexual development was normally promoted , only significantly reduced amounts of VelB and LaeA proteins were enriched by tagged VeA in the absence of MpkB ( Figure 2D , Tables S2 and S3 ) . The MAP kinase does not affect VeA nuclear import , because the interaction of VeA with the importin KapA was not significantly affected in mpkB mutant . Consistently , nuclear import of the subunits of the trimeric VelB-VeA-LaeA complex was not affected in a mkkB mutant lacking the upstream MAP2K AnSte7 ( Figure S2B ) . Lack of laeA normally causes enhanced VeA and VelB expression as well as enhanced complex formation [31] . This suggests that decreased VeA-VelB association is not a result of the reduced levels of LaeA in mpkB mutants . These results suggest that AnFus3 phosphorylates VeA in vitro and interacts with VeA in vivo . Furthermore , AnFus3 is required for enhanced association of VeA with VelB which are components of the VelB-VeA-LaeA velvet complex . MAPKKK ( SteC ) and MAPK ( MpkB ) are necessary for sexual development in A . nidulans [24] , [25] . Yeast Fus3 receives the phosphorylation signal from MAP2K Ste7 . The corresponding filamentous fungus homolog has not yet been described . The ANID_03422 ( mkkB ) locus of A . nidulans encodes a protein , which is conserved in different Aspergilli ( Figure S3 ) and has 25% identity to yeast Ste7 [34] . AnSte7 [MkkB] is also related to N . crassa MAP2K [35] and human MAP2K1 [36] . Overexpressing the corresponding mkkB gene resulted in two fold increase in the number of fruiting bodies and supported a role in sexual development ( Figure S4A–S4C ) . mkkB deletion mutants had a slow growth phenotype and were blocked in early sexual development , which resulted in nest-like structures containing clumps of Hülle cells ( yellow arrows , Figure 3A , 3B ) . Hülle cells support sexual development as specialized nursing cells for the growing fruiting body [31] . AnSte7 is required for hyphal fusion as one of the initial steps of fruiting body formation . Hyphal fusion of wild type strains marked with either synthetic cytoplasmic green fluorescent protein ( sGFP ) or with nuclear monomeric red fluorescent protein ( mRFP ) resulted in hyphae with green cytoplasm and red nuclei ( heterokaryon ) ( Figure 3C ) . In contrast , a mkkB deletion strain was unable to fuse with the wild type strain . We found the same hyphal fusion defect for the steCΔ strain as in the mkkB mutant ( Figure 3C , 3D ) . This further supports that AnSte11 and AnSte7 act in a common pathway . The analysis of putative additional functions of AnSte7 in later phases of sexual development required a by-pass of initial hyphal fusions . Therefore , heterokaryons were artificially produced by fusing protoplasts . An intact mkkB copy of the wild type strain allowed the development of mature fruiting bodies ( red arrows ) , when wild type and mkkB mutant protoplasts were fused . In contrast , two mkkB mutants forced to form heterokaryons were impaired in fruiting body maturation and produced only early structures of development ( yellow arrow , Figure 3D ) . This suggests several functions of MAP2K AnSte7 during sexual development presumably in concert with AnFus3 . We determined whether the A . nidulans kinases may replace functions of its yeast counterparts . Plasmids containing Anste7 [mkkB] and Anfus3 [mpkB] genes expressed under yeast promoters were transformed into ste7 and fus3 deletion strains . mkkB and mpkB did not alleviate the defects in pheromone response of the yeast mutants ( Figure S4D ) . However , MpkB moderately suppressed the defects in pheromone response of a fus3 kss1 double mutant , showing that the MpkB is partially able to take over functions of the MAP kinase pair Fus3/Kss1 . This suggests a partial overlap of the functions of the MAPK pathways of these two organisms . The A . nidulans MAP kinase mating module was further characterized by identifying interaction partners of AnSte7 [MkkB] by TAP purification from different developmental stages ( only vegetative is shown , Figure 4A , 4B , Figure S5 , Tables S4 , S5 ) . Tagged AnSte7 did not recruit AnFus3 , but copurified AnSte11 [SteC] and AnSte50 [SteD] , a protein sharing homology to S . cerevisiae Ste50 . Ste50 functions as an adaptor for membrane recruitment of Ste11 in yeast [12] . Deletion of the corresponding steD in A . nidulans caused a defect in fruiting body formation ( Figure S1A ) . Similar to the other MAPK mutants , steD mutant could not produce heterokaryons in outcrossings ( not shown ) . Thus , the adaptor AnSte50 is as important for accurate fungal development as the other components of the MAPK module . A . nidulans AnSte50 was enriched by AnSte7::TAP in wild type , but not in the steCΔ strain indicating that AnSte11 is required for the AnSte50-Ste7 interaction ( Figure 4A , 4B ) . These data suggest a physical interaction of AnSte50 and two MAPK module components in a AnSte50-Ste11-Ste7 complex . Interaction partners of AnSte50 were identified to explore the entire fungal MAPK mating module . A functional steD::ctap ( Figure S1A , S1B ) recruited the MAP3K AnSte11 and the MAPK AnFus3 but not the MAP2K AnSte7 ( Figure 4C , 4D and Table S6 ) . This further supports that AnSte50-Ste11-Ste7-Fus3 forms a module similar to yeast Ste5-Ste50-Ste11-Ste7-Fus3 with the exception that a counterpart for the yeast Ste5 scaffold is missing in A . nidulans . We analysed whether AnSte11 and AnSte7 act upstream of MAPK AnFus3 . MAPK phosphorylation was monitored by a phospho-specific antibody against the MAPK Thr182XTyr184 motif . Phosphorylated AnFus3 was permanently detectable in vegetative wild type cultures ( Figure 4E ) . In contrast , modified AnFus3 was absent in mutants lacking AnSte11 or AnSte7 , whereas the absence of AnSte12 did not change levels of phosphorylated AnFus3 . In the absence of AnSte50 , reduced phosphorylation of AnFus3 indicates some residual activity of the untethered AnSte11-Ste7 complex . This supports an active A . nidulans MAPK module consisting of AnSte50-Ste11-Ste7-Fus3 which controls fungal sexual development . The role of AnSte50-Ste11-Ste7-Fus3 for secondary metabolism was examined . Impaired secondary metabolism had only been described for the mpkB mutants [28] . The mycotoxin sterigmatocystin ( ST ) levels were drastically reduced in the sterile steC , steD , mkkB , or mpkB mutants whereas ST levels in the sterile steAΔ [AnSte12] were similar to wild type ( Figure 4F–4G ) . Similarly , the expression of the biosynthesis genes for ST ( stcU ) and terrequinone ( tdiA and tdiB ) , and the expression of laeA and the transcription factor encoding aflR , both required for expression of secondary metabolite genes , were distinctly reduced in each mutant of the MAPK module ( Figure 4H ) . These data corroborate that active AnSte50-Ste11-Ste7-Fus3 MAPK is not only required for sexual development but also for secondary metabolite production . The yeast mating MAPK module transmits a signal from the plasma membrane to the nucleus by releasing MAPK Fus3 from the Ste5 scaffold at the membrane [13] , [37] . We analysed how the signal is transmitted through the filament of A . nidulans to nuclear factors as AnSte12 or VeA . Time course immunoblotting ( Figure S1C ) showed that AnFus3 was constantly expressed during development . The mkkB mRNA for the upstream MAPKK was also present throughout all stages ( Figure S5C ) . The corresponding protein AnSte7::sGFP was present in vegetative as well as in the initial phases of asexual and sexual development , but decreased afterwards ( Figure S5C ) . Similarly , the AnSte50::sGFP ( Figure S1C ) seems to be degraded because the protein disappeared during mid and late asexual development . Confocal spinning disc microscopy revealed that functional AnSte7::sGFP fusion protein expressed under native locus promoter was localised during early phase of growth throughout the cytoplasm , but never found in the nucleus ( not shown ) . After becoming competent for differentiation ( 16 hours after germination ) , AnSte7::sGFP accumulated not only at the hyphal tip but also at the plasma membrane and at the septa of hyphae or spore forming cells ( white arrows in Figure 5A ) . The AnSte7 signal was also present on the nuclear envelope . The AnSte7 localization pattern did not change in the absence of the MAP3K AnSte11 ( not shown ) . Like AnSte7 , a functional Ste50::sGFP fusion never entered the nucleus . AnSte50 was cytoplasmic and accumulated at later stages of vegetative growth at the hyphal tip , the septa of spore forming cells , the plasma membrane and the nuclear envelope ( arrows in Figure 5B , 5D ) . A functional AnFus3::sGFP expressed under the native promoter accumulated at the hyphal tip and was as well present in the cytoplasm as in the nucleus in vegetative and spore forming cells ( Figure 5C , 5D ) . This suggests a dynamic and complex distribution of MAPK module subunits from the fungal membrane to the nucleus . It also revealed that the MAPK AnFus3 is as yeast Fus3 the only subunit with the potential to enter the nucleus . AnFus3 [MpkB]::mRFP was expressed constitutively together with AnSte7 [MkkB] and AnSte50 [SteD]::sGFP fusions to validate whether all components of the MAPK module are colocalized within the fungal filament ( Figure 6 ) . Most of the GFP signals of AnSte7 and Ste50 merge with the RFP signal of MpkB at the fungal tip , the plasma membrane and at the nuclear envelope where they might form dynamic protein complexes . Exclusively at hyphal tips we found two types of co-localizations of kinase pairs . In addition to direct co-localizations , similar to plasma membrane or nuclear envelope , there were extended co-localization patterns at the hyphal tip . This could reflect that a fraction of kinases is localized in vesicles at the hyphal tip . Bimolecular fluorescence complementation ( BiFC ) [38] , [39] was applied to examine whether there are direct transient in vivo interactions between AnSte7 and Fus3 , which could not be found by TAP purification ( Figure 7B ) . Similar to the yeast localization of the Ste5-Ste11-Ste7-Fus3 MAPK module at the membrane , AnSte11-Ste7 and AnSte7-Fus3 interacted at the plasma membrane and also at the hyphal tip ( Figure 7 ) . There was an additional strong interaction of AnSte11-Ste7 at septa which border cellular segments as well as at septa of spore forming cells and spores ( Figure 7C–7E ) . Quantification of the fluorescence intensity from the bright enhanced yellow fluorescent protein ( EYFP ) spots of AnSte11-Ste7 , Ste7-Fus3 pairs revealed that they emit upto 10 fold more yellow fluorescence than the single EYFP molecules ( Figure S6 ) , suggesting that the kinase pairs form multimeric complexes . Consistently to the yeast situation , the transcription factor AnSte12 as well as fungus specific factors VeA and LaeA specifically interacted with the MAPK Fus3 in the nucleus ( Figure 1C , 1D ) . AnSte50 also interacted with the kinases at the plasma membrane and hyphal tip ( Figure 8 ) . Only AnSte11-Ste7 strongly interacted at the septa but there was hardly any interaction between AnSte7-Fus3 or between the AnSte50 and any of the kinases at the septa ( Figure 7C and 7D , Figure 8D–8F ) . Yeast Ste7-Ste5-Fus3 migrates to tips of mating projections in pheromone treated cells . Only Fus3 travels to the nucleus upon activation by Ste7 [13] . A . nidulans is a homothallic fungus , which does not require a mating partner . Time lapse images revealed that MAPK module components AnSte7 and Ste50::sGFP can move within the fungal cell along the membrane . During the cellular movements , these molecules shortly touched the membrane then hit the nucleus . Sometimes , fusion protein moved back after contacting the nucleus in the opposite direction . ( Figure 9A and 9B , Videos S1 and S2 ) . The dynamics of the protein interactions of the BiFC expressing strains were further analysed by time lapse movies ( Videos S3 , S4 , S5 , S6 ) . The AnSte7-Fus3 pair moved together along the plasma membrane ( Video S3 , Figure S7A ) towards the first nucleus , then they advanced to the next nucleus while some other spots did not move distinctly . Likewise , AnSte50-Fus3 complexes left one nuclear envelope , touched the membrane and moved to the next nucleus ( Video S4 , Figure S7B ) . Similar movements were also observed for other complexes of the MAPK module ( not shown ) . AnSte11-Ste7 can dissociate from the plasma membrane , cross the cytoplasm and reach the nuclear envelope ( Videos S5 and S6 , Figure S7C , S7D ) . The major difference to the yeast situation is that the MAPK module of A . nidulans travels from the outer border of the fungal cell through the cytoplasm to the nuclear envelope . The AnSte7-Fus3 pair as well as pairs of AnSte50 with all three kinases interacted at the nuclear envelope ( Figure 7 and Figure 8 ) . These data suggest significant differences in the molecular mechanism how a MAPK signal is transmitted in yeast in comparison to a filamentous fungus . The interactions of the AnSte11-Ste7 and AnSte7-Fus3 complexes were examined in steDΔ strain to examine AnSte50 function for cellular location of the module . The interaction of the three kinases at the plasma membrane of wild type ( Figure 7 ) was abolished for AnSte11-Ste7 and drastically reduced for AnSte7-Fus3 in the steD mutant ( Figure 9C , 9D ) . Plasma membrane localizations of the AnSte7 and Fus3::sGFP fusions were also reduced in the steD mutant ( not shown ) . Contrastingly , the localization of the entire module at the hyphal tip or for the partial module AnSte11-Ste7 at the septum seems to be mediated by a mechanism which is largely independent of AnSte50 . These data suggest that AnSte50 supports association of the A . nidulans MAPK module with the plasma membrane but it does not affect the hyphal tip and septum localizations .
We describe here the A . nidulans Fus3 MAPK module which is involved in sexual development and the control of secondary metabolism and releases AnFus3 into the nucleus . Our data suggest a provocative additional hypothesis: AnFus3 is able to travel along the membrane and to cross the cytoplasm to the nuclear envelope in complexes with AnSte7 MAP2K , AnSte11 MAP3K and the adaptor protein AnSte50 . In the nucleus AnFus3 interacts with transcription factor AnSte12 for sexual development . The additional interaction of AnFus3 with VeA or yet unidentifed targets may promote VeA-VelB formation which is required for coordinated development and secondary metabolism ( Figure 9E ) . The A . nidulans Fus3 MAP kinase module is preferentially assembled at distinct intracellular locations , such as the hyphal tip , the septa , the plasma and nuclear membranes . Membrane localisation of the module is presumably relevant to perceive external signals as in yeast . Sexual development is defective when membrane localization of the module is impaired as in strains without intact AnSte50 . Tip localisation could be important for hyphal fusions and cell-cell contacts . MAPKK AnSte7 and MAP3K AnSte11 but not other components interact at septa suggesting additional phosphorylation functions at septa independent of AnFus3 . Corresponding mutants displayed strong deformations in the septa between developing asexual spores and spore forming cells but did not show any abnormal septation pattern in vegetative hyphae ( not shown ) . This suggests a possible additional link between kinases of the module and regulators of asexual development . Intracellular distances in a filamentous fungus are significantly larger than in yeast . Several steps can be distinguished for signal transduction from surface to nucleus of A . nidulans . ( i ) From hyphal tip to plasma membrane: AnSte50 is primarily required for efficiently anchoring the MAPK module to membranes , but not to hyphal tips . AnSte50 might also contribute like in yeast to Ste11 MAP3K activation . The essential function of AnSte50 for signal transduction is supported by the defect of sexual development and lack of AnFus3 phosphorylation in a steD mutant . The AnSte50 independent localization at the hyphal tip suggests an additional yet unknown anchoring function for the AnFus3 module at the hyphal tip . The anchoring mechanism could include small membrane bound vesicles at the Spitzenkörper which could explain some of our localization results ( Figure 6B , Figure 7 , Figure 8 ) . The lack of AnSte11 did not cause any changes in the subcellular localization of AnSte7 , indicating that AnSte11 is not required for proper AnSte7 localization . The lack of AnSte50 had a drastic effect on the localization of MAPK module complexes . AnSte50 interacts with all components of the MAPK module and might provide a binding platform for the other MAPK components which even works when AnSte11 is absent ( Figure 8 ) . ( ii ) In yeast Fus3 dissociates from the Ste5 tethered pheromone pathway module and enters into the nucleus [13] . Transport of the AnFus3 in the AnSte50-Ste11-Ste7 complex ( or subcomplexes ) to the nuclear envelope as additional signal transmission step in A . nidulans might secure that AnFus3 can be kept active over larger distances until it finally reaches the nucleus . It will be interesting to analyse phosphorylation states of kinases at different cellular locations during signal transduction . ( iii ) Import of AnFus3 from nuclear envelope into nucleus: AnFus3 presumably dissociates from the kinase module at the nuclear envelope in a mechanism wihich is unknown . After entry into the nucleus , AnFus3 interacts with AnSte12 , and presumably phosphorylates it . AnFus3 phosphorylates the velvet protein VeA , which efficiently associates with VelB and LaeA . It is yet unclear whether there are additional AnFus3 targets which support VelB-VeA complex formation . VelB-VeA then contributes with AnSte12 to sexual fruiting body development and the trimeric VelB-VeA-LaeA concomitantly promotes expression of distinct genes for secondary metabolites ( Figure 9E ) . These include the production of the mycotoxin sterigmatocystin or antitumor agent terrequinone but not the antibiotic penicillin synthesis . The MAPK module of A . nidulans is presumably involved in integrating multiple signals and enabling an adequate cellular response . Oxylipins represent currently the only known pheromones of Aspergilli but the receptors are unknown [40] . In yeast nitrogen starvation induces the same kinase module as pheromones , and part of the components are also involved in response to osmotic stress . It is likely that the AnSte50-Ste11-Ste7-Fus3 and the septal AnSte11-Ste7 modules have additional targets other than AnSte12 and VeA , which remain to be identified . An interesting open question is whether other organisms also transport their Fus3 MAPK counterpart together with the entire module from surface to nuclear envelope . This results in questions about transport control points and module attachment sites on the nuclear envelope where future work in A . nidulans could deepen insights into the molecular mechanism of information transfer through the cell .
Fungal strains created and used in the course of this study are given in Table S7 . Aspergillus nidulans strains; FGSCA4 ( veA+ ) , TNO2A3 ( veA1 ) [41] , AGB152 ( veA+ ) [42] , AGB154 ( veA+ ) , AGB506 ( veA+ ) [31] , AGB551 ( veA+ ) , AGB552 ( veA+ ) served as wild type transformation hosts for the knock-out , epitope tagging , BIFC , and overexpression experiments . Further details for the strains transformed with various plasmids are given in Table S8 . Culturing fungal strains were described in detail elsewhere [43] . DH5α and MACH-1 ( Invitrogen ) Escherichia coli strains were applied for recombinant plasmid DNA . Aspergillus and E . coli strains were cultured as described previously [30] . Fungal and bacterial transformations were carried out as given in detail [30] . Circular and linear DNA molecules were created based on the standard recombinant DNA technology protocols in detail [30] . Plasmids and oligonucleotides applied and constructed in this study are given in Table S8 and Table S9 . During polymerase chain reaction ( PCR ) different kind of DNA polymerase combinations including Pfu ( MBI Fermentas ) , Phusion ( Finnzymes ) , Platinum-Taq ( Invitrogen ) and Taq ( Fermentas ) were used . Linear and circular DNA constructs were created as given below . For construction of Anste7 [mkkB] deletion fragment , 1 . 1 kb 5 UTR and 0 . 6 kb 3 UTR flanking regions of AN3422 locus were amplified with 3422-A/C and 3422-D/F , respectively . These two fragments were fused with ptrA marker ( amplified from pPTRII ) by fusion PCR ( 3422-B/E ) , creating 3 . 6 kb deletion fragment which was transformed into TNO2A3 ( veA1 ) , AGB154 ( veA+ ) , creating AGB586 , AGB587 strains , respectively . Complementation plasmid pME3854 was constructed by amplifying 4 . 2 kb genomic Anste7 [mkkB] locus ( Comp-A/B ) and subsequent cloning into StuI site of pAN8-1 [44] plasmid carrying the phleomycin resistance marker . Deletion and complementation events were verified by the Southern hybridization ( Figure S8A–S8B ) . Primers OZG302/303 amplified the 1 . 6 kb cDNA of Anste7 [mkkB] from sexual cDNA library [45] . T4 Polynucleotide kinase ( PNK ) treated phosporylated amplicons were inserted into PmeI site of pME3160 [30] under nitrogen source inducible niiA promoter leading to pME3855 that was transformed into AGB152 , which resulted in AGB662 . For the purpose of substitution of the orinigal Anste7 [mkkB] locus by mkkB::gfp and ctap , mkkB promoter including mkkB ORF ( 2 . 85 kb ) and terminator regions ( 0 . 6 kb ) were PCR-amplified from genomic DNA ( 3422-A/OZG380 for gfp , 3422-A/OZG382 for ctap , and OZG314/3422-F ) . Finally , the fragments 3422-A/OZG380 and OZG314/3422-F were fused to sgfp::natR module ( with oligos 3422-B/3422E ) creating 5 . 4 kb mkkB::sgfp::natR fusion construct . Likewise , 3422-A/OZG382 and OZG314/3422-F , and ctap::natR were joined by fusion PCR ( 3422-B/3422E ) resulting in mkkB::ctap::natR cassette for gene replacement . mkkB::sgfp::natR construct ( 5 . 2 kb ) was transformed into TNO2A3 and SWH51 [24] , which yielded AGB590 and 592 , respectively ( Figure S8D–S8E ) . Similarly , mkkB::ctap::natR was introduced into AGB551 and SWH51 resulting in AGB597 and 598 . Anste11 [steC] ORF was amplified from gDNA ( OZG389/OZG392 ) and fused to nyfp ( OZG73/387 ) leading to nyfp::Anste11 [steC] fusion fragment which was cloned in PmeI site of pME3160 plasmid yielding pME3859 ( nyfp::steC ) . Anste7 [mkkB] ORF was PCR-amplified ( for nyfp , OZG389/303 , for cyfp OZG390/303 ) from genomic DNA followed by fusion to nyfp and cyfp , which produced nyfp::Anste7 [mkkB] and cyfp::Anste7 [mkkB] fusions . Similar to Anste11 [steC] cloning , nyfp::mkkB fragment was inserted in PmeI site of pME3160 generating pME3861 plasmid . To test AnSte11/AnSte7 interaction , cyfp::mkkB was cloned in SwaI site of pME3859 leading to pME3860 that was brought in AGB506 , which generated AGB599 . For AnSte7/AnFus3 interactions , Anfus3 [mpkB] cDNA was amplified from cDNA library ( OZG404/403 for nyfp , OZG405/403 for cyfp ) . OZG404/403 and OZG405/403 were fused to nyfp ( OZG73/387 ) and cyfp ( OZG75/388 ) fragments yielding nyfp::mpkB and cyfp::mpkB . nyfp::mpkB fragment was inserted in PmeI site of pME3160 , which led to pME3864 and cyfp::mpkB was cloned in SwaI site of pME3861 generating pME3862 ( nyfp::Anste7 [mkkB]/cyfp::Anfus3 [mpkB] ) . The BIFC plasmid pME3862 was introduced in AGB506 in order to generate AGB600 . Anste12 [steA] gDNA was amplified with oligos OZG400/401 and fused to cyfp by fusion PCR ( OZG75/400 ) . cyfp::veA , cyfp::velB , cyfp::vosA and cyfp::laeA were produced as described in detail [31] . Insertion of cyfp::steA , cyfp::veA , cyfp::velB , cyfp::vosA and cyfp::laeA in SwaI site of pME3864 yielded following plasmids pME3865 ( mpkB/steA , AGB601 ) , pME3866 ( mpkB/veA , AGB623 ) , pME3867 ( mpkB/velB , AGB625 ) , pME3868 ( mpkB/vosA , AGB624 ) , pME3869 ( mpkB/laeA , AGB622 ) . Anste50 [steD] cDNA was amplified from cDNA library ( OZG500/501 ) and joined to cyfp fragment with oligos OZG75/OZG500 . Finally , this fragment was cloned in SwaI sites of pME3859 , 3861 , 3864 , leading to plasmids pME3870 , 3871 and 3927 , respectively . steD deletion , steD::sgfp and steD::ctap linear fragments were created in an identical manner to mpkB constructs . steD deletion; OZG470/472 , ptrA , OZG473/475 were fused by using oligos OZG471/474 in a fusion PCR . steD::sgfp::natR ( 5 . 1 kb ) ; OZG470/564 , sgfp::natR , OZG566/475 were joined by oligos OZG471/474 . steD::ctap::natR ( 4 . 9 kb ) ; OZG470/565 , ctap::natR , OZG566/475 were joined by oligos OZG471/474 . steD deletion cassette was brought into AGB552 and 551 generating , AGB576 and 650 , respectively . steD::sgfp and steD::ctap were used for gene replacement in AGB551 , giving rise to AGB657 and 659 , respectively ( Figure S9A–S9C ) . mpkBΔ::ptrA deletion fragment was constructed by amplification of 1 . 2 kb 5 and 3 flanking regions of mpkB with primers ( for 5 UTR , OZG443/445 , for 3 UTR OZG446/448 ) . OZG443/445 , ptrA marker , and OZG446/448 were fused by oligos OZG444/447 creating 3 . 9 kb mpkBΔ::ptrA construct . Consequently , mpkBΔ::ptrA fragment was transformed into AGB552 , which generated AGB611 . To create Anfus3 [mpkB]::sgfp and ctap linear fragments , mpkB promoter as well as ORF was amplified by ( OZG443/560 for gfp fusion and OZG443/561 for ctap fusion ) . OZG443/560 , gfp::natR , and OZG562/448 were co-fused by oligos OZG444/447 creating 5 . 3 kb mpkB::sgfp::natR fragment . OZ443/561 , ctap::natR , and OZG562/448 linear DNAs were fused to make mpkB::ctap::natR gene replacement fragment ( 5 . 1 kb ) . mpkB::sgfp and mpkB::ctap were transformed into the AGB551 strain , which yielded AGB654 and 659 , respectively ( Figure S9D–S9G ) . For creation of constitutively expressed mpkB::mrfp fusion , gpdA promoter ( OZG735/736 ) , mpkB cDNA ( OZG737/738 ) , and mRFP::H2A terminator ( OZG739/740 ) were amplified and fused together ( OZG735/740 ) . The fusion was cloned in the SwaI site of a pyrG marker bearing plasmid . The final plasmid pME3966 was introduced into AGB590 and AGB657 for co-localization studies . Promoter and terminator regions of STE7 ( promoter OZG679/680 , terminator OZG683/684 ) and FUS3 ( promoter OZG685/686 , terminator OZG689/690 ) were amplified from the wild type yeast genomic DNA . These regions were either fused to the gDNA or cDNAs of Anste7 [mkkB] ( 681/682 ) and Anfus3 [mpkB] ( 687/688 ) genes . Resulting fusions proSTE7::mkkB gDNA::STE7ter , proSTE7::mkkB cDNA::STE7ter , proFUS3::mpkB gDNA::FUS3ter and proFUS3:: mpkB cDNA::FUS3ter were cloned in SmaI site of yeast centromeric plasmid pRS316 ( URA3 ) [46] yielding pME3958 , 3959 , 3960 and 3961 , respectively . STE7 ( OZG679/684 ) , FUS3 ( OZG685/690 ) , and KSS1 ( OZG691/692 ) genomic loci were amplified and similarly cloned into SmaI site of pRS316 resulting in pME3962 , 3963 , and 3964 plasmids , respectively . These control and chimeric constructs were transformed into the appropriate ste7 , fus3 , and fus3/kss1 [47] deletion strains . Southern and Northern hybridizations were carried out as explained in detail [30] , [43] according to protocols [48] , [49] . Immunoblotting experiments for recognition of GFP , TAP fusion , VeA , and actin in protein extracts was performed according to described protocols [31] . α-phospho 44/42 ( 4377 , Cell Signaling Technology Inc ) was used for detection of the phosphorylated AnFus3 [MpkB] . For the detection of the phosphorylated proteins , α-phosphoserine/threonine ( ab17464 , Abcam ) was employed . Manufacturers protocols were followed for incubation times and buffer applications of phosphospecific antibodies . Proteins were expressed in Rosetta 2 ( DE3 ) using ZYM5052 [50] media supplemented with 30 µg/ml Chloramphenicol and 100 µg/ml Ampicillin ( GST-LaeA91 ) or 30 µg/ml Kanamycin ( Velvet proteins ) at 16°C . Cells were harvested by centrifugation , resuspended in lysis buffer ( 30 mM HEPES pH 7 . 4 , 400 mM NaCl , 30 mM Imidazol ) and lysed by passing through a Microfluidics Fluidizer at 0 . 55 MPa . The lysate was cleared by centrifugation at 30000×g for 30 minutes . His-tagged proteins were purified with a 5 ml NiNTA-Sepharose ( GE Healthcare ) and GST-tagged LaeA91 with a 5 ml GSH-Sepharose ( GE Healthcare ) column connected to an ÄKTA Prime chromatography system . After washing with 10 column volumes with lysis buffer , proteins were eluted with elution buffer plus 400 mM Imidazol or 30 mM reduced Glutathione . Velvet proteins were desalted with a HiPrep Desalting 26/10 column ( GE Healthcare ) into storage buffer ( 10 mM HEPES pH 7 . 4 , 400 mM NaCl ) . GST-LaeA91 was cleaved with PreScission Protease at 4°C for 16 h and further purified by gel-filtration using a Superdex 200 26/60 and a final 5 ml GSH-Sepharose column both equilibrated in gelfiltration buffer ( 10 mM HEPES pH 7 . 4 , 150 mM NaCl ) . All proteins were shock-frozen in liquid nitrogen and stored at −80°C until further use . In order to immunoprecipitate GFP fusion proteins , protein crude extracts were prepared from vegetatively grown cultures . 100 µl GFP-Trap sepharose ( Chromotek ) was washed twice with 1 ml protein extraction buffer ( 50 mM Tris pH 7 . 5 , 100 mM KCl , 10 mM MgCl2 , 0 . 1% NP40 , 10% Glycerol , 20 mM β-glycerophosphate , 2 mM Na3VO4 , 5 mM NaF , 0 . 5 mM PMSF , 1 mM benzamidine , 1 mM EGTA , 1 mM DTT ) . 20 ml ( 150 mg total ) protein crude extract was incubated with 100 µl GFP-Trap sepharose ( Chromotek ) at 4°C for 2 hours on a rotating platform . Afterwards , sepharose-extract mixture was centrifuged at 4000 rpm at 4°C for 1 min . Crude extract was removed with a 5 ml pipette . The sepharose was washed twice with 20 ml of protein buffer and centrifuged at 4000 rpm at 4°C for 1 min . This step was repeated one more time . Finally , 1 ml of protein buffer was added and the sepharose was resuspended . Each of the 200 µl sepharose buffer mixture was transferred into 1 . 5 ml eppendorf cups and centrifuged at 4000 rpm at 4°C for 1 min and supernatant was removed . Immunoprecipitated proteins were washed three times with 1 ml kinase reaction buffer ( KRB; 20 mM Tris pH 7 . 5 , 10 mM MgCl2 , 1 mM DTT , 1 mM benzamidine , 1 mM Na3VO4 , 5 mM NaF , 0 . 1 µCi [32P]-ATP ) . In vitro phosporylation assay was performed with modifications according to protocol given in [51] . For in vitro phosphorylation experiment , 30 µl KRB , containing 0 . 1 µCi [32P]-ATP and 10 µg recombinant protein were added to the sepharose beads and incubated at 30°C for 35 minutes with the periodic resuspensions in every five minutes . Afterwards , reaction tubes were centrifuged at 4000 rpm at R/T for 1 min and supernatants containing phosphorylated proteins were transferred into new eppendorf cups . Supernatans and sepharose containing immunoprecipitated proteins were mixed with 3× protein loading dye ( 30 µl supernatant and 15 µl loading dye ) and incubated at 95°C for 10 min . 30 µl of the supernatant fraction was run on 4–15% gradient SDS gel that was dried for 2 h and exposed to Kodak X-omat film for 5 hours . 10 µl of the reaction was used for visualization of the proteins with coomassie staining . 2 µl of sepharose was used for immunoblotting and ponceau staing for validation of equal immunoprecipitated target protein ( MpkB or GFP ) . For non-radioactive kinase experiments , same KRB buffer containing 5 µM ATP was used . Supernatants were treated with 1000 units lambda protein phosphatase ( New England Biolabs ) in the presence of 1 mM MnCl2 at 30°C for 1 hour . Samples were added with 3× loading dye and boiled at 95°C for 10 min . 3 µl of the samples were used for immunoblotting . For the TAP purification of the MkkB , MpkB , SteD , and VeA interacting proteins and further LC-MS/MS identification previously published protocols were applied [30] . A . nidulans strains expressing various fluorescence proteins ( EYFP/sGFP/mRFP ) were inoculated in the 8-well borosilicate coverglass system ( Nunc ) containing the liquid minimal medium . Widefield fluorescence photographs were taken with an Axiovert Observer . Z1 ( Zeiss ) microscope equipped with a CoolSNAP ES2 ( Photometrics ) digital camera . CSU-X1 A1 confocal scanner unit ( Yokogawa ) connected with QuantEM:512SC ( Photometrics ) digital camera was used for laser microscopy . The SlideBook 5 . 0 software package ( Intelligent Imaging Innovations ) was used for fluorescence and laser confocal image and movie recording as well as productions . We defined signals as plasma-membrane localized if we found the signals that are at the border of the silhouette of the fungal cell or even surmount the fungal cell; similarly , we defined signals as nucleus-associated when we found multiple signals at the border of the nuclear silhouette . The EYFP protein was purified by using GFP-Trap as described for GFP protein . EYFP molecules were allowed to attach to poly-L-lysine coated coverslips for 10 minutes , in PBS buffer . Fungal cultures were grown as described above . The preparations were imaged using a SP5 TCS STED microscope ( Leica Microsystems ) , under 514 nm excitation ( provided by an Argon laser ) , using a 100× oil-immersion objective ( 1 . 4 NA , Leica ) . The images were processed by a custom-written routine in Matlab ( The Mathworks Inc . ) . Briefly , the spots were identified by the application of an automatic threshold based on the intensity of the background . We then used Gaussian fits to the spots to determine their intensity , and to correct for the background intensity , which provided the baseline of the fits . Extraction of sterigmatocystin ( ST ) and thin layer chromatography ( TLC ) was carried out as given in detail [43] . Penicillin levels were determined as published previously [52] .
|
Mitogen activated protein ( MAP ) kinase cascades are conserved from yeast to man to transmit an external signal to the nucleus and induce an appropriate cellular response . The yeast Fus3 MAP kinase module represents a textbook paradigm for signal transduction . The pathway is activated by external sexual hormones triggering several kinases that transmit the signal at the plasma membrane to Fus3 . Phosphorylated Fus3 is released from the membrane-associated module , crosses the cytoplasm , and enters the nucleus to activate transcription factors for sexual development . We describe here the Fus3 MAPK pathway of a filamentous fungus that controls sexual development as well as secondary metabolism , which are coordinated processes in filamentous fungi . Aspergillus nidulans is able to release Fus3 as a complex from the membrane . Complexes of Fus3 can include two additional kinases and an adaptor protein , and these complexes can migrate from the membrane to the nuclear envelope where only A . nidulans Fus3 can enter the nucleus to control nuclear regulators . Revealing specific functions of cellular Aspergillus Fus3 complexes in signal transduction to control fungal development and secondary metabolism will be a fascinating future task .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"protein",
"interactions",
"microbiology",
"gene",
"function",
"developmental",
"biology",
"model",
"organisms",
"molecular",
"development",
"molecular",
"genetics",
"microbial",
"growth",
"and",
"development",
"gene",
"expression",
"biology",
"proteomics",
"molecular",
"biology",
"biochemistry",
"signal",
"transduction",
"genetics",
"yeast",
"and",
"fungal",
"models",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
The Aspergillus nidulans MAPK Module AnSte11-Ste50-Ste7-Fus3 Controls Development and Secondary Metabolism
|
The healing of a fracture depends largely on the development of a new blood vessel network ( angiogenesis ) in the callus . During angiogenesis tip cells lead the developing sprout in response to extracellular signals , amongst which vascular endothelial growth factor ( VEGF ) is critical . In order to ensure a correct development of the vasculature , the balance between stalk and tip cell phenotypes must be tightly controlled , which is primarily achieved by the Dll4-Notch1 signaling pathway . This study presents a novel multiscale model of osteogenesis and sprouting angiogenesis , incorporating lateral inhibition of endothelial cells ( further denoted MOSAIC model ) through Dll4-Notch1 signaling , and applies it to fracture healing . The MOSAIC model correctly predicted the bone regeneration process and recapitulated many experimentally observed aspects of tip cell selection: the salt and pepper pattern seen for cell fates , an increased tip cell density due to the loss of Dll4 and an excessive number of tip cells in high VEGF environments . When VEGF concentration was even further increased , the MOSAIC model predicted the absence of a vascular network and fracture healing , thereby leading to a non-union , which is a direct consequence of the mutual inhibition of neighboring cells through Dll4-Notch1 signaling . This result was not retrieved for a more phenomenological model that only considers extracellular signals for tip cell migration , which illustrates the importance of implementing the actual signaling pathway rather than phenomenological rules . Finally , the MOSAIC model demonstrated the importance of a proper criterion for tip cell selection and the need for experimental data to further explore this . In conclusion , this study demonstrates that the MOSAIC model creates enhanced capabilities for investigating the influence of molecular mechanisms on angiogenesis and its relation to bone formation in a more mechanistic way and across different time and spatial scales .
The biological process of fracture healing comprises three main stages: ( i ) the “inflammation phase” , where the trauma site becomes hypoxic and is invaded by inflammatory cells , fibroblasts , endothelial cells and mesenchymal stem cells [1]; ( ii ) the “reparative phase” , which starts with the production of cartilaginous and fibrous tissue resulting in a soft callus , later replaced by a hard callus , through the process of endochondral ossification; ( iii ) in the final “remodeling phase” the woven bone is replaced by lamellar bone and the vasculature is reorganized . The healing of a fracture depends largely on the development of a new blood vessel network ( angiogenesis ) in the callus . Sprouting angiogenesis involves the following steps: first a “tip cell” is selected; this cell extends filopodia sensing the haptotactic and chemotactic cues in the environment and leads the newly formed “sprout” comprised of following , proliferating “stalk cells”; the newly formed sprout , or “branch” then connects with another branch in a process called anastomosis , which results in the formation of a closed loop allowing the initiation of blood flow; finally the newly formed vascular network is stabilized by pericytes [2] . In order to ensure a correct development of the vasculature , the balance between stalk and tip cell phenotypes must be tightly controlled . The process of tip cell selection consists of the following main steps . Firstly a gradient of vascular endothelial growth factor ( VEGF ) is formed by the up-regulation of VEGF-expression and secretion , triggered by hypoxia ( low oxygen concentration ) . The VEGF-mediated activation of the VEGFR-2 receptors induces the up-regulation of Dll4 which activates the Notch1-receptors on the neighboring cells , thereby down-regulating their expression of VEGFR-2 . This process of lateral inhibition , with cells battling to inhibit each other leads eventually to a “salt and pepper” alternating pattern , where cells with high Dll4 levels remain with high VEGFR-2 receptor levels , allowing them to migrate ( and becoming tip cells ) whilst their neighbors become inhibited , making them less susceptible to VEGF , and thus these adopt the non-migratory stalk cell phenotype . In this manner the adequate amount of tip cells , required for a correct sprouting pattern , is established [2]–[5] . Both fracture healing as well as angiogenesis are very complex biological processes involving the coordinated action of many different cell types , biochemical and mechanical factors across multiple temporal and spatial scales . The time scales of individual events that underlie biological processes range from seconds for phosphorylation events to hours for mRNA transcription to weeks for tissue formation and remodeling processes [6] . The spatial scales vary from nanometers at the molecular level to millimeters at the tissue level and meters at the level of the organism [6] , [7] . Thus , it can be concluded that most biological processes have an intrinsic multiscale nature and must be studied and modeled accordingly . Depending on the biological spatial scale of interest a variety of experimental and modeling approaches can be used , which are nicely summarized by Meier-Schellersheim et al . [7] . The modeling approaches can be arranged in two broad categories: continuum and discrete modeling techniques . Continuum models use ordinary differential equations ( ODEs ) or partial differential equations ( PDEs ) to describe the evolution of cell and tissue densities and protein concentrations . The model variables are averages , which makes it difficult to represent individual cell-cell and cell-matrix interactions [8] , [9] . Moreover , since the cells are not individually represented , it is challenging to model the individual intracellular processes . Also , continuum models fail to correctly capture the process of angiogenesis due to the inherent discreteness of vascular networks [10] . Discrete approaches are often used to study small-scale phenomena , e . g . biological processes at the cellular and subcellular levels [11] . However , these techniques often become computationally expensive when used for predictions of larger cell population sizes at the tissue scale [11] . Here we briefly review hybrid , multiscale models of angiogenesis , i . e . models that combine different modeling techniques for various scales mentioned above into one framework , as this is the approach we have adopted here . For comprehensive reviews on ( multiscale ) mathematical models of angiogenesis the reader is referred to Mantzaris et al . , Qutub et al . and Peirce [12]–[14] . Notably none of the hybrid models to date include Dll4-Notch1 tip cell selection . Milde et al . presented a deterministic hybrid model of sprouting angiogenesis where a continuum description of VEGF , MMPs , fibronectin and endothelial stalk cell density is combined with a discrete , agent-based particle representation for the tip cells [15] . The hybrid model describes the biological process of sprouting as the division of tip cells depending on chemo- and haptotactic cues in the environment and a phenomenological “sprout threshold age” . In the model of Lemon et al . the formation of a new branch also occurs at the tip cell position , but is modeled as a random process with an average number of branches per unit length of the capillary [16] . Checa et al . model sprout formation stochastically by making the probability of sprouting from a vessel segment proportional to the segment length . The capillary growth rate is also regulated by the local mechanical stimulus [17] . Peiffer et al . proposed a hybrid bioregulatory model of angiogenesis during bone fracture healing [18] , based on the deterministic hybrid model of Sun et al . [19] . The process of angiogenesis is modeled discretely , including sprouting and anastomosis . The selection of tip cells in the growing vascular network is , however , modeled with phenomenological rules . A three dimensional model of cellular sprouting at the onset of angiogenesis was developed by Qutub et al . [20] . Although this framework describes sprout formation as a function of the local VEGF concentration and the presence of Dll4 , it is only a first approximation of the complex tip-to-stalk cell communication by Dll4/Notch signaling . A more detailed model of the lateral inhibition that underlies the tip cell selection process in angiogenic sprout initialization was presented by Bentley et al . [3] , [21] . They use an agent-based framework to accurately simulate a small capillary comprising 10 endothelial cells that can change shape and sense the local VEGF concentration by extending very thin filopodia . Moreover , every endothelial cell is characterized by its individual protein levels of VEGFR-2 , Dll4 and Notch – and their distribution on the cell membrane , by further subdividing the membrane into separate agents – which does not only allow assessment of the effects of the VEGF environment on tip/stalk cell patterning but also those of Dll4 over- and under-expression and cell shape change . Sprouting angiogenesis involves multiple biological scales: the intracellular scale where gene expression is altered so that different phenotypes ( e . g . tip and stalk cells ) can arise , the cellular scale that involves proliferation and migration and the tissue scale that encompasses the concentration fields of soluble and insoluble biochemical factors . As these scales are highly coupled , multiscale models are needed to study the mechanisms of sprouting angiogenesis . To the best of the authors' knowledge , there is only one model of sprouting angiogenesis with Dll4-Notch1 rigorously implemented [3] but this model simplified the extracellular environment to a uniform or linearly varying field of VEGF concentration , which is constant in time . While this simplification is justified for a detailed study of the short term phenotypic changes of a few neighboring endothelial cells , it is not for more complex , multicellular systems that involve cell-matrix interaction and highly dynamic , extracellular environments . This is certainly true for fracture healing , in which matrix densities and ( gradients of ) extracellular concentrations of soluble signals , like VEGF , are spatially and temporally changing as a result of cellular activity . While efforts have been done to model the interplay of VEGF diffusion and sprouting angiogenesis in the context of skeletal muscle tissue [6] , these multiscale models did not incorporate Dll4-Notch1 signaling . Moreover , in the context of fracture repair multiscale models that consider angiogenesis and that relate tissue , cell and intracellular scales have not been established yet [22] . In this study , we present a multiscale model of osteogenesis and sprouting angiogenesis with lateral inhibition of endothelial cells ( MOSAIC ) which extends the bioregulatory framework of Peiffer et al . [18] with an intracellular model based on the work of Bentley et al . on tip cell selection [3] . We hypothesize that the MOSAIC model creates enhanced capabilities for investigating the influence of molecular mechanisms on angiogenesis and its relation to bone formation . Simulation results will illustrate the interplay between molecular signals , in particular VEGF , Dll4 and Notch1 , endothelial cell phenotypic behavior and bone formation . They will demonstrate the advantages of multiscale modeling in the context of fracture healing , thereby exploring the importance of the model of Bentley et al . [3] for a much more complex and dynamic extracellular environment . At the same time , by comparison to the more phenomenological model of Peiffer et al . [18] the potential of a more mechanistic treatment of tip cell selection will become clear .
The discrete variable cv represents the blood vessel network , which is implemented on a lattice . When a grid volume contains an endothelial cell this variable is set to 1 , otherwise cv = 0 . The vessel diameter is defined by the grid resolution and is always one endothelial cell wide , although the movement of the tip cell is grid independent as explained below . Every endothelial cell ( cv = 1 ) has unique intracellular protein levels , which control the behavior of that specific cell . The intracellular module is adapted from the agent-based model of Bentley et al . [3] and consists of the following variables: the level of VEGFR-2 ( V ) , Notch1 ( N ) , Dll4 ( D ) , active VEGFR-2 ( V′ ) , active Notch1 ( N′ ) , effective active VEGFR-2 ( V″ ) , effective active Notch1 ( N″ ) and the amount of actin ( A ) . The effective active levels ( V″ and N″ ) include the time delay of translocation to the nucleus , thereby representing the levels at the nucleus , influencing gene expression . The amount of actin ( A ) refers to the polymerized actin levels ( F-actin ) inside the cell . In particular , it is associated to actin used for filopodia formation , owing to its importance for tip cell migration . As such , an increase in actin levels can be interpreted as filopodia extension , while a decrease as filopodia retraction . Other intracellular signaling pathways that involve actin , such as energy metabolism [23] , [24] , are not considered . The following equations describe the intracellular dynamics . An overview of all the parameters of the intracellular module can be found in Table 1 . The activation of the VEGFR-2 receptor , described by V′ , is given by: ( 1 ) where the constant Vsink represents the amount of VEGFR-1 receptors that act as a sink ( decoy receptor ) by removing VEGF from the system , t represents the time and δt the time step of the inner loop ( more information on these parameters can be found in the section “implementation details” below ) , Vmax represents the maximal amount of VEGFR-2 receptors , gv is the local VEGF concentration ( at the tissue level ) and Mtot is the total number of membrane agents ( constant for all ECs ) . Equation 1 is adapted from Bentley et al . [3] where every EC is composed of a varying amount of membrane agents , representing small sections of the cellular membrane . In the current framework , however , every EC is represented by one agent so that Mtot was chosen to be constant for all ECs and equal to an intermediate value between the initial and maximal amount of membrane agents in the agent-based framework of Bentley et al . [3] . The level of activated VEGFR-2 remains in a range going from 0 to V . When the VEGFR-2 receptors are activated above a certain threshold ( V′* ) , the actin levels of the endothelial cell are incremented in a constant manner ( ΔA ) . As mentioned earlier , this represents the extension of filopodia by the endothelial cell , which is shown to be regulated downstream of VEGFR-2 [4] . If the endothelial cell fails to extend its filopodia for a certain amount of time D3 , the filopodia retract which is mathematically translated into a reduction of the actin levels in a constant manner ( −10 . ΔA ) . The actin level remains in a range between 0 and Amax . The amount of Notch1 is considered to be constant in every EC , representing a balance between the rate of degradation and expression . At the same time , initial Notch activity levels are assumed to be zero and in the model Notch activity can only be increased through binding with Dll4 from neighboring ECs . The model therefore neglects the role of Notch in EC quiescence and the fact that high Notch activity levels have been measured in quiescent ECs [25]–[27] . Instead , it only focuses on the role of Dll4-Notch in tip cell selection . The number of activated Notch receptors ( N′ ) will be equal to the total amount of Dll4 available ( with an upper bound , given by the total number of Notch receptors N ) . The amount of Dll4 in the environment of an EC is the sum of the amount of Dll4 at the junctions with its neighboring ECs , whereby every cell is assumed to distribute Dll4 uniformly across its cell-cell junctions ( see Figure 2 ) . After a delay of D1 for V′ and D2 for N′ the active VEGFR-2 and Notch levels become the effective active levels ( V″ and N″ ) representing the levels at the nucleus , influencing gene expression . The delays were taken from Bentley et al . which were fitted to a somite clock Delta-Notch system [3] , [28] . Note that there is a delay between receptor activation and gene expression ( transcription ) but not between gene expression and protein synthesis ( translation ) , which is a simplification of the model . The amount of Dll4 is modeled in the following way: ( 2 ) represents the previous amount of Dll4 , the change in Dll4 expression due to the activation of the VEGFR-2 receptor [4] , [29] and is the amount of Dll4 that is removed from the environment due to the activation of Notch-receptors on neighboring ECs . If-conditions are used to ensure that the Dll4 level remains in a range between 0 and Dmax . When Notch signaling is activated in a cell , the amount of VEGFR-2 receptors is down-regulated , suppressing the tip cell phenotype as follows [4] , [5]: ( 3 ) Vmax represents the maximal amount of VEGFR-2 receptors and models the VEGFR-2 expression change due to Notch1 activation . If-conditions are used to ensure that the VEGFR-2 level remains in a range going from Vmin to Vmax . Since the amount of VEGFR-2 ( V ) at the previous timestep ( ) is not present in Equation 3 , the amount of VEGFR-2 is continuously in equilibrium with the amount of effective active Notch1 ( ) . Equation 3 implies that in quiescent cells the number of VEGFR-2 receptors will be maximal , owing to the absence of any Notch activity . As mentioned earlier , the model neglects the role of Notch activity in quiescence and the fact that it will lead to reduced VEGFR-2 levels in quiescent ECs [25]–[27] . Note that Bentley et al . [3] represent every EC by a varying number of agents ( to account for changes in cell shape and cell growth ) , whereas in this study every EC is represented by one agent . However , in order to use the parameter values and equations ( in an adapted form ) of Bentley et al . [3] , Mtot was fixed at a constant value for all ECs . Consequently , the values of V , N , V′ , N′ , V″ , N″ , D and A are evaluated at the cellular level , not at the level of individual membrane agents . This also implies that here cellular polarity is not captured explicitly as receptor and ligand concentrations are uniformly distributed across the membrane junctions . In the current model cell directional behavior follows from gradients of extracellular signals alone . The evolution of the vascular network is determined by tip cell movement , sprouting and anastomosis [18] , [19] , outlined below . At the tissue level , the fracture healing process is described as a spatiotemporal variation of eleven continuous variables: mesenchymal stem cell density ( cm ) , fibroblast density ( cf ) , chondrocyte density ( cc ) , osteoblast density ( cb ) , fibrous extracellular matrix density ( mf ) , cartilaginous matrix density ( mc ) , bone extracellular matrix density ( mb ) , generic osteogenic growth factor concentration ( gb ) , chondrogenic growth factor concentration ( gc ) , vascular growth factor concentration ( gv ) and concentration of oxygen ( n ) . The set of partial differential equations ( PDEs ) accounts for various key processes of bone regeneration . Initially the callus is filled with granulation tissue and the mesenchymal stem cells and growth factors will quickly occupy the regeneration zone . Subsequently the mesenchymal stem cells differentiate into osteoblasts ( intramembranous ossification – close to the cortex away from the fracture site ) and chondrocytes ( central callus region ) . This is followed by VEGF expression by ( hypertrophic ) chondrocytes , which attracts blood vessels and osteoblasts and which is accompanied by cartilage degradation and bone formation ( endochondral ossification ) . The model does not include bone remodeling . The general structure of the set of continuous equations is given by: ( 7 ) ( 8 ) where t represents time , the space and the density of a migrating cell type ( mesenchymal stem cells and fibroblasts ) . represents the vector of the other nine concentrations , ECM densities , growth factor concentrations and oxygen concentrations for which no directed migration is modeled . and are the diffusion coefficients . represents the taxis coefficients for both chemotaxis and haptotaxis . and are reaction terms describing cell differentiation , proliferation and decay and matrix and growth factor production and decay . Detailed information , including the complete set of equations , boundary and initial conditions , parameter values and implementation details can be found in Peiffer et al . [18] and Geris et al . [31] and are provided here as online supplement . The partial differential equations are solved on a 2D grid with a grid cell size of 25 µm . The width of the discrete ECs is determined by the size of a grid cell ( 25 µm ) . Since the ECs in the model of Bentley et al . [3] have a width of 10 µm , the parameter values taken from Bentley et al . are multiplied with a factor of 2 . 5 ( see Table 1 ) . The partial differential equations are spatially discretized using a finite volume method assuring the mass conservation and nonnegativity of the continous variables [32] . The resulting ODEs are solved using ROWMAP , a ROW-code of order 4 with Krylov techniques for large stiff ODEs [33] . The MOSAIC model is deterministic and implemented in Matlab ( The MathWorks , Natick , MA ) . The flowchart in Figure 1B gives a schematic overview of the computational algorithm used in this study . Firstly the continuous variables are calculated . Then the inner loop is iterated which consists of four subroutines: ( 1 ) the current tip cells are evaluated by the tip cell selection criterion and , if necessary , they lose their tip cell phenotype; ( 2 ) the new position of every tip cell is calculated using a central difference scheme in space in combination with explicit Euler time integration; ( 3 ) the code checks whether sprouting occurs , meaning that other endothelial cells also satisfy the criterion for tip cell selection; ( 4 ) the intracellular levels of every endothelial cell are updated . Finally , the inner and outer loops are iterated until the end time of the simulation is reached . The outer loop has a maximal time step size of 8 . 57 hours ( row ) . Since the tip cells do not move more than one grid cell ( 25 µm ) in this time interval ( = 35 µm/day [19] ) , this maximal time step size ( row ) implies that the 11 PDEs can be solved for a constant vasculature . The inner loop has a maximal step size of 1 . 2 hours ( ee ) , similar to Peiffer et al . [18] , and was chosen so that the movement of the tip cells within one grid cell could be accurately followed ( ee≪row ) . To reduce implementation difficulties , the time step of the inner loop ( δt ) is determined by calculating how many maximal inner loop time steps ( ee ) can fit in one outer loop time step ( ΔT ) and dividing the outer loop time step by this number . Consequently , the time step of the inner loop is not constant , which means that D1 , D2 and D3 vary slightly , but this is a minor trade-off for the computational efficiency . Numerical convergence tests have shown that the average inner time step δt is equal to 155 s . Consequently , D1 , D2 , D3 approximate the delays chosen by Bentley et al . [3] . Since the time step δt is approximately 10 times the time step of Bentley et al . [3] , the parameter values of σ and δ have been altered to match the dynamics of the Dll4-Notch system . Numerical tests have shown that similar behavior is retrieved when both σ and δ are multiplied with 3 . 16 ( see Table 1 ) . Simulations were conducted using a quad-core Intel® Xeon® CPU with 12 GB RAM memory . Initially the callus domain is filled with granulation tissue only ( mf , init = 10 mg/ml ) , all other continuous variables are initialized to zero . Boundary conditions are presented in Figure 3 . Further information on the choice of appropriate boundary and initial conditions of the continuous variables can be found in Peiffer et al . [18] and Geris et al . [31] .
The MOSAIC model predicts the evolution of the continuous variables as well as the evolution of the intracellular variables during normal fracture healing . The osteoprogenitor cells enter the callus from the surrounding tissues and differentiate into osteoblasts under the influence of osteogenic growth factors . This leads to rapid intramembranous ossification near the cortex and distant from the fracture line . In the endosteal and intercortical callus the bone is formed through the endochondral pathway , starting from a cartilage template that is mineralized as the blood vessel network is formed to supply the complete fracture zone with oxygen . Figure 4 compares the predictions of the Peiffer-model [18] and the MOSAIC model with the experimentally measured tissue fractions of Harrison et al . in a rodent standardized fracture model [35] . Both models capture the general trends in the experimental data equally well: the bone tissue fraction gradually increases throughout the healing process; the fibrous tissue fraction disappears; the cartilage template is first produced and later replaced by bone . After one , two and three weeks of simulated healing time the surface fraction of the blood vessels in the callus is respectively 2 . 34% , 18 . 20% and 46 . 25% . Experimental results also show that the vascular plexus is very dense in the fracture callus , although quantitative results are lacking [36] , [37] . Images , illustrating the angiogenic and osteogenic process in the fracture callus can be found in Maes et al . and Lu et al . [38] , [39] . These experimental studies report that at the progressing front , there is a tree-like structure of tip cells extending filopodia to sense their environment and to guide the developing sprout . At the back , the vasculature is being remodeled into a more structured network of larger vessels with more quiescent endothelial cells . At present this remodeling phase of the vasculature , which will remove some blunt ends as well as redundant vessels , is not included in the MOSAIC model . Figure 5 shows that the tip cells have high VEGFR-2 levels . The stalk cells are inhibited and have low VEGFR-2 and actin levels . The Dll4-Notch signaling stops when the VEGF-concentration in the callus drops ( the VEGFR-2 levels stay constant ) ( Equations 1–3 ) . The VEGF concentration goes down since the vasculature brings enough oxygen to the fracture site . The endothelial cells far away from the vascular front all have maximal VEGFR-2 levels . The average VEGFR-2 concentration , predicted across all ECs present in the fracture callus , drops at day 7 in the standard condition ( Figure 6 , standard ) . Indeed , after 7 days the ECs start to inhibit each other in gaining the tip cell phenotype , resulting in a prediction of enhanced Notch1-signaling and reduction of the average VEGFR-2 levels at the vascular front . At the back , VEGFR-2 levels are predicted to return to their maximal value , which is a direct consequence of Equation 3 ( effect of Notch activity on VEFGR-2 ) , and the fact that in the model Notch activity levels of an EC are only governed by VEGF-induced Dll4 expression ( in its neighboring cells ) . As mentioned before , the model only focuses on the lateral inhibition between tip cells and stalk cells through Dll4-Notch . It does not address EC quiescence and the fact that Notch activity in quiescent ECs will be associated with reduced VEGFR-2 receptor levels [25]–[27] . Despite this anomaly in terms of the number of VEGFR-2 receptors , the model correctly predicts highly reduced VEGFR-2 activity levels in quiescent cells ( i . e . cells at the back of the vasculature ) , because of the low VEGF concentrations encountered here . This trend in the average VEGFR-2 concentration ( Figure 6 , standard ) was also measured by Reumann et al . [40] . Reumann et al . characterized the time course of VEGFR-2 mRNA expression during endochondral bone formation in a mouse rib fracture model by quantitative RT-PCR [40] . They observed a small drop ( although statistically insignificant ) in the median value of VEGFR-2 mRNA expression at three days post fracture [40] . The simulated average VEGFR-2 concentration ( Figure 6 , standard ) follows a similar trend but drops at later time points ( day 7 versus day 3 ) . The experimental results [40] were , however , determined in a mouse rib fracture model whereas the parameter values of the model presented in this study were derived from a rat femur fracture model [35] . At the same time , it should be mentioned that Figure 6 represents protein values where Reumann et al . [40] measured mRNA levels . Moreover , Reumann et al . [40] measured the total mRNA content , of all cells present in the callus whereas Figure 6 shows the average VEGFR-2 concentration on the cell membranes of the ECs in the callus . Not only ECs but also osteoblasts and other osteogenic cells express VEGFR-2 [41] , which might also explain the temporal difference seen between the experimental and simulated data . If pharmacological blocking of VEGFR-2 receptors is simulated , the vasculature does not develop since the actin production is inhibited , meaning that the ECs cannot extend filopodia and gain the tip cell phenotype . Due to this impaired vascularization only a small amount of bone is predicted intramembranously , resulting in a non-union between the fractured bone ends . If the VEGF concentration is increased ( Figure 7B ) , the vascular density is initially increased since the VEGFR-2 receptors are being more activated in this stimulating environment ( Equation 1 ) . This simulation result is confirmed by many experimental studies that reported an increase in vascularity at the site of VEGF application in a murine femoral fracture healing model [34] , a lapine mandibular defect model [42] , a murine ectopic model [43] and a rat femoral bone drilling defect model [44] . In the simulations the increase in vascular density leads to faster healing , which is also found experimentally [34] , [42] ( Figure 8 ) . The MOSAIC model predicts earlier bone formation , less cartilage formation in both the periosteal , intercortical as well as the endosteal callus . Moreover , the cartilage resorption is predicted to be accelerated , another trend which has been reported in experimental studies [43] , [44] . It is striking , however , that after 35 days , the MOSAIC model predicts that the VEGF-treated callus contains slightly less bone and more remnants of fibrous tissue than the normal condition ( Figure 8 ) . A further increase of the VEGF concentration ( +2% , Figure 7B ( iii ) ) reduces however the vascular density and bone tissue fraction in the MOSAIC model . This is consistent with the trend seen by Street et al . , where an optimal dose of VEGF ( 250 µg ) leads to a maximal amount of callus volume ( both total and calcified ) in a critical rabbit radius segmental gap model [34] . In higher VEGF environments ( e . g . Figure 7B ( iii ) ) the ECs strongly inhibit each other; creating a salt and pepper pattern of high and low VEGFR-2 levels ( Figure 9 ) . In addition , the development of the vasculature ceases after a certain period , as can be seen in Figure 9 . The ECs that fulfill the tip cell criteria ( Equation 6 ) will sprout and will initially move perpendicular to the vessel from which it is originating . In case this “mother” vessel is a growing vessel as well that extends towards the source of the chemotactic and haptotactic signals , this implies that the new sprout will initially move perpendicular to the gradients . In this particular configuration ( Figure 7B ( iii ) ) , the vascular front was progressing in a more “sheet-like” fashion . Consequently , the tip cells persistently want to sprout towards already occupied grid cells , an action that is not allowed in the computational framework . The period after which the vascular development ceases , is shorter in higher VEGF environments . Note that the average VEGFR-2 concentration of all ECs in the callus reaches a constant level which is reduced in high VEGF environments ( Figure 6 ) . When a reduction of VEGF concentration by means of the addition of VEGF-antibodies is simulated , results demonstrate that the VEGFR-2 receptor is not sufficiently stimulated . This leads to an impaired vasculature and a non-union between the fractured bone ends ( see Table 3 , Equation 1 ) . The simulated reduction in vascular density is consistent with the experimental findings of Street et al . who incorporated a fracture hematoma supernatant with neutralizing monoclonal antibody to human VEGF in a Matrigel vehicle , which was then implanted in a murine dorsal wound model [45] . There was a significant decrease in the number of blood vessels formed in the Matrigel vehicle with neutralizing monoclonal antibody when compared to the fracture hematoma supernatant alone [45] . The sensitivity analysis indicates that the model results are greatly influenced by the parameters σ , δ and Vsink and that the MOSAIC model is insensitive to the initial conditions of Dll4 ( D0 ) and actin ( A0 ) . An overview of the results of the sensitivity analysis can be found in Table 3 . Simulations of the heterozygous knockout genotypes ( δ = 50% , σ = 33% ) show an increased sprouting due to a clearly reduced inhibition of the tip cell phenotype ( Equations 2–3 ) . Figure 5 demonstrates that the endothelial cells behind the brown ( tip ) cells with high VEGFR-2 levels are strongly inhibited in the normal case but are weakly inhibited in the simulated knockout . The overexpression of Dll4 ( δ = 200% ) increases the inhibition of the tip cell phenotype resulting in a decrease of the vascular density ( Table 3 ) and a delay in the endochondral bone formation process , particularly in the periosteal callus ( Figure 8 ) . The increase in σ also causes a more potent suppression of the tip cell phenotype in the stalk cells , due to an increased down-regulation of VEGFR-2 by Notch1 ( Equation 3 ) . In turn , this leads to a decrease of the vascular density ( see Figure 5B , Table 3 ) . Thus , if the tip cell phenotype is more inhibited ( by increasing δ , which defines the enhancement of Dll4 expression due to VEGFR-2 activation ( Equation 2 ) or increasing σ , which represents the inhibition of the VEGFR-2 expression due to Notch1 activation ( Equation 3 ) ) , the vascular density is reduced . This simulation result corresponds to experimental observations [5] , [46] , [47] . Hellström et al . [5] showed that the inhibition of Notch signaling ( by inhibiting Notch receptor cleavage and signaling with γ-secretase inhibitors , by heterozygous inactivation of the Notch ligand Dll4 or by endothelial cell specific deletion of Notch1 ) promotes an increase in the number of tip cells in the retina of newborn mice . Conversely , a 35% decrease in filopodia density and a 45% decrease in vessel density were found in a direct gain of function experiment of the Notch1 receptor . Simulating an increase of the decoy receptor VEGFR-1 ( by decreasing Vsink ) results in a reduction of the vascular density since less VEGF remains available for VEGFR-2 activation ( see Figure 7A , Equation 1 ) . This is consistent with Flt-1 ( VEGFR-1 ) loss- and gain-of-function data in zebrafish embryos [34] , [48] . Moreover , Street et al . showed that Flt-IgG treatment decreased the vascularity by 18% and impaired cortical bone defect repair in a murine femoral fracture healing model [34] . Similarly , the in silico results show a delayed or even impaired healing of the fracture due to the reduced vascular density . In Figure 7A ( iv ) the VEGF concentration is too low to activate the VEGFR-2 receptors which stops the angiogenic process . Since only a small amount of bone will be formed intramembranously in the fracture callus of Figure 7A ( iv ) , the impaired vascularization will result in a non-union . If the threshold of VEGFR-2 activation V′* , below which the actin production and tip cell movement is inhibited , is increased , the vascular density decreases ( see Table 3 ) . The initial amount of actin ( A0 ) only slightly influences the final vascular density ( see Table 3 ) . The other variables related to the fracture healing , were not influenced . Similarly , the final vascular density is insensitive to the initial intracellular amount of Dll4 ( D0 ) ( see Table 3 ) .
This study established a novel multiscale model of angiogenesis in the context of fracture healing , by integrating an agent-based model of tip cell selection [3] into a previously developed hybrid model of fracture healing [18] . The bone regeneration process was predicted by the MOSAIC model in accordance with experimental reports and previously validated in silico results [18] . The MOSAIC model was also able to capture many experimentally observed aspects of tip cell selection: the salt and pepper pattern seen in developing vascular structures under normal angiogenic conditions , i . e . a tip cell with high VEGFR-2 and actin levels followed by a stalk cell characterized by strong Notch1 signaling and therefore reduced VEGFR-2 and actin levels [30] , an increased tip cell density and a higher vascular density in case of Dll4 heterozygous knockouts [5] and an excessive number of tip cells ( leading to a very high vascular density ) in high VEGF concentrations [3] , [34] . The sensitivity analysis also indicated the most influential parameters of the MOSAIC model ( δ , σ and Vsink ) . This study has addressed some , but not all of the limitations of the Peiffer-model [18] . In the MOSAIC model the tip cell selection is based on Dll4/Notch1 signaling whereas the Peiffer-model [18] implemented sprouting with phenomenological rules such that ( 9 ) i . e . in the Peiffer-model the VEGF concentration needs to be high enough ( 10 ng/ml ) , there needs to be a minimal separation of 100 µm between two tip cells and the movement direction of the new tip cell should make an angle of >24° with the orientation of its mother vessel . Moreover , the Peiffer-model foresees three healing days between subsequent sprouting events , which has no experimental foundation and has been removed in the MOSAIC model . Consequently , the MOSAIC model is more mechanistic , allowing investigation of different mutant and druggable cases in the signaling pathways , leading to real predictions for experimentation , which was not possible in the Peiffer-model . Since the incorporation of the lateral inhibition mechanism leads to a denser plexus in the MOSAIC model than in the Peiffer-model , we have reduced the oxygen production rate by a factor of two so that the final oxygen concentrations are the same in both models . Experimental results show that the vascular plexus is indeed very dense in the fracture callus [36] , [37] . In the MOSAIC model the tip cell velocity increases with the active VEGFR-2 levels , indicating that both the level of VEGFR-2 and the external VEGF-concentration influence the tip cell speed . This is consistent with the experimental data of Arima et al . [49] . They used time-lapse imaging in a murine aortic ring assay ( with and without VEGF ) to quantify the behavior of the endothelial cells during angiogenic morphogenesis [49] . Arima et al . reported that VEGF-induced vessel elongation was only due to greater displacement per tip cell [49] . Moreover , treatment with Dll4-antibodies also resulted in a greater displacement per tip cell [49] . This is due to the reduced inhibitory actions of Dll4 , causing a greater number of cells to have high VEGFR-2 levels . These results are however contested by Jakobsson et al . who quantified the average migration speed of wild-type ( DsRed and YFP ) and heterozygous Vegfr2+/egfp endothelial cells in different chimaeric embryoid bodies [50] . They observed no difference in migration speed , indicating that VEGFR-2 levels do not determine EC migration velocity [50] . Clearly , more research is necessary to elucidate the above observations and improve the current implementation of tip cell migration in future versions of the model . The MOSAIC model indicates a key role of the decoy receptor VEGFR-1 ( modeled via Vsink ) ( Equation 1 ) . Increasing the amount of VEGFR-1 , results in a decrease of the vascular density ( Figure 7A ) which is also seen in loss- and gain-of-function data [34] , [48] . Both the MOSAIC model and the model of Bentley et al . [3] , use a constant value to represent the decoy-effect of the VEGFR-1 receptor . There is however experimental evidence that both VEGFR-1 and its soluble form are up-regulated in Notch-activated stalk cells [4] , [51] . Hence , the stalk cells phenotype is not only consolidated by a decrease in VEGFR-2 but also by an increase in the competing VEGFR-1 receptor . The results of Krueger et al . also suggest that VEGFR-1 regulates tip cell formation in a Notch-dependent manner [48] . The MOSAIC model displays interesting behavior in high VEGF environments ( see Figure 7 ) . Initially , the increase in VEGF has a positive effect , resulting in a very dense vasculature since the VEGFR-2 receptors are being more activated in this stimulating environment ( +0 . 1%; see Equation 1 ) . This leads to a faster healing , which is also found experimentally [34] , [42] . A further increase ( +2% ) , however , reduces the vascular density in the MOSAIC model . This is consistent with the trend seen by Street et al . [34] . Note that a salt and pepper pattern of high and low VEGFR-2 levels is created and maintained in high VEGF environments ( +2%; Figure 7B ( iii ) and Figure 9 ) . To explain this observation , one needs to look at the beginning of the angiogenic process in the fracture callus . Initially , some endothelial cells gain the tip cell phenotype and start to migrate . Gradually sprouts arise in the developing vasculature which increases the network size and alters the local VEGF-levels due to the influence of the oxygen tension on VEGF-production . The original tip cells maintain their advantage ( e . g . located in a higher VEGF environment ) by strongly inhibiting their neighboring ECs , creating a salt and pepper pattern of VEGFR-2 levels . In standard conditions the vasculature would start to mature , leading to quiescent ECs . In high VEGF environments ( +2% ) , however , this salt and pepper pattern of VEGFR-2 is maintained ( Figure 9 ) , illustrating that some ECs have ( very ) high VEGFR-2 levels leading to a persistent inhibition of their neighboring ECs ( characterized by low VEGFR-2 levels ) . Figure 6 shows that these high VEGFR-2 levels are cancelled out by the low VEGFR-2 levels resulting in a “steady state” level of the average VEGFR-2 concentration . In high VEGF environments ( +2% , +10% ) this “steady state” level is gradually reduced ( Figure 6 ) , implying the dominance of the lower VEGFR-2 levels . Mathematically , this result follows from Equations ( 1 ) and ( 3 ) , indicating that in high VEGF concentrations both the active VEGFR-2 ( V′ ) and Notch ( N′ ) ( and with a delay the effective active VEGFR-2 ( V″ ) and Notch ( N″ ) ) are high , resulting in a reduction of the VEGFR-2 receptor ( Figure 10 ) . Consequently , the average VEGFR-2 concentration is reduced below the threshold for tip cell formation ( Figure 6 ) . In other words , the majority of the ECs have too little VEGFR-2 receptors to assume the tip cell phenotype . In the extreme case , this finally results in the inhibition of the development of the vasculature since there are no tip cells to lead the sprouts towards the VEGF source ( Figure 7B ( iv ) ) . Interestingly , Figure 7D shows that similar results cannot be obtained with the Peiffer-model [18] , i . e . the vascular density is not reduced in high VEGF environments ( +2% , +10% ) . This is due to the phenomenological rules that determine the tip cell selection in the Peiffer-model ( Equation 9 ) . A similar “non-linear” EC response to VEGF concentrations would only be possible with the Peiffer-model if another phenomenological rule would be implemented that e . g . down-regulates tip cell selection at high VEGF responses . In contrast , the “non-linear” response follows naturally from the mechanistic rules of tip cell selection that were implemented in the MOSAIC model . That is , the down-regulation of the tip cell selection in high VEGF environments ( +2% , +10% ) arises from the negative feedback loop in the Notch-Dll4 signaling pathway . Moreover , in high VEGF-environments ( +10% ) and at the back of the developing vasculature , we see an indication that patches of endothelial cells oscillate between cell fates ( switching between high and low VEGFR-2 levels ) . These patches are also predicted by the model of Bentley et al . and are observed during pathological angiogenesis [3] . In the future , we will further investigate the conditions that give rise to these oscillations and their implications on the development of the vasculature . The results of the MOSAIC model are based on the assumption that a tip cell phenotype can only be acquired if the levels of VEGFR-2 and actin are sufficiently high ( Equation 6 ) . If this criterion was changed by replacing the requirement on VEGFR-2 by a similar requirement for the level of active VEGFR-2 , some ECs could become tip cells , since V′ is high in high VEGF environments ( +2% , +10% ) , and the vasculature would fully develop ( Figure 7C ) . These results show the added value of the MOSAIC model: the intracellular module and its related state variables and rules decide on the EC response to the extracellular VEGF environment , in turn determining the healing response at the tissue level ( Figure 8 ) . In case the criterion for tip cell selection is specified in terms of VEGFR-2 ( and actin ) the absence of blood vessel formation will result in a non-union or a delayed union of the fracture . However , when this criterion is replaced by one that relies on the levels of active VEGFR-2 ( and actin ) , a vascular and healing response is retrieved , similar to the Peiffer-model . Clearly , these findings give rise to some interesting biological questions on a proper criterion for the tip cell phenotype . Since the VEGFR-2 levels are strongly reduced in high VEGF environments ( +10% ) , the tip cells lose their tip cell phenotype and stop migrating although there is a strong angiogenic signal present . Can tip cells move in high VEGF environments although they do not have enough VEGFR-2 receptors ? If so , should the tip cell criterion ( Equation 6 ) be based on the active VEGFR-2 levels ( V′ ) , since these remain high in high VEGF concentrations ? Or is the down-regulation of VEGFR-2 receptors in high VEGF environments compensated by other signaling cascades that have VEGFR-2 as one of their downstream targets ( leading to an increase of VEGFR-2 ) ? In this study , the model of Peiffer et al . [18] was combined with a detailed model of Dll4-Notch1 signaling [3] . Some simplifications were however made to the model of Bentley et al . due to computational reasons , i . e . the size and shape of the ECs are fixed in the PDE framework of the MOSAIC model . Consequently , every EC is represented by one agent whereas Bentley et al . use a varying amount of membrane agents for every EC [3] . This does not only allow Bentley et al . to model the change in membrane and cell shape in great detail , but also to include cellular polarity ( non-uniform distribution of receptors and ligands across the cell membrane and cell-cell junctions ) . In the MOSAIC model , filopodia extension is modeled implicitly by an increase in the level of the “actin” variable upon VEGFR-2 activation . This is consistent with current knowledge that activation of Cdc42 by VEGF triggers filopodia formation [4] . Bentley et al . modelled filopodia extension in more detail by adding membrane agents to the cellular membrane . As a result , the number of VEGFR-2 receptors will alter due to filopodia extension , which is proposed to be a mechanism to consolidate the tip cell fate [3] . In the MOSAIC model the accumulation of actin does not lead to an increase in the amount of VEGFR-2 levels or to a change in the microenvironmental range that can be probed by the tip cell . However , if the molecular mechanisms of filopodia extension and its implications on probing the environment and the directionality of tip cell movement are clearer , these can be readily incorporated in the multiscale framework . The mechanism of lateral inhibition is based on Dll4/Notch1 signaling between the endothelial cells of the developing sprout . Delta-Notch signaling is however an evolutionary conserved pathway that is also involved in cell fate specification , tissue patterning and morphogenesis [2] , . In angiogenesis specifically , Notch signaling influences endothelial cell specification [4] , [5] , [30] , [54] , [55] , endothelial proliferation [29] , [30] , cell migration [2] , [30] , filopodia formation [30] , cell adhesion [30] , and post-angiogenic vessel remodeling and endothelial cell quiescence [56] . These effects are not only dependent on Dll4 and Notch1 but also on the other ligands ( Delta-like 1 , Delta-like 3 , Jagged-1 and Jagged-2 ) and receptors ( Notch2 , Notch3 and Notch4 ) [57] . Due to the complexity and interdependency of these pathways , only the influence of Dll4-Notch1 signaling on tip cell selection was modeled . Consequently , in the model once the VEGF levels are reduced due to the restoration of the blood flow and tissue oxygenation , the Dll4-Notch1 signaling pathway is not active anymore . This is predicted to occur in the ECs that are located at the back of the vasculature , returning their VEGFR-2 levels to the maximal value . As mentioned before this contradicts the fact that in quiescent cells VEGFR-2 levels will be minimal and smaller than those of migrating cells , which is consistent with high Notch activity in quiescence [25]–[27] . Since the MOSAIC model does not include the role of Notch in quiescence , the simulation results are only accurate for the initial formation of the vasculature and not for the maturation and stabilization of the vascular plexus . This does not , however , alter the main findings of this work concerning sprouting angiogenesis . Besides VEGFR-2 , also other VEGF receptors , such as VEGFR-1 and VEGFR-3 play a role in angiogenesis . Although the VEGFR-3 receptor is mainly active in lymphangiogenesis , recent experimental evidence indicates that VEGFR-3 is up-regulated in tip cells during pathological angiogenesis [4] , [58] . Blocking this receptor reduces the amount of sprouting and EC proliferation . It appears that VEGFR-2 induces VEGFR-3 expression in tip cells , whereas it is down-regulated in stalk cells by Notch [4] , [59] . However , when more quantitative experimental data become available on the role of VEGFR-1 and VEGFR-3 in sprouting angiogenesis , this can be incorporated in the MOSAIC model . The MOSAIC model only focuses on soluble VEGF , whereas VEGF-isoforms that bind to the extracellular matrix are essential to establish the VEGF gradients required for guided tip cell migration [60] . Some modeling work has already been done in this area [61] , [62] , e . g . Vempati et al . used a detailed molecular model of VEGF ligand-receptor kinetics and transport to investigate the VEGF-isoform specific spatial distributions observed experimentally [61] . Many other factors , such as neuropilin 1 ( NRP-1 ) , fibroblast growth factor ( FGF ) , and platelet-derived growth factor ( PDGF ) regulate the angiogenic response as well [2] . Nevertheless , it has been stated repeatedly that VEGF is “the principal dancer” during angiogenesis [29] , [30] . The proposed MOSAIC model incorporates biological processes at various temporal and spatial scales: an intracellular module that includes Dll4/Notch1 signaling to determine tip cell selection , a discrete representation of the ECs allowing an accurate representation of the developing vascular network and a continuum description of oxygen , growth factors and tissues that finally result in the healing of the fracture by the formation of bone . Our simulation results demonstrate the advantages of such a multiscale approach . Firstly , the interplay between molecular signals , in particular VEGF , Dll4 and Notch1 , endothelial cell phenotypic behavior and bone formation was explored . In this way , the MOSAIC model could be used to verify to what extent gene knockouts , injection of VEGF-antibodies or blockage of VEGF-receptors leads to a “bone phenotype” in terms of rate and amount of bone formation ( see e . g . Figure 8 ) . While some of these simulation results could be ( qualitatively ) compared to experimental data , it is clear that future research efforts must be focused on a more comprehensive quantitative validation . Again , the multiscale nature of the simulation results presents an advantage here , as it allows for a validation at different scales ( molecular , cellular and tissue scale ) . Secondly , the proposed multiscale model is more mechanistic since tip cell selection is based on intracellular dynamics ( Dll4-Notch1 signaling ) , rather than the phenomenological rules that were used in Peiffer et al . [18] . As such , the MOSAIC model enabled to extend the model of Bentley et al . [3] to the context of fracture healing , leading to interesting emergent behavior at the macro-scale . More specifically , whereas the Peiffer-model predicts the presence of a vascular network in high VEGF environments ( +10% ) the MOSAIC model ( depending on the tip cell criterion ) predicts the absence of a vascular network ( see Figure 7 ) , which was a direct consequence of the Dll4-Notch feedback mechanism ( see explanation related to Figure 10 ) . In conclusion , the proposed multiscale method was found to be a useful tool to investigate possible biological mechanisms across different time and spatial scales , thereby contributing to the fundamental knowledge of sprouting angiogenesis and its relation to fracture healing .
|
The healing of a fracture largely depends on the development of a new blood vessel network ( angiogenesis ) , which can be investigated and simulated with mathematical models . The current mathematical models of angiogenesis during fracture healing do not , however , implement all relevant biological scales ( e . g . a tissue , cellular and intracellular level ) rigorously in a multiscale framework . This study established a novel multiscale platform of angiogenesis during fracture healing ( called MOSAIC ) which allowed us to investigate the interactions of several influential factors across the different biological scales . We focused on the biological process of tip cell selection , during which a specific cell of a blood vessel , the “tip cell” , is selected to migrate away from the original vessel and lead the new branch . After showing that the MOSAIC model is able to correctly predict the bone regeneration process as well as many experimentally observed aspects of tip cell selection , we have used the model to investigate the influence of stimulating signals on the development of the vasculature and the progression of healing . These results raised an important biological question concerning the criterion for tip cell selection . This study demonstrates the potential of multiscale modeling to contribute to the understanding of biological processes like angiogenesis .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"biotechnology",
"medicine",
"anatomy",
"and",
"physiology",
"cardiovascular",
"biomedical",
"engineering",
"biological",
"systems",
"engineering",
"bone",
"bioengineering",
"musculoskeletal",
"system",
"theoretical",
"biology",
"biology",
"cardiovascular",
"system",
"systems",
"biology",
"physiology",
"vascular",
"biology",
"computational",
"biology",
"genetics",
"and",
"genomics",
"engineering"
] |
2012
|
MOSAIC: A Multiscale Model of Osteogenesis and Sprouting Angiogenesis with Lateral Inhibition of Endothelial Cells
|
Recommended treatment for severe rabies exposure in unvaccinated individuals includes wound cleaning , administration of rabies immunoglobulins ( RIG ) , and rabies vaccination . We conducted a survey of rabies treatment outcomes in the Philippines . This was a case series involving 7 , 660 patients ( 4 months to 98 years of age ) given purified equine RIG ( pERIG ) at the Research Institute for Tropical Medicine ( Muntinlupa , Philippines ) from July 2003 to August 2004 following Category II or III exposures . Data on local and systemic adverse reactions ( AR ) within 28 days and biting animal status were recorded; outcome data were obtained by telephone or home visit 6–29 months post-exposure . Follow-up data were collected for 6 , 464 patients . Of 151 patients with laboratory-confirmed rabies exposure , 143 were in good health 6–48 months later , seven could not be contacted , and one 4-year-old girl died . Of 16 deaths in total , 14 were unrelated to rabies exposure or treatment . Two deaths were considered PEP failures: the 4-year old girl , who had multiple deep lacerated wounds from a rabid dog of the nape , neck , and shoulders requiring suturing on the day of exposure , and an 8-year-old boy who only received rabies PEP on the day of exposure . This extensive review of outcomes in persons with Category III exposure shows the recommended treatment schedule at RITM using pERIG is well tolerated , while survival of 143 laboratory-confirmed rabies exposures confirms the intervention efficacy . Two PEP intervention failures demonstrate that sustained education and training is essential in rabies management .
Rabies is a zoonotic disease characterized by progressive and incurable viral encephalitis , invariably fatal if untreated and usually transmitted by the bite ( s ) or scratches of an infected animal . Data from the Department of Health show that every year , over 100 , 000 people at risk in the Philippines receive rabies post-exposure prophylaxis ( PEP ) , which varies according to the categorization of the exposure as defined by the World Health Organization ( Table 1 ) . The most severe cases , Category III , require wound cleaning , rabies vaccination , and direct wound infiltration with rabies immunoglobulin ( RIG ) and where possible , observation of the biting animal if it does not already display clinical symptoms of rabies for a period of 10 days [1] , [2] . Infiltration of RIGs into the wound ( s ) is essential in the management of severe bites to provide passive antibody protection during the first 1–2 weeks while the body develops its own immune response to vaccination . The WHO recommends the use of human RIG ( HRIG ) or equine ( ERIG ) in category III exposures [2] . For multiple severe Category III exposure HRIG is recommended , however , when not available or accessible , ERIG or pERIG must be used . As availability of HRIG is constrained by the limited production capacities imposed when using human plasma as the immunoglobulin source , bite victims in highly endemic countries are more likely to receive ERIG or pERIG . F ( ab' ) 2 fragment rabies immunoglobulin ( Favirab , Sanofi Pasteur , Lyon , France ) is a highly purified pERIG , characterized in animal models [3] and in humans [4] and is currently used in over 40 countries . Industrial chromatographic purification results in a product with a high purity with selective extraction of active immunoglobulin molecules ( IgG ) from plasma and a final purification of F ( ab' ) 2 from the IgG peptic digest . The final pasteurized solution for wound infiltration has a high specific activity , containing mainly F ( ab' ) 2 molecules ( 85% ) . The clearance of Favirab is more rapid than ERIG and HRIG , documented by certain experimental animal data , however , this is not considered to influence the efficacy . Fewer than 1% of patients report adverse events to Favirab , these consisting mainly of mild allergic type reactions . We report the results of a review of consultation records and follow-up investigations to determine the health status of persons who received PEP , including Favirab as a source of ERIG , at the Research Institute of Tropical Medicine , Manila .
Whenever possible , as part of standard treatment procedures , and in order to confirm the presence of rabies virus in biting animals that had died or were killed , a direct Fluorescent Antibody Test ( dFAT ) was performed at the rabies laboratory of the RITM following standard procedures [5] .
During follow-up investigations ( by telephone , by home visit or by hospital record ) a total of 7 , 604 ( 99 . 3% ) subjects could be contacted as 56 subjects moved out of the area or their contact address or telephone number were inadequate . A total of 3 , 970 ( 51 . 8% ) subjects were contacted by telephone , 3 , 595 ( 46 . 9% ) subjects by home visit and 18 ( 0 . 23% ) by telephone followed by a home visit . Health status could be documented in 6 , 468 ( 84 . 8% ) subjects , but for 1 , 164 ( 15 . 3% ) subjects no follow-up information was obtained as they had moved from the original address , etc . The interval between PEP-and follow-up event varied between 35 days and 29 months .
This primary objective of the evaluation was to document the health status of subjects given rabies PEP which included use of the purified equine rabies immunoglobulin ( pERIG ) , Favirab . The health status of patients treated after Category II/III exposure with the standard of care described in the RITM guidelines was documented by an active survey by telephone or home visits . The RITM guidelines are in accordance with the WHO guidelines [2] , [6] , the local Philippine recommendations and those developed in other countries [7] . Not unexpectedly , exposure was seen predominantly in children 5 years or younger , with over 23 . 28% of exposures occurring below 5 years of age; twice as many boys exposed when compared to girls . A similar age and sex distribution was reported in Thailand [8] , although the age distribution was different to that recently reported in India [9] . The records made no reference to washing of the wounds , however , the standard recommendations of the RITM are likely to have been implemented carefully . Attending staff of the different animal bite treatment centers are trained in the appropriate management of patients with animal bites and those with Category III exposure are referred to the RITM Admitting Section . Wound washing is followed by an infiltration of pERIG into the wound site and injection of the remaining pERIG by IM route . This split administration was documented in 97 . 8% of the cases , only 108 cases ( 1 . 4% ) having pERIG infiltration into the wound alone . 61 patients received pERIG by IM only; 16 of whom were exposed to rabid patients and 6 had healed wounds at the time of the consultation . Over 54% of subjects had exposure in highly innervated body regions , such as the head , hands or feet , and one 23-year-old subject was bitten on the penis . Specific reference to exposure involving fingers or toes was documented in 24 subjects ( 2–61 years-old ) and following treatment , no compartment syndrome was reported . This experience is similar to that observed in Thailand [10] . A first dose of rabies vaccine was administered to 98 . 5% of the study population . Rabies vaccination for the second and third dose was continued , as documented in the RITM records , in 53 . 8% and 43 . 5% , respectively . During the follow-up investigation , data on rabies vaccination in local animal bite treatment centers was not requested to avoid introducing a recall bias . For the 6 , 468 subjects for whom follow-up information was obtained , the mean interval between exposure and follow-up was 11 . 5 months ( ranging from 35 days to 29 months ) . During the survey window , 16 deaths were recorded in the whole study popualtion , in subjects from 4 to 72 years of age , occurring between 35 days and 16 months following exposure . The causes of 14 deaths were clearly identified as being unrelated to the rabies exposure , but two deaths were considered PEP intervention failures . Other clinical conditions reported by the patients or their parents occurred between 8 and 28 months but none were considered related to the treatment . The documented healthy outcome of 143 subjects exposed to laboratory-proven rabid animals showed that the combination of pERIG , rabies vaccine and wound treatment is effective in protecting against canine rabies virus infection . The results confirm the clinical effectiveness of pERIG when used in PEP , notwithstanding the questions raised by scientific research , such as a more rapid clearance and a reported difference in protection associated with the use of pERIG against different rabies strains in animal models [11] . Nonetheless , no treatment intervention is 100% successful as illustrated by two tragic outcomes . The first case concerned a malnourished girl with severe lacerations in critical anatomical areas [12] . She was the first bite victim of the dog , and therefore probably received a large inoculum of the virus in highly innervated areas . Further , given her extensive lacerations with persistent bleeding , her wounds were sutured . She did subsequently develop an adequate immune response to the vaccine , as described for other malnourished children [13] , but still succumbed to the rabies infection ( Figure 1 ) . The WHO recommendations ( TRS931 ) state that in cases of multiple severe exposures , HRIG if available should be infiltrated in the wounds , otherwise pERIG should be used and a maximum quantity must be infiltrated undiluted in the lesions . We can only speculate whether the use of HRIG , or indeed refraining from suturing the wounds , could have prevented the course of disease in this case . The second case concerned a boy who only received treatment on the day he was bitten by a rabid dog , although this was not confirmed . With no subsequent vaccinations the boy died . What is notable in this case is that a cousin bitten by the same animal on the same day , and who completed the recommended rabies vaccination series through 90 days , was in good health 10 months post exposure . These two cases are defined as rabies PEP intervention failures . In summary , records of 7 , 660 patients given rabies PEP at the RITM during the period July 2003 to August 2004 were considered in this case series . Although the true extent of rabies exposure in all 7 , 660 patients is unknown , of 144 cases of laboratory-proven rabies Category III exposure available for follow-up , there was one rabies PEP intervention failure . A second presumed PEP intervention failure , there being no confirmation of rabies infection in the biting animal , highlights the importance of the potential of rabies infection by animals not being examined for their rabies status and illustrates that the burden of disease is more important . Thus , the RITM rabies PEP guidelines , i . e . , wound cleaning and treatment , antibiotic and tetanus prophylaxis , rabies vaccination and use of pERIG for Category III exposures by potential rabid animals are deemed satisfactory . Whereas further consideration of the development of alternative treatments to the current RIGs , such as monoclonal antibodies , is merited [11] , the real-world , clinical experience presented here emphasizes that , in the meantime , correct implementation of the rabies prevention recommendations is of paramount importance to save lives . Continued training of treating physicians and attending staff should further improve the quality of treatment and care . Extensive lesions in highly innervated body regions are of particular concern and may demand specific clinical interventions , such as immediate suturing . It must be borne in mind that such deviations from the recommendations are often basic and unavoidable when working under field conditions , and must be considered with great caution when interpreting unexpected outcomes . The experience from the RITM is that pERIG , when administered as recommended and as part of the rabies PEP in conjunction with wound treatment and rabies vaccination , is safe and effective and contributes to the prevention of otherwise fatal consequences of rabies infection .
|
Infection from a bite by a rabid animal is fatal unless rapid treatment ( thorough cleaning of the wound , administration of rabies immunoglobulins ( RIG ) , and a full anti-rabies vaccination course ) is provided . Ideally human RIG should be used , but cheaper , more readily available purified horse RIG ( pERIG ) are widely used in developing countries . Follow-up of over 7 , 600 patients previously given pERIG at the rabies treatment reference center in Manila ( Philippines ) provided updated health status for 6 , 458 patients 39 days to 29 months after treatment . A total of 151 patients had been bitten by animals with laboratory-confirmed rabies . Two rabies deaths were reported , one in a 4-year-old girl with bites on the back , shoulder , and neck so severe that stitching was required to prevent bleeding ( against recommended practice ) , and another in an 8-year-old boy who only received rabies vaccination on the day of initial treatment . A 7-year-old cousin of this boy , bitten by the same animal , who did receive the full vaccination course was still healthy 10 months later . Fourteen other reported deaths had causes unrelated to rabies . These data illustrate the effectiveness of pERIG as part of the recommended treatment regimen , while highlighting the importance of adhering to current recommendations .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/infectious",
"diseases",
"of",
"the",
"nervous",
"system",
"infectious",
"diseases/viral",
"infections",
"pathology/neuropathology",
"virology/emerging",
"viral",
"diseases"
] |
2008
|
Rabies Post-Exposure Prophylaxis in the Philippines: Health Status of Patients Having Received Purified Equine F(ab')2 Fragment Rabies Immunoglobulin (Favirab)
|
A meta-analysis of the effects of vector saliva on the immune response and progression of vector-transmitted disease , specifically with regard to pathology , infection level , and host cytokine levels was conducted . Infection in the absence or presence of saliva in naïve mice was compared . In addition , infection in mice pre-exposed to uninfected vector saliva was compared to infection in unexposed mice . To control for differences in vector and pathogen species , mouse strain , and experimental design , a random effects model was used to compare the ratio of the natural log of the experimental to the control means of the studies . Saliva was demonstrated to enhance pathology , infection level , and the production of Th2 cytokines ( IL-4 and IL-10 ) in naïve mice . This effect was observed across vector/pathogen pairings , whether natural or unnatural , and with single salivary proteins used as a proxy for whole saliva . Saliva pre-exposure was determined to result in less severe leishmaniasis pathology when compared with unexposed mice infected either in the presence or absence of sand fly saliva . The results of further analyses were not significant , but demonstrated trends toward protection and IFN-γ elevation for pre-exposed mice .
Vector-borne diseases are a major cause of morbidity and mortality in many areas of the world . In addition to their cost to human health , vector-borne diseases can have a high economic cost primarily affecting impoverished nations and the people with the least resources . While there have been efforts to control or eradicate certain vector-borne diseases , these goals have proved frustratingly elusive and the incidence of some vector-borne infections , such as leishmaniasis , is rising [1] . Emerging and reemerging diseases such as Chikungunya threaten to become major public health concerns . More familiar diseases , like malaria and dengue fever , are infecting new populations due to lapses in vector control programs , human migration and increasing vector habitat due to climate change and other human activities [1]–[8] . Although vaccines have been developed for some vector-borne diseases ( e . g . yellow fever , ) the vast majority and the most problematic still lack vaccines and viable treatment options . The quest for vaccine development has included assessing the potential protective effect of long-term exposure to insect vector saliva . Results have been mixed at best , and there is some controversy as to whether saliva exacerbates disease or protects against its more severe manifestations . When an arthropod vector bites a host and transmits a pathogen , it releases some of its own saliva into the bite site as well as the pathogen . It is well established that this saliva is highly immunogenic , containing vasodilatory and immunosuppressive compounds [9] . Perhaps the most studied vectors in this regard have been that of sand fly vectors of leishmaniasis . A landmark study in 1988 by Titus and Ribeiro demonstrated that Lutzomyia longipalpis saliva exacerbates Leishmania major infection in naïve mice [10] . Many similar studies have followed , consistently demonstrating that naïve animals either infected via sand fly or coinoculation with salivary gland homogenate along with Leishmania parasites have generally developed larger , longer lasting lesions than animals inoculated with parasites alone [11]–[25] . Furthermore , these effects appear to be consistent across all sand flies , though the salivary composition differs widely between species . In Lutzomyia species , the vasodilatory peptide maxadilan has been implicated in upregulating Th2 cytokines ( e . g . IL-4 and IL-10 ) and down-regulating Th1 cytokines ( e . g . IFN- γ ) in vitro and in vivo , presenting a potential mechanism for the observed differences in disease progression [18] , [26]–[31] . Belkaid et al . further demonstrated that disease enhancement is IL-4 driven , as Phlebotomus papatasi saliva did not enhance disease in IL-4 deficient mice . Furthermore , disease enhancement was even greater in IL-12p40 deficient mice [12] . The infection-enhancing effects of saliva , however , have been demonstrated to be negated by prior exposure to uninfected sand fly saliva [11] , [13] , [18] , [24] , [32]–[45] . Immunity to the salivary peptides is theorized to elicit a strong Th1 response in the host , which adversely affects Leishmania parasites . This effect appears to apply to immunization with uninfected sand fly bites and with individual salivary proteins , though laboratory-colonized sand fly saliva is much more effective than wild-caught in providing protection against disease [11] , [13] . Studies assessing the effects of mosquito saliva began soon after those of sand flies , with Bissonnette et al and Cross et al demonstrating that Aedes aegypti saliva inhibits IFN-γ , TNF-α , and IL-2 release from murine cells [46] , [47] . As with the sand fly studies , the reports that followed have consistently demonstrated that mosquito saliva from all genera also up-regulates Th2 cytokines and down-regulates Th1 cytokines [46]–[56] . Mosquito saliva has also been shown to increase infectivity of various viruses [57]–[62] , as well as enhancing viral replication [63] , mortality [64]–[66] and even being necessary for infection [57] . However , there has been some controversy with regard to its effect on malaria , with some studies claiming exacerbation of disease and others claiming no effect or even protection from prior immunization [67]–[70] . Hard ticks are a third group of well-studied arthropod vectors with immunomodulatory saliva . These ticks can take up to two weeks to take a complete bloodmeal , so it is necessary for them to secrete these compounds to avoid rejection from the host . Tick saliva has been demonstrated to inhibit pro-inflammatory ( Th1 ) cytokine production [71]–[79] , T cell proliferation [71] and neutrophil activity [80] . Accordingly , it has also been implicated in increasing Borrelia and viral infectivity , and immunity against tick saliva may also correspond to decreased effectiveness of the pathogen [81]–[90] . While several review papers on this topic have been published , to date there has not been an analytical comparison of these studies . Here we present a meta-analysis of the effects of vector saliva on disease progression as it applies to three outcomes: pathology , pathogen load , and cytokine levels . Only transient-feeding vectors were included ( i . e . sand flies and mosquitoes ) , as long-term feeding results in a more complicated and not directly comparable interaction . The proportion of mosquito experiments included in each of the analyses varied ( 22–52% for pathogen load , 25–37 . 5% for cytokine levels , and 0% for pathology ) due to the limited number of published studies including these parameters . Also as a result of paucity , human studies and research on trypanosomes and their vectors were also excluded . For comparability , only in vivo infection , as opposed to macrophage and other in vitro cell studies , and quantification by ELISA and PCR were used for the cytokine evaluation . Furthermore , only IFN-γ , IL-4 , and IL-10 were included , as they were the most often studied . Experiments were placed into two groups: naïve animals exposed to saliva during infection compared with a control group exposed to only pathogens , and animals pre-exposed to saliva before infection compared with a control group of naïve animals exposed to saliva only during infection . A third group , pre-exposed animals compared with those that were needle inoculated and not exposed to saliva at all , was included in the leishmaniasis pathology evaluation . Other than expanding our knowledge of the biology of infection , the results of the analyses concerning the first group could have ramifications for vector control programs and vaccine studies . If control programs are allowed to lapse , newly naïve populations could end up with more severe disease . As for vaccine trials , it would be important to test against vector-borne infection as opposed to needle inoculation . This has been a problem especially with vaccines against leishmaniasis; they may work for needle-inoculated mice but fail to protect against infection via sand fly [91] , [92] . The second group mimics natural conditions for endemic populations and naïve ones such as travelers and deployed military service members . It is important to understand potential elevated risks in these populations , as well as potential for vaccine studies . The third group assesses whether immunity induced by saliva pre-exposure just negates the exacerbative effect of saliva , or if there is an added protective effect . While there are certainly limits to this type of analysis , it can be very useful in determining whether observed trends across the published literature are statistically significant effects .
For consistency and comparability , this analysis included only murine studies concerning transient-feeding vectors . A thorough literature search was performed using Pubmed ( http://www . ncbi . nlm . nih . gov/pubmed/ ) for papers published until May 2013 . Search terms combined vector saliva , immune response , and specific vectors and diseases such as leishmaniasis , malaria , dengue , sand fly saliva , and mosquito saliva . Other papers were found using the references in previously located articles . Criteria for inclusion were studies using wild-type mouse strains ( as opposed to certain immunodeficient ) that contained information on one of three outcomes: pathology ( leishmaniasis papers only ) , cytokine levels in vivo ( ELISA or PCR ) , and infection level ( parasite or viral load in tissues or parasitemia/viremia ) . Due to the constraints inherent in a meta-analysis , we limited our focus on pathology to studies evaluating leishmaniasis . The statistical analyses required a certain number of data points and we were unable to find enough studies assessing other pathogens to make comparisons on their own and we could not combine the studies with leishmaniasis pathology studies because the types of assays were not comparable ( e . g . lesion size compared to mortality analysis ) . Similarly , flow cytometric analysis of cytokine expression was excluded because there was not enough conformity across studies in the experimental set up , cells assessed , and gating strategies to be controlled properly . The Studies were organized into three broad categories of experiments for analysis: Cytokines were divided into IFN-γ , IL-4 , and IL-10 groups , as these proteins were most commonly measured . Mean values and standard deviations of the mean for each outcome were extracted from either data reported in the papers or from figures using the “grabit” function in MATLAB ( Mathworks ) . When standard errors were reported , they were converted to standard deviations with the formula SE = SD/√N . When no standard deviation or error was reported , standard deviation was calculated as 1/N . The mean was taken for multiple measurements over time . The data were analyzed with the metafor package in R ( r-project . org ) . A random effects model was used as there is considerable variation in both mouse strain and vector and pathogen species . The natural log of the ratio of the experimental mean to the control mean was taken [Yi = ln ( Xe/Xc ) ] . Using a ratio allowed us to directly compare studies and provided a means of controlling for differences in experimental design . Variance was calculated by the formula Vi = [SDe2/ ( Ne*Xe2 ) ]+[SDc2/ ( Nc*Xc2 ) ] . The code used in R was as follows: dat1<-read . csv ( “[file name]” , header = TRUE ) res1<- rma ( Yi , Vi , data = dat1 ) summary ( res1 ) forest ( res1 , slab = paste ( dat1$Author , dat1$Year , sep = “ , ” ) ) This code sequence provided a summary of the analysis ( most importantly overall effect and p value ) and a forest plot of the data for each group .
The infection level analysis combined measurements of parasite and viral load in tissues and parasitemia or viremia . Various sand fly and mosquito vectors were included , as were various pathogen species ( namely Leishmania species , Plasmodium species , and West Nile virus ) ( Table S1 ) . Infection of naïve mice in the presence of vector saliva was found to significantly increase infection level ( estimate 1 . 2440 , p value 0 . 0029 ) compared to pathogen alone ( Fig . 1 ) . These results are broadly applicable , considering the variation in vectors ( Lu . longipalpis , Ph . papatasi , Culex tarsalis , and Ae . aegypti ) and pathogens ( L . amazonensis , L . major , L . braziliensis , West Nile Virus , and Rift Valley Fever Virus ) . Pre-exposure to saliva , however , was not demonstrated to significantly decrease infection level across all vectors and pathogens ( estimate −0 . 6266 , p value 0 . 0868 , Table S2 ) or even across just the sand fly vectors and leishmaniases ( estimate −0 . 8063 , p value 0 . 0865 , Table S2 ) . The general trend , however , did indicate protection . There was unfortunately not enough information available to perform an analysis of the third group , that of pre-exposed mice compared with control mice infected without saliva . In some of the studies , a salivary protein was used as a proxy for saliva as a whole ( maxadilan [18] and rLMJ11 [38] ) . The analysis was conducted both including these studies ( Fig . 1; Table S2 ) and excluding them ( Table S2 ) , and the results were not significantly different from each other . Although saliva is a complex cocktail of proteins and the protocols utilized for vaccination utilize greater amounts of a single protein than is found in salivary extracts , this result indicates that maxadilan and LmJ11 are both likely major factors in saliva's immunogenic properties , and that they alone have nearly the same effect as the entire salivary gland homogenate . Due to the low number of studies concerning other aspects of pathology , here pathology is synonymous with the size of Leishmania induced lesions . Though all of the studies in this analysis concerned sand flies and Leishmania , they varied considerably with regard to mouse strain , sand fly species , Leishmania species , and experimental design ( e . g . infected ear or footpad , experimental group infection by vector feeding or inoculation , amount and times of pre-exposure , etc ) . Consistent with the infection level results , naïve mice infected in the presence of saliva had significantly larger lesions than those in the control group ( estimate 0 . 319 , p value<0 . 001 ) ( Fig . 2 ) . These results were consistent regardless of whether the saliva came from the natural vector or another species of sand fly ( natural vector estimate 0 . 6183 , p value<0 . 0001; other vector estimate 0 . 7837 , p value<0 . 0001; Table S2 ) , or even whether the vector was of the natural genus ( natural genus estimate = 0 . 6388 , p = <0 . 0001 , other genus estimate 0 . 8644 , p value<0 . 0001 ) ( Table S2 ) . Saliva also appeared to increase the duration of the lesions , though this factor was not included in the analysis due to inconsistencies in the lengths and intervals of time measured between studies . Pre-exposure to saliva , however , was shown to significantly decrease lesion size ( estimate −0 . 7781 , p value<0 . 0001 ) when compared with naïve mice infected in the presence of the same saliva ( Fig . 3 ) . Interestingly , the only study to show the opposite [35] was also the only study conducted on the natural pairing of L . braziliensis and L . intermedia . However , the overall results again remained significant regardless of whether the saliva came from the natural vector or even the natural genus ( natural vector estimate − . 5839 , p value 0 . 0074 , other vector estimate −1 . 0174 , p value<0 . 0001 , natural genus estimate −0 . 6909 , p value 0 . 0008 , other genus estimate −09326 , p value 0 . 0010 ) ( Table S2 ) . It is noteworthy that two of the experiments ( bThiakaki Fig . 2A and cThiakaki Fig . 2A [24] ) included , trend more toward enhancement , though not statistically significant . The mice in these studies were pre-exposed to Ph . papatasi and Ph . sergenti saliva , respectively , and subsequently exposed to Lu . longipalpis saliva upon infection . The third experiment by the same authors ( aThiakaki Fig . 2A [24] ) , where mice were pre-exposed to Lu . longipalpis saliva , demonstrated protection . These results imply that the protection gained by prior exposure may be somewhat species ( or at least genus ) - specific . When compared with a control group infected without any saliva at all , mice pre-exposed to saliva developed smaller lesions ( estimate −0 . 4889 , p value 0 . 0254 ) ( Fig . 4 ) . As with the infection level studies , the analysis did not vary by excluding the studies using only maxadilan or rLMJ11 ( Table S2 ) . Studies included in the cytokine analysis were those that measured IFN-γ , IL-4 , or IL-10 by either ELISA or PCR . Other cytokines and those measured via flow cytometry were excluded for consistency and due to low numbers and only measurements from in vivo infections were included . IFN-γ analysis of pre-exposed versus naïve mice ( n = 5 ) , as well as , of naïve mice infected in the presence versus absence of saliva ( n = 9 ) were inconclusive ( Table S2 ) . Though general trends were observed , they were not significant ( IFN-γ levels were lower in naïve mice exposed to saliva than in control mice and higher in pre-exposed mice than in the control group , Table S2 ) . Naïve mice exposed to saliva during infection , however , had significantly higher IL-4 levels than control mice exposed only to the pathogen ( estimate 1 . 7196 , p value 0 . 0185 ) ( Fig . 5 ) . Likewise IL-10 levels were shown to be significantly higher in naïve mice exposed to saliva during infection ( estimate 0 . 8398 , p value<0 . 001 ) ( Fig . 6 ) . Unfortunately there were not enough measurements of IL-4 or IL-10 in mice pre-exposed to saliva versus naïve mice to conduct an analysis . Both of the cytokine findings were consistent across studies using both mosquito and sand fly vectors and various pathogens ( parasitic and viral ) , suggesting a common mechanism of disease enhancement in the saliva of diverse vectors .
Here we performed a meta-analysis of available data concerning the effects of vector saliva on host immunity . While a vast amount of heterogeneity existed between studies , the use of a ratio allowed us to control for the variability . Overall , our study indicates that saliva enhances infection in naïve mice . Pathogen levels in host blood and tissues are consistently higher in those mice exposed to saliva during infection and this effect holds true for both sand fly and mosquito vectors and for different pathogen species . As such , one would imagine that these results could be extended to other transient-feeding vectors as well , and indeed that has been demonstrated to be the case with Glossina morsitans morsitans/Trypanosoma brucei brucei [93] , [94] and Rhodnius prolixus/Trypanosoma cruzi [95] . Both of these studies report higher parasitemia in naïve mice infected in the presence of saliva . More studies need to be performed on these and other vectors , however , to see if the findings are truly consistent . In addition to its effects on infection level , vector saliva also influences leishmaniasis pathology . Here we demonstrated that sand fly saliva enhances Leishmania-induced lesion size . Furthermore , higher levels of morbidity and mortality in mice infected with West Nile virus in the presence of mosquito saliva have been reported [61] , [64]–[66] . Thus , the higher infection levels that result from saliva exposure have a demonstrable effect on the disease pathology . An important consideration in this analysis was whether the vector/pathogen pairing was natural or unnatural . Several sand fly/Leishmania studies used an unnatural combination of Lu . longipalpis saliva with L . major , where the natural vector is one of several Phlebotomus species [10] , [18] , [22] , [23] , [38] , [41] , [96] . Likewise there have been studies using Lu . longipalpis saliva paired with L . amazonensis or e . braziliensis [14]–[16] , [18] , [19] , [21] , [22] , [24] , [97] , [98] . While the genus is correct , these Leishmania species are naturally transmitted by different sand fly species [99] . These studies have the potential be both helpful and misleading . What is true for an unnatural pairing may not hold for a natural combination and thus the results may have little practical application . On the other hand , if the effects are similar regardless of the pairing , there may be potential for a more comprehensive vaccine , or at the least important implications for travelers already exposed to different sand fly species . We found that including or excluding the unnatural pairings made no difference in the overall results of the analyses , and that both natural and unnatural pairings ( species and genus ) generally demonstrated the same results in all categories . A proposed mechanism for the salivary enhancement of infection has been the up-regulation of host Th2 cytokines . Indeed our analysis demonstrates a marked increase in IL-4 and IL-10 levels in groups exposed to saliva , both sand fly and mosquito , suggesting a strong Th2 response . These results , taken with the lack of enhancement in IL-4 deficient mice [12] , strongly imply that the proposed Th2 driven mechanism is in fact correct . In vivo , cytokines function in a milieu of other cytokines and factors and it is the relative balance ( or ratio ) or these proteins that set the tone of a particular immune response . Whether Th1 cytokines are were regulated in response to saliva exposure is another question we investigated . While IFN- γ levels were generally lower in mice exposed to saliva , the results were not significant . However , upon further examination of the data , the only study to report the opposite also contained the only unnatural vector/pathogen pairing ( [96] , Lu . longipalpis/L . major ) . Eliminating this study lowered the p value , but not enough that the results were significant . The potential for vaccines developed from vector saliva has been an important research topic in recent years . Therefore , a major aim of this study was to determine whether pre-exposure to vector saliva results in less severe infection . In the infection level analysis , while the trend was toward lower levels in pre-exposed mice , the results were not significant and therefore inconclusive . However , the leishmaniasis pathology analysis demonstrated less severe lesions in pre-exposed mice , and this result holds true even when compared with mice unexposed to saliva even during infection . Therefore , with respect to leishmaniasis pathology , pre-exposure does not just negate the infection-enhancing effects of saliva in naïve mice , it actually confers a significant protective effect compared to infection in the absence of saliva . It is interesting to note , however , that while pre-exposure to Lu . intermedia saliva does decrease infection level , it appears to have the opposite effect on lesion size [35] . More studies are necessary to investigate this phenomenon . While a comprehensive cytokine analysis would be extremely informative with regard to the mechanism of the demonstrated protective effect , unfortunately there were not enough pertinent studies to conduct an analysis on IL-4 or IL-10 levels , and the IFN- γ analysis results were inconclusive . Kamhawi et al . found little change in the level of IL-4 producing cells in pre-exposed mice compared with naïve mice [39] , though IFN- γ levels were elevated in pre-exposed mice . Interestingly , all of the studies reported much higher IFN- γ levels in pre-exposed mice except for one using a natural pairing of Ph . papatasi/L . major in BALB/c mice [12] . The same pairing with C57BL/6 mice indicated elevated IFN- γ in pre-exposed mice . This study is the only incidence in the analyses where mouse strain makes a difference , but it illustrates that while these results may be true for some mouse strains and vector/pathogen combinations , they may not be true for other strains or indeed other animals or humans . Not surprisingly , studies assessing human immune responses to insect bites in disease settings are few and do not present a unifying theme for all vector-transmitted diseases or for a single disease or vector . Some studies suggest that saliva exposure skews the human immune response toward Th2-type immunity [100]–[103] and others suggest a more mixed response [104]–[106] . This meta-analysis has demonstrated conclusively the infection-enhancing effect of transient-feeding vector saliva in murine models of infection and the Th2 driven mechanism behind it; however , more studies need to be conducted on the effects of pre-exposure . A significant protective effect exists with regard to sand fly saliva and leishmaniases , but the mechanism still needs to be clarified . More cytokine studies are needed , as well as additional studies with other . Overall , the vaccine potential of saliva needs to be further investigated . There are many important considerations in the potential development of vaccines , not least that humans may be affected very differently than specific mouse strains , saliva differs widely between vectors , and immunity to saliva has only been demonstrated to result in less severe disease , not prevent infection entirely . Indeed , while the human response to vector saliva has been demonstrated to be similar to the murine one in that saliva enhances infection in naïve human cells [107]–[109] , the effects of pre-exposure have been more controversial and appear to be more complicated than in mice [100]–[102] , [105] , [106] . This study , perhaps most importantly , emphasizes the importance of maintaining vector control programs once started . If allowed to lapse , not only will the protective immunity be lost when vector populations rebuild , but disease may be much more severe in newly naïve populations .
|
Arthropod vectors transmit a wide variety of diseases resulting in substantial human morbidity and economic costs worldwide . When hematophagous arthropods blood feed , they release saliva into the host . This saliva elicits a strong immune response and has recently been a focus for vaccine research . There is evidence that the saliva enhances infection in naïve hosts , but that prior exposure to saliva results in less severe infection . This analysis endeavored to determine whether there was a statistically significant enhancement or protective effect with regard to saliva exposure and the progression of disease , and to determine the underlying immune mechanism driving these effects . We found that saliva does indeed enhance infection levels of vector-transmitted pathogens and leishmaniasis pathology in naïve mice and elevates Th2 cytokine levels ( IL-4 and IL-10 ) . We also determined that pre-exposure to saliva results in less severe pathology of experimental leishmaniasis in mice . These results are important for vaccine trials and vector control programs , though more studies are needed with regard to pre-exposure .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"infectious",
"disease",
"immunology",
"veterinary",
"diseases",
"zoonoses",
"medicine",
"and",
"health",
"sciences",
"clinical",
"immunology",
"leishmaniasis",
"biology",
"and",
"life",
"sciences",
"immunology",
"vector-borne",
"diseases",
"veterinary",
"science",
"parasitology"
] |
2014
|
Meta-analysis of the Effects of Insect Vector Saliva on Host Immune Responses and Infection of Vector-Transmitted Pathogens: A Focus on Leishmaniasis
|
The Notch pathway controls proliferation during development and in adulthood , and is frequently affected in many disorders . However , the genetic sensitivity and multi-layered transcriptional properties of the Notch pathway has made its molecular decoding challenging . Here , we address the complexity of Notch signaling with respect to proliferation , using the developing Drosophila CNS as model . We find that a Notch/Su ( H ) /E ( spl ) -HLH cascade specifically controls daughter , but not progenitor proliferation . Additionally , we find that different E ( spl ) -HLH genes are required in different neuroblast lineages . The Notch/Su ( H ) /E ( spl ) -HLH cascade alters daughter proliferation by regulating four key cell cycle factors: Cyclin E , String/Cdc25 , E2f and Dacapo ( mammalian p21CIP1/p27KIP1/p57Kip2 ) . ChIP and DamID analysis of Su ( H ) and E ( spl ) -HLH indicates direct transcriptional regulation of the cell cycle genes , and of the Notch pathway itself . These results point to a multi-level signaling model and may help shed light on the dichotomous proliferative role of Notch signaling in many other systems .
The Notch signal transduction pathway plays a central role during animal development , and is also critical for tissue homeostasis during adulthood [1] . Notch signaling typically acts as a short-range , cell-cell communication system , which can trigger a multitude of cellular responses , including proliferation , differentiation and programmed cell death . The outcome of Notch activation is highly context-dependent , and with respect to e . g . , proliferation , Notch can act both as an anti- and pro-proliferative regulator [2] . The dynamic response of the genome to Notch receptor activation is multi-faceted [3 , 4] . The immediate response involves a tripartite protein complex consisting of the intracellular domain of Notch ( NICD ) , the DNA-binding factor Su ( H ) ( mammalian RBPJ ) and the co-factor Mastermind ( mammalian Maml1/3 ) [5 , 6] . In Drosophila , main direct targets of the NICD-Mam-Su ( H ) activator complex are the genes of the Enhancer-of-split Complex ( E ( spl ) -C ) ; founding members of the HES gene family of bHLH transcriptional repressors [7–9] . A delayed response to Notch activation therefore likely involves the repression of secondary target genes by the E ( spl ) -HLH factors . During early neurogenesis , these E ( spl ) -HLH factors act by antagonizing the activity and expression of the proneural bHLH factors [10] . However , the full repertoire of HES/E ( spl ) -HLH targets remains largely unknown . Additionally , E ( spl ) -HLH gene activation by NICD-Su ( H ) -Mam is context-dependent i . e . , different E ( spl ) -HLH genes are activated in response to Notch in different tissues [11] . Therefore , the precise flow of events from receptor cleavage to diverse target gene regulation is often unclear: which specific E ( spl ) -HLH genes are activated , which other target genes are regulated , and at what level ( s ) ? For instance , while Notch signaling is known to regulate cell cycle genes [12] , it is unclear whether this regulation is direct via NICD-Mam-Su ( H ) , or indirect via the E ( spl ) -HLH factors; chiefly because the genome-wide binding profiles of E ( spl ) -HLH factors have not been addressed . Finally , whether differences in E ( spl ) -HLH expression and function contribute to the cell-specific response to Notch receptor activation remains completely unknown , primarily because extensive genetic redundancy has precluded the identification of single-gene mutations and functions for any one of these genes [13–16] . Here , we address the connection between Notch signaling and proliferation control using the Drosophila embryonic CNS as model . The CNS is established by some 1 , 200 neuroblasts ( NBs ) that delaminate from the neurogenic ectoderm ( Fig 1A ) [17–20] . NBs divide asymmetrically to self-renew and produce daughter cells with a more limited proliferation potential [21] . For the majority of NBs , early-born daughter cells divide once , to generate two neurons/glia; denoted Type I proliferation mode [22] ( Fig 1B ) . We recently demonstrated that many , perhaps all , NBs undergo a programmed proliferative switch , to generate daughters that directly differentiate into neurons; Type 0 proliferation mode [23] ( Fig 1B ) . This Type I>0 proliferation switch requires critical input from a few key cell cycle genes , including Cyclin E ( CycE ) , string ( stg; mammalian cdc25 ) , E2f and dacapo ( dap; p21CIP1/p27KIP1/p57Kip2 ) . In this study , we find that Notch/E ( spl ) -HLH signaling is globally required to regulate the Type I>0 switch . To dissect the Notch downstream events and the role of the different E ( spl ) -HLH genes , we utilized TILLING and CRISPR/Cas9 mutagenesis , as well as BAC recombineering , to generate novel individual mutants for all seven E ( spl ) -HLH genes . Strikingly , in spite of their reported genetic redundancy , we find that , when placed over a genomic deletion removing all seven genes , individual E ( spl ) -HLH mutations can significantly affect the Type I>0 daughter proliferation switch . Intriguingly , different E ( spl ) -HLH genes affect the switch in different NB lineages . With respect to cell cycle components , Notch signaling regulates several key cell cycle proteins , including CycE , E2f , Stg and Dap . Moreover , ChIP-seq and DamID-seq demonstrates binding of Su ( H ) , E ( spl ) m5-HLH and E ( spl ) m8-HLH to E ( spl ) -C , CycE , stg , E2f and dap . These results help resolve the Notch pathway with respect to the Type I>0 switch , by identifying the main Notch components , the critical downstream targets , as well as the molecular and genetic interactions involved . We propose an intriguing multi-levelNotch signaling cassette involved in the Type I>0 daughter proliferation switch , where primary-level Notch signaling results in activation of E ( spl ) -HLH and cell cycle genes , and second-level Notch signaling results in E ( spl ) -HLH repressing a partly overlapping set of cell cycle genes . This multi-levelmode of Notch signaling may help ensure precise timing and fidelity of the Type I>0 switch , and may shed light upon the sensitivity and dynamics of Notch signaling , as well as its dichotomous nature with respect to proliferation control , in many other systems .
The embryonic Drosophila CNS can be subdivided into the brain and the ventral nerve cord ( VNC ) ; here we focus on the VNC . Each embryonic VNC hemisegment contains ~30 lateral NBs [24 , 25] , most , if not all of which commence neurogenesis by proliferating in the Type I mode [22] . Subsequently many , perhaps all , switch to the Type 0 mode ( Fig 1B ) [23] . We previously used two model lineages to study the Type I>0 switch; NB5-6T and NB3-3A , both of which can be uniquely identified by transgenic reporters and a number of markers . NB5-6T undergoes nine rounds of Type I proliferation , followed by five rounds of Type 0 proliferation , while NB3-3A undergoes one Type I round , followed by 11 Type 0 rounds ( Figs 1Q and S1G ) [23 , 26 , 27] . The last four Type 0 neurons in NB5-6T are denoted Apterous ( Ap ) neurons [26] and can be identified by Eyes absent ( Eya ) [28] . Similarly , the Type 0 neurons in NB3-3A can be identified by Even-skipped ( Eve ) [19 , 26 , 29] . The Notch pathway is critical for the Type I>0 switch in NB5-6T [27] ( S1A–S1F Fig ) . We find similar effects in NB3-3A ( Fig 1F–1J ) . Early and strong Notch pathway perturbation results in a failure of lateral inhibition , and as an effect thereof the generation of supernumerary NBs [30 , 31] . However , kuzbanian ( kuz ) mutants , presumably due to the maternal expression of kuz , showed extra Ap neurons without extra NB5-6T [27] , and we also find extra Eve neurons without extra NB3-3A neuroblasts ( Fig 1N and 1O ) . In line with these findings , we found no change in overall NB numbers in kuze29-4 ( Fig 1C–1E ) . In spite of kuze29-4 being a likely null allele [32] , we did not observe a full penetrance of the phenotype i . e . , with all Type 0 daughters converting to Type I , again likely due to the maternal expression of kuz . Addressing other Notch signaling components during the Type I>0 switch revealed roles for Su ( H ) , Tom and neuralized ( neur ) , as well as the Delta but not Serrate ligand [27] ( S1C–S1F Fig ) . Hence , canonical Notch signaling is involved in the Type I>0 switch in both NB5-6T and NB3-3A ( Figs 1Q and S1G ) . Next , we analyzed global proliferation pattern in both the abdominal and thoracic VNC . To this end we stained VNCs with Deadpan ( Dpn ) , Prospero ( Pros ) and phosphorylated histone H3 ( pH3 ) , allowing us to discriminate between dividing NBs ( Dpn+ and cortical/asymmetric Pros ) and dividing daughters ( Dpn-negative and cellular Pros; [23] ) ( Fig 2A and 2B ) . We analyzed NB and daughter proliferation at three stages , in thoracic T2-T3 and abdominal A1-A2 segments . We did not find any global NB proliferation effects in kuze29-4 ( Fig 2C and 2D and 2G and 2H ) . In contrast , kuze29-4 showed increase in dividing daughter cells , in both thorax and abdomen , at both St14 and St15 ( Fig 2E and 2F and 2G and 2H ) . Notch signaling has been shown to be involved in Programmed Cell Death ( PCD ) in the VNC , specifically of postmitotic cells [33] . This raised the possibility that reduced daughter proliferation observed in Notch pathway mutants may result from reduced PCD of Type I daughters , rather than conversion of Type 0 daughter to Type I . To address this issue , proliferation analysis was also conducted in PCD mutants ( Df ( 3R ) ED225 ) , as well as in kuz , ED225 double mutants , in NB3-3A , NB5-6T and globally . We found minimal global proliferation effects in ED225 , apparent only in thoracic daughters at St14 and abdominal NBs at St14 and St15 ( Fig 2G and 2H ) . kuze29-4; ED225 double mutants showed increased NB proliferation similar to ED225 single mutants , and increased daughter proliferation similar to kuze29-4 single mutants ( Fig 2G and 2H ) . Similar effect of ED225 was observed also in NB3-3A and NB5-6T ( Figs 1H–1J and S1E and S1F ) . These results are in line with previous published findings on PCD and lineage progression , and demonstrates that Notch signaling does not trigger the Type I>0 switch by merely triggering PCD ( see S1H Fig and S1 Fig legend for details and references ) . In summary , canonical Notch pathway signaling is globally involved in the Type I>0 daughter proliferation switch in the VNC , but does not control this switch via PCD . To begin addressing the downstream events involved in the Notch-mediated Type I>0 switch , we focused on the Enhancer-of-split complex ( E ( spl ) ) effectors in the Notch pathway . This complex contains seven HES/E ( spl ) -HLH genes , displaying well-known genetic redundancy [13–16]; to date , no single gene loss-of-function phenotype has been described . We first analyzed a series of deletions in the regions , and found strong effects on Ap cell numbers ( Eya+ ) in NB5-6T ( S2A–S2F Fig ) . Extra Ap neurons observed in E ( spl ) complex mutants arose from both failure of NB selection and Type I>0 switch ( S2E Fig ) , with weaker genotypes only affecting the switch and stronger genotypes affecting both the switch and NB selection i . e . , lateral inhibition ( S2F and S2G Fig ) . Together , these results point to a role in the VNC for: m7 and/or m8; m3 and/or m5; mδ , mγ and/or mβ; as well as gro . To resolve the individual roles of the seven E ( spl ) -HLH genes , TILLING was performed to identify EMS-induced mutations from a genome-wide mutagenesis project . A number of mutations in all seven genes were identified , out of which 15 nonsense and missense mutations , predicted to affect protein function , were chosen for further study ( Fig 3A and S1 Table ) . In addition , we utilized recombineering to generate complete deletions of three genes , as single or double mutants: m3null; m3null , mδnull; and m3null , mβnull ( Fig 3B ) . Finally , mγnull was engineered by CRISPR/Cas9 mediated deletion of the coding region of the gene ( Fig 3B ) . TILLING alleles , as well as the CRISPR/Cas9 mγnull allele , were tested over a deletion for the region ( Df ( 3R ) BSC751 ) which removes all seven E ( spl ) genes and gro ( S2A Fig ) . The recombineering alleles ( m3null , mδnull and mβnull ) were deleted in a BAC , inserted on chromosome 2 ( 51D ) , and crossed into a Df ( 3R ) gro32 . 2 , P-gro/ ( E ( spl ) -CΔmδ-m6 genetic background . We initially focused on NB5-6T , and found increase in Ap cell numbers for four genes; mδ ( mδL56Q ) , m5 ( m5C37S , m5K72* , m5Q127* ) , m7 ( m7G86E ) and mγ ( mγnull ) ( Fig 3C–3F and 3Q ) . None of the three null alleles for m3 , mδ or mβ showed increase in Ap cell numbers ( Fig 3R ) . The reason mδL56Q showed effect while mδnull did not may be due to that mδL56Q was tested in a gro heterozygous background , whereas mδnull was in a wild type background with respect to gro . The m8V59M mutant did not show any effect ( Fig 3Q ) . Analysis of the NB5-6T lineage revealed that extra Ap neurons resulted from defects in the Type I>0 switch , as evident by pH3+ , late-born ( Type 0 ) daughters in the lineage ( Fig 3G–3I ) . Underscoring the redundant nature of the region , homozygous m5 mutants did not show increase in Ap cell numbers ( S3A Fig ) . To address if E ( spl ) -HLH gene involvement may vary between NBs , we next analyzed the E ( spl ) -HLH mutants for effects upon NB3-3A development . Similar to our findings on NB5-6T , the m5 , mδ and mγ mutants displayed increase also in Eve cells ( Fig 3J–3M and 3S and 3T ) . However , in contrast to NB5-6T , we did not find effects in m7 mutants in NB3-3A ( Fig 3S ) . Instead , while the m8V59M mutant did not affect NB5-6T , it did show effect on NB3-3A ( Fig 3L and 3S ) . In line with this finding , we observed expression of an m8-GFP reporter in the NB3-3A neuroblast ( S3B Fig ) . Moreover , the recombineering alleles revealed effects for m3null , and as expected for m3null , mδnull ( Fig 3T ) . However , in m3null , mβnull double-mutants Eve cell numbers were not increased beyond that observed in m3null alone ( Fig 3T ) . Together with lack of phenotype in the mβ TILLING alleles , this argues against any role for mβ . Analysis of pH3-positive cells in the NB3-3T lineage revealed that extra Eve neurons resulted from defects in the Type I>0 switch , evident by pH3-positive , late-born ( Type 0 ) daughters in the lineage ( Fig 3N–3P ) . These studies demonstrate that in spite of redundancy between the E ( spl ) genes in relation to other Notch functions , with respect to the Type I>0 switch we observe weak but significant effects in single gene mutants for six of the seven E ( spl ) -HLH genes , revealed when placed over a deficiency removing the entire E ( spl ) region . Strikingly , we furthermore find evidence for selective utilization of different E ( spl ) genes in different NBs , with m3 and m8 only acting in NB3-3A and m7 in NB5-6T . Next we aimed to identify the downstream targets of Notch/E ( spl ) -HLH signaling with regards to the Type I>0 switch . While the proneural genes are well-known to be regulated by Notch [34 , 35] , our studies indicate that they are not the key targets of Notch signaling in the switch ( S4A–S4F Fig ) . We recently demonstrated that proliferation control in the developing Drosophila VNC requires four key cell cycle factors: Cyclin E ( CycE ) , E2f , String ( Stg; mammalian Cdc25 ) and Dacapo ( Dap; mammalian p21CIP1/p27KIP1/p57Kip2 ) ; mutation and/or misexpression of these cell cycle genes affects the Type I>0 daughter proliferation switch [23 , 36] . These findings prompted us to test for genetic interactions and cross-rescue between Notch and these genes , again using the Ap neurons in NB5-6T ( Eya+ cells ) and the Eve+ neurons in NB3-3A as readouts for a defective Type I>0 switch . First , we tested for trans-heterozygotic interaction between kuz and dap , and strikingly , noted an increase in both Ap and Eve neurons ( Figs 4A–4C and 4H and S5A ) . We also noted genetic interaction between kuz and E ( spl ) -C ( Fig 4H ) . Second , we attempted to rescue kuz by transgenic expression of dap , and observed suppression of the number of Ap and Eve neurons ( Fig 4D and 4G and 4I and 4J ) . These genetic interaction and cross-rescue effects were due to specific effects upon the Type I>0 switch , as evident by the alterations in daughter but not NB divisions in the NB5-6T lineage at St13 ( Fig 4M–4P ) . In contrast to kuz/dap and kuz/E ( spl ) -C interactions , we noted no interaction between dap and a E ( spl ) complex deletion ( Fig 4E and 4F and 4H ) . Third , we attempted to suppress the increase of Ap neurons in m5 and m7 hemizygous mutants by reducing CycE gene dosage , and indeed observed reduction of supernumerary Ap neurons ( Fig 4K and 4L ) . We conclude that , with respect to the Type I>0 daughter proliferation switch , there is genetic interaction between the Notch/E ( spl ) -HLH pathway and the dap and CycE cell cycle genes . A number of gain-of-function studies have demonstrated strong effects when expressing the Notch-Intracellular Domain truncation ( NICD ) [37] . To address the sufficiency of Notch signaling to trigger the Type I>0 switch , we therefore misexpressed NICD using the insc-Gal4 driver , a driver expressed by most if not all NBs from St11 and onwards . We analyzed NB and daughter proliferation in both the thorax and abdomen , at two different stages . In line with the selective role for Notch signaling in controlling daughter but not NB proliferation , we did not observe any effect on NB proliferation at any stage , neither in thorax nor abdomen ( S5B Fig ) . We did however observe significant reduction of daughter proliferation , evident in both thorax and abdomen at St12 ( S5B Fig ) . We thus find that NICD can trigger the Type I>0 switch . In contrast to the frequent use of the broad Notch pathway activator NICD , fewer studies have demonstrated effects from misexpressing the various E ( spl ) -HLH genes . We tested a number of available E ( spl ) -HLH UAS transgenes , but observed little if any effects upon the number of Ap cells in NB5-6T . The E ( spl ) -HLH genes are controlled by miRNAs [38] , and to circumvent this level of regulation , we generated a novel UAS transgene for m8; avoiding both the 5´and 3´UTR , and codon-optimizing the open-reading-frame ( S1 Data ) . A FLAG epitope tag was furthermore added to the N-termini ( S5A Fig ) . Surprisingly , these changes to the RNA sequence did not result in any clear expression when driven from pros-Gal4 , as judged by FLAG epitope antibody stain ( S5B and S5C Fig ) . In addition to miRNA control of E ( spl ) -HLH expression , these genes are however also controlled at the post-translational level e . g . , by phospho-degron mediated proteolysis on a Casein Kinase 2 ( CK2 ) site [39–41] . We therefore mutated the CK2 site in m8 ( S1 Data; S6A Fig ) , and observed that the UAS-m8CK2 transgene showed robust FLAG-tag expression in the embryonic CNS ( S6D and S6G Fig ) . Using this transgene , we found that expression of m8CK2 from pros-Gal4 resulted in reduction of daughter proliferation , evident in both the thorax ( St12 ) and abdomen ( St12 and St14 ) ( Fig 5A and 5B and 5E and 5F ) . We also noted effects on NB proliferation , but only in the thorax at St12 ( Fig 5E and 5F ) . In line with these results , analysis of the NB5-6T lineage revealed a reduction in the total number of cells generated in this lineage in pros>m8CK2 , from around 17 cells in control to some 13 cells in misexpression ( S7A Fig ) . We conclude that ectopic E ( spl ) -HLH expression can trigger a premature Type I>0 switch , without strongly affecting NB proliferation , and that triggering a premature switch logically leads to reduction of cell numbers generated in a lineage . We recently found that the Type I>0 daughter proliferation switch is under control also of the late temporal gene castor ( cas ) and the Hox gene Antennapedia ( Antp ) [23] . Cas is part of the temporal cascade of transcription factors ( Hb>Kr>Pdm>Cas>Grh ) playing out in most , if not all , NBs [42] . Antp is gradually expressed in NBs over time , and hence also shows a temporal expression profile [23 , 43] . Previous studies did not reveal cross-regulation in NBs between cas , Antp or Notch signaling [23 , 27] . We therefore addressed if m8 can act combinatorially with cas and Antp . First , looking at pros>cas-Antp co-misexpression , as anticipated from previous studies [23] , we noted reduction of daughter proliferation in both thorax and abdomen ( Fig 5E and 5F ) . In contrast to Notch signaling , both cas and Antp are also involved in the control of NB proliferation exit at the end of lineage development [23] . Indeed , pros>cas-Antp co-misexpression resulted in reduction also of NB proliferation , in both thorax and abdomen , at both St12 and St14 ( Fig 5E and 5F ) . Next , we co-misexpressed m8CK2 with cas-Antp , and observed striking combinatorial reduction of daughter proliferation , in both thorax and abdomen , at both St12 and St14 ( Fig 5C and 5D and 5E and 5F ) . Similar to cas-Antp co-misexpression , we also noted reduced NB proliferation in m8CK2-cas-Antp co-misexpression , but this was not significantly increased from that observed in cas-Antp co-misexpression ( Fig 5E and 5F ) . We conclude that stabilized m8 can act strongly combinatorially with cas and Antp to trigger a premature Type I>0 switch . In addition , misexpression of all three genes can to a lower degree reduce NB proliferation , but does not act combinatorially in this regard . To further address the connection between Notch signaling and the cell cycle , we analyzed the expression of the key cell cycle proteins described above . Focusing first on NB5-6T , we found upregulation of CycE and E2f in kuze29-4 mutants , while Dap was down-regulated ( Fig 5M and 5N ) . In the Df ( 3R ) gro32 . 2 , P-gro deficiency , which removes all E ( spl ) genes , E2f and CycE were strongly up-regulated , while Dap was unaffected . In insc>NICD we found down-regulation of CycE and upregulation of Dap , whereas E2f was unaffected . Both pros>m8CK2 and pros>cas-Antp resulted in CycE and Dap down-regulation , while E2f was unaffected . Triple co-misexpression; pros>m8CK2-cas-Antp , did not differ from m8CK2 alone or cas-Antp co-misexpression ( Fig 5M and 5N ) . In addition to the effects observed after detailed quantification in NB5-6T , several changes were readily observed globally in NBs: down-regulation of CycE in pros>m8CK2-cas-Antp , down-regulation of Dap in pros>m8CK2 , and Stg down-regulation in pros>m8CK2 ( Figs 5G–5L and S7B ) . Hence , we find changes in protein expression of all four key cell cycle proteins in Notch/E ( spl ) -HLH mutants and misexpression embryos . Our genetic interaction and protein expression analysis shows that the Notch/E ( spl ) -HLH pathway regulates four key cell cycle genes . To address the molecular mechanisms underlying this regulation , we performed Chromatin-Immuno-Precipitation ( ChIP ) on Su ( H ) and m8CK2 , as well as DNA adenine methyltransferase identification ( DamID ) on m8 and m5 , both un-driven and driven by Gal4 . Duplicates were conducted for all experimental set-ups , apart from ChIP of m8 , which was only conducted once , with similar results . Previous studies of Su ( H ) have involved ChIP analysis in cell lines and wing disc cells , and have identified binding to the E ( spl ) complex , as well as the CycE and stg cell cycle genes [4 , 12 , 44 , 45] . To direct our analysis specifically to the developing CNS , we expressed a FLAG-tagged Su ( H ) construct ( S1 Data ) , driven by pros-Gal4 , and used the FLAG tag to immunoprecipitate Su ( H ) . In both replicates we identified peaks at the E ( spl ) complex ( Fig 6A ) . Focusing on the key cell cycle genes , we also identified peaks at CycE , dap and stg ( Figs 6B and S8A and S8B ) . The E ( spl ) -HLH proteins have not been previously analyzed with respect to chromatin interactions , presumably due to their instability . We performed ChIP on pros>m8-FLAG embryos but , not surprisingly , were unable to obtain sufficient amounts of DNA for sequencing . Hence , we turned to pros> m8CK2-FLAG embryos , and were now able to obtain sufficient amounts of DNA for sequencing . As has been predicted from previous genetics , m8 binds to the E ( spl ) complex ( Fig 6A ) . In addition , we find peaks on CycE , dap and stg ( Figs 6B and S8A and S8B ) . To complement the ChIP analysis we turned to DamID , and because of the sensitivity of this assay , performed this technique on embryos only carrying UAS-m8-DamID or UAS-m5-DamID , allowing for the leakiness of the UAS transgene to provide low expression levels [46] . However , given the low stability of the m5 and m8 proteins , we also performed DamID on embryos where each UAS was driven by pros-Gal4 . These experiments also revealed binding of both m8 and m5 to the E ( spl ) complex , as well as to CycE , dap and stg ( Figs 6A and 6B and S6A and S6B ) . Strikingly , Su ( H ) , m5 and m8 peaks overlap with known CNS enhancers , particularly for dap , and to some extent for CycE and stg ( Figs 6B and S8A and S8B ) .
The Type I proliferation mode is to great extent controlled by the Pros homeodomain transcription factor . Pros is expressed by most , if not all NBs , is cytoplasmically located in NBs , and distributes asymmetrically to the daughter cell where it enters the nucleus [48–50] . Once in the daughter nucleus , Pros acts to repress key cell cycle genes i . e . , CycE , stg and E2f [46 , 51] , thus ensuring that the daughter can only divide one time; Type I . In contrast , Pros does not appear to play a central role for the Type 0 proliferation mode [23 , 27] . Instead , the Type I>0 switch is controlled by several cues emerging late in NB lineage progression . In the thorax , these include the temporal gene cas and the Hox gene Antp , both of which are selectively expressed during latter stages of NB lineage development [23 , 43] . Additionally , our studies demonstrate that the Notch pathway is also involved in the Type I>0 switch ( this study; [27] ) . Because the Notch pathway is off in early delaminating NBs–a prerequisite for the generation of NBs–but is gradually activated in NBs [27] , Notch also acts as a temporally gated cue for the Type I>0 switch . Misexpression of cas and Antp can trigger a premature Type I>0 switch [23] , and here , we find that activation of the Notch pathway ( NICD or m8CK2 ) can act similarly . Our previous studies demonstrated that Notch , cas and Antp do not regulate each other [23 , 27] . In line with this finding , we find that m8-cas-Antp co-misexpression shows combinatorial effects on the Type I>0 switch . A fascinating feature of Drosophila embryonic NB lineages is that the Type I>0 switch occurs at different and reproducible stages of lineage development in each NB type e . g . , a short Type I window in NB3-3A , but a long Type I window in NB5-6T . Our results indicate that temporal , Hox and Notch input , acting in parallel pathways on partly overlapping but also distinct cell cycle genes , combinatorially contribute to high fidelity and lineage-specific flexibility for the timing of the Type I>0 switch . In doing so , they must somehow integrate with earlier pattering mechanisms to ensure the NB-specific timing of the Type I>0 switch . There is a well-established regulatory connection between Su ( H ) and the E ( spl ) -HLH genes . This stems from DNA-binding and enhancer studies , demonstrating Su ( H ) binding to key regulatory elements in the enhancers of several E ( spl ) -HLH genes [7 , 9] . In addition , several studies , in cell lines and wing disc cells , have demonstrated binding of Su ( H ) to the E ( spl ) complex using ChIP [4 , 12 , 44 , 45] . These studies also revealed binding of Su ( H ) to CycE and stg . In contrast , the direct targets of the E ( spl ) -HLH proteins are less clear , and only a subset of Notch targets have been identified as direct targets [52] . To our knowledge , there are no genome-wide data for any HES protein , presumably due to the instability of these proteins . Analyzing our ChIP and DamID results , we find that the target genes E ( spl ) -C , CycE , dap and stg fall into several categories . For the dap target gene there is overall agreement between the two methods used and the three different proteins ( Su ( H ) , m5 and m8 ) ; peaks are overlapping , and fit with two known enhancer elements ( Figs 6A and 6B and S6A and S6B ) . In contrast , for the CycE target gene , a more complex picture emerges . First , although DamID for m5 and m8 show very similar profiles , there are striking differences in the peak profiles of driven versus un-driven m5 and m8: un-driven m5 and m8 show one major peak in the CycE promoter , whereas driven m5 and m8 show a set of peaks in the intronic region . Second , m8 ChIP only partially overlaps with driven m5 and m8 DamID . Third , ChIP for Su ( H ) shows a somewhat different profile , with several peaks not matching m5 and m8 DamID and ChIP peaks ( Figs 6A and 6B and S6A and S6B ) . For stg there is overall agreement between driven and un-driven m5 and m8 , but here the ChIP for m8 stands out , with a set of three very strong peaks in the upstream region . Regarding differences between different DamID experiments , one reason for different profiles when comparing driven versus un-driven may be that un-driven DamID relies upon low-level ubiquitous leakiness of the UAS transgene , whereas driven DamID is activated by a CNS-specific Gal4 driver . Regarding differences between DamID and ChIP , it is generally assumed that DamID detects also transient binding , whereas ChIP relies more upon persistent binding to the DNA . Regardless of these differences in profiles , observed using the different experimental approaches , we believe that the ChIP and DamID results support the notion of direct regulation of key cell cycle genes by NICD-Mam-Su ( H ) and E ( spl ) -HLH . A number of studies have attempted to address the possible specificity of the seven Drosophila E ( spl ) -HLH genes . Loss-of-function studies , using deletions , have resulted in the notion that these seven genes are highly redundant , supported also by the fact that no individual loss-of-function phenotypes have been identified [13–16] . In contrast , gain-of-function studies have lent support for a notion that while different E ( spl ) -HLH proteins indeed have similar function , they may differ in their efficiency in promoting different developmental outcomes [53] . Here , by using single-lineage analysis , and in a sensitized background–removing half a copy for all E ( spl ) -HLH genes–we find that six out of the seven E ( spl ) -HLH genes are involved in the Type I>0 switch ( Fig 7C ) . The fact that m3 and m8 only act in NB3-3A and m7 in NB5-6T likely reflects differential gene expression–NB specificity and/or levels–rather than differential protein function , since the biological output is the same: the Type I>0 switch . All seven E ( spl ) -HLH genes are known to be expressed in the developing VNC , in a salt-and-pepper fashion [54] , and we previously used an m8-GFP reporter to reveal that Notch signaling commences in the NB5-6T NB during latter stages of lineage development [27] . This reporter expression was dependent upon Notch signaling , evident by the loss of expression in NB5-6T in kuze29-4 . Here , we also find expression of m8-GFP also in NB3-3A , in line with the role of m8 in this lineage . We have made extensive efforts aimed at generating reporter transgenes for all seven E ( spl ) -HLH genes , and antibodies to their protein products , but this has not resulted in reproducible detection of expression in NBs . Hence the details of the expression of all seven E ( spl ) -HLH in different NBs remain unclear . It is tempting to speculate that rather than a high degree of specificity of expression in different NBs , E ( spl ) -HLH genes may act in a generic additive manner . This notion is in part supported by our findings: NB3-3A has an early Type I>0 switch , which involves five E ( spl ) -HLH genes , while NB5-6T shows a later switch , involving four genes . Another intriguing idea is that different E ( spl ) -HLH genes may be utilized for the Type I>0 switch during different time-windows i . e . , a temporal E ( spl ) -HLH cascade . However , we find no evidence for this idea in our results , since the different E ( spl ) -HLH mutants show similar numbers of aberrant daughter proliferation at the same stage ( Fig 3G–3I and 3N-3P ) . The Notch pathway is controlled at a number of different levels , including miRNA and protein-stability control of most , if not all components [55–57] . Our findings here add further complexity to Notch regulation , by proposing feedforward activation and negative feedback between primary- and secondary-level TFs in the pathway , as well as by both activation and repression of an overlapping set of key cell cycle regulators ( Fig 7C ) . This regulatory model is especially intriguing when viewed against a growing body of evidence that points to the importance of oscillations of Notch signaling with respect to differential biological outcomes [58] . Support for complex interplay between Notch signaling and the cell cycle recently emerged also from mathematical modeling [59] . This regulatory interplay combines for a highly flexible and dynamic signaling output , and suggests that variations in Notch signal strength and length may help explain the anti- or pro-proliferative output from this pathway .
A DNA fragment encoding Stg protein was expressed in bacteria . Protein was PAGE-gel purified and injected into guinea pigs , mice and rats . See S1 Text: Extended Experimental Procedures for details . E ( spl ) -HLH TILLING alleles were obtained by TILLING ( Targeted Induced Local Lesions IN Genomes ) of all seven E ( spl ) -HLH genes on the Fly-TILL platform [60] . Gene-specific deletion mutants were generated from a functional E ( spl ) -C BAC [61] , that was modified in three consecutive steps of recombineering mediated gap-repair [62] . The resulting transgene was integrated at the M[3xP3-RFP , attP]51D attP site using phiC31-mediated integration [62] . CRISPR/Cas9-mediated homologous recombination was used to generate a null allele of E ( spl ) -HLH- mγ . See S1 Text: Extended Experimental Procedures for details . Novel UAS transgenes were generated for Su ( H ) and m8 by de-novo gene synthesis ( Genscript , Piscataway , NJ , USA ) . DNAs were inserted into the pUASattB vector , and transgenes generated by PhiC31 transgenic integration [63] ( BestGene Inc . Chinmo , USA ) . UAS-TF-Dam transgenic flies were generated by P element transformation ( BestGene Inc . Chinmo , USA ) . See S1 Text: Extended Experimental Procedures for details . Drosophila DNA adenine methyltransferase identification ( DamID ) was carried out according to a modified protocol based on a method from Vogel et . al . [64] and A . Brand ( www . flychip . org . uk ) . See S1 Text: Extended Experimental Procedures for details . Chromatin preparation was carried out according to the protocol from Négre et al . , ( http://wiki . modencode . org/project/uploads/6/6b/ChIP_protocol_NN_07v1 . 2 . pdf ) . Immunoprecipitation was conducted according to MERCK Millipore protocol ( Manga ChIP protein A/G beads ) , using αFLAG ( m ) 1:200 ( BPS Bioscience cat: 25003 ) . See S1 Text: Extended Experimental Procedures for details . Sequencing was carried out on the Illumnina HiSeq2500 platform . DNAstar Seqman NGN software ( DNASTAR , Inc . version 12 . 2 ) was used for sequence assembly . Normalization was done with RPM , Qseq was used for peak detection and the wig-files were aligned to genome assembly dm6 on the UCSC genome browser for visualization [65] . See S1 Text: Extended Experimental Procedures for details .
|
Communication between cells is critical for controlling proliferation , and the Notch signal transduction pathway plays a well-established and evolutionary conserved role during these processes . However , in spite of numerous studies of this pathway over the years , the genetic sensitivity of the pathway , combined with complexity in the nuclear response to Notch activation , has often precluded an in-depth molecular understanding of the pathway . In addition , findings in many systems point to both anti- and pro-proliferative roles of Notch signaling . Here , we use a number of novel genetic strains–mutants and misexpression transgenes–and focus on a particular role of the pathway; daughter cell proliferation in the embryonic Drosophila central nervous system . Combined with genome-wide chromatin binding assays , we are able to decode the pathway and identify both the nuclear effectors downstream Notch , as well the key cell cycle genes involved . We find that Notch activity is gated by a process of direct and indirect transcriptional output , which acts to balance the proliferation decision with high fidelity . These findings shed light on the dichotomous nature of Notch signaling with respect to proliferation control and may point to widely used aspects of the pathway .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"abdomen",
"gene",
"regulation",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"neuroscience",
"animals",
"notch",
"signaling",
"animal",
"models",
"drosophila",
"melanogaster",
"model",
"organisms",
"genome",
"analysis",
"drosophila",
"research",
"and",
"analysis",
"methods",
"genomics",
"animal",
"cells",
"gene",
"expression",
"insects",
"arthropoda",
"signal",
"transduction",
"cellular",
"neuroscience",
"cell",
"biology",
"anatomy",
"thorax",
"neurons",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"computational",
"biology",
"gene",
"prediction",
"cell",
"signaling",
"organisms"
] |
2016
|
Control of Neural Daughter Cell Proliferation by Multi-level Notch/Su(H)/E(spl)-HLH Signaling
|
Cryptosporidium epidemiology is poorly understood , but infection is suspected of contributing to childhood malnutrition and diarrhea-related mortality worldwide . A prospective cohort of 108 women and their infants in rural/semi-rural Tanzania were followed from delivery through six months . Cryptosporidium infection was determined in feces using modified Ziehl-Neelsen staining . Breastfeeding/infant feeding practices were queried and anthropometry measured . Maternal Cryptosporidium infection remained high throughout the study ( monthly proportion = 44 to 63% ) . Infection did not differ during lactation or by HIV-serostatus , except that a greater proportion of HIV-positive mothers were infected at Month 1 . Infant Cryptosporidium infection remained undetected until Month 2 and uncommon through Month 3 however , by Month 6 , 33% of infants were infected . There were no differences in infant infection by HIV-exposure . Overall , exclusive breastfeeding ( EBF ) was limited , but as the proportion of infants exclusively breastfed declined from 32% at Month 1 to 4% at Month 6 , infant infection increased from 0% at Month 1 to 33% at Month 6 . Maternal Cryptosporidium infection was associated with increased odds of infant infection ( unadjusted OR = 3 . 18 , 95% CI 1 . 01 to 9 . 99 ) , while maternal hand washing prior to infant feeding was counterintuitively also associated with increased odds of infant infection ( adjusted OR = 5 . 02 , 95% CI = 1 . 11 to 22 . 78 ) . Both mothers and infants living in this setting suffer a high burden of Cryptosporidium infection , and the timing of first infant infection coincides with changes in breastfeeding practices . It is unknown whether this is due to breastfeeding practices reducing pathogen exposure through avoidance of contaminated food/water consumption; and/or breast milk providing important protective immune factors . Without a Cryptosporidium vaccine , and facing considerable diagnostic challenges and ineffective treatment in young infants , minimizing the overall environmental burden ( e . g . contaminated water ) and particularly , maternal Cryptosporidium infection burden as a means to protect against early infant infection needs prioritization .
The World Health Organization reports that the most common diarrhea-causing protozoan parasite worldwide is Cryptosporidium [1] , and a recent , large , multi-country investigation reported Cryptosporidium as the second most common pathogen indentified among care-seeking African and Asian infants 0 to 11 months [2] . The significance of this infection was underscored as this study revealed that infection was associated with a greater than two-fold increase in mortality of children 12 to 23 months [2] . Despite these indications of the potential global scope and impact of Cryptosporidium infection , a full understanding of the epidemiology of infant and early childhood infection remains limited due to logistical and methodological difficulties in conducting such research in impoverished high-burden urban and rural settings [2] . Cryptosporidium is a pathogen transmitted via the oral-fecal route from human ( C . hominis ) and animal ( predominately C . parvum ) reservoirs . Infection risk factors include a contaminated environment with elements such as: unsafe water , poor sanitation and hygiene , and close proximity to infected livestock , while severe clinical disease risk factors include: malnutrition and compromised immunity , particularly HIV-associated immunosuppression [1] , [3] . Symptoms include: nausea , vomiting , voluminous and watery diarrhea , dehydration , abdominal discomfort , anorexia , fever , fatigue , and respiratory problems [4] , [5] , with chronic and life-threatening symptoms possible amongst immunocompromised individuals due to the increased duration and severity of illness [4] . However an unknown number of individuals experience asymptomatic Cryptosporidium infection [6] . This clinically silent infection may remain undetected and untreated and therefore may contribute to malnutrition , growth impairment , and long-term cognitive and functional deficits in infants and children [7] , [8] . The primary aim of this research was to determine the prevalence of Cryptosporidium in young infants living in rural and semi-rural Tanzania by indentifying the timing of the first and subsequent Cryptosporidium events in both symptomatic and asymptomatic infections . Secondarily , we aimed to evaluate potential infant infection risk factors including: infant nutritional status , infant feeding practices , infant HIV-exposure , maternal nutritional status , maternal HIV infection , and , uniquely , maternal post-partum Cryptosporidium infection .
This study was a prospective birth cohort enrolling newborns and their HIV-seropositive or –negative mothers living in the rural and semi-rural areas of Kisesa Ward ( population 30 , 000 ) [9] in northwestern Tanzania . Pregnant women receiving antenatal care at Kisesa Health Centre ( KHC ) , a Tanzanian government-administered , publically accessible primary care facility were recruited from March through December , 2012 , a period that included both the dry and rainy seasons . Women gave birth between April , 2012 and January , 2013; the study follow-up appointments for mothers and infants were conducted between May , 2012 and July , 2013 . Eligibility criteria were gestation <37 weeks at consent , singleton birth , known maternal HIV serostatus ( screening with Determine HIV-1/2 [Inverness Medical] , confirmation with Uni-Gold HIV-1/2 [Trinity Biotech] ) , maternal ability to speak and understand the local language of Kiswahili , and stated intention to reside within the clinic catchment at delivery and through six months post-partum . The study was advertised through health workers at KHC as well as rural government-run health dispensaries in the region . All HIV-positive women were receiving anti-retroviral treatment ( ART ) for their own care or for prevention of mother-to-child transmission by the time of delivery . Infants born to HIV-positive women were given nevirapine daily for six weeks and tested for HIV-infection by dried blood spot DNA-PCR at the regional hospital laboratory at the Month 3 follow-up visit . The study protocol was approved by the ethics review committees of the Tanzania National Health Research Ethics Review Committee and Cornell University . Written informed consent was obtained from mothers for themselves and on behalf of their infants at enrolment with verbal assent re-confirmed at follow-up . All women were encouraged to deliver at KHC unless otherwise medically advised . As many women in this region do not deliver at health clinics , and preliminary research revealed that transportation expenses were the primary barriers to accessing healthcare [10] , the study provided transportation compensation and other clinical expenses typically borne by mothers for delivery and follow-up visits . For women who delivered elsewhere , including home births , mothers and infants were requested to attend a follow-up clinic visit within three days of delivery . The study flow chart is summarized in Figure 1 . If a mother-infant pair did not return for a regularly scheduled follow-up visit , a field worker traveled to their last known address to invite them to return to the clinic for a follow-up appointment . At each follow-up , the research nurse , under supervision of the study coordinator , administered the Infant Feeding and Health Questionnaire to mothers . This questionnaire was designed to obtain data on a range of feeding , health , and environmental risk factors . Exclusive breastfeeding ( EBF-WHO ) was defined according to the WHO definition where “the infant receives breast milk ( including expressed breast milk or breast milk from a wet nurse ) and allows the infant to receive oral rehydration solution ( ORS ) , drops , syrups ( vitamins , minerals , medicines ) , but nothing else” [11] . Duration of EBF-WHO was defined as the time from birth until an infant first received food or liquids other than breast milk or medicines . Diarrhea was defined as loose or watery stools ≥ three times per day that represented a pattern atypical for that individual [2] . The questionnaire included: 1 ) infant nutrition: breastfeeding and complementary feeding practices; 2 ) mother-reported infant morbidity: cough , difficulty breathing , fever , convulsions , vomiting , skin rash , anorexia , unscheduled clinic/hospital visits , and episodes of diarrhea; and 3 ) environment: food security , using an index composed of questions relating to the mother's food consumption pattern , and sanitation and hygiene practices , such as hand-washing behavior , access to safe water , and toilet facilities . Infants exhibiting symptoms of illness were referred to the clinical officer at KHC for follow-up . Anthropometric assessments were collected at each follow-up visit . Maternal height and weight were measured using a standard stadiometer ( Health O Meter , Inc . , Bridgeview , IL ) to the nearest 0 . 2 kg and nearest 0 . 1 cm , respectively . Maternal mid-upper arm circumference ( MUAC ) and triceps skinfold thickness ( TSF ) were measured to the nearest 0 . 1 cm and 0 . 5 mm , respectively . Infant weight and length were measured using a calibrated digital infant scale ( Seca 334 Digital Baby Scale ) to the nearest 0 . 01 kg and a standard infant length board to the nearest 0 . 1 cm , respectively . Infant MUAC , TSF , and head circumference were measured to the nearest 0 . 1 cm , 0 . 5 mm , and 0 . 1 cm , respectively . Active case detection was of interest so maternal and infant fecal samples were collected irrespective of self-reported intestinal symptoms at each follow-up visit . Cryptosporidium infection was detected using fresh stool samples that were stored in a cooler with ice packs for ≤5 hours before being transferred and stored at 4°C in the parasitology laboratory of the Tanzanian National Institute for Medical Research ( NIMR ) , Mwanza Research Centre . Within 24 hours of collection , approximately 5 g of stool was mixed with 5 mL 10% v/v formalin and stored at 4°C until analysis . Presence of Cryptosporidium was confirmed using a modified Ziehl-Neelsen staining procedure [12] , which is estimated to have a sensitivity ranging from 32 to 79% and a specificity ranging from 89 to 100% [13]–[15] . After staining , slides were examined by a single technician , without knowledge of participant clinical status , using a light microscope ( Olympus model CX41RF ) to detect Cryptosporidium oocysts and estimate oocyst burden . Cryptosporidium infection was defined as ≥1 oocyst detected in stained fecal smears . A second technician re-examined a sample ( 10% ) of the slides and inter-observer agreement was 96% . Data were analyzed in STATA10 ( STATA Corporation , Texas , USA ) . Means of normally distributed continuous variables were compared using Student's t-test and proportions of categorical variables were compared using the χ2 test and Fisher's Exact test . Results were considered statistically significant at α = 0 . 05 , two-sided . Univariate and multivariate logistic regression models were used to estimate the odds ratio ( OR ) and 95% confidence interval ( 95% CI ) of a priori considered potential risk factors for infant Cryptosporidium infection ( HIV-exposure , exclusive breastfeeding , maternal Cryptosporidium infection , and household factors , such as animal ownership , sanitation , wealth , and maternal education ) . This study is registered with ClinicalTrials . gov , number NCT01699841 . The sponsors ( Cornell University and the National Science Foundation ) were not involved in the design or oversight of the study . Members of the writing team had full access to the study data . The authors had final responsibility for the decision to submit for publication .
During the study period , 108 infants were born , and of these , six infants exited the study because of death , migration , or withdrawal of consent prior to the Month 1 study visit ( Figure 1 ) and were not included in follow-up analyses . Birth anthropometrics were statistically different between HIV-exposed and HIV-unexposed infants ( Table 1 ) . A greater proportion of HIV-exposed infants had low birth weight ( LBW; defined as birth weight <2500 g ) compared to HIV-unexposed infants ( HIV-exposed vs HIV-unexposed = 15 vs 3% , respectively; p = 0 . 026 ) . Likewise , a greater proportion of HIV-exposed infants were stunted at birth ( defined as birth length <44 . 7 cm ) compared to HIV-unexposed infants ( HIV-exposed vs HIV-unexposed = 18 vs 2% , respectively; p = 0 . 004 ) . No HIV-exposed infant tested positive for HIV between birth and three months of age . Maternal and household characteristics did not differ based on HIV-status of the mother , other than marital status , where HIV-positive women were more likely to be divorced than HIV-negative women ( HIV-positive vs HIV-negative = 21 vs 0% , respectively; p = 0 . 002 ) . The proportion of all mothers ( HIV+ and HIV− combined ) infected with Cryptosporidium ranged from a low of 44% ( 31/70 ) at Month 1 to a high of 63% ( 45/71 ) at Month 6 post-partum , and this proportion was not statistically different across time points . The majority of all mothers experienced Cryptosporidium infection at some point during the study follow-up period , with 82% experiencing Cryptosporidium infection at least once and 16% infected at every time point . Self-reported diarrhea was not related to Cryptosporidium infection and symptomatic infection ranged from a low of 0% at Month 3 to a high of 14% at Month 6 . While the majority ( 60% ) of mothers experienced self-recovery from Cryptosporidium infection between visits based on the presence/absence of oocysts in their feces , 15% of mothers who recovered later became re-infected on a subsequent visit . All infants remained free from Cryptosporidium infection until Month 2 and infection remained uncommon through Month 3 . By Month 6 , the increase in infection was dramatic with 33% ( 23/69 ) of infants exhibiting evidence of infection . Statistically significant differences in maternal Cryptosporidium prevalence based on HIV-serostatus were not evident , with the exception of the Month 1 study visit ( p = 0 . 012 ) ( Figure 2 ) . There were no statistically significant differences in infant Cryptosporidium infection based on HIV-exposure ( p = 0 . 284 ) . As overall EBF-WHO declined , the proportion of infant Cryptosporidium infection increased ( Figure 3 ) . Post-partum , there was a higher proportion of HIV-positive mothers practicing EBF-WHO compared with HIV-negative mothers and this difference was statistically significant at both Month 1 ( proportion HIV-positive vs HIV-negative: 44 vs . 23% , p = 0 . 03 ) and Month 2 ( proportion HIV-positive vs HIV-negative: 26 vs . 10% , p = 0 . 04 ) . Notably , of the four infants who continued EBF-WHO until six months , none had evidence of Cryptosporidium infection even though they were living in a Cryptosporidium environment as confirmed by evidence of maternal Cryptosporidium infection in all four cases . There was a pattern of lower proportion of Cryptosporidium infection in infants with a greater proportion of the diet consisting of breast milk ( EBF-WHO vs . partial/no breastfeeding ) and this was significant at Month 6 ( p = 0 . 030 ) ( Figure 4 ) . Neither maternal nor infant Cryptosporidium infection was associated with reported symptoms of infection that included diarrhea , anorexia , vomiting , and in mothers only , abdominal pain and nausea . Care-seeking behavior , operationalized as an unscheduled clinic or hospital visit , was uncommon for both mother ( 4% ) and infant ( 8% ) between each scheduled follow-up visit and was not associated with Cryptosporidium infection . Table 2 summarizes the contribution of infant Cryptosporidium infection risk factors in this setting . In univariate analyses , only maternal Cryptosporidium infection at Month 1 ( unadjusted OR = 3 . 18 , 95% CI = 1 . 01 to 9 . 99 ) was associated with infant infection . While EBF-WHO was not significantly associated with lower odds of infant Cryptosporidium infection , there was a consistent trend between longer duration of EBF-WHO and lower infant infection . In the multivariate model , maternal hand washing prior to infant feeding was significantly associated with an increased likelihood of infant Cryptosporidium infection ( adjusted OR = 5 . 02 , 95% CI = 1 . 11 to 22 . 78 ) . Maternal nutritional status , defined by body mass index ( BMI ) and MUAC , was not associated with maternal Cryptosporidium infection . Likewise , birth weight was not associated with infant Cryptosporidium infection nor was infant growth faltering up to six months a predictor of infant infection . Maternal food security index was negatively correlated with the practice of EBF-WHO at each visit; meaning that the more food secure a household , the less likely the infant was EBF-WHO . Similarly , the wealthier a household , the less likely the infant was EBF-WHO and the more educated a mother , the less likely the infant was EBF-WHO .
This is the first report of maternal-infant Cryptosporidium infection in Sub-Saharan Africa and the prevalence of infection was high . Post-partum infection was detected at least once in the majority of women and , for many , on multiple occasions . The Cryptosporidium burden in infants increased dramatically between three and six months of age , a period that corresponds to changes in breast feeding practices . Our results indicate that young infants living in rural and semi-rural Tanzania are susceptible to Cryptosporidium infection in early infancy with approximately 1/3 of infants showing evidence of infection by six months of age . This study confirms and extends the importance of Cryptosporidium infection in young infants reported in the GEMS study [2] that included both rural and urban settings . Our results are comparative to the findings of a sub-sample of young Tanzanian infants in urban , hospital-based studies where 25% of infants 0 to 6 months had evidence of either G . lamblia or Cryptosporidium parvum [16] , though the burden of Cryptosporidium parvum was not individually reported . In studies conducted in the Tanzanian capital of Dar es Salaam , only 9% of children three months to nine years and 18 . 9% of children 0 to 60 months had evidence of Cryptosporidium infection [5] , [16] and this may represent an urban-rural difference in young infant burden in Tanzania . Previous studies in Tanzania of HIV-positive adults report a Cryptosporidium prevalence between 7 and 17% [5] , [6] and HIV infection has been identified as a risk factor for Cryptosporidium and cryptosporidiosis in some studies [6] , [17]–[20] but not others [2] . Maternal HIV infection did not appear strongly related to Cryptosporidium infection in our study and this may be explained in part because the majority of HIV-positive women were otherwise healthy and not severely immunocompromised based on their CD4 cell counts . Previous studies that identified HIV infection as a risk factor were primarily conducted in the pre-ARV era and greater immunosuppression may explain differences [6] , [17] , [20] . Likewise , HIV-exposure was not a significant risk factor for Cryptosporidium infection in infants and this might be due in part to more optimal feeding methods in the HIV-exposed infants due to infant feeding counseling for HIV-positive mothers . While HIV infection may not be a significant risk factor for infection in this setting , it remains relevant for the clinical management of cryptosporidiosis in immunocompromised individuals given the lack of effective Cryptosporidium treatment other than ARV's to improve HIV immunocompetency [21] . While maternal Cryptosporidium infection was associated with greater infant infection , previously ( or even currently ) infected mothers may also be providing protective passive immunity in utero or in breast milk . A recent study of Bangladeshi infants reported that protection from Cryptosporidium infection was associated with high anti-Cryptosporidium IgA in breast milk [22] . Despite possible passive immunity and/or risk elimination ( from contaminated food/water ) , EBF-WHO was uncommon in our study population and was not sustained for the universally recommended duration of six months . In this study , using the WHO definition of EBF , only a third of mothers were practicing EBF-WHO at Month 1 . Previous Tanzanian studies indicated much higher levels of “EBF” ranging from 49% within 3 days after birth [23] , 90% at Month 1 [24] , and 80% at Month 2 [25] , but these large differences are likely due to the less strict non-WHO-EBF definitions and/or maternal recall methods used [24] , [25] . Additionally , two of these studies included HIV-positive women only and HIV maternal care includes infant feeding counseling that is typically unavailable to HIV-negative mothers in this setting [24] , [25] . Indeed , we found significantly higher rates of EBF-WHO in HIV-positive mothers and this may explain why infant HIV-exposure was associated with lower infant Cryptosporidium infections . Globally , knowledge of the epidemiology of Cryptosporidium infection in early infancy is scarce and , in Tanzania , such data are unavailable . When the lack of prevalence data is combined with barriers to diagnosis , the disease rarely features on the clinician's diagnostic radar . This leads to a cycle that likely perpetuates the underestimation of the Cryptosporidium burden leading to an inappropriately lower global health and research priority . This cycle reinforces ineffective clinical and public health management of Cryptosporidium . In our study , maternal hand washing prior to infant feeding was counterintuitively associated with infant infection , although given the wide 95% confidence interval , we recommend caution in the interpretation of this finding . Previous studies have indicated that household sanitation and hygiene , including hand washing , were related to reduced Cryptosporidium infection [17] . Since Cryptosporidium has notoriously robust survival and transmissibility [26] , [27] , and mothers may wash their hands with contaminated water and then feed their children , our result is plausible in this setting . It may also be that the practice of hand washing is a proxy indicator for women who lived in more contaminated environments . Further research could include testing water sources and/or analysis of the species of Cryptosporidium in order to determine probable transmission routes of infection . Such investigations would help interpret this finding in relation to major public health messages related to hand washing in similar settings . Our study had a number of limitations . First , at each follow-up visit , only one stool sample was collected from each mother and infant . Due to the intermittent shedding of Cryptosporidium oocysts , collection of a single stool sample may result in an underestimate of the true Cryptosporidium prevalence [28] . Additionally , our study used modified Ziehl-Neelsen staining , the most common diagnostic technique to detect the presence of Cryptosporidium oocysts in stool samples , however , the sensitivity and specificity of this method are not 100% leading to possible misclassification [13] . Lastly , our results may not be generalizable to other geographical settings due to urban/rural differences and geographical variation in Cryptosporidium contamination . In conclusion , there is a high prevalence of infant and maternal Cryptosporidium infection in this setting . Public health interventions promoting EBF-WHO among all women , including HIV-negative mothers should be strengthened . Modeling the message of breast milk as an immunologically protective substance to prevent certain infectious diseases common in childhood may be effective in regions where there are high rates of vaccination coverage . Additionally , further research is needed to address efforts to minimize the maternal and environmental Cryptosporidium burden as a means of protecting young infants in the absence of effective vaccines , diagnostics , and treatment for early infancy cryptosporidiosis .
|
Early infancy and childhood Cryptosporidium infection is associated with poor nutritional status , stunted growth , and cognitive deficits , yet minimal research is available regarding the burden and risk factors worldwide . Since there is no vaccine available , and because diagnostic challenges exist and treatment for children younger than one year is ineffective , prevention of early infancy infection through a better understanding of basic epidemiology is critical . This study was designed to investigate symptomatic and clinically silent infection amongst HIV-seropositive and HIV-seronegative mothers and their infants in a longitudinal cohort , and to indentify potential risk factors . Findings indicate that infants are living in a Cryptosporidium environment as demonstrated by the chronically high level of maternal infection throughout the 6-month post-partum period . Despite this , infant infection prevalence remains low until six months of age when it dramatically rises . The increase in infant infection corresponds to a reduction in exclusive breastfeeding . As expected , maternal infection is associated with increased infant infection , but unexpectedly , so is maternal hand washing prior to infant feeding . Since prevention may indeed be the “best medicine” for infants , investigation of beneficial breastfeeding practices , protective correlates in breast milk , and ways to reduce the maternal and environmental Cryptosporidium burden are needed .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"neonatology",
"hiv",
"infections",
"medicine",
"and",
"health",
"sciences",
"maternal",
"health",
"geographical",
"locations",
"parasitic",
"diseases",
"parasitology",
"pediatrics",
"health",
"care",
"age",
"groups",
"nutrition",
"women's",
"health",
"tanzania",
"infants",
"sanitation",
"cryptosporidiosis",
"africa",
"infectious",
"diseases",
"epidemiology",
"people",
"and",
"places",
"breast",
"feeding",
"environmental",
"health",
"biology",
"and",
"life",
"sciences",
"population",
"groupings",
"viral",
"diseases",
"intestinal",
"parasites"
] |
2014
|
Cryptosporidium Prevalence and Risk Factors among Mothers and Infants 0 to 6 Months in Rural and Semi-Rural Northwest Tanzania: A Prospective Cohort Study
|
Capillaries are the prime location for oxygen and nutrient exchange in all tissues . Despite their fundamental role , our knowledge of perfusion and flow regulation in cortical capillary beds is still limited . Here , we use in vivo measurements and blood flow simulations in anatomically accurate microvascular network to investigate the impact of red blood cells ( RBCs ) on microvascular flow . Based on these in vivo and in silico experiments , we show that the impact of RBCs leads to a bias toward equating the values of the outflow velocities at divergent capillary bifurcations , for which we coin the term “well-balanced bifurcations” . Our simulation results further reveal that hematocrit heterogeneity is directly caused by the RBC dynamics , i . e . by their unequal partitioning at bifurcations and their effect on vessel resistance . These results provide the first in vivo evidence of the impact of RBC dynamics on the flow field in the cortical microvasculature . By structural and functional analyses of our blood flow simulations we show that capillary diameter changes locally alter flow and RBC distribution . A dilation of 10% along a vessel length of 100 μm increases the flow on average by 21% in the dilated vessel downstream a well-balanced bifurcation . The number of RBCs rises on average by 27% . Importantly , RBC up-regulation proves to be more effective the more balanced the outflow velocities at the upstream bifurcation are . Taken together , we conclude that diameter changes at capillary level bear potential to locally change the flow field and the RBC distribution . Moreover , our results suggest that the balancing of outflow velocities contributes to the robustness of perfusion . Based on our in silico results , we anticipate that the bi-phasic nature of blood and small-scale regulations are essential for a well-adjusted oxygen and energy substrate supply .
Tissues depend on a continuous supply of oxygen and energy substrates delivered via the bloodstream . This is particularly evident in the brain , which contains limited energy reserves and undergoes rapid and substantial increases in blood flow during neuronal activation ( neurovascular coupling ) [1] . Robustness of perfusion is crucial to guarantee a sustained nutrient supply throughout the tissue . Occlusion experiments have been used to study robustness of perfusion [2–6] . Their results suggest that single vessel occlusion at the pial and capillary level does not lead to a complete cessation of flow but to a redistribution of flow . Indeed , for capillaries , three branches downstream from the site of occlusion the red blood cell ( RBC ) flux recovers to 45% of its baseline value [5] . Occlusions of penetrating vessels prove to be more severe [5 , 6] . However , these studies focus on the effect of the occlusion and it remains largely unknown if the observed flow redistribution results exclusively from the vascular topology , or if further mechanisms are relevant . Up-regulation of flow in response to neuronal activation is another key feature of the cortical blood supply . The increase in flow rate results from vasodilations of arterioles and capillaries [7–11] . It is still matter of on-going debate if dilations at capillary level are active or passive [10–12] . Either way , capillaries are the ideal location for nutrient exchange and the most abundant vessel type [13] . Those aspects underline that the perfusion of the capillary bed is at the very basis of nutrient supply in all tissues . Nonetheless , many open questions regarding capillary perfusion patterns during baseline and activation remain to be answered . Various experimental and numerical studies show that the perfusion of the capillary bed is highly heterogeneous [12 , 14–23] . This heterogeneity is evident for multiple perfusion characteristics , e . g . hematocrit and velocity distribution [14–17 , 23] as well as capillary transit time ( CTT ) and capillary outlet saturation [18 , 24–26] . These characteristics are relevant for oxygen and nutrient supply [15 , 18 , 19 , 26] , and are also linked to diseases and aging [24 , 27 , 28] . For example it has been shown that CTT homogenizes during activation [18 , 19 , 29] and that this is beneficial for oxygen extraction [24] . However , in a mouse model of Alzheimer’s disease CTT homogenizes less during activation than in wild type mice [27] . Little is known about the mechanisms leading to the homogenization of capillary flow during activation . As the homogenization is initiated before the up-regulation of flow [19] it seems likely that dilations and constrictions at the level of individual capillaries are relevant . However , in vivo such alterations are difficult to measure [11] and quantitative numerical investigations are lacking . Here , we use numerical simulations in anatomically accurate microvascular networks ( MVNs ) [6 , 12] and in vivo measurements to quantitatively describe and analyse the flow field in the capillary bed . In further structural and functional analyses we investigate the changes resulting from the dilation of individual capillaries and quantify the impact of single capillary dilation . Capillary dilation is also used as an example scenario to study robustness of perfusion in response to local alterations . In all investigations we highlight the impact of the bi-phasic nature of blood ( plasma and RBCs ) [23 , 30–34] . This is motivated by the significant impact of RBCs on the microvascular flow field . The term “impact of RBCs” refers to the effects due to the presence of RBCs in comparison to pure plasma flow without RBCs . On the scale of MVNs these effects become apparent in a threefold manner: ( 1 ) a general increase in flow resistance [35] , ( 2 ) the non-homogeneous distribution of RBCs [17 , 20 , 23 , 36–38] and ( 3 ) the altered flow and pressure field due to the non-homogeneous RBC distribution [32] . All three effects are direct consequences of the RBC dynamics in microvascular flow ( Fahraeus-Lindqvist effect and phase-separation effect , Methods ) . So far , most studies addressing the impact of RBCs have been performed in the vasculature of the muscle or the mesentery , and the analysed vascular networks were at maximum 1 , 000 vessels in size [23 , 32–34 , 39–43] . These studies show that the particulate nature of blood induces temporal fluctuations in the flow field . Moreover , they indicate that phase-separation is the key mechanism leading to hematocrit heterogeneity . A recent work performing direct numerical simulations of bi-phasic blood flow in MVNs with ~50 vessels provides novel evidence that these effects are most pronounced at capillary level [23] . It has been shown , that the continuum approach to model the RBC-phase [41] tends to under predict the impact of RBCs [32 , 34] . Therefore , we believe that the tracking of individual RBCs is crucial to accurately model and study the impact of RBCs . This becomes even more relevant for large realistic MVNs , where the capillary bed forms an interconnected mesh-like structure [6] with on average six capillary segments between arteriole and venule [44] . However , most numerical models working with large MVNs do not track individual RBCs [20 , 36 , 41 , 45] . Thus , to the best of our knowledge this is the first study investigating the impact of RBCs in cortical MVNs with more than 10 , 000 vessels . In contrast to previous works , our focus is on quantitatively describing the impact of RBCs across scales , i . e . from the very local impact at individual bifurcations to the large-scale impact across the entire MVN . Our local investigations are complemented by in vivo measurements at individual cortical capillary bifurcations . Additionally , we studied the role of RBCs during capillary diameter changes and for robustness of perfusion . We also comment on capillary dilation as a candidate mechanism for small-scale regulations .
We measured RBC velocities in mice to analyse perfusion heterogeneity at capillary bifurcations ( Fig 1A–1C , S1 and S2 Figs ) . The measurements were performed in all vessels of 70 randomly chosen bifurcations ( Methods ) . Qualitative comparison of the velocities in the daughter vessels of divergent bifurcations reveals that at individual bifurcations the outflow velocities are of similar magnitude ( Fig 1D ) . This is an interesting result because globally the velocity distribution in the capillary bed is highly heterogeneous ( S3 Fig ) [12 , 14–16] . How can these similar outflow velocities be explained and what is different at convergent bifurcations ? To quantify the velocity difference at individual bifurcations we define the relative velocity difference |Δrv|=2|vb1−vb2|vb1+vb2 . ( 1 ) At divergent bifurcations vb1 and vb2 are the velocities in each daughter vessel while at convergent bifurcations the velocities in the mother vessels are used . The flow field and the instantaneous relative velocity difference fluctuate in time ( Fig 1D ) [14 , 23 , 46–50] . Please note , that the following studies are based on the time-averaged flow field ( Methods ) , i . e . the relative velocity difference is computed from the median velocity in the individual capillaries . Comparing the |Δrv|-distribution for divergent and convergent bifurcations shows that the relative velocity difference at divergent bifurcations is significantly smaller than at convergent ones ( Fig 1F–1G and 1K , Methods , p-value = 0 . 037 , one-sided Mann-Whitney U Test ) . We introduce the generic terms well-balanced and unbalanced bifurcations for bifurcations with a relative velocity difference < 20% and > 40% , respectively . These categories will be used to compare the flow dynamics at bifurcations with different relative velocity differences . The threshold value for well-balanced bifurcations is chosen based on the raw data for the in vivo measurements at divergent bifurcations ( Fig 1F , lower plot ) . At levels above approximately 20% the distribution of the raw data is less dense . Additionally , the difference between the histograms for divergent and convergent bifurcations is largest for |Δrv|<20% ( Fig 1F–1I ) . The threshold for unbalanced bifurcations was chosen to clearly separate the two bifurcation categories from each other . It is important to note that the transition from well-balanced to unbalanced bifurcations is smooth and does not have a precise cut-off value . The threshold value of 20% has only been introduced for quantitative analysis . To support our in vivo result we analyse the relative velocity difference at all divergent and convergent capillary bifurcations in our blood flow simulations with realistic MVNs . The numerical model has been introduced by Schmid et al . [12] and is summarized in the Methods . In brief , for a known distribution of RBCs we use an adjusted version of Poiseuille’s law [41] to compute the vessel resistance . Based on the continuity equation we set up a system of linear equations , which we solve for the pressure . The model is a close approximation to the situation in vivo , i . e . the presence of RBCs increases the flow resistance ( Fahraeus-Lindqvist effect ) and the RBCs distribute with a different ratio than the bulk flow ( phase separation ) . Indeed , our flow simulations in the two realistic MVNs ( S4 Fig ) from the mouse cortex confirm the reduced relative velocity difference at divergent bifurcations ( Methods , Fig 1H–1I ) . A detailed comparison of the distributions of the relative velocity difference for the convergent and divergent bifurcations and for in vivo measurements and simulations is provided in the Methods . Fig 1E shows an example , where the outflow velocities in the daughter vessels fluctuate around the perfectly balanced flow situation . The negative correlation between the two outflow velocities is a direct consequence of the alternating distribution of RBCs at the divergent bifurcation . This behaviour has already been observed at individual bifurcations in simulations in small MVNs [23 , 33] and recent direct numerical simulations nicely demonstrate how the outflow velocities affect the RBC lingering at the bifurcation [23] . However , to the best of our knowledge , it has not yet been investigated to which extent the velocity balancing takes place on the network scale . Based on our simulations 26% of all divergent bifurcations are considered well-balanced ( Fig 1H ) and for 9% the relative velocity difference is even smaller than 5% . It is important to note that at most bifurcations no negative correlation between the outflow velocities can be identified from the results ( S6C and S6E Fig ) . That is , a velocity increase in one daughter vessel is usually not reflected by a simultaneous decrease in the other daughter vessel . We suspect that this is due to the combination of dynamic effects occurring simultaneously at other bifurcations in the vicinity . Further , investigations of the transient flow field are necessary to prove the existence of a negative correlation between the outflow velocities at divergent capillary bifurcations . We conjecture that RBC dynamics play a key role in reducing the relative velocity difference at divergent bifurcations for the following reasons: First , because the fundamental difference between the two bifurcation types is that at divergent bifurcations RBCs are distributed while at convergent ones they are reassembled . Second , RBCs increase the flow resistance of capillaries [35] and thus the RBC distribution has a large impact on the flow field [23 , 32 , 33] . Please note , that all analysis presented in the following sections are based exclusively on the results of our blood flow simulations in realistic MVNs . To confirm that RBC dynamics induce the velocity balancing we use two different numerical models to simulate blood flow in the realistic MVNs ( Methods ) . As described above , the first model ( with RBCs ) is a close approximation of the situation in vivo . In the second numerical model RBCs are treated as passive particles ( with pPs ) that do not affect the vessel resistance and which , at divergent bifurcations , distribute with the same ratio as the bulk flow . For the simulation with pPs the median relative velocity difference at divergent bifurcations is 74% and for the simulation with RBCs it is 56% . Consequently , we find that with pPs the median velocity difference is significantly larger than for the simulation with RBCs ( Fig 1H and 1J , p-value = 5 . 1e-58 , one-sided Mann-Whitney U Test ) . Moreover , there are fewer well-balanced bifurcations ( |Δrvdb≤20%| ) in the simulation with pPs ( 15% ) than in the simulation with RBCs ( 26% , Fig 1H and 1J ) . In addition to this , we analyse the absolute velocity difference at divergent bifurcations for the simulation with RBCs and with pPs ( Fig 2A and 2B ) at well-balanced and unbalanced bifurcations ( |Δrvdb>40%| ) . We evaluate the set of bifurcations that are divergent in both simulation setups ( 91% ) . In total there are 7 , 285 divergent bifurcations in the simulation with RBCs . Comparing Fig 2A and 2B reveals that the impact of RBCs on the balancing is larger at well-balanced bifurcations than at unbalanced bifurcations . This becomes apparent if we look at the bifurcations that are well-balanced in both numerical experiments . In this subset of bifurcations the relative velocity difference is reduced on average by 32% due to the presence of RBCs ( S7 Fig , p-value = 6 . 2e-22 , Wilcoxon signed-rank test ) . The corresponding analysis for unbalanced bifurcations yields a reduction in the relative velocity difference by only 8% ( S7 Fig , p-value = 1 . 3e-113 , Wilcoxon signed-rank test ) . A p-value of 1 . 8e-18 confirms that the velocity reduction at well-balanced bifurcations is significantly larger than at unbalanced bifurcations ( Mann-Whitney U Test , S7 Fig ) . Altogether , our results indicate that the presence of RBCs does not only lead to a larger number of well-balanced bifurcations , but that RBCs generally reduce the velocity difference at divergent bifurcations . These results confirm the balancing of outflow velocities at divergent bifurcations ( Fig 1D–1I , Fig 2A and 2B ) and clearly identify RBC dynamics as the main source of this effect ( Fig 1H and 1J , S6 Fig ) . Hematocrit heterogeneity is another relevant characteristic of the perfusion of the capillary bed , because it is directly related to the oxygen supply capacity of the microvasculature [17 , 21 , 26 , 51] . As the precise mechanisms leading to this heterogeneity on the network scale are still poorly understood we aimed to separate heterogeneities resulting from topology from those attributable to the presence of RBCs . Once again , we used the simulation setup with RBCs and with pPs . We analyse the hematocrit distribution in the outflow vessels of capillary divergent bifurcations ( Fig 2C ) . While the hematocrit distribution is relatively flat for the simulation with RBCs , the distribution for pPs has a pronounced peak at the level of the inflow hematocrit and shows a smaller variance ( Fig 2D–2F ) . In vivo measurements confirm a flat hematocrit distribution and thus agree qualitatively with the simulation with RBCs [17] . Consequently , the phase separation in combination with the Fahraeus-Lindqvist effect ( Methods ) is likely to be the key source of hematocrit heterogeneity ( S8 Fig ) . This observation agrees with the results of the direct numerical simulation of bi-phasic blood flow by Balogh and Bagchi [23] , where it has been shown that hematocrit heterogeneity is a direct consequence of RBC lingering at bifurcations . It is important to note , that in contrast to Balogh and Bagchi [23] our model does not resolve RBC deformations but uses a simplified bifurcation rule to describe the motion of RBCs at divergent bifurcations ( Methods ) . The qualitative agreement between the two works provides evidence that our simplified bifurcation rule captures the dominant aspects of phase separation . A more quantitative comparison would be necessary to further comment on the accuracy of our simplified description . A prerequisite for the phase separation is an unequal flow partitioning at divergent bifurcations . This heterogeneous flow field is caused by the vascular topology and as such the vasculature indirectly contributes to the resulting hematocrit heterogeneity . In summary , the impact of RBCs leads to globally increased hematocrit heterogeneity while it simultaneously locally reduces the velocity heterogeneity . So far , we have shown that the impact of RBCs balances the outflow velocities at divergent bifurcations . But what are the benefits of the velocity balancing at divergent bifurcations ? In a previous numerical study , well-balanced bifurcations proved to be useful for a localized up-regulation of RBCs [32] . This suggests they might be relevant for regulatory purposes at the capillary level . Consequently , in the second part of this work we focus on identifying the role of well-balanced bifurcations . In line with this , we investigated the potential of small-scale regulation by capillary dilation . To this end , we modified 25 well-balanced and 35 unbalanced bifurcations by dilating the diameter of one daughter capillary by 10% [9] ( Methods , further dilation factors in S9 Fig ) . Each daughter vessel has been dilated in a separate simulation . Thus , in total we simulated 120 distinct capillary dilation scenarios and compared the relative change in flow rate and in number of RBCs . As the relative change in flow rate is also affected by the length of the dilated segment ( S11 Fig ) , the relative change in flow rate is normalized with the length of the dilated segment and multiplied by 100 μm , i . e . we compare the relative change in flow rate with respect to an equivalent dilation along a 100 μm segment ( Methods ) . To investigate the role of phase-separation during capillary dilation we introduce a further modification of our numerical model where we only turn off the phase-separation but keep the effect of RBCs on the vessel resistance ( Methods ) . For all scenarios the largest change in flow rate and in the number of RBCs occurs in the dilated vessel itself ( Fig 3A–3D , S10 Fig ) . The largest average change in the number of RBCs in the dilated vessel is found at well-balanced bifurcations for the simulation with RBCs ( 27% , Fig 3B–3D , S2 Table ) . In the second daughter vessel the flow rate remains constant and the number of RBCs decreases ( -11% , S1 Table ) . This specific redistribution of RBCs , as well as the preservation of flow in the second daughter vessel , is only present at well-balanced bifurcations ( S10 Fig ) . Moreover , the significant increase in the number of RBCs in the dilated vessel can only be obtained if phase-separation is active ( Fig 3B and 3D ) . To study whether the chosen threshold for classifying well-balanced and unbalanced bifurcations affect the average values of the relative change in response to capillary dilation , we performed a sensitivity analysis on the impact of the threshold on the results presented in Fig 3 ( S12 and S13 Figs ) . Changing the threshold by ± 10% does not lead to significant differences in the results . As previously stated , the bifurcation categories are mostly introduced for analysis purposes , because it facilitates the comparison of bifurcations with a small and a large relative velocity difference . Effectively , there is no precise cut-off value but a smooth transition from well-balanced to unbalanced bifurcations . This implies that the trends described will be more pronounced the more well-balanced or unbalanced the bifurcation is . To study the impact of RBCs during capillary dilation , we analyse how the flow ratio at well-balanced divergent bifurcations changes in response to capillary dilation . The flow ratio is defined as the flow rate in the dilated vessel divided by that in the mother vessel ( r = qd1/qmother ) . The quotient of flow ratios awithRBCs=rdilationwithRBCsrbaselinewithRBCs ( 2 ) compares the flow ratio during baseline and activation ( e . g . capillary dilation ) and is > 1 for all scenarios ( Fig 3F ) , e . g . the fractional flow in the dilated vessel increases for all cases . The impact of RBCs becomes most apparent if we compare the quotient of flow ratios for the simulation with RBCs and with pPs ( Fig 3F ) . In 76% of the tested scenarios awith pPs is greater than awith RBCs , that is , the flow ratio at well-balanced divergent bifurcations is changing less if RBCs are present . In summary , the impact of RBCs leads to two beneficial effects during capillary dilation: ( 1 ) The changes in the RBC distribution are more pronounced at well-balanced bifurcations . Therewith , RBCs enhance the effectiveness of dilation ( Fig 3B–3D ) . ( 2 ) The presence of RBCs helps to preserve the baseline flow ratio ( Fig 3F ) . Consequently , RBCs are crucial for efficient local up-regulation and for a robust perfusion of the capillary bed . To investigate the impact of capillary dilation on the surrounding network we examined the changes in all vessels three generations up- and downstream of the site of dilation ( Fig 4A and 4B , Methods ) . The median relative change up- and downstream is < 5% for the number of RBCs and the flow rate ( Fig 4C and 4D ) . Moreover , as for the previous analysis , the largest changes mostly occur in the dilated vessel itself . We conclude that the effects of single capillary dilation are very localized . The relative change in flow rate depends not only on the dilation factor ( S9 Fig ) but also on the length of the dilated segment ( S11 Fig ) and on the overall network topology . For a capillary dilation of 10% we obtain an average increase in flow rate of 23% per 100 μm dilation . The average increase for 100 μm dilation is slightly smaller at well-balanced bifurcations ( 21% ) than at unbalanced bifurcations ( 25% , S10 Fig ) . However , this difference is not statistically significant ( p-value = 0 . 104 , S2 Table ) . In the un-dilated second daughter vessel the flow rate does not change in response to capillary dilation at well-balanced bifurcations , while the flow rate decreases at unbalanced bifurcations ( see S1 Table and S2 Table for statistics ) . This is another example of how the velocity balancing at divergent bifurcations and the presence of RBCs contribute to preserving the baseline flow rates . The relative change in the number of RBCs is not a function of the length of the dilated segment ( S11 Fig ) . The average increase for a 10% dilation is 27% at well-balanced bifurcations and 13% at unbalanced bifurcations ( p-value = 3 . 15e-10 , see Methods and S2 Table for details ) . If well-balanced bifurcations are crucial for robustness and for regulative purposes , they ought to be distributed throughout the cortical vasculature . Thus , our final investigations focus on the spatial distribution of well-balanced bifurcations in the MVN . First , we study differences with respect to cortical depth and divide the MVNs into five analysis layers ( ALs ) , each 200 μm thick [12] ( Fig 5A ) . We computed: 1 . the relative number of well-balanced bifurcations per AL , 2 . the minimum Euclidean distance between well-balanced bifurcations , 3 . the minimum Euclidean distance between well-balanced bifurcations and descending arteriole ( DA ) /ascending venule ( AV ) and 4 . the minimum path length from well-balanced bifurcation to DA and AV ( Fig 5B , S14 Fig , Methods ) . It is important to note that these characteristics can also be affected by topological differences over depth . The relative number of well-balanced bifurcations per AL varies between 26% and 40% for MVN 1 and between 33% and 39% for MVN 2 ( S14A Fig ) . While in MVN 1 we observe a minimum in the relative number of well-balanced bifurcations for AL 3 , this trend is not confirmed in MVN 2 . As the coefficient of variation is 15% for MVN 1 and 5% for MVN 2 , we conclude that the relative number of well-balanced bifurcations does not vary significantly over depth . The Euclidean distance between well-balanced bifurcations decreases on average by 16% from AL 1 to AL 2 ( S14B Fig , S4 Table ) and remains approximately constant in the layers below . The results for the Euclidean distance and the minimum path length between well-balanced bifurcations and the closest penetrating vessel exhibit similar trends . For the Euclidean distance and the minimum path length between well-balanced bifurcations and DA ( S14C Fig , Fig 5B , S5 Table , S7 Table ) we observe the shortest distances/ path lengths for AL 2–4 . The median Euclidean distance of AL 1 and AL 5 is on average 28% larger than the median distance of AL 2–4 . For the minimum path length we find an average difference of 43% between AL 2–4 and AL 1 and AL 5 . For the Euclidean distance between well-balanced bifurcations and AV we observe an increase over depth ( S14D Fig , S6 Table ) . In MVN 1 this trend is similar for the minimum path length between well-balanced bifurcations and AV . However , in MVN 2 the minimum path length increases from AL 1 to AL 2 but no significant changes are notable for the layers below ( Fig 5B , S8 Table ) . Taken together , most characteristics show a homogeneous distribution between AL 2–4 and the largest differences are observed with respect to AL 1 and AL 5 . For the distance and the minimum path length between well-balanced bifurcations and AV we observed an increase over depth . Comparing the minimum path lengths from well-balanced bifurcations to DA and to AV shows that in AL 2–5 well-balanced bifurcations are on average 91 μm closer to the DA ( Fig 5B , S14E Fig , S9 Table ) . This configuration is plausible for two reasons: 1 . Preserving robust perfusion is more relevant at locations further upstream and 2 . Contractile mural cells are only present close to DAs [52 , 53] . Fig 5C shows the distributions for the distance to the closest outflow vessel of a well-balanced bifurcation in MVN 1 and 2 ( Fig 5D ) , respectively . A median distance of 40 . 0 μm ( MVN 1 ) and 36 . 7 μm ( MVN 2 ) proves that most tissue points are close to a well-balanced bifurcation . If we define a region of influence based on the average inter-capillary distance ( ~50 μm ) [44] , 69% of the tissue can be effectively influenced by capillary dilation .
We identified the balancing of outflow velocities at divergent bifurcations as key element of microvascular flow , which is directly induced by RBC dynamics . This result is important for various aspects related to microvascular perfusion and is likely an evolutionary benefit of the bi-phasic nature of blood . First , our study provides evidence for the significant role of RBC dynamics and thereby confirms existing theoretical considerations [32 , 40 , 43] . Second , from a functional point of view , well-balanced bifurcations are relevant for regulatory purposes . This hypothesis is reinforced by the convenient spatial distribution of well-balanced bifurcations and the more efficient up-regulation of RBCs during capillary dilation . We hypothesize that different bifurcation types ( well-balanced and unbalanced , convergent and divergent ) fulfil different regulatory tasks . It seems likely that those differences are also reflected anatomically , for example in mural cell types , mural cell densities or different diameter distributions at the bifurcation . A recent study shows that pericyte morphology differs significantly along the capillary path [53] and consequently different functional tasks seem plausible . Further studies are necessary to refine the role and the development of well-balanced bifurcations . The velocity balancing at individual bifurcations indicates that RBCs dampen the flow field . Based on the physics of the RBC dynamics , RBCs distribute to minimize the outflow velocity differences at bifurcations . However , this mechanism is very local , and decreasing the velocity difference at one bifurcation might increase it at another bifurcation . Additionally , the number of available RBCs and a large difference in flow rates can limit the balancing . As a result , the flow field and the RBC distribution seem to fluctuate around the most well-balanced state possible . As stated previously , we focused on the time-averaged flow field . Nonetheless , we want to point out that the analysis of the transient flow field could allow further insights on the flow dynamics at capillary bifurcations . In our numerical model the velocity fluctuations are a direct consequence of the fluctuating RBC distribution . Consequently , the analysis of transient changes is an important aspect for a profound understanding of the impact of RBCs on the flow field . Especially if we keep in mind , that temporal fluctuations are most pronounced in the capillary bed [23] . Taken together , our knowledge on temporal fluctuations in microvascular flow remains limited . We believe that a profound analysis of the transient flow field in large realistic MVNs needs to be performed to provide the basis for future investigations . By analysing the preservation of the flow ratio in response to capillary dilation we extended our studies addressing the damping effects of RBCs . We observed that the impact of RBCs reduces the change in flow ratio at well-balanced bifurcations . It is important to note that this result is not only valid for capillary dilations , but also for any downstream alteration that induces a change in flow rate . However , preserving the flow ratio comes at the cost of local alterations in RBC distribution . This implies that under certain circumstances preserving perfusion might be more crucial than preserving the RBC distribution . Altogether we suggest that both mechanisms ( RBC up-regulation and preservation of the flow ratio ) are important for well-adjusted oxygen and nutrient supply . The most fascinating aspect of the described mechanisms is that the impact of RBCs is an intrinsic feature of the bi-phasic nature of blood . Our results confirm the global hematocrit and RBC velocity heterogeneity in the capillary bed [14–22] . While the global RBC velocity heterogeneity is caused by the vascular topology , the global hematocrit heterogeneity results from the heterogeneous flow rates in combination with the RBC dynamics . Interestingly , the local velocity balancing by RBCs is not visible at the network scale . What are the possible benefits of the heterogeneous perfusion for nutrient supply ? The existing hematocrit heterogeneity bears potential for an increase in oxygen extraction fraction by hematocrit homogenization [24] . However , this advantage might come at the risk that areas with low hematocrit values are more sensitive to tissue hypoxia . Furthermore , hematocrit heterogeneity will increase the heterogeneity in oxygen saturation of RBCs . As such , it affects the tissue cylinder radius supplied by a capillary and the diffusive interaction between RBCs [54 , 55] . The RBC velocity heterogeneity might be necessary for the redistribution of flow during activation . This hypothesis is fostered by in vivo measurement that show that high and low flux capillaries respond differently to neuronal activation [19 , 22 , 29 , 47] and that RBC velocity increases as well as decreases can be observed [16] . This redistribution of flow leads to a reduction of capillary transit time heterogeneity ( CTH ) [18 , 19 , 29 , 47] . We suggest that flow homogenization is a secondary regulation mechanism that together with the overall up-regulation of flow refines nutrient supply during activation . Our functional study on the impact of capillary dilation reveals that single capillary dilation is able to locally alter perfusion and RBC distribution . As previously mentioned , it is essential that the level of the relative change does not only depend on the dilation factor and the dilated vessel length , but also on the entire vascular topology . This can be explained by fact that the cortical vasculature is comparable to an interconnected resistor network , where each local change affects the entire network . Nonetheless , we are able to show that the largest changes occur in the dilated vessel and as such the effects of capillary dilation can be considered to be quite local . We also want to underline that oxygen availability is governed by the RBC velocity and the hematocrit ( e . g . number of RBCs ) . Only few in vivo studies measure both quantities [17 , 21 , 37] . Frequently only RBC velocity changes in response to stimulation are considered . However , the RBC velocity alone gives only limited insight on the change in RBC flux . In the olfactory bulb glomeruli it has been shown that the RBC velocity increases in response to stimuli [37] . However , for the hematocrit increases as well as decreases have been observed [37] . Our results show that capillary dilation has a large effect on the RBC distribution . This may suggest that capillary dilation might be more relevant for altering the RBC distribution than for increasing the flow rate . Therefore , we hope that changes in RBC distribution will be addressed more frequently in in vivo measurements . To facilitate comparison , we only analysed the impact of single capillary dilations . However , it has been observed in vivo that multiple capillaries respond simultaneously . Additionally , the response pattern can vary between capillaries and even along capillaries [56] . While the impact of such a response scenario is more difficult to study , it also bears the potential for larger and more sophisticated adaptations of flow and RBC distributions . For the presented results it is irrelevant , whether a capillary dilates actively or passively , or whether the vessel resistance is reduced by an increased RBC deformability [57] . For active and passive capillary dilation pericytes are the mural cells of interest . A reduced pericyte tone could directly induce capillary dilation or lead to a larger vessel distensibility , which subsequently would result in vasodilation . In summary , we suggest that the bi-phasic nature of blood is a convenient intrinsic feature that increases robustness of perfusion and facilitates regulation . Capillary dilation proves to be an efficient mechanism to locally alter perfusion and RBC distribution . Consequently , from a functional point of view , it seems likely that capillary diameters changes are relevant for neurovascular coupling .
Surgical and experimental procedures , as well as animal husbandry protocols , were approved by the Veterinary Office , Canton of Zürich , and performed according to Swiss law ( Federal Act on Animal Protection 2005 and Animal Protection Ordinance 2008 ) . For in vivo two-photon imaging the mice were anaesthetized using the triple anaesthetic mixture ( fentanyl 0 . 05 mg/kg , Sintenyl , Sintetica; midazolam 5 mg/kg , Dormicum , Roche; and medetomidine 0 . 5 mg/kg , Domitor , Orion Pharma ) , re-administered after ~45 minutes for maintenance of anaesthesia . We used six adult , female C57BL/6J mice ( Charles River ) for in vivo imaging experiments . The mice were housed under an inverted 12-hour light/dark cycle , with food and water ad libitum , in cages of 2–4 littermates . At the time of the first surgical procedure , the mice were 8–12 weeks old , weighing 20-25g . The surgical procedures used to prepare the mice for imaging have been described previously [58] . Under isoflurane anesthesia ( AbbVie , 4% for induction , 1–2% for maintenance ) , a custom-made aluminium headpost was attached to the skull using dental cement ( Synergy D6 Flow , Coltene , cured using blue light ) . After 24–48 hours , using a triple anaesthetic mixture ( fentanyl 0 . 05 mg/kg , Sintenyl , Sintetica; midazolam 5 mg/kg , Dormicum , Roche; and medetomidine 0 . 5 mg/kg , Domitor , Orion Pharma ) , a craniotomy was performed over the somatosensory cortex and a 3x3mm sapphire glass coverslip ( Valley Design ) was positioned over the exposed brain and secured with more dental cement . The mice were given pain relief ( buprenorphine , 0 . 1mg/kg s . c . every six hours during the day and in drinking water overnight , 0 . 3 mg/ml ) for 3 days following surgery and were allowed to recover in their home cage for at least one week before imaging . Surgical and experimental procedures , as well as animal husbandry protocols , were approved by the Veterinary Office , Canton of Zürich , and performed according to Swiss law ( Federal Act on Animal Protection 2005 and Animal Protection Ordinance 2008 ) . In vivo imaging experiments were performed with a custom-built two-photon microscope [59] . The mice were anaesthetized using the triple anaesthetic mixture described earlier , with midazolam ( 5 mg/kg ) re-administered after ~45 minutes for maintenance of anaesthesia . The blood plasma was labelled with FITC-Dextran ( 5% , 59-77kDa , Sigma ) injected via the tail vein . In plane bifurcations up to ~300 μm below the cortical surface were randomly selected for imaging . A total of 38 diverging and 32 converging bifurcations were used in the study . Ideally , the RBC velocity would be measured simultaneously in each vessel of the bifurcation . However , as this is technically not possible , each vessel was measured for three ~10 s windows , interleaved with measurements of the other vessels from the same bifurcation . This measurement protocol ensured that our individual measurements are steady over a time window of 60 s . Therewith , we are confident that our individual measurements are representative for the time-averaged flow field at the bifurcation . The consecutive line-scans were performed immediately after one and another . The RBC velocities have been calculated with the Radon-transform-algorithm [60] implemented in the open-source MATLAB toolbox CHIPS [61] ( Cellular and Hemodynamic Image Processing Suite ) . For each measurement we use the median velocity as the representative velocity for this capillary . Generally , the agreement between the three consecutive measurements is very good ( median difference is 5 . 5% , S1 and S2 Figs ) . However , as we tried to keep averaging to a minimum , we use the RBC velocity obtained in the second in vivo measurement . The numerical simulations are performed in two realistic microvascular networks ( MVN ) from the mouse parietal cortex [6] . Generally , each MVN can be represented as a graph consisting of a set of nodes ( bifurcations ) connected by edges ( vessels ) . The realistic MVNs used in this study perfuse an approximately cubic domain representing a tissue volume of ~1 mm3 . They were acquired with two-photon laser scanning microscopy [62 , 63] . The labelling of vessels and the diameter distribution of the original MVNs have been slightly modified [12] . The vessels are labelled to differentiate between pial arterioles , descending arterioles , capillaries , ascending venules and pial venules . In order to assign the vessel types , the topology and the vessel diameters are taken into account . Further information on the MVNs under investigation can be found in Blinder et al . [6] and Schmid et al . [12] . The numerical model was first described in Obrist et al . [43] and Schmid et al . [32] and was extended to large realistic MVNs in Schmid et al . [12] . For an in depth description please consult Schmid et al . [12 , 32] . The flow rate in each vessel is computed by Poiseuille’s-law qij=pi−pjRije , ( 3 ) where qij is the flow rate in the vessel connecting nodes i and j; pi and pj are the respective pressure values at these nodes . The effective resistance Rije is a function of the hematocrit [35] . For pure plasma flow in a vessel with circular cross section the resistance is Rij=128μLijπDij4 , ( 4 ) where Lij and Dij are the length and the diameter of vessel ij and μ is the dynamic viscosity of blood plasma . The mass balance at every node i in combination with boundary conditions for each in- and outflow result in a system of linear equations that can be solved for the pressure . The distribution of RBCs influences the flow and pressure fields and vice versa . Three RBC related phenomena must be considered to accurately model the bi-phasic character of blood: 1 . the Fahraeus-Lindqvist effect [35 , 41] , 2 . the Fahraeus effect [41 , 64] and 3 . the phase separation at vessel bifurcations [12 , 65] . The Fahraeus-Lindqvist effect describes the impact of RBCs on the resistance of the vessel [35] . We use the empirical formulation derived by Pries et al . [41] to account for RBCs . The second RBC-related flow phenomenon is the Fahraeus effect [64] , which leads to a reduced tube hematocrit because RBCs move on average faster than the bulk flow as they tend to travel in the centre of the vessel [66 , 67] . In our simulations we use an empirical function by Pries et al . [41] to account for the Fahraeus effect . Phase separation is a phenomenon that is mostly relevant at divergent capillary bifurcations [65] . It states that the fractional plasma- and RBC-flows are not equal in the daughter vessels , i . e . RBCs are distributed with a different ratio than plasma . This effect is most pronounced if the diameter of the mother vessel is < 10 μm . Consequently , in our numerical model we use two distinct formulations to describe the phase separation . For vessels 10 ≥ μm we use the empirical equations by Pries et al . [65] . In vessels < 10 μm we assume that RBCs follow the path of the largest pressure force ( bifurcation rule ) [12 , 32] . This assumption is based on a simplified analysis of the forces on a single RBC at divergent bifurcations and is justified in more detail in Fung [40] or Schmid et al . [12] . Along with the bifurcation rule , the motion of RBCs at divergent bifurcations can be affected by RBC ‘traffic jams’ . At convergent bifurcations traffic jams occur if two RBCs arrive at approximately the same time . At divergent bifurcations the different RBC velocities in the daughter vessels ( Fahraeus effect ) can lead to RBC jams in the mother vessel or a distribution of RBCs that does not follow the bifurcation rule . For simulations with RBCs , all RBC-related flow phenomena are considered . To test the impact of the different phenomena we work with variations of the standard simulation setup . In the simulations with passive particles ( with pPs ) we switch off the Fahraeus-Lindqvist effect and the phase separation . Thus , RBCs do not influence the resistance of the vessel and at divergent bifurcations they distribute with the same ratio as the bulk flow except for traffic jam effects . In the simulations without phase separation we only switch off the phase separation but keep the impact of RBCs on the vessel resistance . These different simulation setups allow us to separate effects resulting from the phase separation and from the Fahraeus-Lindqvist effect . It is important to note , that some uncertainty exists with respect to the empirical equations used to model the hydrodynamical effects of RBCs . However , as long as the general trends described are correct our qualitative results will not be affected . For example , if the impact of RBCs on the vessel resistance would be larger than predicted by the empirical equations currently used we would still observe a velocity balancing at divergent bifurcations . In this case , the velocity balancing would most likely be even more pronounced than in our current setup . Recent evidence suggests that especially in smaller vessels a deviation from Poiseuille’s law is possible [23] . This deviation occurs temporarily in situations where a lingering RBC temporarily blocks downstream vessels . In small vessels the blockage can be very frequent and consequently also the time-averaged relation between pressure drop and flow rate can be affected . We do not account for the deviation from Poiseuille’s law in our numerical model . However , vessel blockage is a temporary effect and for most vessels the correlation between pressure drop and flow rate is positive [23] . Therefore , we are confident that neglecting the temporary deviation from Poiseuille’s law does not significantly affect our time-averaged analysis of the flow field . In order to solve the linear system of equations for the pressure we need boundary conditions at all in- and outflow vertices . In previous work [12] we developed and validated a new approach to assign suitable pressure boundary conditions . The inflow hematocrit is set to 0 . 3 at all inflow nodes [17 , 68] . A special feature of our modelling approach is that we track RBCs individually . This allows us to describe their motion at divergent bifurcations based on simplified physical assumptions and reduces the amount of modelling by empirical relations . Moreover , RBCs have a finite volume and thus situations such as RBC traffic jams are modelled more accurately than if using infinitely small particles . The simulations are initialized with homogeneous hematocrit distributions . The simulation time step is set to 0 . 75 ms for MVN 1 and to 0 . 5 ms for MVN 2 ( see Schmid et al . [12] for details ) . Over time , the hematocrit distribution and the flow field converge to their statistical steady state . Nonetheless , the hematocrit distribution and the flow field continue to fluctuate around the statistical steady state . For our subsequent analysis we use the time-averaged flow field and RBC distribution . In order to define the averaging interval for our simulations we compute the turn over time for each vessel . The turn over time for a vessel is defined as the time necessary to completely perfuse the vessel once , e . g . the vessel length divided by the flow speed . Because of the large range of flow velocities the vessel turn over time of different vessels varies significantly . Our averaging interval is chosen such that 90% of all vessels are completely perfused at least 10 times ( averaging interval: MVN 1: 12 s , MVN 2: 5s ) . To ensure that the statistical steady state is reached , the simulations are run for at least two averaging intervals before we start the time averaging . As the flow field in the simulations fluctuates strongly , we employ a moving average for the illustration of the time courses in Fig 1E and S6 Fig . The moving average is computed over 100 time steps and at every 50 time steps . In our analysis we focus on outflow vessels of divergent capillary bifurcations . Two reasons motivate this approach . First , divergent bifurcations play a crucial role in the distribution of RBCs and blood flow . Second , by limiting our comparison to outflow vessels of divergent bifurcations we know that we are looking at comparable vessel types in experiments and simulations . It is important to note that in a few cases the bifurcation type ( divergent or convergent ) differs in the simulation with RBCs and with pPs . In Fig 1F–1I we compare the relative velocity difference at divergent and convergent bifurcations for the simulation with RBCs and for in vivo experiments . We postulate that the distributions are different for divergent and convergent bifurcations . To be more precise , we expect a lower median and a larger positive skew at divergent bifurcations . Both aspects are confirmed in vivo and in the simulation with RBCs ( S3 Table ) . The larger skew is also reflected in the smaller value for the first quartile ( Q1 ) for divergent bifurcations ( S3 Table ) . We use the one-sided Mann-Whitney U Test to test whether the median of the relative velocity difference at divergent bifurcations is smaller than at convergent bifurcations . Indeed , a p-value of 0 . 037 for the in vivo measurements confirms our hypothesis ( simulation with RBCs: p-value = 3 . 2e-70 ) . Additionally , we use the one-sided Kolmogorov-Smirnov-Test to compare the cumulative densities of the relative velocity difference between divergent and convergent bifurcations ( Fig 1K ) . Here , a p-value of 0 . 042 reinforces that the cumulative density of the relative velocity difference is larger for divergent bifurcations , e . g . the distribution is more positively skewed ( simulation with RBCs: p-value = 4 . 02e-61 ) . The general trends are in accordance for the in vivo measurements and the simulation with RBCs . However , the median and the standard deviation of the relative velocity difference are larger in the simulation with RBCs than in the in vivo experiments for both bifurcation types . Various aspects could cause these discrepancies . Regarding the in vivo measurements it is important to note that our sample size is relatively small and that we do not have any relative velocity differences greater than 130% . Additional in vivo measurements would be necessary to quantitatively comment on those differences . The absolute values of our numerical simulations could also be affected by various modelling assumptions . The most important ones are: ( 1 ) The chosen inflow hematocrit of 0 . 3 is on the low side . A higher inflow hematocrit leads to more RBCs in the MVN and thus increases the capability to balance velocity differences [32] . ( 2 ) The uncertainty in the vessel diameter estimates can have a significant impact on the local flow velocities at divergent bifurcations . ( 3 ) The empirical equations also affect the velocity balancing at divergent bifurcations . Because of the existing uncertainties it is difficult to quantitatively compare in vivo measurements and numerical simulations with each other . Nevertheless , these uncertainties do not affect the comparison between divergent and convergent bifurcations within the same setup . For all boxplots ( Fig 2D , Fig 3A–3D , Fig 4C and 4D , S7–S10 Figs , S12 and S13 Figs ) we use the following definitions: The box extends from the lower ( Q1 ) to the upper quartile ( Q3 ) of the underlying data . The thick line depicts the median . The upper and the lower whisker extend to the last data point < Q3 + 1 . 5 ( Q3-Q1 ) and to the first data point > Q1–1 . 5 ( Q3-Q1 ) , respectively . Data points outside the range of the whiskers are illustrated as separate outliers . In order to perform representative capillary dilations , we define a set of criteria to choose suitable divergent bifurcations . All criteria are based on the time-averaged simulation results of the simulation with RBCs . The first criterion is based on the relative velocity difference during baseline . Here , we either choose well-balanced ( |Δrvdb|≤20% ) or unbalanced divergent bifurcations ( |Δrvdb|>40% ) . To avoid any impact from the boundary we ensure that the divergent bifurcation is relatively close to the centre of the MVN ( i . e . the distance of the bifurcation to the centre of the MVN has to be smaller than 0 . 6 times the maximum distance of all nodes to the centre ) . As we are also interested in the role of RBCs during capillary dilation we select bifurcations where the hematocrit in the mother vessel is ≥0 . 3 such that sufficient RBCs are present . From the set of suitable divergent bifurcations , we randomly choose 25 well-balanced and 35 unbalanced divergent bifurcations and check that they are well distributed over the whole depth of the cortex . For each of those bifurcations we perform two simulations , in which each daughter vessel has been dilated once . Thus , in total we simulated 50 distinct capillary dilation scenarios for well-balanced bifurcations and 70 scenarios for unbalanced bifurcations . As previously stated , at divergent capillary bifurcations the RBCs follow the path of the largest pressure force . The pressure force is a function of the cross-section of the vessel and consequently , changing the capillary diameter at the bifurcation would significantly affect the bifurcation rule . To eliminate this effect , we keep the capillary diameter at the divergent bifurcation constant and only dilate a segment of the chosen capillary ( S9 Fig ) . Therefore , the capillary is split into two segments . The length of the segment adjacent to the divergent bifurcation is set to six times the length of an RBC in that vessel . The capillaries are dilated by 10% , i . e . the dilation factor is fdil = Ddilated/Dbaseline = 1 . 1 [9] , where Dbaseline and Ddilated are the vessel diameters before and after dilation . Simulation results for further dilation factors are provided in S9 Fig . It should be noted , that these results are not limited to capillary dilation but that they are also valid for other scenarios , where the resistance of individual vessels decreases , e . g . by more deformable RBCs [57] . The effects of capillary dilation are only studied in MVN 1 . As previously stated , for most of our numerical analyses we use the time-averaged flow field and RBC distribution . To comment on the changes in response to capillary dilation ( activation ) we compute the relative difference for each vessel . As the change in flow rate is a function of the length of the dilated segment , the relative change ΔcapillaryDil . rqij=qijactivation−qijbaselineqijbaseline⋅100μmLijdilated , ( 5 ) is normalized by 100μm/Lijdilated , where qijbaseline and qijactivation are the flow rates in vessel ij during baseline and activation , respectively . Lijdilated is the difference between the original vessel length Lij and the constant vessel length Lijconstant at the bifurcation ( S9 Fig ) . The relative change ΔcapillaryDil . rnRBCij=nRBCijactivation−nRBCijbaselinenRBCijbaseline ( 6 ) in the number of RBCs is not a function of the dilated segment length and thus normalization is not necessary . To investigate how local the effects of capillary dilation are , we analyse all changes in the vessels three generations up- and downstream of the site of dilation . The site of dilation is defined as the whole bifurcation at which capillary dilation is performed , e . g . the mother and the two daughter vessels . All vessels that deliver blood to the mother vessel of the divergent bifurcation are upstream vessels of generation I . The bifurcations that bring blood to the vessels of generation I are upstream vessels of generation II and so on . The equivalent definition is used to define the vessels downstream of the site of dilation . Here , the first vessels downstream of the dilated capillary are downstream vessels of generation I ( Fig 4A ) . Depending on the topology and the flow situation there can be from 3 to 39 vessels up-/downstream of the site of dilation . All up-/downstream vessels of the 50 capillary dilation scenarios are grouped together for the boxplots illustrated in Fig 4C and 4D . The first step in analysing the relative changes in response to capillary dilation is to test which changes are in fact significantly different from 0 . The resulting p-values are summarized in S1 Table . If we want to compare the changes between different vessel types for one simulation setup , we need a statistical test that is suitable for paired samples ( Fig 3A–3D , S9 and S10 Figs ) . Here , we chose a nonparametric test to compare the median values of the samples ( Wilcoxon signed-rank test ) . Next , we compare the relative changes between different simulation setups . As we want to understand for which scenario we observe the larger/smaller change , we use the one-sided Mann-Whitney U Test that allows for the provision of an alternative hypothesis ( S2 Table ) . To analyse the impact of capillary dilation on the proximity of the site of dilation we look at the changes in the vessels three generations up-/downstream of the site of dilation . As the vessels are not directly connected to each other the samples can be considered independent . Consequently , we use the two-sided Mann-Whitney U Test . In order to analyse the distribution of well-balanced bifurcations we calculated different measures to describe their position in the cortical vasculature . Here , we provide additional information on how these measures have been computed . Several of the subsequently described measures are given with respect to descending arterioles ( DA ) / ascending venules ( AV ) or more precisely with respect to the main branch of DAs and AVs . The first measure is the relative number of well-balanced bifurcations , which is the ratio of the number of well-balanced divergent bifurcations to the total number of divergent bifurcations . The minimum Euclidean distance between well-balanced bifurcations , the second measure , is computed by identifying the closest neighbouring well-balanced bifurcation for each well-balanced bifurcation . The third measure is the Euclidean distance between a well-balanced bifurcation and the closest point along a DA/AV . Lastly in the forth measure we computed the minimum path length from well-balanced bifurcation to DA/AV . To compute the minimum path length , we move up-/downstream from each well-balanced bifurcation until the DA/AV is reached . For each well-balanced bifurcation we obtain a set of possible paths to the DA/AV . The shortest path length of this set is the minimum path length to the DA/AV for this well-balanced bifurcation . Based on the cortical depth of the well-balanced bifurcation the minimum path length is averaged for each of the five ALs . Our final analysis regarding the distribution of well-balanced bifurcations is on the distance from each tissue point to the closest outflow vessel of a well-balanced bifurcation . Here , we need a discrete representation of the tissue in which the MVN is embedded . Therefore , the tissue is divided into 100x100x132 ( MVN 1 ) / 100x100x114 ( MVN 2 ) cubes , which results in a cube size of 8 . 2x8 . 2x8 . 2 μm for MVN1 and 8 . 6x11 . 2x9 . 9 for MVN 2 . Now , for each centre of the cube we calculate the distance to the closest outflow vessel of a well-balanced bifurcation . This results in 1 , 320 , 000/ 1 , 140 , 000 data points for the histograms in Fig 5C for MVN 1 and MVN 2 , respectively .
|
Glucose and oxygen are key energy sources of the brain . As energy storage capabilities are limited in the brain , a continuous supply of oxygen and glucose via the bloodstream is crucial for the brain’s functioning . The bulk of discharge occurs at the level of capillaries , which are the smallest and most frequent vessels of the cortical vasculature . Nonetheless , our understanding of perfusion and topology of the capillary bed is still limited . Here , we use in vivo two-photon based blood flow measurements and numerical simulations in large realistic microvascular networks to study the flow in the cortical microvasculature . Our results reveal that the impact of red blood cells enhances the robustness of microvascular perfusion and increases the heterogeneity in red blood cell distribution . It is well established that higher neuronal activity leads to an increase in blood flow . However , the precise regulation mechanisms and their spatial extent remain largely unknown . We show that small-scale regulations locally alter flow and red blood cell distribution . We suggest that these mechanisms are key for an efficient and flexible circulatory system . Moreover , our results reveal a novel role of the bi-phasic nature of blood .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"classical",
"mechanics",
"fluid",
"mechanics",
"cardiovascular",
"anatomy",
"oxygen",
"blood",
"counts",
"capillaries",
"red",
"blood",
"cells",
"arterioles",
"blood",
"vessels",
"animal",
"cells",
"flow",
"field",
"chemistry",
"fluid",
"dynamics",
"hematology",
"continuum",
"mechanics",
"physics",
"flow",
"rate",
"hematocrit",
"blood",
"flow",
"blood",
"anatomy",
"cell",
"biology",
"physiology",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"chemical",
"elements"
] |
2019
|
Red blood cells stabilize flow in brain microvascular networks
|
Organisms cope with physiological stressors through acclimatizing mechanisms in the short-term and adaptive mechanisms over evolutionary timescales . During adaptation to an environmental or genetic perturbation , beneficial mutations can generate numerous physiological changes: some will be novel with respect to prior physiological states , while others might either restore acclimatizing responses to a wild-type state , reinforce them further , or leave them unchanged . We examined the interplay of acclimatizing and adaptive responses at the level of global gene expression in Methylobacterium extorquens AM1 engineered with a novel central metabolism . Replacing central metabolism with a distinct , foreign pathway resulted in much slower growth than wild-type . After 600 generations of adaptation , however , eight replicate populations founded from this engineered ancestor had improved up to 2 . 5-fold . A comparison of global gene expression in wild-type , engineered , and all eight evolved strains revealed that the vast majority of changes during physiological adaptation effectively restored acclimatizing processes to wild-type expression states . On average , 93% of expression perturbations from the engineered strain were restored , with 70% of these occurring in perfect parallel across all eight replicate populations . Novel changes were common but typically restricted to one or a few lineages , and reinforcing changes were quite rare . Despite this , cases in which expression was novel or reinforced in parallel were enriched for loci harboring beneficial mutations . One case of parallel , reinforced changes was the pntAB transhydrogenase that uses NADH to reduce NADP+ to NADPH . We show that PntAB activity was highly correlated with the restoration of NAD ( H ) and NADP ( H ) pools perturbed in the engineered strain to wild-type levels , and with improved growth . These results suggest that much of the evolved response to genetic perturbation was a consequence rather than a cause of adaptation and that physiology avoided “reinventing the wheel” by restoring acclimatizing processes to the pre-stressed state .
Physiological stressors affect organisms across individual and evolutionary timescales: they invoke in individuals processes that work to restore homeostasis , and become over evolutionary timescales the selective pressures that drive adaptation in populations . How organisms generate innate and evolved responses to stressors – often termed physiological acclimation ( or phenotypic plasticity ) and adaptation , respectively – is a driving question today in many different fields of science , from the origins of drug resistance to the effects of global climate change . A common goal in many of these areas is to move from case-by-case studies towards a predictive understanding of how organisms will adapt to future stressors . However , whereas acclimatizing responses are generally “prewired” and relatively uniform between individuals of a population , the paths and outcomes of adaptation can be many and varied . Even under a simplified scenario of consistent selective pressures across replicate populations , evolution is not deterministic . There are many potential explanations for this variability - such as the randomness of mutations , escaping drift , epistasis , and clonal interference - all of which can give rise to multiple and sometimes quite disparate evolutionary outcomes [1]–[7]; yet , in other instances , adaptation is remarkably parallel between independently-evolved lineages , even down to the genetic level [8]–[10] . In replicate populations of laboratory-evolved organisms , parallelism is commonly interpreted as a sign of selection in either genetic [8] or phenotypic [11] data . Most studies determine the basis and parallelism of adaptation by comparing ancestral versus evolved states . However , in cases of adaptation to an environmental or genetic perturbation , there exists a third “wild-type” state that existed prior to the exposure to stressors that is often ignored . Exposure to genetic or environmental stressors invokes numerous processes that shift organisms from a wild-type to a perturbed physiological state , and it is this perturbed physiological state that is optimized over evolutionary timescales by natural selection . Thus , during experimental evolution all evolved strains share an initial set of acclimatizing responses that could be resolved differently by natural selection across replicate lineages . Given only a comparison of the ancestral ( perturbed ) and evolved states , it would be unclear how much of parallel adaptation represents convergent evolution of truly novel physiology , versus a wholesale restoration of cellular function to the pre-perturbed state . This has the potential to greatly conflate which physiological changes are likely causes versus consequences of improved fitness , and falsely identify highly parallel instances of adaptive evolution . To our knowledge , only one other study [12] has explicitly addressed the extent to which organisms adopt novel versus restored physiological states during adaptation to an environmental stressor , versus a genetic alteration . By including data on the wild-type state prior to an environmental or genetic perturbation , it becomes possible to distinguish which evolved changes were truly novel versus simply altering the acclimatized state . This allows physiological changes to be categorized into four patterns ( Figure 1A ) : restored , unrestored , or reinforced refer to whether acclimatizing responses were reversed , left unchanged , or enhanced through evolution , whereas novel changes did not manifest during initial acclimation , but appeared only later during evolution . Importantly , these classifications can be applied to various levels of physiological processes – from alterations in gene expression , to protein activity , metabolite concentrations and flux , and even higher-order properties such as growth rate or fitness – and could conceivably differ between levels . Ultimately , this framework provides a “direction” to orient the interpretation of physiological changes that occurred during adaptation , revealing the level and degree to which adaptation either restores prior cellular states or finds novel solutions to improve growth or fitness . We hypothesized that physiological changes that are simply restorative would occur commonly , and would frequently arise in parallel between replicate evolved lineages . By sorting out these restorative changes , the novel and reinforcing changes that remain should more clearly reflect the physiological bases of adaptation . Particularly when these novel or reinforcing changes occur in parallel , they may identify loci in which the causative , beneficial mutations occurred . As a model system in which to examine the interaction between acclimation and adaptation to perturbations , we employed a combination of metabolic engineering plus experimental evolution to study physiological and evolutionary responses to a novel , sub-optimal central metabolism in Methylobacterium extorquens AM1 . As a facultative methylotroph , M . extorquens AM1 is capable of utilizing one-carbon ( C1 ) compounds like methanol as a sole source of carbon and energy , as well as other multi-carbon compounds like succinate [13] . Its metabolism of C1 compounds is a complex process that requires over 100 different genes [14] , many of which were acquired via horizontal gene transfer [15] , [16] . C1 substrates such as methanol or methylamine are oxidized first to formaldehyde , and in wild-type ( WT ) , this toxic intermediate is then oxidized to formate using a pathway linked to tetrahydromethanopterin ( H4MPT ) , an analog of folate [15] , [17] ( Figure 1B ) . From formate , C1 units can be further oxidized into CO2 for the production of NADH , or assimilated into biomass [18] , [19] . To create an engineered Methylobacterium ( EM ) strain , the native H4MPT-based pathway of formaldehyde oxidation was disabled and replaced by a functionally analogous , yet non-homologous C1 pathway . Two genetic changes were required to make EM: ( 1 ) the deletion of the mptG locus , which encodes the enzyme that drives the first committed step in H4MPT biosynthesis and is necessary for growth or survival in the presence of methanol [17] , and ( 2 ) the introduction of an expression plasmid with two genes – flhA and fghA , both from Paracoccus dentrificans – that drive the oxidation of formaldehyde to formate using glutathione ( GSH ) as a C1 carrier [20] . The introduction of the engineered GSH-dependent pathway restores the ability of the ΔmptG strain to grow on methanol , however this EM strain is approximately 3-times slower growing than WT . Furthermore , the EM strain exhibits morphological abnormalities that arose from overexpression of the foreign GSH pathway [20] . Eight replicate populations ( F1–F8 ) were founded from an EM ancestor and propagated on methanol for over 600 generations in batch culture to study adaptation to a novel metabolic module . Adaptation in the F populations was substantial , rapid , and largely methanol-specific [20] . The cellular abnormalities that emerged as a consequence of introducing the foreign pathway were also eliminated , representing an example of a restored ( morphological ) change . Several beneficial mutations have been identified in these evolved lines , including four from an isolate from the population with the highest fitness gains ( F4 ) . Notably , all four of these beneficial mutations altered gene expression . The targets and apparent physiological pressures acting upon these beneficial mutations are as follows . ( 1 ) Increased pntAB expression: switching from the native to the engineered pathway of formaldehyde oxidation eliminated the cell's only direct source of NADPH production , and a transhydrogenase encoded by pntAB can overcome this limitation by reducing NADP+ to NADPH using NADH [21] . ( 2 ) Increased gshA expression , which encodes an enzyme in GSH biosynthesis: GSH is needed to react with formaldehyde in the engineered pathway , and its recruitment into central metabolism might dilute GSH away from its native functions to protect against oxidative stress [22] . ( 3 ) Increased icuAB , which encodes a cobalt transporter: this mutation allowed cells to overcome metal limitation in the medium [10] . And ( 4 ) , decreased expression of the introduced GSH pathway ( i . e . flhA and fghA ) [20] , [23]: Foreign genes and plasmids introduced through engineering or natural gene transfers are often sub-optimal in terms of their sequence , expression , or activity for their new host and function [23]–[25] . Correspondingly , mutations that decreased expression of flhA and fghA balanced the benefits of formaldehyde oxidation with the costs of gene expression , and these occurred in all eight evolved populations through a variety of genetic mechanisms [23] . While these mutations to pntAB , gshA , icuAB , and the foreign pathway are known to have improved fitness in one or more lineages , many other changes in cellular physiology were also altered as a consequence . It is unclear whether these mutations produced novel or restorative physiological states , nor the extent to which these changes occurred in parallel across replicate populations . Here , we sought to examine the extent to which evolution creates truly novel physiological states in the F lines , versus simply restoring acclimatizing processes towards WT-like levels . To this end , we used DNA microarrays to analyze changes in global gene expression from WT , to EM , to each of the eight F strains , and classified significant changes into patterns of restored , unrestored , reinforced , and novel gene expression as in Figure 1A . Without knowledge of acclimatizing processes , the substantial transcriptional changes observed in the evolved lineages would have been perceived as novel; however , our analysis revealed an overwhelming trend towards restoring gene expression to the WT state . Furthermore , whereas over 300 genes restored expression in parallel across all eight replicates , novel or reinforced changes tended to be unique to one or a few populations . Rare examples of parallelism amongst the novel or reinforced changes were particularly enriched for the loci with known beneficial mutations described above , or other probable candidates . One example of a highly parallel and beneficial reinforced change – PntAB transhydrogenase – translated into a restorative change in physiology , as it appeared to return NAD ( P ) ( H ) pools perturbed in EM back toward WT levels . Thus , incorporating information from physiological acclimation to a genetic or environmental perturbation can “orient” the interpretation of evolutionary adaptation , thereby distinguishing restorations from novelty , and greatly enriching for physiological changes that were causes , and not just consequences of increased fitness .
To interpret gene expression and other physiological data , we first characterized the growth rate and competitive fitness of WT , EM , and isolates of each F population after 600 generations of evolution . Relative growth rates measured using a high-throughput , automated robotic system [26]–[28] indicated the evolved isolates were now 1 . 95 to 2 . 5 times that of their EM ancestor on methanol , while WT was 3 times as fast ( Figure 1C ) . Improvements in the F isolates were similar to the gains previously measured at the population level [20] , suggesting that our isolates were representative of their respective evolved populations . Furthermore , these improvements in growth rate correlated well with increases in relative fitness , as determined by head-to-head competition [29] , confirming that selection focused largely on exponential growth ( Figure 1C ) . Given that the sole difference between the slow EM strain and WT was the replacement of the formaldehyde oxidation pathway , we hypothesized that adaptation in the F lines would largely focus upon this stage of methanol metabolism . To test this hypothesis , we determined the specific growth rate of strains on two additional C1 substrates: methylamine , and formate . Growth on methylamine is nearly identical to growth on methanol , except that formaldehyde enters C1 metabolism by way of methylamine dehydrogenase [30]; while , in contrast , growth on formate skips the steps of formaldehyde oxidation altogether ( Figure 1B ) . Relative to EM , the improvement of strains on methylamine was nearly comparable to their respective gains on methanol , while there were much smaller gains on formate ( Figure 1D ) . The large difference between improvement in the selective environment ( with methanol ) versus formate , or succinate [20] , contrasts with the generic improvements across substrates that was observed after adaptation of WT on methanol [29] . Overall , these data suggest that selection in the evolved lineages was focused predominantly focused on the formaldehyde oxidation pathway of C1 metabolism required for both methanol and methylamine growth . To investigate large-scale changes in physiology arising due to the replacement ( acclimation ) and subsequent evolution ( adaptation ) of the formaldehyde oxidation pathway , we used DNA microarrays to examine differences in global gene expression . We identified 878 genes that were differentially expressed relative to EM: 455 of which arose as acclimatizing responses to metabolic engineering , while the remaining 423 genes appeared only in the evolved isolates . Patterns of restored , unrestored , reinforced , or novel gene expression were categorized by following the fate of EM perturbations ( if present ) into each of the evolved lineages . Due either to experimental noise or an intermediate reversal in gene expression , a significant number of genes fell in-between our criteria for restored and unrestored , and were thus classified as a fifth group of “partially restored” changes . We present changes in gene expression in two ways ( Figure 2A ) . First is a scatter plot that depicts both the changes that occurred during acclimation to the introduced pathway ( WT vs . EM , x axis ) , versus those that occurred during adaptive evolution ( EVO vs . EM , y axis ) . Second , we present a histogram ( grey box ) that compiles these data solely in terms of the changes that occurred during adaptation . The majority of gene expression changes that occurred in the evolved strains were not novel , but restored perturbations to a WT-like state . Genes whose expression was fully or partially restored greatly outnumber the other categories . This is apparent by the large number of restored and partially restored genes in the scatter plot ( Figure 2A ) , as well as by tabulating the number of genes satisfying each category across independent evolved isolates ( Figure 2B , blue and purple ) . The next most numerous category was novel changes , followed by unrestored and then reinforced . As an additional method to explore similarity between transcriptional profiles , we used principal component analysis ( PCA ) . Including all significant expression changes , PC1 clearly separats EM from WT and the evolved isolates . In contrast , PC2 distinguishes three evolved isolates from the remainder: separating F4 from F1 and F8 ( Figure 2C ) . This highlights that , despite the great degree of parallelism in restorative gene expression , the transcriptomes of a few F lines appear to be quite distinct . Considering just those genes that are perturbed in EM ( i . e . , acclimatizing responses , with no novel changes ) , all evolved isolates cleanly fall between EM and WT , while F4 , F1 , and F8 remain quite distinct ( Figure S1A ) . Considering just novel changes , only F4 and the pair of F1 and F8 are distinct from the rest ( Figure S1B ) . Given that most of the 455 genes with perturbed expression in EM were restored during adaptation , we hypothesized that this class of changes may be particularly likely to occur in parallel across the F lines . For each gene with significant expression changes , we tabulated how many instances of each class occurred across the F lines ( Figure 3 ) . Only about 10% ( 46/455 ) of perturbed genes satisfied the strict criterion for restoration in all eight populations ( solid blue ) . By grouping strictly restored genes with cases of partial restoration , 72% ( 328/455 ) perturbed genes were restored across all eight populations ( dashed-blue ) , and 98% ( 444/455 ) moved toward WT in at least four populations . In contrast , partially restored changes had little affect when combined with fully unrestored genes . This tremendous degree of parallelism was not observed for novel expression changes . Over 70% ( 330/483 ) of these occurred in just one strain , and of those that occurred in two populations , 81% ( 83/102 ) of these were specific to the F1 and F8 isolates that exhibited a particularly distinct transcriptional pattern . Gene expression that was restored or unrestored in perfect parallel highlights the major acclimatizing responses of EM to perturbations invoked by metabolic engineering . The 46 genes that were restored across all lineages function in heat shock and stress responses ( including recA ) , C1 metabolism ( components of methanol dehydrogenase ) , chemotaxis response regulators , and various genes putatively of phage origin . Conversely , only two genes – a glycine riboswitch ( META1_misc_RNA_19 ) and a conserved hypothetical protein ( META2_0338 ) were never restored in any lineage . In the adaptation of the F lines , we hypothesized that a greater number of restored genes would indicate a closer return to the wild-type state , and thus faster growth . This could occur either directly through mutations to pathways controlling the expression of perturbed genes , or secondarily as other physiological processes are restored ( e . g . , increase in growth rate or decrease in stress ) . Contrary to this hypothesis , however , growth rate did not correlate with either increased instances of restored or partially restored gene expression , or with a decrease in unrestored expression ( Figure S2A–S2C ) . Furthermore , none of the four aforementioned loci known to have experienced beneficial mutations fell into this class . This suggests that the overall extent to which expression of evolved isolates returned to the WT-like state is not a good indicator of growth improvement . Many if not most evolution experiments focus only on changes that are novel with respect to their ancestor . Provided with only knowledge of the EM state and the commonly-used threshold of 2-fold differential expression , our analysis would have wrongly classified many instances of fully or partially restored and reinforced expression as being novel in the evolved lineages ( see histogram in Figure 2A ) . However , by incorporating WT physiology , we were able to identify 423 genes whose expression is wholly novel in at least one evolved lineage . The number of genes with novel expression varied between the F strains from only 12 in F2 , to 217 in F4 ( Figure 2B ) . Most instances of novel expression were unique to one or a few evolved lineages , with the exception of a few loci . One might therefore expect that an increased number of novel changes would be correlated with higher fitness , however no correlation was found between growth rate and increased instances of novel gene expression ( Figure S2D ) . In fact , the F1 and F8 strains share a large number of uniquely derived changes in gene expression - with functions in DNA transcription and translation , DNA synthesis , and a number of C1-related genes – yet are amongst the least improved lineages . In F4 , many novel down-regulated genes ( Figure 2A ) are in fact instances of gene loss from a previously identified deletion on the M . extorquens AM1 megaplasmid [20] , [31] that has been shown to be beneficial and recurring across experiments [32] . While individual cases of novel gene expression are no doubt important to growth and fitness gains in the F isolates , we found that these are in general less frequent than restorative changes , mostly restricted to one or a few strains , and on the whole a poor indicator of improvements gained in the F lines . The rarest , and perhaps most interesting class of gene expression changes were those that were reinforced , in which the acclimatizing response of EM to metabolic engineering was augmented through the evolutionary process . We identified only 30 genes with reinforced expression in at least one evolved isolate ( 7% of perturbed genes ) , which include the increased expression of the pntAB operon , the up-regulation of two genes with putative functions in cobalamin biosynthesis , the down-regulation of genes with predicted functions in fatty acid metabolism , and other genes with poorly-annotated functions that were down-regulated . Most genes with reinforced expression were unique to one or a few F strains , and remained unrestored or were restored in the other isolates ( Figure 3 ) . Unlike the above tests , instances of reinforced expression were strongly correlated with improvements in growth rate ( Figure S2E; R2 = 0 . 87 , p = 0 . 005 ) , however the sample size of reinforced changes is small . As described above , pntAB was known to contain a beneficial mutation in its promoter in F4 [20] , and these data now show that increased expression at this locus was not novel , but rather a response that arose first in the acclimation of EM and was reinforced through evolution . We hypothesized that highly parallel instances of novel or reinforcing changes in gene expression might be enriched for loci with beneficial mutations . Although 306 genes showed parallel changes in expression across at least six populations , and 453 genes were either novel or reinforcing , only 5 instances were observed that satisfied both criteria , and they were all novel . We identifed those loci with known beneficial mutations ( gshA and icuAB ) ; one other gene with parallel increases ( META1_0936 , a putative type I secretion membrane fusion protein ) ; and two genes with parallel decreases ( META1_2657 , a putative soxC sulfite oxidase; and META2_1007 , a putative beta-lactamase ) . Regarding the parallelism of reinforcing changes , the three loci composing the pntAB operon were increased in 8/8 lineages relative to EM , but only significantly so in 4/8 lineages . This suggests that beneficial mutations may be particularly common in expression changes that are both parallel and buck the trend of restoration to the WT-like state . PntAB transhydrogenase functions in redox homeostasis , and thus it was intriguing to find that perturbed pntAB expression was not restored but actually increased further away from WT levels . We hypothesized that the consequence of increased pntAB could actually be restorative to the levels of pyridine nucleotides NAD ( H ) and NADP ( H ) , despite its enhanced expression . We first examined the evolved isolates for mutations in the pntAB locus beyond that known for F4 , and only one other strain – F3 – had a similar mutation in the upstream region ( Figure 4A ) . Next , we investigated whether increased expression of pntAB equated to increased enzyme function . Transhydrogenase activity measured in WT , EM , and each of the evolved isolates closely mirrored changes in the expression of pntAB measured in the microarray analysis ( Figure 4B ) . Our data suggest that , outside of F3 and F4 , increased transhydrogenase in the F strains occurs either as a consequence of outside physiological changes ( e . g . , via allostery ) or through trans-acting factors that drive increased expression in these lineages . We further examined the relationship between transhydrogenase levels and growth rate , and found a highly correlated positive relationship amongst the evolved F isolates ( Figure 4C ) . To determine whether the effect of increased transhydrogenase activity in the evolved strains was to restore the redox balance of pyridine nucleotides , we examined strain differences in the ratios of NADPH/NADP+ and NADH/NAD+ . Interpreting changes in the steady-state concentrations of metabolites such as NADPH is complicated by the fact that these values represent a balance between production ( such as by transhydrogenase ) and consumption via biosynthesis . As there is relatively little degeneracy in the network of biosynthetic reactions , the rate of NADPH use should be nearly directly proportional to growth rate , such that mutations that increase the cell's capacity to grow can actually decrease the steady-state concentration of currency metabolites . Indeed , data from a variety of other organisms , such as Escherichia coli [33] and Lactococcus lactis [34] grown at different rates in chemostats have confirmed this intuition . Consistent with the above expectations , the slow-growing EM strain possessed a much higher ratio of both NADPH/NADP+ and NADH/NAD+ than WT . Including the evolved isolates , the ratios ( or redox state ) of reduced to oxidized NADP ( H ) and NAD ( H ) were both highly negatively correlated with growth rate , such that faster-growing strains possess substantially lower ratios for each ( Figure 5A and 5B , respectively ) . Variation in levels of NADPH/NADP+ also correlated well with changes in pntAB expression: strains with significant increases in pntAB ( n = 4 strains ) showed significantly lower NADPH/NADP+ ratios than those with marginal increases ( p<0 . 05 , Welch two-sample t-test ) ; however , the same was not true for NADH/NAD+ ratios . Even amongst strains with significantly increased pntAB expression , those with cis-acting mutations ( F3 and F4 ) were significantly faster and had lower NADPH/NADP+ ratios than strains with significant increases apparently driven in trans ( F2 and F7 ) . Importantly , almost all strains statistically significantly restore the redox states of NADP ( H ) and NAD ( H ) towards WT-like levels . Overall , these data suggest that the reinforcement of transhydrogenase activity increased the rate of NADPH production and drove the restoration of pyridine nucleotide metabolism back toward a WT-like state through an apparent variety of adaptive mechanisms .
Organisms are constantly pressured by ever-changing and potentially disruptive cellular and environmental conditions . Large-scale changes in physiology can occur due to ecological or environmental transitions , or upon sudden changes in genomic composition due to mutation , horizontal gene transfer , or genetic engineering in the laboratory . When a perturbed , sub-optimal physiology persists over multiple generations , transient acclimatizing responses begin to overlap with responses from evolutionary adaptation . Conceptually , processes of physiological acclimation and adaptation are intimately linked: as beneficial mutations should revert many acclimatizing processes from a perturbed to a baseline physiological state . In practice , the interplay between acclimatizing and adaptive responses to perturbations has often been ignored , leading to the scenario where large-scale , parallel restoration of physiology to a pre-stress state will appear as novel . We argue that a proper interpretation of evolved physiological states is only possible given knowledge of the initial acclimation to a new environment or genomic composition . Our work sought to determine the extent to which cells adopt novel versus restorative physiological states by examining acclimatizing and adaptive responses to a novel central metabolism . We utilized a strain of M . extorquens AM1 ( EM ) that was metabolically engineered to utilize a foreign , GSH-based central pathway to oxidize formaldehyde during growth on C1 compounds , and was subsequently propagated in eight replicate F populations for over 600 generations of evolution to optimize growth using the engineered pathway . The physiology of the EM ancestor was perturbed in many ways: it was three-fold slower; adopted an elongated , curved or branched cell morphology [20] , and exhibited a unique density-threshold for growth on methanol [35] . Here we document two additional levels of physiological perturbation: microarray analyses revealed 455 genes with altered expression from WT to EM , as well as perturbations in the central redox cofactors , NAD ( H ) and NADP ( H ) . By orienting our analyses based upon the initial acclimation from WT to EM , we categorized evolved changes as restored , unrestored , reinforced , or novel as in Figure 1A . Given the particularly interesting connection between acclimatizing and adaptive processes in reinforcement , we further examined the systems-level consequences of enhanced PntAB activity . The major pattern seen for evolved changes in physiology was an overwhelming trend to return to a wild-type state . Our work highlights a few general trends to be explored in other systems . First , the majority of gene expression differences distinguishing the ancestor and the evolved isolates were not novel , but instead restorative . Most restored genes were not themselves targets of beneficial mutations , but altered in response to other changes such things as NAD ( P ) ( H ) levels , or indirectly , improved growth rate ( increased methanol dehydrogenase ) , or reduced stress ( decreased recA and heat shock proteins ) . So much of gene expression was restorative that it outweighed instances of novel expression in all evolved strains . Similarly , PCA analysis confirmed that expression in the evolved isolates was more like WT than their common EM ancestor . One interesting future direction would be to examine the temporal component of adaptation , studying the degree to which physiology is restored as populations acquire sequential beneficial mutations . Second , the restoration of WT physiology occurred highly in parallel . Indeed , the vast majority of genes were restored , at least partially so , in all eight lineages . This is perhaps intuitive as a shared set of acclimatizing processes from EM were simply “turned off” in the case of stress-related responses , or “turned up” in the case of growth related genes , in the evolved lines . Without specific knowledge of these acclimatizing processes , however , most of these restorative changes would be wrongly classified as novel ( Figure 2A , histogram ) . Third , some acclimatizing processes were left unrestored because physiological adaptation cannot , or has not yet , addressed these perturbations . These may represent fundamental and perhaps inescapable differences separating WT and EM physiologies . And finally , changes that are both highly parallel and either novel or reinforced are potentially enriched for loci targeted by beneficial mutations , and thus causal changes during adaptation . Increases in expression in gshA ( 6/8 novel ) , icuAB ( 6/8 novel ) , and pntAB ( 4/8 reinforced ) are all outliers when comparing the parallelism of changes in each category across genes ( Figure 3 ) . In fact , by filtering out ( highly parallel ) restorative changes , we find only 19 genes ( out of 878 ) that are novel or reinforced changes in half or more of the evolved strains . Including the parallel , beneficial decreases in the expression and/or activity of the foreign pathway that occurred in 8/8 strains [23] , parallel changes in gene expression that are not restorative appear to be particularly enriched for beneficial mutations that drove adaptation . Looking closer , we did find variation in how the various F lines adapted to an engineered C1 metabolism . Novel expression of genes very rarely occurred in more than one strain , and where observed , it was nearly always to the F1 and F8 strains . These isolates consistently showed different transcriptional profiles than the other F isolates , not only amongst novel genes , but also in the number , types , and degree to which genes are restored . Interestingly , both F1 and F8 are also amongst the slowest growing of the F strains , suggesting perhaps the presence of a multi-peaked fitness landscape in which these strains have found a local optimum . While it appears that the F populations restored many genes in parallel , and share at least a few common molecular and physiological mechanisms , additional work is needed to understand the full extent to which these strains found parallel versus divergent paths to optimize growth using an engineered central metabolism . Reinforcing changes to physiology , while rare in our system , are an important link between processes of physiological acclimation and adaptation . We focused on one particular instance of reinforcement – the up-regulation of pntAB transhydrogenase – to investigate both the genetic basis for enhancing expression beyond acclimation and to uncover its physiological consequences . Normally , pntAB is expressed during multi-C and not C1 growth [36] , however in EM , the only direct source of NAD ( P ) ( H ) production was lost with the deletion of the native pathway of formaldehyde oxidation . This perturbation might invoke increased pntAB to maintain NAD ( P ) ( H ) homeostasis during growth on methanol . Supporting this hypothesis , the deletion of pntAB was found to be neutral for C1 growth in WT but lethal in the EM strain ( H . -H . Chou , data not shown ) , and the mutation in the F4 lineage that drives increased pntAB expression provides a 10% selective benefit in the ancestral background [20] . These results demonstrate the irreplaceable role of pntAB as an acclimatizing response in EM , and the benefit of reinforcing this function even further through adaptation . While the increased expression and activity of PntAB transhydrogenase was reinforcing , this translated into a restorative effect upon metabolism . All eight F strains increased transhydrogenase activity significantly , despite significant increases in expression for only half of these . Upon sequencing the genomic neighborhood of pntAB , we identified only two strains – F4 and F3 – that possess known or candidate mutations to drive increased expression . As for the physiological consequence of increased transhydrogenase activity , all evolved strains tend to restore NAD ( P ) ( H ) metabolism , and strains with greater increases to pntAB – particularly the pair with mutations in the upstream region – have levels of NAD ( H ) and NADP ( H ) that are the closest to WT . The reinforcement of pntAB expression and transhydrogenase activity , as well as the restoration of NAD ( P ) ( H ) levels , are both well correlated with increased growth rate in the F populations . By increasing activity during acclimation , and reinforcing this response further during adaptation , transhydrogenase activity appears to have been critical in maintaining and improving growth in the EM strain . Information on acclimatizing and adaptive responses in the engineering and evolution of EM allowed us to develop a framework in which to examine the true nature of evolved physiological change . We defined four basic patterns to describe not only novel changes to physiology , but also changes that restore , disregard , or reinforce the initial acclimatizing responses to perturbations ( Figure 1A ) . To our knowledge , this linkage between immediate physiological acclimation and subsequent adaptation has explicitly been explored only once before [12] . This paper described a large number of “compensatory” changes in gene expression that effectively restored the wild-type ( glucose-grown ) state during the acclimation and experimental evolution of E . coli to sub-optimal carbon sources . The commonalities between adaptation to a poor environment versus a novel , suboptimal metabolic pathway are remarkable: whereas their study showed that 87% of genes were restored after adaptation to a poor substrate , we found that on average that 93% of genes were restored after adaptation to the foreign pathway; their change was environmental , ours genetic . Furthermore , reinforcing changes are reminiscent of the fixation of traits via genetic accommodation or assimilation [37]–[39] , in that both processes stem from exposure to genetic or environmental stressors to reveal beneficial phenotypes that are “assimilated” and possibly reinforced by positive selection . However , in genetic assimilation , stress-induced phenotypes arise from cryptic genetic variation in populations [40] while , at least for the reinforced up-regulation of pntAB expression , the initial response required no standing genetic variation at all . In fact , the initial acclimatizing response of EM to increase pntAB was merely a generic response to NADPH shortage typically experienced during growth on multi-carbon substrates such as succinate [36] , that was co-opted for methanol growth in EM , and further increased and optimized by selection during adaption of the F lines . Overall , our results suggest that much of evolutionary adaptation effectively relieves processes of physiological acclimation . Rather than “reinvent the wheel” of C1 metabolism , a few causal mutations in the adaptation of the F populations propagated through physiology to restore WT homeostasis . In fact , more changes in gene expression occurred as a result of acclimation to genetic engineering ( n = 455 ) than novel changes seen in any of the isolates after 600 generations of experimental evolution ( 12 to 217 ) . Beneficial mutations were enriched toward novel and reinforcing changes that occurred in parallel . By distinguishing acclimatizing versus adaptive processes , a more accurate depiction on the nature and parallelism of physiological evolution is revealed .
All growth was performed using a modified “Hypho” minimal medium as in [20] . One liter of Hypho was prepared from 799 mL of deionized water , 100 mL phosphate salts ( 25 . 3 g of K2HPO4 plus 22 . 5 g NaH2PO4 in 1 L deionized water ) , 100 mL sulfate salts ( 5 g of ( NH4 ) 2SO4 plus 0 . 98 g MgSO4 in 1 L deionized water ) , and 1 mL of modified , high-iron “Vishniac” trace metal solution [10] , [20] . All solutions were autoclaved separately and combined under sterile conditions , and the final medium was stored in the dark . Carbon substrates added just prior to inoculation consisted of: 20 mM methanol , 3 . 5 mM sodium succinate , 15 mM methylamine hydrochloride , or 20 mM sodium formate . Growth experiments were initiated by inoculating 10 µL of freezer stock into 9 . 6 mL Hypho in a 50 mL Erlenmeyer flask containing 10 mM methanol and 1 . 75 mM succinate plus 50 µg/mL kanamycin . Flasks were grown at 225 rpm in a 30°C shaker-incubator until reaching stationary phase ( 2–4 days ) . A second acclimation cycle was accomplished by transferring 150 µL of saturated culture into 9 . 45 mL fresh medium with 0 . 5× kanamycin plus the carbon substrate to be tested; then transferred again into the same conditions for experimental ( measured ) growth . A 1∶64 dilution of cultures with the given substrate concentrations allowed for six doublings per growth cycle . All physiological assays ( e . g . , microarray analyses , enzyme assays , metabolite concentrations ) were performed using cells that had reached half-maximal density following transfer from acclimation cultures also grown on methanol . This protocol results in eleven doublings of growth in a consistent environment while ensuring cells were still growing exponentially at the time of harvest . This gave the maximal possible time to approach steady-state physiology while staying within the constraints of the selective conditions . Furthermore , since it was previously found that the EM ancestor exhibits a unique cell-density threshold for growth [35] , it would not have been possible to have diluted the cultures much more than the 1/64 used here . Specific growth rates were determined in 48-well plates using a high-throughput , robotic system that automates measurements of optical density ( i . e . , OD600 ) in growing cultures at timed intervals [27] . This system consists of a plate-shaking tower , a plate reader , a robotic arm , and de-lidding station that transfer cultures between growth and measurements , all of which is scheduled with an open software manager program [26] . Strains for growth measurements were inoculated first into flasks , transferred to plates with for an acclimation phase , and transferred once more for measurement during the third cycle . All growth was performed in 640 µL total medium and were transferred in a 1/64 dilution ( 10 µL culture into 630 µL medium ) . To limit clumping and reduce noise in OD600 measurements in growing cultures , 0 . 1 mg/mL of prepared cellulase enzyme ( Sigma-Aldrich , St . Louis , MO ) was added to the growth medium ( SMC , unpublished ) . The specific growth rate was calculated from the log-linear phase of growth for at least triplicate cultures of each strain using an open software analysis package [28] . Strains and plasmids relevant to this study are listed in Table S1 and were generated previously , unless otherwise noted . The ancestral strains for the F populations were described previously [20] . Briefly , they derive from two WT M . extorquens AM1 strains - one that is naturally pink ( CM501 ) , and another that is white ( CM502 ) due to a neutral mutation in carotenoid biosynthesis [41] – to limit contamination between cultures . The EM strain was constructed in two steps: 1 ) the H4MPT-dependent pathway was disabled by deleting the mptG locus ( encoding β-ribofuranosylaminobenzene 5′-phosphate synthase ) , the product of which drives the first committed step in the H4MPT biosynthesis [42]; and 2 ) the introduction of a GSH-dependent formaldehyde oxidation pathway on the plasmid pCM410 – which expresses the genes flhA ( encoding S-hydroxymethyl-GSH dehydrogenase ) and fghA ( encoding S-formyl-GSH hydrolase ) from Paracoccus denitrificans –into the ΔmptG backgrounds , generating completed pink ( CM701 ) and white ( CM702 ) EM strains [20] . Eight replicate populations were founded from either the pink ( odd populations; CM701 ) or white ( even populations; CM702 ) EM strains and evolved for over 600 generations in 9 . 6 mL Hypho medium plus 15 mM methanol in batch culture with transfers of 1/64 of the volume every four days for the first 300 generations , and every two days thereafter . These evolved “F” populations ( F1-8 ) were streaked at generation 600 onto Hypho agar plates to isolate colonies for further characterization . In addition to the previously characterized isolate from the F4 population , CM1145 [20] , we chose for this study the second of three random isolates from each of the other F populations for further investigation ( Table S1 ) . Other strains relevant to this study were as follows . Fluorescence-based fitness assays required an EM reference strain ( CM1232 ) that had been generated by replacing the katA locus with mCherry driven by a constitutive Ptac promoter [20] . To standardize the use of kanamycin in all cultures , we used a WT strain in which the kan resistance marker was inserted into katA ( CM611 ) [29] . The relative fitness of WT and evolved strains was assessed in a head-to-head competition of co-cultures with a fluorescently-labeled reference as in [29] . Briefly , fully-grown cultures of WT and each evolved isolate were mixed in roughly equal optical densities with an EM strain expressing mCherry ( CM1232 ) . A sample of this mixture ( T0 ) was diluted with Hypho plus 8% DMSO and stored at −80°C in 96-well plates; the rest was diluted 1∶64 into 640 µL of Hypho methanol medium in a 48-well plate and incubated with shaking at 30°C for 4 days , after which samples of the co-culture after competition ( T1 ) were frozen for later analysis using flow cytometry . Because of the 4-day growth cycle , this amortizes fitness over all growth phases ( i . e . , lag , exponential , and stationary ) . The ratio of labeled to unlabeled cells before and after co-culture growths was measured using a BD LSR Fortessa flow cytometer with an HTS attachment for 96-well plates ( BD Biosciences , San Jose , CA ) . Recently it was found that the forward scatter ( FSC ) and side scatter ( SSC ) settings used in earlier work [20] systematically underestimated fitness increases relative to EM because of the cells' larger size . Here we set both scatter measurements set to 300 V to accommodate small bacterial cell sizes [23] , and the flow-rate was adjusted to the lowest setting to produce reliable measurements of labeled and unlabeled events in dilute co-cultures . The ratio of nonfluorescent to fluorescent cells before ( R0 , from T0 ) and after ( R1 , from T1 ) competition were used to calculate the fitness ( W ) of strains relative to the EM reference ( CM1232 ) using the following formula , assuming a 64-fold expansion of cells from six doublings per growth cycle: Triplicate cultures of strains were grown to half-maximal OD600 in 15 mM methanol before harvesting and total RNA extraction using the RNeasy kit ( Qiagen , Valencia , CA ) . Genomic DNA was removed using the TURBO DNA-free kit ( Ambion , Austin , TX ) and the RNA samples were concentrated using Amicon Ultra centrifugal filters ( Millipore , Billerica , MA ) . Microarray analyses for all ( n = 30 ) samples were performed by MOgene , Inc ( St . Louis , MO ) using one-color cDNA labeling and hybridization . The array probes and platform were designed previously [43] to include 60-mer oligonucleotides that provide two or more probes for confirmed and predicted ORFs in the Methylobacterium genome [31] . Raw and normalized expression data are available from the Gene Expression Omnibus , accession GSE42116 . Pre-processing , normalization , and analysis of expression data was performed using the limma package [44] , [45] with Bioconductor [46] and R [47] . Differentially expressed genes were identified by the proportion of differentially expressed probes in a limma contrast given: 1 ) at least two-thirds probes significant at p<0 . 05 in the moderated t-statistic , 2 ) at least one-half of probes significant at p<0 . 01 , and 3 ) all significant probes with uniform changes either up or down . Probes that met these criteria were averaged in each strain to estimate the log2 difference in expression relative to EM . Genes differentially expressed in both EVO:EM and EVO:WT contrasts , and in the same direction , were classified as novel . Expression perturbations from acclimation were identified in a WT:EM contrast and further partitioned given information from EVO:EM and EVO:WT contrasts to define patterns of: restored expression , given an EVO:EM change back in the direction of WT expression; unrestored expression , given no EVO:EM difference but a significant EVO:WT difference; and reinforced expression , having an EVO:EM difference in the same direction ( up or down ) as the change from WT to EM ( Figure 1A ) . Partially restored genes showed no EVO:EM difference and were not significant in a EVO:WT contrast . Principal component analysis was used to cluster and contrast the expression profile of WT , EM , and evolved strains , and was calculated using the prcomp function in R with scaling to account for large variance of expression changes between genes . Cultures for the determination of TH activity and NAD ( P ) ( H ) ratios ( below ) were grown to half-maximal OD600 on methanol , spiked with another 15 mM methanol , and allowed to return to mid-exponential growth for approximately 16 hours to increase yield . Cultures for transhydrogenase activity measurements were pelleted and washed with 50 mM Tris-HCl ( pH 7 . 5 ) before storage at −80°C . Upon thawing , cells were re-suspended in 2 mL Tris buffer and lysed by bead beating ( MP Biomedicals , Solon , OH ) . Cell extracts were centrifuged for less than 15 s to collect the beads . The supernatant was removed and combined with a reaction mix consisting of: 20 µL of 40 mM MgCl2 ( 10× ) , 20 µL 5 mM NADPH ( 10× ) , 20 µL 10 mM 3-acetylpyridine adenine dinucleotide ( 10× ) , plus Tris buffer to equal 200 µL , total , in a 96-well plate . The increase in absorbance at 375 nm was measured immediately after addition of the reaction mix and the slope of the linear regression was used to calculate transhydrogenase activity ( µmole of 3-acetylpyridine adenine dinucleotide reduced sec−1 mg−1 ) as follows: TH activity ( µmole sec−1 mg−1 ) = slope ( sec−1 ) ×1/exctinction coefficient ( 0 . 0051 mol cm L−1 ) ×1/path length ( 0 . 42 cm−1 ) ×reaction volume ( 0 . 2 mL ) ×1/cell protein ( mg ) ×1000 ( conversion to µmole L−1 ) . Cell extracts for the measurement of pyridine nucleotide concentrations were prepared as follows . Metabolism in mid-exponential cells was quenched using vacuum-filtration and rapid immersion into hot extraction solutions . Oxidized pyridine nucleotides ( NAD+ and NADP+ ) were selectively preserved in an acidic extraction solution consisting of 100 mM HCl plus 500 mM NaCl; reduced species ( NADH and NADPH ) were extracted using a basic solution of 100 mM NaOH plus 500 mM NaCl . For both acidic and basic extractions , 750 µL of culture was vacuum-filtered onto 0 . 45 µm nylon membranes ( Millipore , Billerica , MA ) , immediately immersed into the appropriate extraction solution , briefly vortexed , and heated to 95°C for 5 m . Extracts were again briefly vortexed , centrifuged at maximum speed for 30 s , and the supernatant removed , flash frozen , and stored at −80°C for later use . Three biological replicates stemming from separate inoculations were extracted for each strain . Pyridine nucleotides in cell extracts were quantified using enzymatic cycling [48] with alcohol dehydrogenase ( ADH ) or glucose-6-phosphate dehydrogenase ( G6PDH ) to measure NAD ( H ) and NADP ( H ) , respectively . Each assay was performed using 20 µL of either acidic extraction solutions for oxidized species , basic solutions for reduced , or a serial dilution of ( reduced ) standards . For NAD ( H ) , to 20 µL of cell extract or standard was added 180 µL of master solution consisting of: 20 µL 1 M bicine ( pH 8 . 0 ) plus 40 mM EDTA ( 10× ) , 20 µL of 16 . 6 mM phenazine ethosulfate ( 10× ) , 20 µL of 4 . 2 mM thiazolyl blue tetrazolium bromide ( 10× ) , 20 µL of 100% ethanol , 2 µL of ADH ( Sigma-Aldrich , St . Louis , MO ) at 0 . 1 U/µL , and 98 µL water . The same mixes were used for the determination of NADP ( H ) with G6PDH , except that ethanol and ADH were replaced by 20 µL of 50 mM glucose-6-phosphate ( 10× ) and 2 µL of G6PDH at 0 . 1 U/µL . Assays were conducted in 96-well plate format and measured in a Safire2 spectrophotometer ( Tecan , Morrisville , NC ) at 30°C by following the increase in absorbance at 550 nm over time .
|
Acclimatizing and adaptive ( evolutionary ) processes allow organisms to thrive despite cellular and environmental perturbations . Our work examined whether adaptation restores stress responses towards wild-type ( pre-stressed ) versus novel physiological states during adaptation by studying a bacterium ( Methylobacterium extorquens AM1 ) that was experimentally engineered and evolved with a novel central metabolism . The engineered strain was much slower and less fit than wild-type , but eight replicate populations evolved for six hundred generations showed substantial improvements . We found that changes in gene expression during adaptation consistently restored acclimatizing processes to the wild-type state , often in 8/8 evolved lines . Novel changes were common and largely restricted to one lineage; however , highly parallel novel changes revealed loci harboring beneficial mutations . Even rarer were reinforced changes , such as pntAB transhydrogenase , which increased beyond immediate acclimation during evolution to restore NAD ( P ) ( H ) metabolism and improve growth . Overall , a few novel or reinforcing changes drove the mass-restoration of physiology back to wild-type .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome",
"expression",
"analysis",
"microbial",
"metabolism",
"gene",
"regulation",
"population",
"genetics",
"microbiology",
"parallel",
"evolution",
"microbial",
"evolution",
"molecular",
"genetics",
"forms",
"of",
"evolution",
"microbial",
"physiology",
"metabolic",
"pathways",
"biology",
"evolutionary",
"genetics",
"biochemistry",
"adaptation",
"natural",
"selection",
"genetics",
"genomics",
"evolutionary",
"biology",
"metabolism",
"evolutionary",
"processes"
] |
2013
|
Evolution after Introduction of a Novel Metabolic Pathway Consistently Leads to Restoration of Wild-Type Physiology
|
Human embryonic stem cells ( hESCs ) and neural progenitor ( NP ) cells are excellent models for recapitulating early neuronal development in vitro , and are key to establishing strategies for the treatment of degenerative disorders . While much effort had been undertaken to analyze transcriptional and epigenetic differences during the transition of hESC to NP , very little work has been performed to understand post-transcriptional changes during neuronal differentiation . Alternative RNA splicing ( AS ) , a major form of post-transcriptional gene regulation , is important in mammalian development and neuronal function . Human ESC , hESC-derived NP , and human central nervous system stem cells were compared using Affymetrix exon arrays . We introduced an outlier detection approach , REAP ( Regression-based Exon Array Protocol ) , to identify 1 , 737 internal exons that are predicted to undergo AS in NP compared to hESC . Experimental validation of REAP-predicted AS events indicated a threshold-dependent sensitivity ranging from 56% to 69% , at a specificity of 77% to 96% . REAP predictions significantly overlapped sets of alternative events identified using expressed sequence tags and evolutionarily conserved AS events . Our results also reveal that focusing on differentially expressed genes between hESC and NP will overlook 14% of potential AS genes . In addition , we found that REAP predictions are enriched in genes encoding serine/threonine kinase and helicase activities . An example is a REAP-predicted alternative exon in the SLK ( serine/threonine kinase 2 ) gene that is differentially included in hESC , but skipped in NP as well as in other differentiated tissues . Lastly , comparative sequence analysis revealed conserved intronic cis-regulatory elements such as the FOX1/2 binding site GCAUG as being proximal to candidate AS exons , suggesting that FOX1/2 may participate in the regulation of AS in NP and hESC . In summary , a new methodology for exon array analysis was introduced , leading to new insights into the complexity of AS in human embryonic stem cells and their transition to neural stem cells .
The human central nervous system is composed of thousands of neuronal subtypes originating from neural stem cells ( NSCs ) that migrate from the developing neural tube . Such neuronal complexity is generated by a vast repertoire of molecular , genetic , and epigenetic mechanisms , such as the active retrotransposition of transposable elements [1] , alternative promoter usage , alternative RNA splicing ( AS ) , alternative polyadenylation , RNA editing , post-translational modifications , and epigenetic modulation [2] . Understanding the processes that generate neuronal diversity is key to gaining insights into neuronal development and paving new avenues for biomedical research . Human embryonic stem cells ( hESCs ) are pluripotent cells that propagate perpetually in culture as undifferentiated cells and can be induced to differentiate into a multitude of cell types both in vitro and in vivo [3] . As hESCs can theoretically generate all cell types that make up an organism , they serve as an important model for understanding early human embryonic development . In addition , the hESCs are a nearly infinite source for generating specialized cells such as neurons and glia for potential therapeutic purposes [4 , 5] . In recent years , methods have been introduced to induce hESCs to differentiate into neural progenitors ( NPs ) [6 , 7] and neuronal and glial subtypes [8–12] . The therapeutic interest in understanding the molecular basis of pluripotency and differentiation has led to many studies comparing transcriptional profiles in different hESC lines and the study of expression changes during the differentiation of hESCs to various lineages [13–17] . NSCs and progenitor cells ( NPs ) are present throughout development and persist into adulthood [18–20] . They are critical for both basic research and developing approaches to treat neurological disorders , such as Parkinson disease and amyotrophic lateral sclerosis ( ALS ) , and stroke or head injuries [21 , 22] . NSCs and NPCs can be isolated from human fetal brain tissue [23–26] , as well as from several regions of the adult human brain , such as the cortex , hippocampus , and the subventricular zone ( SVZ ) of the lateral ventricles [26–35] . Several studies have explored expression patterns of NPCs . For example , Wright et al . identified “expressed” and “not expressed” genes in NPCs isolated from the human embryonic cortex [24]; Cai et al . used the massively parallel signature sequencing profiling ( MPSS ) technique to analyze expression of fetal NPCs in comparison to hESCs and astrocyte precursors [27]; Maisel et al . used Affymetrix Gene Chip arrays to compare adult and fetal NPCs propagated in neurospheres [35] . However , as with hESCs , the focus thus far has been primarily on transcriptional differences , which ignores differential RNA processing such as AS , polyadenylation , degradation , or promoter usage . AS is frequently used to regulate gene expression and to generate tissue-specific mRNA and protein isoforms [36–39] . Recent studies using splicing-sensitive microarrays suggested that up to 75% of human genes undergo AS , where multiple isoforms are derived from the same genetic loci [40] . This functional complexity underscores the challenge and importance of elucidating AS regulation . AS appears to play a dominant role in regulating neuronal gene expression and function [41 , 42] . Examples of splicing regulators that are enriched and function specifically in neuronal cells include the brain-specific splicing factor Nova [43 , 44] and neural-specific polypyrimidine tract binding protein ( nPTB ) , which antagonizes its paralogous PTB to regulate exon exclusion in neuronal cells [45–47] . Finally , an early report estimating that 15% of point mutations disrupt splicing underscores the importance of splicing in human disease [48] . Indeed , the disruption of specific AS events has been implicated in several human genetic diseases , such as frontotemporal dementia and parkinsonism , Frasier syndrome , and atypical cystic fibrosis [49] . While insights into the regulation of AS have come predominantly from the molecular dissection of individual genes [36 , 49] , it is becoming clear that molecular rules can be identified from large-scale studies of both constitutive splicing and AS [40] . Most systematic global analyses on AS have focused on comparisons across differentiated human tissues [50–52] . Only one study , utilizing expressed sequence tag ( EST ) collections from stem cells , has attempted to find AS differences between embryonic and hematopoietic stem cells [53] . However , utilizing ESTs to identify AS has intrinsic problems , as ESTs tend to be biased for the 3′ ends of genes , and full coverage of the genome by ESTs is severely limited by sequencing costs . The commercial availability of Affymetrix exon arrays provides an alternative approach to interrogate the expression of every known and predicted exon in the human genome . The Affymetrix GeneChip Human Exon 1 . 0 ST array contains ∼5 . 4 million features used to interrogate ∼1 million exon clusters ( collections of overlapping ) of known and predicted exons with more than 1 . 4 million probesets , with an average of four probes per exon . Our goal was to identify and characterize AS events that distinguish pluripotent hESCs from multipotent NPs , paving the way for future candidate gene approaches to study the impact of AS in hESCs and NPs . However , as different hESC lines were established under different culture conditions from embryos with unique genetic backgrounds , we expected that hESCs and their derived NPs might have distinct epigenetic and molecular signatures [54] . As both common and cell-line specific alternatively spliced exons are likely to be important in regenerative research , in our study two separate hESC lines were used , with independent protocols for differentiating the hESCs into NPs positive for Sox1 , an early neuroectodermal marker . As an endogenously occurring population of NPs , human central nervous system stem cells grown as neurospheres ( hCNS-SCns ) were utilized as a natural benchmark for derived NPs . We developed an approach called REAP ( Regression-based Exon Array Protocol ) , which is based on robust regression that analyzed signal estimates from Affymetrix exon array data to identify AS exons . Experimental validation revealed alternative exons that distinguish hESCs from NPs; some of them also distinguish hESCs from a variety of differentiated tissues . A comparison of REAP-predicted alternative events with independent methods , such as using publicly available transcripts ( ESTs and mRNAs ) and computational predictions based on genomic sequence information alone [55] , showed a strong concordance of REAP-identified AS exons with AS events identified from these orthogonal methods . Finally , using analysis of the sequences flanking REAP-identified alternative exons , we were able to discover known and novel cis-regulatory elements that potentially regulate these AS events .
NPs were independently derived from two hESC lines , and RNA extracted from the cell lines was processed and hybridized onto Affymetrix Human 1 . 0 ST exon arrays . Immunohistochemical and reverse-transcriptase polymerase chain reaction ( RT-PCR ) analyses demonstrated that the hESCs expressed pluripotent marker genes , and the derived NPs expressed multipotent and neurogenic markers similar to hCNS-SCns . Undifferentiated Cythera ( Cyt-ES ) and HUES6 ( HUES6-ES ) hESC lines were maintained in culture as previously described [12 , 23 , 56] . Utilizing specific antibodies , we observed that undifferentiated Cyt-ES and HUES6-ES cells were positive for the pluripotent markers Oct4 , SSEA-4 , and Tra-1–80 ( unpublished data ) . NPs were derived from the hESC cell lines using protocols optimized for each line ( see Materials and Methods ) . Greater than 90% of derived NP cells ( Cyt-NP from Cyt-ES and HUES6-NP from HUES6-ES ) were positive for Sox1 , an early neuroectodermal marker , and Nestin ( Figure 1A ) , and negative for Oct4 ( unpublished data ) . As a natural benchmark for the derived NPs , we utilized hCNS-SCns , which were previously isolated from fresh human fetal brain tissues using antibodies to cell-surface markers and fluorescence-activated cell sorting [12 , 23] . The hCNS-SCns form neurospheres in culture which are greater than 90% Nestin and Sox1 positive , and differentiate into both neurons and glial cells in vitro [12 , 23] . Immunohistochemical analysis confirmed that hCNS-SCns were negative for Oct4 ( unpublished data ) and positive for Sox1 and Nestin ( Figure 1A ) . Here , known molecular markers were subjected to RT-PCR measurements , which were compared to gene-level signal estimates generated from the exon array data . Total RNA was extracted , and labeled cDNA targets were generated from three independent preparations of each cell type , namely Cyt-ES , HUES6-ES , Cyt-NP , HUES6-NP , and hCNS-SCns . To facilitate downstream analyses , instead of utilizing the meta-gene sets available from the manufacturers , we generated our own gene models by clustering alignments of ESTs and mRNAs to annotated known genes from the University of California Santa Cruz ( UCSC ) Genome Browser Database . After hybridization , scanning , and extraction of signal estimates for each probeset on the exon arrays , gene-level estimates were computed based on our gene models using available normalization and signal estimation software from Affymetrix . For every gene , a t-statistic and corresponding p-value were computed representing the relative enrichment of the expression of the gene in hESC versus NP , such as in Cyt-ES versus Cyt-NP . After correcting for multiple hypothesis testing using the Benjamini-Hochberg method , a p-value cutoff of 0 . 01 was used to identify enriched genes . Close inspection of all pairs of hESC-NP comparisons revealed a generally significant overlap from 31% to 85% of the smaller of two compared sets of enriched genes ( see Figure S1 ) . Thus for the purpose of identifying overall pluripotent and neural lineage-specific genes , the collective set of NPs ( Cyt-NP , HUES6-NP , and hCNS-SCns ) was compared to the collective set of hESCs ( Cyt-ES and HUES6-ES ) . Oct4 and Nanog , which are important in maintaining the pluripotent state of embryonic stem cells ( ESCs ) , were highly expressed in hESCs but were significantly lower in NPs ( Figure 1B ) . RT-PCR of Oct4 and Nanog mRNA levels accurately reflected the signal estimates from the array ( Figure 1C ) . Interestingly , Nestin was not significantly higher in NPs as compared to the hESC from the gene-level estimates ( p-value 0 . 065 ) , which was further confirmed by RT-PCR ( Figure 1C ) . Notch was recently identified to be important in promoting the neural lineage entry in mouse ESCs [57] and was shown to regulate stem cell proliferation in somatic mouse and hESC [58] . Gene-level signal estimates suggested that Notch was significantly higher in hCNS-SCns relative to hESCs , but levels of Notch were not significantly different in the derived NPs compared to hESCs . Delta/Notch-like EGF-related receptor ( DNER ) , a neuron-specific transmembrane protein , was recently shown to bind to Notch at cell–cell contacts and activates Notch signaling in vitro [59] . RT-PCR validation of DNER confirmed array-derived signal estimates , indicating an enrichment of DNER in NPs relative to hESCs ( Figure 1C ) . Finally , Sox1 , a HMG-box protein related to SRY , was shown to be one of the earliest transcription factors expressed in cells committed to the neural fate [60] . Here the gene-level estimates indicated that Sox1 was expressed significantly higher in NPs relative to hESCs ( p-value 0 . 00013 , Figure 1B ) , a finding that was confirmed by RT-PCR ( Figure 1C ) . From these examples , we concluded that RT-PCR validation correlated well with gene-level estimates from the exon array . In addition , the derived NPs had decreased levels of pluripotent markers Oct4 and Nanog but had levels of Sox1 that were comparable to hCNS-SCns . This finding confirmed that the derived NPs were committed to a neural fate and validated the use of hCNS-SCns as a benchmark for NPs . Next we asked whether the highest enriched genes in hESCs relative to NPs reflected our existing knowledge in the literature . Using the above-mentioned groupings of hESCs ( Cyt-ES , HUES6-ES ) and NPs ( Cyt-NP , HUES6-NP , and hCNS-SCns ) , 2 , 945 genes were enriched in hESCs relative to NPs; and 552 genes were enriched in the NPs relative to hESCs , at a p-value significance cutoff of 0 . 01 ( correcting for multiple hypothesis testing using the Benjamini-Hochberg method ) . The 15 most enriched genes in hESCs included genes such as teratocarcinoma-derived growth factor 1 ( TDGF1/cripto; p-value < 10−12 ) , zinc finger protein 42 ( Zfp42/Rex1; p-value < 10−12 ) , Oct4 ( p-value < 10−12 ) , Nanog ( p-value < 10−10 ) , lin-28 homolog ( p-value < 10−10 ) , cadherin 1 preprotein ( p-value < 10−10 ) , claudin 6 ( p-value < 10−9 ) , ephrin receptor EphA1 ( p-value < 10−9 ) , and erbB3 ( p-value < 10−9 ) . TDGF1/cripto was first shown to stimulate DNA synthesis and cell proliferation of both undifferentiated and differentiated embryonic carcinoma cells [61] and was later shown to be important for cardiomyocyte formation from mouse ESC [62] . Oct4 , reviewed in [63] , and Nanog [64] are crucial for the pluripotency of hESCs . Recently , knockdown of Zfp42/Rex-1 in mouse ESC caused the cells to differentiate [65] . Our gene-level exon array analysis confirmed that the hESCs and NPs were molecularly distinct . To reveal global functional differences between the enriched genes in hESCs or NPs , the enriched genes were subjected to a Gene Ontology ( GO , http://www . geneontology . org ) analysis as described previously [55] . Enriched genes in hESCs were more likely to be in molecular function categories , such as “RNA binding” ( p-value < 10−12 ) , “structural constituent of ribosome” ( p-value < 10−51 ) , “exonuclease activity” ( p-value < 10−6 ) , “cytochrome-c oxidase activity” ( p-value < 10−5 ) , and “ATP binding” ( p-value < 10−6 ) , and in biological processes involved with “tRNA processing” ( p-value < 10−6 ) and “protein biosynthesis” ( p-value < 10−48 ) , consistent with our knowledge of hESCs as a rapidly proliferating population of cells ( Figure 2A ) . Similar analysis of the enriched genes in NPs revealed an overrepresentation in molecular functional categories , such as “calcium ion binding” ( p-value < 10−8 ) and “structural molecule activity” ( p-value < 10−5 ) , and in biological processes involved with “neurogenesis” ( p-value < 10−38 ) , “cell adhesion” ( p-value < 10−13 ) , “cell motility” ( p-value < 10−4 ) , “development” ( p-value < 10−6 ) , “neuropeptide signaling pathway” ( p-value < 10−4 ) , and “endocytosis” ( p-value < 10−4 ) ( Figure 2B ) . Considering that these were the only categories that were significantly enriched out of more than 18 , 000 GO terms , and that randomly selected sets of similar numbers of genes did not reveal statistical differences in GO categories , our results confirmed that the global molecular profiles derived from exon array analysis were consistent with known differences between hESCs and NPs . To summarize , firstly immunohistochemical and RT-PCR evidence validated that the cells exhibited expected characteristics; secondly , stage-specific marker gene differences by RT-PCR were reflected accurately by gene-level estimates from the exon arrays; thirdly , the hESC-enriched genes were coherent with known genes that controlled pluripotency and self-renewal; and lastly , the global functional profiles exemplified expected biological differences between hESC and NP cells . Convinced that the signal estimates from the exon arrays reflected expected molecular and biological differences between hESCs and NPs , we sought to identify AS events . We compared Cyt-ES to hCNS-SCns to illustrate our approach . First we normalized the data and generated signal estimates with Robust Multichip Analysis ( RMA ) and estimated the probability that each probeset was detected above background ( DABG ) using publicly available Affymetrix Power Tools ( APT ) . We analyzed probesets that ( i ) comprised three or more individual probes; ( ii ) were localized within the exons of our gene models with evidence from at least three sources ( mRNA , EST , or full-length cDNA ) ; and ( iii ) were detected above background in at least one of the cell lines . In total , 17 , 430 gene models were represented by probesets that satisfied these criteria . Next we asked whether the probeset expression within each gene model was positively correlated for any two cell lines . To do this we calculated the Pearson correlation coefficient between the vectors of median signal estimates across replicates in Cyt-ES versus hCNS-SCns . The vast majority of genes ( >80% ) was found to have probeset-level Pearson correlation coefficients of greater than 0 . 8 ( Figure 3A ) . Next we randomly permuted the association between the median signal estimates and the probesets for each gene in hESCs ( or hCNS-SCns ) and observed that the distribution of Pearson correlation coefficients for the permuted sets was centered at zero , as expected ( Figure 3A ) . This indicated that the signal estimates for probesets between hESCs and hCNS-SCns were highly correlated and suggested that a scatter plot of probeset signal estimates between hESCs and hCNS-SCns would reveal a linear relationship for the majority of genes . We hypothesized that a linear regression to determine if some probesets behaved unexpectedly in one cell type compared to the other might be a reasonable approach to identify AS exons . Here , a possible representation of the data was explored . If we had N replicates in one condition and M replicates in the other , we could consider N*M points if we analyzed every possible pairing . For instance , three replicate signal estimates for every probeset per cell line , such as signal estimates a , b , and c in hESCs and d , e , and f in hCNS-SCns , would translate to pairing every signal ( d , a ) , ( d , b ) , ( d , c ) … ( f , a ) , ( f , b ) , ( f , c ) for linear regression ( Figure 3B ) . Instead , pairing the signal estimates of all replicates in one condition to the median of the other would only require N + M − 1 points and would capture the variation of the signal estimates of each probeset . For example , we considered ( d , b ) , ( e , a ) , ( e , b ) , ( e , c ) , and ( f , b ) points where b and d were the median intensities for the replicates in Cyt-ES and hCNS-SCns , respectively ( Figure 3B ) . A scatter plot of all probesets of the EHBP1 ( EH domain binding protein , RefSeq identifier NM_015252 ) is shown in Figure 3C in the format described . Each probeset was represented by 5 points of log-transformed ( base 2 ) values; and each point on the scatter plot reflected the extent of inclusion of an exon in hESCs and in hCNS-SCns ( Figure 3C ) . A classical linear regression model could be proposed to fit the response variable yij , the log2 expression of probeset i in cell-type j ( for example , j is Cyt-ESC ) to explanatory variables xik , and the log2 expression of probeset i in cell type k ( for example , k is hCNS-SCns ) . However , classical linear regression by least-squares estimation is biased because the least squares predictions are strongly influenced by the outliers , leading to completely incorrect regression line estimates , masking of the outliers , and incorrect predictions of outliers . Therefore , we applied M-estimation robust regression to estimate the line , which is less sensitive to outliers . Fitting was performed using an iterated , re-weighted least squares analysis . Our assumption was that most of the points were “correct , ” i . e . , that most of the exons were constitutively spliced . Thus , robust regression would find the line that was least dependent on outliers , which would be potential AS exons . This assumption was substantiated by our observation that , using publicly available ESTs and mRNAs , a minority of human exons ( 7% ) have evidence for exon-skipping , the most common form of AS . Using robust regression , the regression line for Cyt-ESC versus hCNS-SCns in the EHBP1 gene is illustrated in Figure 3C . The boxed points belonged to a probeset that was enriched in hESCs but depleted in hCNS-SCns , which was suspected to be due to AS . The difference between the actual and regression-based predicted value , normalized by the estimate of its standard deviation , is called the studentized residuals . Studentized residuals were computed for all probeset pairs in EHBP1 , and the histogram depicting their distribution is illustrated in Figure 3D . As expected , the mean of the distribution was close to zero , and the distribution was approximated by a t-distribution with n-p-1 degrees of freedom , where n was the number of points on the scatter plot , and the number of parameters p was 2 . The boxed points had studentized residuals of 1 . 829 , 3 . 104 , 2 . 634 , 3 . 012 , and 2 . 125 with p-values of 0 . 034 , 0 . 00119 , 0 . 00477 , 0 . 00158 , and 0 . 01780 , respectively , computed based on the t-distribution ( Figure 3C ) . At a stringent p-value cutoff of 0 . 01 , four of the five studentized residuals were designated as significant “outliers , ” indicating that the probeset was “unusual . ” RT-PCR confirmed that the exon , represented by the probeset , was indeed differentially included in hESCs and skipped in hCNS-SCns ( Figure 7B ) . Applying this approach to all gene models revealed that , as expected , the majority of studentized residuals are centered at zero ( Figure 3E ) . Thus far in the example , our analysis was based on regression of hESCs ( y-axis ) versus hCNS-SCns ( x-axis ) ( Figure 3B–3D ) . However , robust regression as described was not symmetrical , i . e . , parameter estimation of y as a function of x was not the same as that of x as a function of y . The negative slope revealed that probesets enriched in hESCs versus hCNS-SCns ( positive valued ) , were expectedly depleted when hCNS-SCns was compared to hESCs ( negative valued; Figure 3F ) . As our method for predicting candidate alternative exons was based on identification of outliers using robust regression , we named the method REAP . In the process of experimentally validating our predictions , we encountered three main sources of false positives ( FP ) from robust regression . First , we identified genes with probeset signal estimates that were poorly correlated and were not amenable to our method . As an example , the median probeset signal estimates in hESCs and hCNS-SCns of the FIP1L1 gene ( gene identifiers BC011543 , AL136910 ) had a Pearson correlation coefficient of 0 . 38 , and the distribution of points was not amenable to robust regression ( Figure 4A ) . To avoid inappropriate application of REAP and generating false predictions , we empirically determined that a gene had to have a Pearson correlation coefficient cutoff of 0 . 6 before being amenable to REAP analysis . Next , we managed two additional sources of FPs , namely “high-leverage” and “high-influence” points , which we were able to identify by computing the following metrics . For every point , we computed ( i ) the studentized residual ( as described above ) , ( ii ) the influence , and ( iii ) the leverage ( see Materials and Methods for more details ) . Leverage assessed how far away a value of the independent variable was from the mean value; the farther away the observation the more leverage it had . The influence of a point was related to its covariance ratio: a covariance ratio larger ( or smaller ) than 1 implied that the point was closer ( or farther ) than was typical to the regression line , so removing it would hurt ( or help ) the accuracy of the line and would increase ( or decrease ) the error term variance . Influence was computed as the absolute difference between the covariance ratio and unity . To illustrate further , a point was classified as an “outlier” if it had a large studentized residual ( p < 0 . 01 ) and low leverage ( boxed point “a” ) ; as a “high-leverage” point if it had a low studentized residual and high leverage ( boxed point “b” ) ; and as a “high-influence” point if it had a high studentized residual , high leverage , and high influence ( boxed point “c”; Figure 4B ) . Points that resembled boxed point “a” were designated as potential AS events . For example , four of the five boxed points in Figure 3C were “outliers , ” and RT-PCR validation indicated that the exon represented by the probeset was indeed skipped in hCNS-SCns ( EHBP1 , Figure 7B ) . Points that were “high-leverage , ” such as the five points in the CLCN2 gene , were experimentally verified to be a FP ( Figure 4C; unpublished data ) . Points that were “high-influence , ” such as the four of five boxed points in the ABCA3 gene were also experimentally verified to be a FP ( Figure 4D; unpublished data ) . In conclusion , in order to reduce the FP rate , all points were evaluated according to the metrics described , and points that were significant “outliers” were considered putative AS events . REAP was applied to identify AS events in NPs compared to hESCs: Cyt-NP versus Cyt-ES; HUES6-NP versus HUES6-ES; hCNS-SCns versus Cyt-ES , and hCNS-SCns versus HUES6-ES . After removing potential FPs , 11 , 348 genes containing 158 , 657 probesets were scored by REAP . As described above , for each pair of cell lines compared , each probset was represented by five points , where each point was defined a significant outlier if it had a high residual ( p < 0 . 01 ) , low influence , and high leverage . Points per probeset should be correlated; in other words , if one point was a significant outlier , the other points were expected to be outliers as well . To ensure that this was the case , we counted the number of probesets with N significant outliers , where N was varied from 0 to 5 . Next , the identity of the probesets and points derived from them were exchanged with other probesets , keeping constant the total number of points that were considered significant outliers . At N = 0 , we observed approximately equal numbers of probesets in the actual versus shuffled controls . In contrast , we observed that there were 1 . 5 times more probesets with N = 2 significant outliers relative to shuffled controls; 12–31 times more probesets with N = 3; and 17–612 times more probesets that had N = 4 significant outliers ( Figure 5A; see Table S1 ) . For example , in hCNS-SCns compared to Cyt-ES , approximately 0 . 39% ( 490 of 124 , 604 ) of probesets had three significant outliers and 0 . 25% ( 308 probesets ) had four significant outliers , relative to 0 . 02% and 0% of shuffled controls , respectively . Next we asked whether the overlap between related comparisons was higher than expected . Comparing the significant probesets between hCNS-SCns versus Cyt-ES and hCNS-SCns versus HUES6-ES revealed 672 significant probesets ( N ≥ 2 ) , whereas if we shuffled the associations between probeset identity and significant outliers , only four significant probesets ( N ≥ 2 ) were identified—a 168-fold enrichment ( Figure 5B , Table S1 ) . A total of 236 significant probesets overlapped when we compared the derived NPs to hESCs ( Cyt-NP versus Cyt-ES and HUES6-NP versus HUES6-ES ) , relative to seven significant probesets ( 34-fold enrichment ) . At a cutoff of two significant outliers , 1 , 737 probesets contained in internal exons were defined as positive REAP predictions ( hereafter called REAP[+] ) exons—candidate AS events that distinguished NP from hESC . Surprisingly , we observed that the majority of REAP[+] exons were specific to the pair of hESC and NP that was compared , likely reflecting differences in genetic origins and/or culturing and differentiation conditions of the cell lines: 614 REAP[+] events were unique to hCNS-SCns versus HUE6-ES; 220 were unique to hCNS-SCns versus Cyt-ES; 439 were unique to HUES6-NP versus HUES6-ES; and 250 were unique to Cyt-NP versus Cyt-ES . The shared events between pairs of comparisons made up a minority of the total number identified: 102 REAP[+] events were found to be in common between hCNS-SCns versus Cyt-ES and hCNS-SCns versus HUES6-ES; 48 between hCNS-SCns versus HUES6-ES and HUES6-NP versus HUES6-ES; and only 17 between hCNS-SCns versus Cyt-ES and Cyt-NP versus Cyt-ES ( Table S2 ) . Traditionally , AS exons were discovered by using EST alignments to genomic loci , and also more recently by computational algorithms that used sequence information extracted from multiple genomes . Here , we compared REAP predictions to both approaches . In the first comparison , publicly available ESTs and mRNA transcripts were aligned to the human genome sequence . 13 , 934 exons with evidence for exon-skipping and/or inclusion ( EST-SE for EST-verified skipped exons ) were generated , comprising ∼7% of all internal exons . First we analyzed Cyt-ES versus hCNS-SCns . If we required that none of the points per probeset ( exon ) was significant , 6% ( 4 , 402 of 71 , 731 ) of exons ( after probeset mapping ) had evidence for EST-SE ( Figure 6A ) . Shuffling the mapping between these probesets and exons resulted in 8% ( 5 , 777 of 71 , 731 ) of exons with evidence for EST-SE ( Figure 6A ) . These percentages were not significantly different from the 7% of exons with EST evidence for AS observed from using all exons . By raising the requirement that probesets had to contain at least one significant point to five significant points , the percentage of EST-SE increased dramatically from 11% ( 531 of 4 , 898 exons ) to 26% ( 33 of 126 ) . In comparison , the shuffled probesets at the same requirements remained at ∼8% , rising slightly to 11% at five points , due to small sample sizes . Similar trends were observed with hCNS-SCns versus HUES6-ES and the derived NPs versus hESCs ( Figure 6A ) . Therefore , we concluded that REAP[+] exons were enriched for AS events independently identified by a transcript-based approach . Next , we compared REAP predictions to a computational approach of identifying exons with AS conserved in human and mouse , ACEScan [55] . ACEScan receives as input orthologous human–mouse exon pairs and flanking intronic regions and computes sequence features and integrates the features into a machine-learning algorithm to assign a real-valued score to the exon . A positive score indicated a higher likelihood of being AS in both human and mouse . ACEScan was updated in the following ways . Firstly , instead of relying on orthology information by Ensembl , and then aligning flanking introns in “orthologous” exons , conserved exonic and intronic regions in human and mouse from genome-wide multiple alignments were extracted . Secondly , whereas in our previous analysis exons from the longest transcript in Ensembl were utilized , now we collapsed all the transcripts available at the UCSC genome browser and analyzed all exons in the entire gene loci . ACEScan was utilized to assign ACEScan scores to all ∼162 , 000 internal exons in our genes . Exons annotated as first or last exons in Refseq mRNAs were excluded from our analysis , resulting in 4 , 487 positive-scoring exons , 2-fold more exons than originally published . Here we repeated our analysis with exons with positive ACEScan scores ( ACE[+] ) instead of EST-SEs . If we required that none of the points per probeset ( exon ) was significant , 2% ( 1 , 645 of 71 , 731 ) of exons ( after probeset mapping ) were ACE[+] ( Figure 6B ) . Shuffling the mapping between these probesets and exons resulted in 3% ( 2 , 044 of 71 , 731 ) of exons being ACE[+] ( Figure 6B ) . These percentages were not significantly different from the 2 . 7% observed from all exons ( 4 , 487 of the 162 , 000 exons that were scored by ACEScan ) . By raising the requirement that probesets had to contain five significant points , the percentage of ACE[+] exons increased from 4% to 11% . However , the sample sizes were small . In comparison , the shuffled probesets at the same requirements remained at ∼4% . Similar overall trends were observed with hCNS-SCns versus HUES6-ES and the derived NPs versus hESCs ( Figure 6B ) . In total , 7 . 5% ( 131 of 1 , 737 ) of REAP [+] exons were designated as ACEScan[+] compared to 2 . 4% ( 2 , 328 of 97 , 437 ) of REAP[−] exons . This result suggested that a small but significantly enriched fraction of AS events in hESCs versus NPs was likely to be evolutionarily conserved in human and mouse . In conclusion , our results suggested that REAP predictions were congruent with predictions from two independent , orthogonal methods . The sensitivity and specificity of REAP in the identification of REAP[+] exons was tested by RT-PCR . To validate REAP[+] alternative exons , RT-PCR primers were designed in the flanking exons to amplify both isoforms . To be a positively validated candidate , the PCR products on a gel had to satisfy all of the following criteria: ( i ) at least one isoform with the expected size must be visible in each cell type; ( ii ) the relative abundance of the two isoforms must be altered between two cell types and the direction of change have to be consistent with the REAP studentized residuals: in our study positive residuals implied inclusion in hESCs and skipping in NPs , and negative residuals implied inclusion in NP and skipping in hESCs; and ( iii ) the results were replicable in at least two experiments . For simplicity of design , we tested candidates predicted from Cyt-ES versus hCNS-SCns . Fifteen REAP[+] exons with at least two significant outliers ( out of five ) were randomly chosen as predicted alternative events and thirty-five exons with less than two significant outliers were randomly chosen as constitutive events ( Table S3 ) . Nine of the fifteen exons ( 60% ) were validated as AS events by our criteria . The sensitivity and specificity of the algorithm at the cutoff of two is 69% and 77% . Increasing the cutoff to three increased the specificity to 85% , with a slight decrease in sensitivity to 67% ( Figure 7A ) . The patterns of AS in hESCs were similar in both Cyt-ES and HUES6-ES for all AS events validated , but the NPs ( Cyt-NP , HUES6-NP , and hCNS-SCns ) had more varied AS . The pattern of AS in the REAP[+] exons in the SLK ( serine/threonine kinase 2 ) and POT1 ( protection of telomeres 1 ) genes showed remarkable agreement within derived NPs and hCNS-SCns ( Figure 7B ) . The AS exon in SLK was observed to be included in hESCs and completely excluded in NPs; the AS exon in the POT1 gene was included more in hESCs and a smaller isoform persisted in NPs . The AS patterns of the other verified REAP[+] exons were consistently similar in hESCs but were more varied in the NP . Interestingly , the patterns of AS in the derived NPs ( Cyt-NP and HUES6-NP ) were not always identical to those of hCNS-SCns . For example , the AS exon in the EHBP1 ( EH domain binding protein 1 ) gene was included in hESCs but skipped in hCNS-SCns , and both isoforms were present in the derived NPs ( Figure 7B ) . As another example , the AS exon in the SORBS1 ( sorbin and SH3 domain containing 1 ) gene was skipped in hESCs and included in hCNS-SCns , but exhibited an intermediate pattern in the derived NPs . However , in some cases , the AS patterns in the derived NPs were different from both hESCs and hCNS-SCns ( such as in the AS exon in UNC84A , SIRT1 , and MLLT10 ) . First , given three independent samples each from two conditions , we concluded that REAP was able to identify AS events with high specificity but with moderate sensitivity . Second , AS events in hESCs were more similar , whereas the AS events in derived NPs were consistent with or intermediate to the benchmark hCNS-SCns , likely reflecting differences in the cell lines and/or differentiation protocols . In addition , we tested the AS patterns of REAP[+] exons from EHBP1 , SLK , and RAI14 in a panel of differentiated human tissues ( Figure 7C ) . The REAP[+] alternative exon in the RAI14 ( retinoic acid induced 14 ) gene was observed to have the same AS pattern in NPs as in frontal and temporal cortex and in several other , non-brain adult tissues , such as heart and spleen . The AS pattern of the REAP[+] exon in the SLK gene in NPs was similar to most differentiated tissues; however , the relatively strong inclusion of the exon in hESCs was unique . Even in esophagus , kidney , liver , and prostate , both isoforms were present . The relative ratio of the exon-included to exon-skipped isoforms in SLK likely represents an ESC-specific AS signature . The alternative exon in the EHBP1 gene was unusual . The exon was included in hESCs but also in frontal cortex and temporal cortex , a finding that was unexpected given the exclusion of the exon in hCNS-SCns ( Figure 7C ) . The AS pattern in hCNS-SCns may represent a transient , early neuronal molecular change . In total , 1 , 500 genes were identified that contained 1 , 737 REAP[+] exons , 68% of which lacked prior transcript ( EST/cDNA ) evidence for AS . To determine whether genes that contained REAP[+] exons , which we refer to as REAP[+] genes , are biased toward particular biological activities , REAP[+] genes were compared to a set of REAP analyzed genes not found to have REAP[+] exons ( REAP[−] genes ) . A Gene Ontology analysis revealed that REAP[+] genes are enriched for GO molecular function categories “ATP binding , ” “helicase activity , ” “protein serine/theronine kinase activity , ” “small GTPase regulatory/interacting protein activity , ” and “thyroid hormone receptor binding” ( Table 1 ) . In terms of GO biological process categories , REAP[+] genes were more frequently involved in “ubiquitin cycle . ” Similar results were obtained when we compared REAP[+] genes to all human genes that did not contain REAP[+] exons ( Table 1 ) [55] . Next we asked if REAP[+] genes are differentially expressed in hESCs compared to NPs and vice versa . For this analysis , the t-statistics computed above measuring the enrichment of a gene in hESCs relative to NPs was utilized for only REAP-analyzed genes . At a defined absolute-valued cutoff , genes were divided into three categories: “enriched in hESCs , ” “enriched in NP , ” or “unchanged” ( Figure 8A ) . Increasing the t-statistic cutoff from one to five , the fraction of REAP[+] genes relative to REAP-analyzed genes remained constant in the “unchanged” categories ( Figure 8B ) . However , the fraction of REAP[+] exons decreased significantly in “enriched in hESCs” and “enriched in NPs” categories . If we increased the cutoffs on genes that were randomly assigned as REAP[+] and REAP[−] , controlling for the same number of genes in each category , we observed that the fraction of REAP[+] exons remained unchanged for all three categories ( Figure 8C ) . To illustrate , at a cutoff of five , 10% ( 29 of 267 ) of enriched NP genes were REAP[+] genes and 8 . 8% ( 102 of 1 , 162 ) of enriched hESC genes were REAP[+] , significantly different ( p < 0 . 000005 ) from the random control where ∼14% of enriched NP and enriched hESC genes were REAP[+] . At a cutoff of five , 14% ( 1 , 368 of 9 , 636 ) of genes that were expressed at similar levels between hESCs and NPs were REAP[+] . Our results suggested that a strategy of focusing on differentially expressed genes would miss at least 14% of transcriptionally unchanged genes that may nevertheless have functional AS differences between hESCs and NPs . Many , if not most , alternative exons undergo cell type–specific regulation by the binding of trans-factors to splicing regulatory cis-elements located proximal to or within the exons . As many tissue-specific splicing cis-regulatory elements were localized in intronic regions of AS exons , we focused on the identification of intronic splicing regulatory elements ( ISREs ) proximal to REAP[+] exons . In addition , we wanted to identify both common and cell type–specific ISREs . Three sets of exons were generated: ( i ) REAP[+] exons that were predicted to be included in NPs and skipped in hESCs ( REAP[+]NP ) ; ( ii ) REAP[+] exons that were predicted to be included in hESCs and skipped in NPs ( REAP[+]hESC ) ; and ( iii ) all REAP[−] exons . Regions of 400 base pairs flanking the exons were targeted for search . Initially , 5-mers that were significantly enriched between the upstream and downstream intronic regions of REAP[+]NP and REAP[+]ES relative to REAP[−] exons were enumerated . We were not able to identify 5-mers that were statistically significantly different . Next , we focused on splicing signals that were conserved across mammalian genomes as a way of enhancing the signal of detecting functional splicing regulatory sequences [66] . Exons that were orthologous across human , dog , rat , and mouse were obtained and the flanking intronic regions were aligned ( 400 bases upstream and downstream separately; Figure 9A ) . We enumerated k-mers that were perfectly conserved across all four genomes in the upstream ( and downstream ) intronic regions . Each conserved k-mer was attributed a χ score representing its enrichment in a set of exons relative to another set of exons . The higher the score , the more frequent the conserved k-mer was in the first set relative to the second set . As a negative control , the associations between REAP scores and exons were shuffled . The enrichment scores for all downstream intronic 5-mers for shuffled REAP[+]NP versus set REAP[−] exons ( x-axis ) , and for shuffled REAP[+]ES exons versus REAP[−] exons ( y-axis ) were displayed ( Figure 9B ) . At a χ cutoff of three , which corresponded to a p-value of 0 . 0015 , the majority of 5-mers were not significantly enriched in either shuffled set . Confident that no association of k-mers with shuffled REAP exons were found; we repeated the analyses for upstream and downstream intronic 5-mers for the original unshuffled sets . We identified 68 conserved 5-mers enriched upstream of REAP[+]NP exons; and 34 5-mers enriched upstream of REAP[+]ES exons ( Figure 9C; Table S4 ) . Of the 5-mers that were significantly enriched upstream of REAP[+]NP exons , we identified a U-rich motif ( UUUUU ) , a GU-rich motif ( GUGUG ) , and a CU-rich motif ( CCUCU , CUCUC , UCUCU , GCUCU ) . It is known that the heterogeneous ribonucleoprotein C ( hnRNP C ) binding site obtained by SELEX is five “U”s [67] . GU-rich sequences in flanking intronic regions were shown to bind to splicing factor ETR-3 to regulate AS [68] . CU-rich sequences were shown to bind the splicing factor PTB [69] . Of the 5-mers enriched upstream of REAP[+]ES exons , we observed CUAAC , which resembled the splicing branch-signal . Of the six 5-mers that were enriched upstream of both REAP[+]NP and REAP[+]ES exons , we identified GCAUG , which was previously shown to be an intronic splicing cis-element for the mammalian fibronectin and calcitonin/CGRP genes [70–72] . More recently , both mammalian Fox1 and 2 have been demonstrated to regulate alternatively spliced exons via UGCAUG binding sites in neighboring introns in neuronal cell cultures [73] . Eighteen conserved 5-mers were significantly enriched in the downstream introns of REAP[+]ES exons; and 76 5-mers were enriched downstream of REAP[+]NP exons ( Table S4 , Figure 9D ) . We identified a motif CUCAU resembling the Nova binding site YCAY [74] , and a G-rich motif ( AGGGG , GGGGA , GGGGC , GGGGG , GGGGU ) enriched in the introns downstream of REAP[+]ES exons . G-rich motifs had previously been shown to be part of a bipartite signal that silences AS exons [75] . Of the five 5-mers that were enriched downstream of both REAP[+]NP and REAP[+]ES exons , GCAUG and a U-rich motif ( UUUUU ) were identified . We concluded that potential ISREs were enriched proximal to a subset of REAP[+] exons; in particular , the Fox1/2 binding site GCUAG may play a regulatory role in controlling AS events in hESCs and NPs .
The ability of ESCs to generate all three embryonic germ layers has raised the exciting possibility that hESCs may become an unlimited source of cells for transplantation therapies involving organs or tissues such as the liver , pancreas , blood , and nervous system , and become tools to explore the molecular mechanisms of human development . Despite such interests , relatively little is understood about the molecular mechanisms defining their pluripotency and the molecular changes important for hESCs to differentiate into specific cell types . To understand these events , protocols are still being developed to differentiate ESCs into a variety of lineages . Of particular biomedical interest is in the capacity of hESCs to be differentiated into a self-renewing population of NPs that can be then further coaxed into a variety of neuronal subtypes , such as dopaminergic neurons that are important in the treatment of Parkinson disease or cholinergic neurons for ALS ( amyotrophic lateral sclerosis ) . While many microarray studies have explored molecular differences between hESCs and derived NPs , most , if not all , have focused on transcriptional changes . These studies have largely ignored intermediate RNA processing events prior to and during translation . In recent years , AS has gained momentum as being important in development , apoptosis , and cancer . REAP , a regression-based method for analyzing exon array data was introduced , and was applied to discover AS events in hESCs , their derived NPs , and in hCNS-SCns . REAP was based on the assumptions that most exons in the gene of interest and in the genome are constitutively spliced and that outliers in a linear pairwise comparison of the signal estimates for probesets in a gene could be detected using a robust regression-based approach . REAP predictions were found to correlate well with transcript-based methods for identifying alternative exons , which interestingly suggested that current databases of transcript information , albeit not specifically enriched for hESC or NPs , in aggregate are nevertheless predictive of AS events in hESC and NP . In addition , REAP[+] exons were also enriched for ACEScan-predicted evolutionarily conserved exons [55] . As ACEScan utilized a different set of information from REAP , the agreement between both algorithms served to further validate the predicted alternative exons . Additional studies in mouse ESCs and neural derivatives will be necessary to determine if these AS events are indeed preserved in these analogous and orthologous cell types . Our finding that only a minority of AS events was common between various hESC to NP comparisons is intriguing . A possible explanation is that the cell lines were not only genetically different , but were also exposed to different isolation and culture conditions . In addition , the different differentiation protocols established as optimal for generating Nestin and Sox1 positive neural precursors may lead to vastly different molecular changes . It is likely that post-transcriptional changes such as AS may be more variable despite the cells being at acknowledged “end-points” defined by a limited set of immunohistochemical markers . Our results are consistent with a recent study that showed that while two well-established hESC lines differentiate into functional neurons , the two lines exhibited distinct differentiation potentials , suggesting that some preprogramming had occurred [76] . In particular , microRNA profiling revealed significant expression differences between the two hESC lines , suggesting that microRNAs , known post-transcriptional regulators , may sway the differentiation properties of the cell lines [76] . We postulated that AS events may serve also to bias the differentiation spectrum of the cells , an important avenue for future work . Experimental validation of REAP[+] exons suggested a high specificity at the expense of relatively moderate sensitivity . We believe that the high FP rates may arise from cross-hybridization effects that remained unaccounted for . However , our specificity of 77% at the cutoff of two significant outliers per probeset allowed us to estimate that at least 1 , 336 of 1 , 737 REAP[+] exons were true AS events that changed during neuronal differentiation of hESC cells , and/or were different between endogeneous NPs and hESC . On average , 7% of all human exons have been estimated by transcript data to undergo AS; thus REAP's validation rate of 60% at the cutoff of two is 73-fold ( 60/7 ) higher than expected . In addition , we validated nine novel AS events that distinguish hESCs and NPs . Consistent with our computational results , we observed that the AS patterns in hCNS-SCns were not always similar to those of the derived NPs . It was important to point out that while transcriptional expression of these genes did not distinguish these cells from one another , in several instances the REAP-predicted AS event was able to separate derived NPs and hCNS-SCns . A notable exception was the alternative exon in the SLK gene , encoding a serine/threonine kinase protein , which was commonly included in both hESCs , i . e . , the exon-excluded isoform was not present in hESCs compared to NPs , as well as in a variety of differentiated tissues . Closer inspection of the REAP[+]-validated AS exon in the SLK gene revealed strong conservation in the intronic region flanking the exon , a hallmark feature of evolutionarily conserved AS exons [55 , 77 , 78] . A study analyzing the expression patterns of the SLK gene suggested a potential functional role during embryonic development and in the adult central nervous system [79]; however , to our knowledge , our identification of the SLK alternative exon is the first report of a hESC-biased AS pattern during neuronal differentiation and across a myriad of differentiated tissues . In agreement , GO analysis suggested that genes containing REAP[+] exons were enriched in serine/threonine kinase activity , of which SLK is a family member . Future work will be required to study the impact of AS in these genes in hESCs and NPs . We predict it is unlikely that the alternative exon in the SLK gene is the only case common across hESC and different from differentiated tissues , but further studies will be necessary to identify other hESC-specific exons . REAP[+] exons were underrepresented in genes that were differentially transcriptionally regulated in hESCs and NPs . Our results act as a reminder that focusing only on genes that are differentially expressed will overlook RNA processing events that may be biologically relevant to the system of interest . Finally , we identified potential cis-regulatory intronic elements conserved and enriched proximal to the REAP[+] exons . In particular , the FOX1/2 binding site , GCUAG , was conserved and enriched in the flanking introns of a subset of REAP[+] exons . Further studies will be required to explore the importance of FOX1 family members in early neuronal differentiation . In conclusion , our introduction of REAP and its application to identifying AS events has revealed new and unanticipated insights into hESC biology and their transition to NP cells . Collectively , these exons represent a set of molecular changes that are likely to be important for studying human neural differentiation with applications in neuronal regenerative medicine .
hESC line Cy203 ( Cythera ) was cultured as previously described [12] . To differentiate into neuroepithelial precursor cells , colonies were manually isolated from mouse embryonic fibroblasts ( MEFs ) and cut in small pieces . These pieces were transferred to a T75 flask with hESCs differentiation media ( same hESC medium but 10% KSR and no FGF-2 ) . Medium was changed the next day by transferring the floating hESC aggregates to a new flask . After culturing for a week , the hESC cell aggregates formed mature embroid bodies ( EBs; ∼10 um round clusters with dark centers ) . EBs were plated on a coated 10-cm dish in hESC differentiation media . The next day , the medium was changed to DMEM/F12 supplemented with ITS and fibronectin . Medium was changed every other day for a week or until the cells formed rosette-like columnar structures that were isolated manually . These structures were then transferred to coated dishes in neural induction medium ( DMEM/F12 supplemented with N2 and FGF-2 ) for a week . Elongated single cells were separated from leftover aggregates using non-enzymatic dissociation . After one to two passages , the cells formed a monolayer of homogeneous NPs ( negative for Sox1 immunostaining ) . Upon confluence , cells will form neurospheres that can also be isolated from the neuroepithelial precursor cells ( positive for Sox1 immunostaining ) . At any of these two stages , pan-neuronal differentiation can be achieved after three to four weeks . hESC line HUES6 was cultured on MEF feeders as previously described ( http://www . mcb . harvard . edu/melton/hues/ ) or on GFR matrigel coated plates . Cells grown on matrigel were grown in MEF-conditioned medium and FGF-2 was used at 20 ng/mL instead of 10 ng/mL for cells grown on MEFs . To differentiate neuroepithelial precursors , colonies were removed by treatment with collagenase IV ( Sigma ) and washed three times in growth media . The pieces of colonies were resuspended in HUES growth media without FGF2 in an uncoated bacterial Petri dish to form EBs . After one week , EBs were plated on polyornathine/laminin coated plates in DMEM/F12 supplemented with N2 and FGF2 . Rosette structures were manually collected and enzymatically dissociated with TryPLE ( Invitrogen ) , plated on polyornathine/laminin coated plates , and grown in DMEM/F12 supplemented with N2 and B27-RA and 20 ng/mL FGF-2 . Cells could be grown as a monolayer for up to at least ten passages . Cells were Sox1 and nestin positive and readily differentiated into neurons upon withdrawal of FGF-2 . Human central nervous system stem cell line FBR1664 ( StemCells ) which is referred to as hCNS-SCns in the main text was cultured as previously described [23] . The cells were cultured in medium consisting of Ex Vivo 15 ( BioWhittaker ) medium with N2 supplement ( GIBCO ) , FGF2 ( 20 ng/mL ) , epidermal growth factor ( 20 ng/mL ) , lymphocyte inhibitory factor ( 10 ng/mL ) , 0 . 2 mg/ml heparin , and 60 ug/mL N-acetylcysteine . Cultures were fed weekly and passaged at ∼two to three weeks using collagenases ( Roche ) . The following antibodies and corresponding dilutions were utilized for the immunohistochemical analysis of marker genes in Cyt-ES and HUES6-ES: Sox2 ( Chemicon , 1:500 ) , Oct4 ( Santa Cruz , 1:500 ) , Sox1 ( Chemicon , 1:500 ) , Nestin ( Pharmingen , 1:250 ) ; hCNS-SCns: Sox2 ( Chemicon , 1:200 ) , Nestin ( Chemicon , 1:200 ) . Total RNA from cells was processed as follows . Cells were lysed in 1 mL of RNA-bee ( Teltest ) . The RNA was isolated by chloroform extraction of the aqueous phase , followed by isopropanol precipitation as per the manufacturer's instructions . The precipitated RNA was washed in 75% ethanol and eluted with DEPC-treated water . Five ug of RNA was treated with RQ1 DNAase ( Promega ) according to the manufacturer's instructions . One ug of total RNA for each sample was processed using the Affymetrix GeneChip Whole Transcript Sense Target Labeling Assay ( Affymetrix ) . Ribosomal RNA was reduced with the RiboMinus Kit ( Invitrogen ) . Target material was prepared using commercially available Affymetrix GeneChip WT cDNA Synthesis Kit , WT cDNA Amplification Kit , and WT Terminal Labeling Kit ( Affymetrix ) as per manufacturer's instructions . Hybridization cocktails containing ∼5 ug of fragmented and labeled DNA target were prepared and applied to GeneChip Human Exon 1 . 0 ST arrays . Hybridization was performed for 16 hours using the Fluidics 450 station . Arrays were scanned using the Affymetrix 3000 7G scanner and GeneChip Operating Software version 1 . 4 to produce . CEL intensity files . cDNAs were generated from total RNA with Superscript III reverse transcriptase ( Invitrogen ) . PCR reactions were performed with primer pairs designed for AS targets ( annealing at 58 °C and amplification for 30 or 35 cycles ) . PCR products were resolved on either 1 . 5% or 3% agarose gel in TBE . The Ethidium Bromide-stained gels were scanned with Typhoon 8600 scanner ( Molecular Dynamics ) for quantification . The number of true positives ( TP; false negatives , FN ) was computed as the number of REAP[+] ( REAP[−] ) exons that were validated by RT-PCR as AS . The number of true negatives ( TN; or FPs ) was computed as the number of REAP[−] ( REAP[+] ) exons that were validated by RT-PCR as constitutively spliced . The true ( false ) positive rate was computed as TP ( FP ) divided by the total number of REAP[+] exons in the experimentally validated set . The true ( false ) negative rate was computed as the TN ( FN ) divided by the total number of REAP[−] exons in the experimentally validated set . Sensitivity was computed as TP/ ( TP+FN ) and specificity was computed as TN/ ( FP+TN ) . Genome sequences of human ( hg17 ) , dog ( canFam1 ) , rat ( rn3 ) , and mouse ( mm5 ) were obtained from UCSC , as were the whole-genome MULTIZ alignments [80] . The lists of known human genes ( knownGene containing 43 , 401 entries ) and known isoforms ( knownIsoforms containing 43 , 286 entries in 21 , 397 unique isoform clusters ) with annotated exon alignments to human hg17 genomic sequence were processed as follows . Known genes that were mapped to different isoform clusters were discarded . All mRNAs aligned to hg17 that were greater than 300 bases long were clustered together with the known isoforms . Genes containing less than three exons were removed from further consideration . A total of 2 . 7 million spliced ESTs were mapped onto the 17 , 478 high-quality genes to infer AS . Exons with canonical splice signals ( GT-AG , AT-AC , GC-AG ) were retained , resulting in a total of 213 , 736 exons . Of these , 197 , 262 ( 92% of all exons ) were constitutive exons , 13 , 934 exons ( 7% ) had evidence of exon-skipping , 1 , 615 ( 1% ) exons were mutually exclusive alternative events , 5 , 930 ( 3% ) exons had alternative 3′ splice sites , 5 , 181 ( 2% ) exons had alternative 5′ splice sites , and 175 ( <1% ) exons overlapped another exon , but did not fall into the above classifications . A total of 324 , 139 probesets from the Affymetrix Human Exon 1 . 0 ST array were mapped to 208 , 422 human exons , representing 17 , 431 genes . These probesets were used to derive gene and exon-level signal estimates from the CEL files . The four-way mammalian ( four-mammal ) whole-genome alignment ( hg17 , canFam1 , mm5 , rn3 ) was extracted from the eight-way vertebrate MULTIZ alignments ( hg17 , panTrol1 , mm5 , rn3 , canFam1 , galGal2 , fr1 , danRer1 ) obtained from the UCSC Genome Browser . Four-way mammal alignments were extracted for all internal exons , and 400 bases of flanking intronic sequence , resulting in a total of 161 , 731 conserved internal exons . A total of 145 , 613 ( 90% of total ) conserved internal exons were constitutive exons , 13 , 653 exons ( 8% ) had evidence of exon-skipping , 1 , 576 exons were mutually exclusive alternative events , 5 , 818 exons had alternative 3′ splice sites , 5 , 046 exons had alternative 5′ splice sites , and 168 exons overlapped another exon . The Affymetrix Power Tools ( APT ) suite of programs was obtained from http://www . affymetrix . com/support/developer/powertools/index . affx . Exon ( probeset ) and gene-level signal estimates were derived from the CEL files by RMA–sketch normalization as a method in the apt-probeset-summarize program . To determine if the signal intensity for a given probeset is above the expected level of background noise , we utilized the DABG ( detection above background ) quantification method available in the apt-probeset-summarize program as part of Affymetrix Power Tools ( APT ) . Briefly , DABG compared the signal for each probe to a background distribution of signals from anti-genomic probes with the same GC content . The DABG algorithm generated a p-value representing the probability that the signal intensity of a given probe was part of the background distribution . We considered a probeset with a DABG p-value lower than 0 . 05 as detected above background . The statistic thCNS-SCns , ESC = ( μhCNS-SCns − μESC ) / sqrt ( ( ( nhCNS-SCns − 1 ) σ2hCNS-SCns + ( nESC − 1 ) σ2ESC ) ( nhCNS-SCns + nESC ) ) / ( ( nhCNS-SCnsnESC ) ( nhCNS-SCns + nESC − 2 ) ) ) , where nhCNS-SCns and nESC were the number of replicates , μhCNS-SCns and μESC were the mean , and σ2hCNS-SCns and σ2ESC were the variances of the expression values for the two datasets used to represent the differential enrichment of a gene using gene-level estimates in hCNS-SCns relative to hESCs . Multiple hypothesis testing was corrected by controlling for the false discovery rate ( Benjamini-Hochberg ) . The log2 signal estimate xij for probeset i in cell-type j had to satisfy two conditions , otherwise the probeset was discarded: ( i ) 2 < xij < 10 , 000 for all conditions/cell-types j; and ( ii ) DABG p-value < 0 . 05 for all replicates in at least one condition/cell-type j . A gene had to have five probesets that satisfied the two conditions above in order to be considered for robust regression analysis . After generating the points ( as described in the Results section ) , we utilized the robust regression method rlm in R-package “MASS” ( version 6 . 1–2 ) with M-estimation and a maximum iteration setting of 30 to estimate the linear function yi = αxi + β . For each probeset , we computed the error term ei , , which was the difference between the actual value yi and the estimated value ξi , from the estimated function ξi = Axi + B , where A and B were estimates of α and β . The error term variance was estimated by se2 = Σei2/ ( n − p ) , which was used to estimate the variance of the predicted value , sξi2 = se2 ( n−1 + ( xi − μx ) 2 / sx2 ( n − 1 ) ) . Here , n referred to the number of points ( generated for each gene ) , and p referred to the number of independent variables ( p = 2 in our method ) ; and μx = Σxi2/n; sx2 = n−1 Σ ( xi − μx ) 2 . Following Belsley et al . [81] , we defined the leverage hi of the ith point as hi = n−1 + ( xi − μx ) 2 / sx2 ( n − 1 ) . Here we considered a point to have high leverage if hi > 3p/n . Next , we calculated the covariance ratio , covi = ( si2/sr2 ) p/ ( 1 − hi ) , which is the ratio of the determinant of the covariance matrix after deleting the ith observation to the determinant of the covariance matrix with the entire sample . We considered a point to have high influence if |covi − 1| > 3p/n . Lastly , we computed the studentized residuals , rstudenti = ei / ( s ( i ) 2 ( 1 − hi ) 0 . 5 ) , where s ( i ) 2 = ( n-p ) se2 / ( n-p-1 ) – ei2 / ( n-p-1 ) ( 1 − hi ) , the error term variance after deleting the ith point . As rstudenti was distributed as Student's t-distribution with n-p-1 degrees of freedom , each rstudenti value was associated with a p-value . We considered a point to be an “outlier” if p < 0 . 01 . The enrichment score of a sequence element of length k ( k-mer ) in one set of sequences ( set 1 ) versus another set of sequences ( set 2 ) was represented by the nonparametric χ2 statistic with Yates correction , computed from the two by two contingency table , T ( T11: number of occurrences of the element in set 1; T12: number of occurrences of all other elements of similar length in set 1; T21: number of occurrences of element in set 2; T22: number of occurrences of all other elements of similar length in set 2 . All elements had to be greater than 5 . To correct for multiple hypothesis testing , p-values were multiplied by the total number of comparisons .
|
Deriving neural progenitors ( NP ) from human embryonic stem cells ( hESC ) is the first step in creating homogeneous populations of cells that will differentiate into myriad neuronal subtypes necessary to form a human brain . During alternative RNA splicing ( AS ) , noncoding sequences ( introns ) in a pre-mRNA are differentially removed in different cell types and tissues , and the remaining sequences ( exons ) are joined to form multiple forms of mature RNA , playing an important role in cellular diversity . The authors utilized Affymetrix exon arrays with probes targeting hundreds of thousands of exons to study AS comparing human ES to NP . To accomplish this , a novel computational method , REAP ( Regression-based Exon Array Protocol ) , is introduced to analyze the exon array data . The authors showed that REAP candidates are consistent with other types of methods for discovering alternative exons . In addition , REAP candidate alternative exons are enriched in genes encoding serine/theronine kinases and helicase activities . An example is the alternative exon in the SLK ( serine/threonine kinase 2 ) gene that is included in hESC , but excluded in NP as well as in other differentiated tissues . Finally , by comparing genomic sequences across multiple mammals , the authors identified dozens of conserved candidate binding sites that were enriched proximal to REAP candidate exons .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"homo",
"(human)",
"computational",
"biology",
"neuroscience"
] |
2007
|
Alternative Splicing Events Identified in Human Embryonic Stem Cells and Neural Progenitors
|
Immunostimulatory therapy is a promising approach to improving the treatment of systemic fungal infections such as paracoccidioidomycosis ( PCM ) , whose drug therapy is usually prolonged and associated with toxic side effects and relapses . The current study was undertaken to determine if the injection of a T helper ( Th ) 1–stimulating adjuvant in P . brasiliensis–infected mice could have a beneficial effect on the course of experimental PCM . For this purpose , mice were infected and treated with complete Freund's adjuvant ( CFA ) , a well-established Th1 experimental inductor , or incomplete Freund's adjuvant ( IFA - control group ) on day 20 postinfection . Four weeks after treatment , the CFA-treated mice presented a mild infection in the lungs characterized by absence of epithelioid cell granulomas and yeast cells , whereas the control mice presented multiple sites of focal epithelioid granulomas with lymphomonocytic halos circumscribing a high number of viable and nonviable yeast cells . In addition , CFA administration induced a 2 . 4 log reduction ( >99% ) in the fungal burden when compared to the control group , and led to an improvement of immune response , reversing the immunosuppression observed in the control group . The immunotherapy with Th1-inducing adjuvant , approved to be used in humans , might be a valuable tool in the treatment of PCM and potentially useful to improve the clinical cure rate in humans .
Paracoccidioides brasiliensis is a thermally dimorphic human pathogenic fungus that causes paracoccidioidomycosis ( PCM ) , the most prevalent human systemic mycosis in Latin America , being endemic in Brazil , Argentina , Venezuela and Colombia . This infection is acquired by inhalation of airborne propagules found in nature , which reach the lungs and are converted to the yeast form [1] , [2] . The yeasts can either be eliminated by immune-competent cells or disseminated into tissues through lymphatic or hematogenous routes . PCM is characterized by granulomatous inflammation , intense immunological involvement with suppression of cellular immunity and high levels of non-protective antibodies in serum [3] . The disease may present a broad spectrum of clinical and pathological manifestations ranging from asymptomatic pulmonary infection to severe and disseminated forms [4] , [5] . The chronic progressive form of the disease ( CF ) is the most common clinical presentation and predominantly affects adult males , with frequent pulmonary , mucosal , cutaneous and adrenal involvement . Although the outcome of the infection can be due to several factors , it is especially dependent on the protective capacity of the host immune system . The cell-mediated immune response represents the main mechanism of defense in PCM [1] . Conversely , it has been reported that a high level of humoral immune response is associated with increased disease dissemination [6] . The mechanisms underlying resistance or susceptibility to PCM remain to be elucidated . The development of the appropriate CD4+ T helper ( Th ) subset is important for PCM resolution and several studies have shown that different disease outcomes can be derived from the commitment of precursors to either Th1 or Th2 lineage [7] , [8] . Resistance to P . brasiliensis infection has been related to interferon-γ ( IFN-γ ) and other Th1-type cytokines [9]–[11] , while susceptibility has been linked to the preferential production of the Th2-type cytokines , i . e . , interleukin ( IL ) -4 , IL-5 , and IL-10 [12]–[14] . Several investigators have suggested that progressive disseminated forms of PCM in humans are associated with various degrees of suppressed cell-mediated immunity [1] , [15] , [16] . This anergy can be reversed after successful therapy , when normal levels of T cell function are partially or completely restored [17] . The prognosis of PCM has been improved through antimycotic drugs , however treatment regimens require an extended period of time often associated with relapses . P . brasiliensis has the peculiarity of responding to treatment with sulpha drugs . Nevertheless , regimens with these agents often require extended period of maintenance therapy that may range from months to years . Clinically , the antifungal drugs most commonly used for PCM include amphotericin B , sulpha derivatives and azoles , but their toxicity can be a limiting factor in treatment [18] , [19] . These concerns , together with the elucidation of the protective immune response against PCM have renewed interest in the development of alternative therapeutic strategies such as immunotherapeutic procedures , which can be useful for controlling PCM . The present study was designed to verify if immunomodulation with CFA could play a protective role in experimental PCM leading to a less severe infection with decreased fungal burdens in the lungs .
Yeast cells of virulent Pb 18 strain of P . brasiliensis were cultured at 37°C in YPD ( Yeast Extract/Peptone/Dextrose ) Medium ( Difco Laboratories , Detroit , USA ) for 7 days and washed three times in 0 . 01 M phosphate-buffered saline ( PBS ) , pH 7·2 . Viability of yeast cells was determined by the fluorescein diacetate-ethidium bromide treatment [20] . BALB/c mice , aged 6–8 wk , were bred and maintained under standard conditions in the animal house of the Medical School of Ribeirão Preto , University of São Paulo , Ribeirão Preto , SP , Brazil . All animal experiments were performed in accordance with protocols approved by the School of Medicine of Ribeirão Preto Institutional Animal Care and Use Committee . Mice were inoculated intravenously with 1×106 viable yeast cells in 100 µl of PBS . On day 20 postinfection , mice were injected subcutaneously with 100 µl of CFA or IFA ( Sigma Chemical Co . , St . Louis , USA ) , both emulsified in PBS in a ratio of 1∶1 . Mice were killed on day 30 after treatment and their lungs were aseptically removed . One lung from each mouse was used for histopathology analyses and the other for quantification of fungal burden and cytokines . The lungs were fixed in 10% neutral buffered formalin for 24 hours and embedded in paraffin . Tissue sections ( 5 µm ) were stained with hematoxylin and eosin ( H&E ) or silver methenamine ( Grocott ) to detect the mycotic structures using standard protocols . Samples were analyzed by light microscopy in an Axiophot photomicroscope ( Carl Zeiss , Jena , Germany ) coupled with a JVC TK-1270 camera ( Victor Company of Japan Ltd , Tokyo , Japan ) . The area of individual granulomas , as well as the total area of the lung sections and the area taken by granulomas per slide , was measured by computer-aided image analysis ( ImageJ 1 . 37v , National Institutes of Health , Bethesda , USA ) . The following data were thus generated: granuloma area ( mean area of all granulomas in each lung section ) , granuloma relative area ( % represented by total granuloma area/total area of the lung sections ) and number of granuloma cells per area ( total number of cells from a granuloma section/the area of the respective granuloma section ) of each mouse . The lungs were weighed and homogenized in 1 ml of sterile PBS using tissue homogenizer ( Ultra-Turrax T25 Basic , IKA Works , Inc . , Wilmington , USA ) . To determine the number of CFU , lung homogenates were diluted 1∶10 in PBS . Aliquots of 100 µl of each sample were dispensed into Petri dishes containing brain heart infusion agar ( BHI , Difco ) supplemented with 4% ( v/v ) of heat-inactivated fetal calf serum ( FCS , Gibco BRL , Gaithersburg , USA ) . The plates were incubated at 37°C , the colonies were counted 14 days later , and then , the number of CFU per gram of tissue was calculated . For cytokine determination , remaining lung homogenates were centrifuged at 5 , 000×g for 10 minutes and the supernatants stored at −20°C until cytokine determination . Supernatants were analyzed as duplicate samples from replicate wells . A sandwich-type ELISA was used to determine IL-12 , IFN-γ , TNF-α , IL-4 , IL-10 , and TGF-β levels , using OptEIA ELISA kits ( BD PharMingen , San Diego , USA ) , according to the manufacturer's recommendations . Inh-ELISA was performed as previously described [21] . Briefly , inhibition standard curve was constructed by adding different concentrations of P . brasiliensis gp43 ( from 1 ng to 30 µg/ml ) in 100 µl of normal serum and then adding 100 µl of the standardized concentration of monoclonal antibody ( MAb ) anti-gp43 ( 10 µg/ml ) . Serum samples ( 100 µl ) were added to 100 µl of MAb anti-gp43 . Normal serum was used as a negative control . Polystyrene plates ( Corning Costar Co . , Corning , USA ) were coated with 500 ng of gp43 in 0 . 06 M carbonate buffer ( pH 9 . 6 ) per well ( 100 µl/well ) overnight at 4°C . After , the plates were blocked by incubation with 200 µl of 1% bovine serum albumin in PBS per well for 1 h at 37°C; washed 3 times and 100 µl from inhibition standard curve , samples and controls were added per well and allowed to stand for 2 h at 37°C . After being washed 3 times , 100 µl of goat anti-mouse immunoglobulin G-peroxidase ( Sigma ) was added , and the plates were incubated for 1 h at 37°C . After further washings , the reaction was developed with a solution of o-phenylenediamine ( 0 . 5 mg/ml; Sigma ) and 0 . 005% H2O2 . The reaction was stopped with 4 N H2SO4 after 8 to 10 min of incubation in the dark . Optical densities were measured at 490 nm on a PowerWave X microplate reader ( Bio-Tek Instruments , Inc . , Winooski , USA ) . The degree of inhibition in MAb binding was shown to be reciprocal to the concentration of circulating antigen in the sample . The cutoff point was established as the receiver operator characteristic ( ROC ) curve . Statistical determinations of the difference between means of experimental groups were performed using two-tailed Mann-Whitney U-test . Differences which provided P<0 . 05 were considered to be statistically significant . All experiments were performed at least three times .
The depression of cell-mediated immune responses has been associated with severe PCM in humans and in the experimental host [1] , [15] , [16] , [22] . However , the propensity for persistence of the fungus in infected tissues appears to be consequence of cell-mediated immune dysregulation with suppression of Th1 and overexpression of Th2 responses [12]–[14] . To evaluate whether therapeutic immunostimulation is able to interfere in experimental murine PCM and restore the host immune response , we selected immunomodulators for therapy strategy based on the induction of Th1 or Th2 immune response . Since CFA supports a Th1 status , while incomplete Freund's adjuvant ( IFA ) promotes a Th2 status [23] , BALB/c mice were divided into two groups and treated with CFA or IFA on day 20 after infection with P . brasiliensis . The progression of P . brasiliensis infection was determined by lung histopathology and analysis of colony-forming unity ( CFU ) , parameters that are considered trustworthy to discriminate susceptible and resistant mice to systemic fungal infection [9] , [12] , [19] , [24] . At 20 days of infection the mice presented 5 . 8×104 CFU/g of lung tissue ( Figure 1A , dashed line ) and compact granulomas ( data not shown ) , for this reason , this time was chosen for the treatment regimens . On day 30 after treatment ( 50 days postinfection ) , the lungs from IFA-treated mice presented multiple sites of focal and confluent epithelioid granulomas with lymphomonocytic halos circumscribing a high number of viable and nonviable yeast cells ( Figure 2A , C and E ) . Morphometric analysis of the lungs from IFA-treated mice revealed a number of granulomas of 41±5 . 2 , with a relative area of 40 . 7±6 . 2% . These granulomas presented 12 . 2±1 . 8% of yeast cells and 6±0 . 6% collagen ( data not shown ) . In contrast , in the P . brasiliensis-infected mice treated with CFA , no granulomas or yeast cells were seen in the pulmonary sections examined and a well-preserved alveolar architecture was observed on day 30 after treatment ( Figure 2B , D and F ) . Most importantly , the treatment with CFA induced a 2 . 4 log reduction in the fungal burden when compared to the IFA-treated mice , corresponding to 99% less CFU ( Figure 1A ) . The CFU data are in agreement with the histopathology analyses , pointing out that therapeutic immunostimulation led to an increased clearance of fungal burden from lungs . In order to evaluate the impact of the treatment with adjuvant , the animals were weighed weekly until the study end point . We observe that the animals of therapy group gained more weight ( 20% ) than the control group ( data not shown ) . These results can be correlated with a good prognostic in the PCM . When we analyzed the production of pro and anti-inflammatory cytokines in the supernatants of lung homogenates from the P . brasiliensis-infected BALB/c mice treated with CFA or IFA , we observed that the IFA-treated group produced low levels of IFN-γ , IL-4 , IL-12 , TNF-α , IL-10 and TGF-β ( Figure 1B–G ) , suggesting a suppression of the immune response in these animals . In contrast , CFA-treated mice produced high levels of these pro and anti-inflammatory cytokines ( Figure 1B–G ) . Although many reports have demonstrated that the Th2 pattern is associated with a severe disease , whereas a Th1-biased immune response is linked to the asymptomatic and mild forms of PCM [9]–[14] , others have shown that the induction of inflammatory cytokines , such as IFN-γ and TNF-α , can lead to overproduction of nitric oxide that has been associated with suppression of cell immunity [25]–[27] . Recently , it was demonstrated that the anti-inflammatory Th2 cytokine IL-4 has a dual role in PCM , leading to a protective or a disease-promoting effect depending on the genetic background of the host [28] . Regarding TGF-β , we observed that this cytokine is produced by pulmonary epithelium , so we hypothesized that it might contribute to the lung tissue renewal ( unpublished data ) . In this study we obtained an effective protection against P . brasiliensis infection even in the presence of anti-inflammatory cytokines , suggesting that , in this therapy model , the protective effect against PCM seems to be dependent on the induction of a mixed Th1/Th2 immune response pattern . The production of both inflammatory and anti-inflammatory cytokines is extremely helpful to balance the immune response , since anti-inflammatory cytokines can control the inflammatory responses , which can result in local pathology and systemic and centrally controlled adverse events . CD4+ T cells also play a role in the regulation of inflammation [29] . On the basis of the differences between the groups treated with CFA or IFA , we suggest that the protection induced by CFA injection was due to a noticeable increase in the pulmonary levels of cytokines , which probably broke the immunosuppression status observed in the infected mice treated with IFA . Nonetheless , we cannot exclude the involvement of other mechanisms , such as modulation by regulatory T cells [30] , apoptosis in the antigen-specific T cells [31] , and Fas-FasL and CTLA-4 expression [32] . The levels of circulating antigen in the mice infected and treated with IFA were two-fold higher than those treated with CFA ( Figure 1H ) . These results supported by other reports that showed that the depression of cell-mediated immunity is associated with the high levels of specific circulating antibodies or soluble antigens in disseminated disease [13] , [15] , [21] . Although many studies on protection against PCM have been performed , only few of them have reported the efficacy of the immunostimulatory therapy . In one of these studies , the therapy with peptide p10 from gp43 , emulsified in CFA , and chemotherapy was used in an attempt to improve the treatment of PCM [33] . The combined treatment showed a beneficial effect when administered at 48 h or 30 days after challenge . However , the control mice that received only CFA and the non-immunized mice presented similar lung fungal burden . These data are in contrast to those observed herein . This difference might be due to distinct experimental protocols used , such as challenge route , dose , and treatment regimen . Nevertheless , other reports have demonstrated that the use of immunostimulatory therapy can lead to a positive prognostic in fungal diseases [34]–[36] . Basically , therapeutic immunostimulation can be used by reinforcing or broadening defenses when specific immune responses are unable to do this during the natural course of the PCM . The present study demonstrated that a single-dose administration of the Th1-inducing adjuvant ( CFA ) in P . brasiliensis-infected mice was sufficient to break the anergy observed in these animals restoring their ability to mount an effective immune response to the fungus . While the control mice presented large amount of yeasts and extensive sites of parenchymal lung injury , the CFA-treated mice were capable to control not only the fungal systemic dissemination but also its growth , leading to a noticeable fungal clearance without apparent lung injury . Our results indicate that Th1-inducing adjuvant proved to be a valuable tool in the treatment of PCM . Overall , these data open new possibilities for the potential use of Th1-inducing adjuvant not only as a sole therapy but also as an adjunct to conventional antifungal therapy against PCM , improving the regular chemotherapy and reducing the time of treatment .
|
P . brasiliensis is a thermally dimorphic human pathogenic fungus that causes paracoccidioidomycosis ( PCM ) , the most prevalent human systemic mycosis in Latin America , whose drug therapy is usually prolonged and associated with toxic side effects and relapses . Although immunostimulatory therapy is a promising approach to improving the treatment of fungal infections as PCM , few studies have been reported . In the current study , we verified that a single-dose administration of an adjuvant that induces T helper ( Th ) 1 immune response ( complete Freund's adjuvant [CFA] ) in P . brasiliensis–infected mice was sufficient to break the lack of immune response to the fungus observed in infected mice . Four weeks after treatment , the CFA-treated mice presented a mild infection in the lungs characterized by preserved lung structure and small fungal burden , whereas control mice that had been treated with incomplete Freund's adjuvant presented many granulomatous lesions and high fungal burden . The immunotherapy with Th1-inducing adjuvant might be a valuable tool in the treatment of PCM and potentially useful for faster and efficient cure of PCM in humans .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results/Discussion"
] |
[
"infectious",
"diseases",
"immunology/immunomodulation",
"infectious",
"diseases/fungal",
"infections",
"immunology",
"immunology/immunity",
"to",
"infections"
] |
2008
|
T Helper 1–Inducing Adjuvant Protects against Experimental Paracoccidioidomycosis
|
We study the elongation stage of mRNA translation in eukaryotes and find that , in contrast to the assumptions of previous models , both the supply and the demand for tRNA resources are important for determining elongation rates . We find that increasing the initiation rate of translation can lead to the depletion of some species of aa-tRNA , which in turn can lead to slow codons and queueing . Particularly striking “competition” effects are observed in simulations of multiple species of mRNA which are reliant on the same pool of tRNA resources . These simulations are based on a recent model of elongation which we use to study the translation of mRNA sequences from the Saccharomyces cerevisiae genome . This model includes the dynamics of the use and recharging of amino acid tRNA complexes , and we show via Monte Carlo simulation that this has a dramatic effect on the protein production behaviour of the system .
The translation of mRNAs by ribosomes is one of the steps in protein synthesis , and as such underpins all cellular processes . Control pathways at the transcriptional level are a long studied phenomenon , but it is becoming clear that control of protein production could also be exercised at the level of translation [1]–[4] . Proteins are assembled from their constituent amino acids by molecular machines called ribosomes , which move along the open reading frame ( ORF ) of an mRNA . Translation of the mRNA proceeds in three separate stages: initiation , where ribosomes form at the end of the mRNA , and scan along until they encounter a “start codon” ( almost always AUG ) ; elongation , where amino acids are provided to the ribosome via chaperoning transfer RNA ( tRNA ) molecules , and are added to the growing polypeptide; and finally termination , when a ribosome detaches from the mRNA at a stop codon , and the polypeptide chain is released ready for folding or further processing by the cellular machinery . At each stage there is opportunity for control of protein production . In this paper we consider control during elongation , employing a model which takes into account the varying rates of translation of different codons . Ribosomes translate the ORF in a stepwise manner . Each codon ( three nucleotides ) codes for a specific amino acid . The ribosome waits at each codon until the correct amino acid tRNA ( aa-tRNA ) complex binds with its A site [5]; the amino acid is then transferred to the growing peptide chain , and the ribosome advances to the next codon . Bare tRNAs are released back into the cytoplasm , where they are reused after being “recharged” with a new amino acid . In Saccharomyces cerevisiae there are 41 tRNA species , each carrying a specific one of the 20 common amino acids - i . e . , in general there is more than one tRNA species carrying the same amino acid . It is thought that the rate at which the ribosome translates a specific codon type depends on the abundance of the relevant aa-tRNA molecule [3] , [6] . Some tRNAs are very abundant , whilst others are relatively rare; in fact there are amino acids for which there exists both an abundant tRNA and a rare tRNA . This poses the question as to what benefit there could be for the cell to sometimes use a codon which codes for a rare tRNA ( which we shall call a slow codon ) , when a more quickly translated alternative exists . That is , what benefit is there in introducing ribosome bottlenecks or pauses to translation ? The answer to this question is likely to be multifaceted , for example it may reduce the error rate and risk of premature termination . Here we consider whether bottlenecks might also be used to enact control on protein production; this could have major impact on our understanding of the role of translation both in wide type and synthetic biology applications . In this paper we show that it is the interplay between the demand for and the supply of tRNA resources which determines the existence of bottlenecks to translation , and ultimately how this controls protein production . For example if the demand for a particular tRNA is very high , then the elongation of the corresponding codons can become the rate limiting step of translation , even if the abundance of that tRNA is high . That is to say , the availability of a species of charged aa-tRNA depends not only on the tRNA abundance , as assumed in previous works , but also on the demand for that species . We examine how translation of different mRNAs is coupled through a common pool of resources . We note that the present work is in contrast to previous studies which have considered the effect of a finite pool of ribosomes [7] , which leads to very different effects on the translation dynamics . In general several ribosomes can elongate the same mRNA at once; this can lead to the formation of queues of ribosomes , as they cannot overtake each other . Thus the occupancy by ribosomes of different parts of an mRNA gives information about the translation of that gene [8] . Elongation is often treated using traffic models , and here we apply a model where excluding “particles” take discrete steps along a one dimensional lattice; this has been detailed extensively in the non-equilibrium statistical mechanics literature [9]–[11] . This model , known as the totally asymmetric exclusion process ( TASEP ) , has been recently extended by Brackley et al . [12] to take into account the fact the abundance of different aa-tRNA molecules can actually vary with time . Previous work [13]–[15] has assumed that all tRNAs are always bound to an amino acid . That is , they assume that aa-tRNA abundances , and therefore different codon types' translation rates , are constant . We show by relaxing this assumption , that it is not only the abundance of tRNAs which determines translation rates , but one must also consider the dynamics of both the supply of and the demand for tRNAs . Importantly , the balance between supply and demand is likely to change due to environmental influences . In the next section we describe the model and the method by which we perform simulations . We then investigate how the rate of translation initiation affects protein production , studying several mRNA sequences from the Saccharomyces cerevisiae genome , and comparing with results from a model where aa-tRNA levels are fixed . We first consider each mRNA sequence separately , performing simulations with multiple copies of the same mRNA . Finally we consider different mRNA species using the same tRNA resource pool . We study how competition for different resources can change protein production rates depending on the number of each species of mRNA . We present results from simulations with two mRNA species , and larger scale simulations which contain a representative mixture of up to 70 mRNA species all in contact with the same pool of tRNAs . In the large scale simulations we consider changes to the abundance of some mRNA types on a scale which will occur during the normal life cycle of the cell , and show that this can result in a significant change in the production rate of some proteins .
Simulations proceed via a similar scheme used in many previous studies of the TASEP ( for example see [9] ) , where codon sites are chosen at random . We use continuous time Monte Carlo methods as this is very efficient [28] . If a codon is being read by a ribosome , the ribosome advances with rate provided the next codon is vacant and there is a aa-tRNA available . In each simulation we treat copies of a particular mRNA attached to the same pool of tRNAs . To model initiation we also include a codon for each mRNA , which always contains a ribosome ready to enter the lattice with rate . To include recharging , we not only pick from the codon sites , but also from the tRNAs . Unbound tRNAs are recharged with rate per tRNA . In order to eliminate any transient effects due to the initial conditions , we disregard the first Monte Carlo steps ( MCS ) and run for a further MCS; i . e . , all results shown are for the steady state . Throughout this paper we use parameters which match those found experimentally for the widely studied yeast Saccharomyces cerevisiae . A typical yeast cell contains a total of codons ( based on mRNA abundances from [29] ) and tRNAs [30] . In order to be able to efficiently perform simulations we study smaller systems , typically containing codons . We therefore scale all the other parameters accordingly , i . e . , since it is the ratio between the total number of tRNAs and total number of codons which is important , we match this to a real cell . We typically use . Accurate measurements for the numbers of each individual tRNA species in a real cell are not available for all 41 species; in [31] it is shown that the gene copy number for each tRNA species correlates well with the tRNA abundances where these have been measured . We therefore determine the proportions of each type of tRNA using the gene copy numbers from the S . cerevisiae genome ( as given in [31] ) , i . e . , , where is the gene copy number for the tRNAs of type . We fix the constant in Eq . ( 1 ) , such that the mean hopping rate is , matching that observed experimentally [5] . For the recharging we need values for the constant , and the maximum charging rate , for each synthetase . The maximum charging rate is given by ( 7 ) where , known as the turnover number , is the rate at which one enzyme molecule can recharge one tRNA , and is the number of enzyme molecules present . Measured values for and can be found in the literature for some synthetases [32]–[36] , but not for all; for this reason , and also because many of the known values are of the same order of magnitude , we take an average of the values from the references above and assume that all enzymes have the same properties . Thus we use a turnover rate of , and a value . The values used in the calculation can be found in the supporting information ( Text S2 ) . has units of concentration , but since our system has no spatial extent we must convert this into a number of molecules by multiplying via the effective volume - this is the volume a cell would have if it were reduced in size by the same proportion by which we have reduced the number of codons in our system , compared to a real cell . We take as the actual volume of a typical yeast cell . The number of molecules of each type of enzyme in a typical cell has been measured by Haar [37] , and from this data we can calculate the number of enzymes per tRNA molecule; to be consistent with our assumption that all synthetases have the same properties , we use the mean value of . We use ribosomes of width [38]; for convenience it is assumed that it is the rightmost covered codon for which the ribosome is awaiting a tRNA ( and this choice does not affect the results [21] ) .
In this section we consider separately several different mRNAs , and examine the steady state ribosome current and density at different values of the initiation rate . In each simulation we include mRNAs , with chosen such that there are approximately codons in total in the system; the other parameters are scaled as detailed in the methods section . In all cases we find that for small the system is in an LD phase , but as increases above some critical value , queues form behind some codons . In order to understand which codon types are causing these queues we introduce the following quantities: the intrinsic relative speed of the codons ( 8 ) which represents the supply of each tRNA type; and representing the demand for tRNAs , the relative abundance of the codons ( 9 ) and is the number of codons on each mRNA . In the following subsections we examine each mRNA in turn . We label the mRNAs A–D , and list them in table 1; we consider two ribosomal and two other mRNAs . The particular open reading frames which we present have been chosen somewhat arbitrarily , but they are of typical length and codon make up . In each case we match the supply of tRNAs to that of a real cell; Fig . 2 shows the supply of each tRNA type . The full codon sequence and further information about each mRNA is given in the supporting information ( Text S2 ) . Fig . 3 ( a ) shows the supply of the tRNA for the codon at each position on the mRNA; this also gives a measure of the intrinsic speed associated with a codon , i . e . , it is proportional to the translation rate in the absence of steric interactions and when resources are not limited . The figure can therefore be interpreted as the intrinsic codon speed profile for this mRNA . Fig . 3 ( b ) shows the frequency of usage for each codon type , assuming that the whole population of mRNAs are of type A . We note that there are no particularly slow ( small ) codons , and there is a high abundance of codons of type 1 . Figs . 4 ( a ) and ( b ) show how the current and density vary with ; we see that initially and ( and also ) increase with , before reaching a plateau - a profile strikingly similar to that of a simple mono-codon mRNA [20] . In Figs . 4 ( c ) and ( d ) we plot the density profile , i . e . , the time average occupation of each site , for two different values of respectively . We again consider two different measures of density: the reader density , i . e . , for the rightmost site covered by a ribosome only , and the total coverage density . As one might expect , the coverage density is approximately times the reader density; however we shall see below that different features can sometimes be seen in each kind of profile . From these figures we see that for small the system is in the LD phase , but for an initiation rate above some critical value we have behaviour which approximates a queue to the left of codon . We highlight the different scales on the vertical axis of the two plots . Queueing is consistent with the experimental observation [8] that on average the density of ribosomes decreases along the mRNA . The queue might seem surprising given the information in Fig . 3 ( a ) alone , as there are no especially slow codons near . In Figs . 4 ( e ) and ( f ) we plot the steady state relative charging level of each tRNA type , which we define as ( 10 ) where here is the steady state average number of charged tRNAs and , as before , is total number of tRNAs ( charged and uncharged ) . The plots shown are for the same two values of as in 3 ( c ) and ( d ) . We note that the charging level of tRNAs of type has decreased significantly at large , i . e . , due to the finite recharging rate and high demand for that tRNA type , codons have become slow codons . Examining the mRNA sequence shows several clusters of type 1 codons around site 80 , which are responsible for the queue ( marked as red dots in Figs . 4 ( c ) and ( d ) ) . This is consistent with previous work [39] which shows that the effect of slow codons is greatly enhanced when they appear in clusters . In summary , the aa-tRNA species for which there is most demand ( largest ) becomes depleted for large , leading to queueing behind clusters of this type of codon . In Fig . 5 we show similar results for a model where all tRNAs are assumed to be charged at all times ( i . e . , the limit ) and the are based on gene copy numbers ( i . e . , tRNA supply ) only , as has been assumed in previous work . We note that the behaviour is very different; as there are no particularly slow codons , the system does not display queueing; instead it reaches a maximal current ( MC ) due only to the steric repulsion between the ribosomes . We also find that even if the are rescaled so as to take into account the demand as well as supply of tRNAs , the results are still different from those of the more complete model presented here ( results not shown ) . Fig . 6 shows plots of for each site on a type B mRNA , and the abundance of the different codon types now assuming that all of the mRNAs are of type B . In contrast to mRNA A , here there are many low codons distributed throughout the mRNA . Fig . 7 shows results analogous to those for mRNA A . We see from Fig . 7 ( f ) that here it is the charging level of tRNAs of type which becomes most depleted at large , and observe queueing behind codons of this type . In the density profile at large ( Fig . 7 ( d ) ) we note that not only are queues clearly visible , but also that there is some periodic structure in the profile for both of the density measures . The peaks in the reader density and the features in the coverage density are caused by the extended volume of the ribosomes , and have a width equal to that of the ribosomes - codons ( this length is indicated by a red bar in the figures ) . This was not observed for mRNA A because the slow codons appeared in clusters and were separated by distances less than - the effect was smeared out . In mRNA B the slow codons ( , shown as red dots ) are separated by much larger distances , and queues are found behind each . Another interesting feature of the density profile in Fig . 7 ( d ) is the shape of the profile immediately to the left of the slowest codons: queues towards the right side of the mRNA usually show a concave decay ( e . g . left of codon 432 ) , whilst some queues towards the left side show a convex decay ( e . g . left of codon 221 ) . Previous work on sequences which only contain two different codon species [40] suggest that these features depend somewhat on the width of the regions between the slowest codons , as well as the elongation rates of these codons , but this is far from fully understood and is beyond the scope of the current work . The situation for mRNA B further differs from that of mRNA A because codons of type ( the slow codons ) do not have a high value . To explain this behaviour we introduce the quantity ( 11 ) which is the ratio between the demand for and supply of tRNAs , and is a measure of a type of codon's propensity to cause queueing . Fig . 8 ( b ) shows this for mRNA B , and we note that has the largest value . From comparison with Fig . 7 ( f ) we find that is also an indicator of how the charging level of tRNAs of type will be affected . Fig . 8 ( a ) shows for mRNA A , correctly identifying codons of type as those which become rate limiting . It is clear that the queueing behaviour arises because one of the aa-tRNA species has become depleted , i . e . , we have entered a limited resources ( ) regime . This is characterised by a reduction in ( as shown in Figs . 4 ( e ) and ( f ) and 7 ( e ) and ( f ) ) at some critical initiation rate where the rate at which tRNAs are being used ( which we denote ) reaches the rate at which they can be recharged ( denoted ) . The critical initiation rate for queueing is therefore ( 12 ) Consider the LD regime where we assume that the current depends on the average supply of each tRNA type ( in this regime tRNAs can be assumed to be fully charged ) , i . e . , ( 13 ) The angled brackets denote the average over . The rate at which aa-tRNAs are used is therefore ( 14 ) where as before ( Eq . ( 8 ) ) , is the number of codons on each mRNA , and is the total number of mRNAs . Notice that by definition . From Eq . ( 3 ) the maximum recharging rate for tRNAs is ( 15 ) Equating and gives the critical initiation rate for tRNAs of type ( 16 ) whereThe with the smallest value gives a reasonable estimate for , shown as dotted vertical lines in Figs . 4 ( a ) and ( b ) , and 7 ( a ) and ( b ) . From Eq . ( 16 ) , by assuming ( which is true for realistic parameters ) and expanding to first order , we also find , i . e . , ( 17 ) which is consistent with the observation stated in the previous subsection that the codon species associated with the largest value of ( as defined in Eq . ( 11 ) ) causes queueing . In both of the examples above we see induced queueing . In the first case ( mRNA A ) an otherwise averagely abundant aa-tRNA becomes depleted due to the high usage frequency of that codon type . In the second case it is an intrinsically slow codon which leads to queueing . These two different types of behaviour show that even in a simulation with only one type of mRNA , the dynamics are highly sensitive to the precise usage of codons . We have investigated two further examples of mRNAs treated individually with tRNA supply matched to that of a real yeast cell as before . The results are very similar to those of mRNAs A and B , so we include these as supporting information ( Text S3 ) . One slight difference is that the point of the onset of the induced queueing is less well defined than in the previous cases . It has already been documented that slow codons appearing in close proximity to the initiation site can lead to a smoothed onset of queueing [15] , but here there is an additional effect in that there are several codon species which become depleted . That is to say more than one codon species acts as a bottleneck , and the regime is entered at slightly different values of for each .
In this subsection we use a very simple “designer mRNA” to help explain the nature of the queueing regime . We consider a system with only two types of codon and tRNA , with an mRNA sequence of length where all codons are of type , except the central codon which is of type ; i . e . , , and ( where is defined in Eq . ( 9 ) ) This is shown schematically in Fig . 9 ( a ) . We consider the different regimes as the initiation rate is increased whilst , as before , assuming that the termination rate is not limiting ( ) . In the original TASEP ( the limit ) , where hopping rates have fixed values , there are two possibilities as is increased: if there is a smooth transition from an LD to a maximal current ( MC ) phase; if in contrast there is a sharp transition from LD to a queueing phase ( QP ) , where ribosomes queue behind the site [11] , [23]–[27] . The system is in a QP for initiation rates larger than ( 18 ) In the finite recharging model we have observed a third possibility . One or more of the tRNA types can start being used up at a rate comparable to the recharging rate , i . e . , its charging level is reduced and it becomes a limited resource: there is induced queueing . As detailed above , we can calculate an approximation for the initiation rate at which the system will move into this regime . Queues of ribosomes build up behind codons; crucially , since the onset of is smooth , the onset of queueing will also be smooth . There is therefore a clear difference between induced queueing and a QP transition . It has been shown in [20] that for realistic recharging parameters , the LR regime is always reached before the MC phase . For our toy mRNA sequence , whether we observe or QP depends on which transition is reached first . In this case Eq . ( 18 ) gives . Fig . 9 ( b ) shows as a function of ( where since we have only two codon types ) . We obtain three regions , as labelled in the figure: In this narrow region – shown in the blow-up on the right of the figure – ; hence the system will reach a induced queueing regime when is above the shown critical value . Therefore there is a queue behind the codon . As is increased through the critical value the smooth induced queueing transition occurs . In this region , and hence the system reaches a QP for initiation rates above the critical value shown . Ribosomes will queue behind the codon ( due to the low value of ) without any tRNAs becoming limited . As is increased through the critical value there is a sharp transition . At higher values of there may be a further LR regime within the QP , but the point at which this regime is entered cannot be estimated in the framework discussed here; instead we refer the reader to Ref . [20] . In this third region , and therefore it is the codons which become depleted first , and the system enters a regime for initiation rates above the shown critical value . There is a transition as is increased . Notice that there is no queueing since the slow codons make up the bulk of the mRNA , including at the beginning of the sequence [15] . For a real mRNA sequence , since there are many tRNA types , the regime will most likely lead to queueing . Although in theory it is possible to reach a real QP transition before any tRNAs become limited , this has not been observed for any realistic mRNA sequence analysed .
Here we show the effect of varying the initiation rate on the ribosome current , density , and tRNA charging level , for several different mixtures of mRNAs of types A and B ( with lengths and respectively ) . We use the same initiation rate for each mRNA . We examine systems with ( i ) an equal amount of each mRNA by codon ( i . e . , there are the same number of codons in all of the mRNAs of type A as there are in all of the mRNAs of type B ) , ( ii ) with the number of codons in type A mRNAs having the ratio 20% to 80% of those in type B mRNAs , and ( iii ) the ratio 80% to 20% type A to type B by number of codons . In each case we include approximately codons in total ( scaling the parameters accordingly as in earlier sections ) . This means that in each case we have where and are the numbers of mRNAs of type A and B in the system respectively . Comparing these with the numbers of mRNA copies found in a real cell ( see supporting information Text S2 ) , the 80∶20 proportion is the most realistic . Figs . 10 ( a ) – ( e ) and 10 ( f ) – ( j ) show simulation results for the 50∶50 and 80∶20 mRNA mixtures respectively . In each case we plot the current ( which corresponds to the protein production rate per mRNA ) and the reader density as functions of , and the charging levels of each tRNA ( defined in Eq . ( 10 ) ) for the largest value of investigated . We also show the reader and coverage density as a function of position for large ( during induced queueing ) for each mRNA . In each case we indicate the where the first tRNA species becomes depleted , and indicate the positions of these codons ( ) with a blue dot . The critical initiation rate can be estimated as before , but now the tRNA use rate is given bywhere we sum over mRNA species , and is the number of codons on type mRNAs . This equation assumes that the LD current is the same through both types of mRNA , i . e . , it assumes the average of the codons on each mRNA is approximately equal to . We note that only the long mRNA B contains the “blue” codons; in each case the current on mRNA B reaches a maximum at due to “blue”-LR induced queueing ( is indicated by a blue vertical line in the figures ) . Since mRNA A does not contain these queueing codons , the current there ( denoted ) continues to rise; the sharp change in at a larger indicates a queueing transition rather than a maximal current transition [15] . We do indeed see that a second tRNA species ( ) also becomes depleted ( indicated in green ) . In the case of the 50∶50 ratio of A to B the transition to queueing in mRNA A is at an initiation rate several times , whereas in the 80∶20 case , the transition is at just slightly greater than . The 20∶80 mixture shows results qualitatively the same as the 50∶50 mixture , so for conciseness we present those results in the supporting information ( Text S4 ) . In Fig . 11 we show the charging level of the marked tRNA types as a function of ; in each case we see the relatively sharp reduction of the charging level of the blue tRNAs at . For the 50∶50 and 20∶80 cases there is a much more gradual decrease in the charging level of the green tRNA , whereas in the 80∶20 case ( where there is an abundance of mRNA A which contains the green labelled codon ) the decreases is much sharper . We cannot use the above method to estimate where the second queueing transition will occur , since as soon as queueing starts on one mRNA species , the current can no longer be estimated using Eq . ( 13 ) . We now look at simulations with different mixtures of mRNAs C and D; again one is short ( ) , and the other is much longer ( ) , but here we find some quite different results to those discussed above . We consider three different simulations with the following proportions by number of codons of each mRNA type where here the 20∶80 mixture is the closest to a real cell when considering the mRNA copy number ( see supporting information Text S2 ) . In Figs . 12 ( a ) – ( e ) we present results for the case where there is an equal number of codons in all mRNAs of type C and in all mRNAs of type D; the aa-tRNA type which becomes depleted first ( , labelled blue ) is only present on the long mRNA . As in the previous section , even once queueing begins to occur on that mRNA , the current of ribosomes along the short mRNAs continues to increase . A strikingly different outcome here is that the current through the long mRNA then begins to decrease again . This happens because initially a queue forms behind the blue codons near the beginning of mRNA D; since there are no blue codons on mRNA C , the current there continues to increase with . As increases , a second type of tRNA ( labelled green ) becomes depleted . The large cluster of green codons near the end of mRNA D begins to cause a more serious queue than the two blue codons near the start - the current along mRNA D decreases . This decrease leads to a lower rate of blue tRNA use , and the charging level therefore increases . This can be seen in Fig . 13 ( b ) . Consider now decreasing the numbers of mRNA C compared to D , i . e . , consider the 20∶80 C to D mixture ( Figs . 12 ( f ) – ( j ) ) . There are now more blue codons , but the same number of blue tRNAs . The blue codons become queueing at first , and as before continues to increase . Although the green codons are again the second species to become depleted , this time the codons are not slower than the blue codons . The demand for green codons is not sufficient to make the queue behind the large green cluster on mRNA D more severe than the queue behind the blue codons . At the second transition a slight increase in the density on mRNAs of type D is seen - Fig . 12 ( g ) ; this is because although the green codons are not the slowest codons , there is a slightly increased density behind them ( e . g . , at several points between and in Fig . 12 ( j ) ) . If we increase the number of type C mRNAs ( the 80∶20 C to D mixture ) , we have a different outcome again . The situation is very similar to that of the 50∶50 mixture , but now the demand for green tRNAs ( ) is so large , that these become depleted almost immediately after the blue tRNAs as is increased . These results are presented in the supporting information Text S4 . The reduction of the current through mRNA D is so severe that the charging level of blue tRNAs returns almost to full . This can be seen in Fig . 13 ( c ) . We have shown that changing the relative numbers of mRNAs can be very important in determining the dynamics of the system . We match the tRNA supply to that of the real cell , and even though the considered demand is not realistic , we show that different patterns of codon usage can lead to very different behaviour in terms of protein production rate . Therefore we have demonstrated that protein production can be controlled at the translation elongation level by means of the interplay between demand and supply of tRNAs . This is likely to be highly important since levels of different mRNAs are likely to be vary for many reasons , e . g . , as a response to environmental stress , or throughout the different phases of the cell cycle .
In this paper we have shown that it is the interplay between demand and supply which determines the existence of bottlenecks in translation elongation , and that this could be used by the cell to control protein production . We apply a recent model of the elongation step of mRNA translation , which includes the dynamics of the use and recharging of aa-tRNAs , to realistic mRNA sequences from the Saccharomyces cerevisiae genome . We show that that including the use and recharging of aa-tRNAs in the model has a significant effect on the dynamics . We obtain a regime where a particular tRNA type becomes depleted leading to the relevant codons becoming “slow” , and causing queueing . Whilst previous authors [15] , [27] have assumed that it is the type of codon associated with the tRNA with the lowest abundance which is most important for queueing , we have shown that which one of the codon types is ( or becomes ) the slowest depends both on the supply of ( ) , and the demand for ( ) the relevant tRNA species ( defined in Eqs . ( 8 ) and ( 9 ) ) . We have also found that merely taking both supply and demand of tRNAs into account in a TASEP model does not give the same results as fully describing the dynamics of the recharging process as we have done here . In the simulations we choose the supply of tRNAs based on the numbers of each tRNA type found in a real cell , i . e . , we have matched the supply of tRNAs to that of a real cell . At low initiation rate , when none of the tRNA charging levels are significantly reduced , the slowness of each codon type depends only on the tRNA supply . At high initiation rate , for some tRNA types the charging levels become reduced: which ones depends on the demand for each type of tRNA . In the case of simulations where only one type of mRNA is included we have shown that the behaviour can be predicted by considering the quantity , defined as the ratio between the tRNA demand and the supply ( Eq . ( 11 ) ) . The value of can be used to predict which codons will be the first to become queue causing as the initiation rate is increased . This might lead one to ask whether a full dynamic treatment of recharging is really necessary . We investigated this hypothesis using a model with fixed hopping rates ( the original TASEP ) , choosing ( data not shown ) . Although we saw queueing behind the same type of codon as in the results presented here , this was obviously due to a QP transition rather than induced queueing . Also the onset of the regime was at a different initiation rate - e . g . for mRNA B in the model with fixed hopping rates this was on the order , compared to in the results presented here . Whilst this is only a minor difference in the case of single mRNAs , none of the interesting “competition” effects observed in the case of simulations with multiple mRNA types would be observed in a model with fixed hopping rates . Since in each simulation we treat a small subset of mRNAs , the demand for tRNAs is not the same as in a real cell . We conclude that in situations where the demand is important , it is difficult to predict the effect on protein production from a specific mRNA without considering the entire mRNA set , which is a computationally ambitious task . Nevertheless , we have shown that the interplay between demand and supply is what determines which codons become rate limiting for translation . Other authors [13] have attempted to treat real mRNA sequences using an iterative mean field approach . If this could be combined with our model of finite recharging , it could offer significant improvement to brute force simulation of the entire genome . One might consider comparing values of with known measures of slow codon usage such as the codon adaptation index ( CAI ) [42] . The CAI is a property of an mRNA sequence and is a measure of the translation efficiency , or more precisely the synonymous codon usage bias of the sequence . It is calculated based on a quantity known as the relative synonymous codon usage ( RSCU ) . The RSCU for codon species which encodes for amino acid species is defined , where is the total number of codons of species within a set of highly expressed genes [42] , is the number of species which encode for amino acid , and the sum is over all of these species . The maximum RSCU value for a given amino acid is denoted . The CAI for a given mRNA is given by the geometric mean of for each codon in the sequence . Our quantity is a similar measure to the RSCU in that it also measures the translation efficiency of a codon , but with respect to how likely that codon is to cause queueing . We can therefore compute a new index for an mRNA by taking the geometric mean of the for each codon in an mRNA , i . e . where the product is over all codons in the sequence . We term this the queueing likelihood index ( QLI ) . A comparison between this and the CAI is given in the supporting information Text S5 . We find that there is a strong correlation between these quantities ( a Pearson correlation coefficient of −0 . 808 ) , so the QLI can also be used as an alternative measure of translation efficiency . The strong correlation is expected since both quantities use codon usage data; the QLI differs from the CAI in that is explicitly includes tRNA availability data as well as codon usage . An additional advantage of the QLI is that it gives a prediction of how translation will be effected by changes in supply or demand . In a real cell the demand for tRNAs changes throughout the cell cycle , both due to different patterns of transcription , and via mechanisms such as storage , release and degradation of mRNAs in P-bodies [43] . Although small changes in the levels of , for example , a single mRNA are unlikely to have a major impact on the total tRNA demand , we would expect that significant changes in demand would result from , for example the 15% of mRNAs which change their expression level between the G1 and G2 phases of the cell cycle [41] . We have presented simulation results that , although still only treating a small subset of mRNAs , show that a change in mRNA abundances of this magnitude can significantly alter the production rate of some proteins . We can also apply our analytic treatment to estimate the initiation rate at which the first tRNA species will become depleted . By assuming that all mRNAs have the same initiation rate , and using measured data for the mRNA abundances in a typical cell [29] along with tRNA gene copy number data [31] , we can calculate for each tRNA species using Eq . ( 17 ) . We find that some tRNAs will never become depleted ( i . e . , another codon type will become rate limiting first ) , whilst those most likely to become bottlenecks include Leu5 with and Gln2 with . A crude estimate of a typical initiation rate of ( based on a translation rate of and an inter ribosome reader separation of [17] ) allows one to speculate that a two-fold increase in the initiation rate may be enough to cause queueing . Such an increase in the initiation rate could be achieved through for example a nutrient up-shift leading to ribosome biogenesis up-regulation . We have shown in this paper that changes in the supply and demand can drastically alter the behaviour of the protein production mechanism , and different patterns of slow codon usage can act as a means for control . It is known that control of protein production rates is also exercised heavily at the initiation stage of translation , via , for example , structure in the untranslated region which varies across different mRNAs [2] , or more globally through regulation of initiation factors such as eIF2 [44] . It is also thought that variation of initiation rates across different mRNAs is used to effect “translation on demand” [1] . We show here how changes in the initiation rate could be used in conjunction with changes in supply and demand of tRNAs , for example to move from queueing to non-queueing behaviour . Other feedback mechanisms which could be executed by the cell to prevent charged tRNA depletion include production of extra aminoacylation enzymes . Another consideration is that in a real cell the availability of ribosomes could be an important factor: a typical cell contains of the order ribosomes [37] , which is about 0 . 05 per ORF codon; in our simulations for queues the mRNA coverage can reach around 0 . 08 ribosomes per ORF codon in the case of simulations with one or two mRNA species , or 0 . 03 ribosomes per ORF codon in the larger scale simulations . If significant numbers of mRNAs in a cell were to have queues , the amount of free ribosomes in the cytoplasm could become depleted likely leading to a reduction in initiation rate - this itself could act as a feedback to reduce queueing . Finite numbers of ribosomes have previously been considered in a TASEP model [7] , but not in the biological context of finite tRNA recharging . In our simulations we ignore the effect of wobble base pairing . It is known that some tRNAs can still recognise a codon when only the first two of the three nucleotides match correctly; the cost of this mismatch is that the hopping rate for such codons reduces by approximately one third [45] . Some authors [46] compensate for this effect in models by rescaling the number of tRNAs for the “wobble” tRNAs; as the current work has shown , the number of tRNAs is crucial to the dynamics , so we do not follow this strategy here . A more realistic approach would be to have a codon type dependent intrinsic hopping rate . Other improvements which could be made to the current model include considering multiple internal states for ribosomes [47] , or using a more realistic model for aminoacylation which considers the differences between each enzyme , and takes into account the availability of each amino acid . A reformulation of Eq . ( 3 ) to more realistically describe an enzymatic reaction with multiple substrates ( such as in [48] , [49] ) could the allow amino acid starvation conditions to be studied in this framework . We also do not consider here effects such as so called “no-go decay” , where mRNAs upon which there are stalled ribosomes are selectively degraded [50] . This could be considered a feed back effect to release resources . A phenomena related to no-go decay is ribosome drop off , the probability of which increases due to stalling at slow codons [51]; this could also be incorporated into future models , although since it occurs at a low rate and in yeast is more likely to be due to secondary structure than slow codons [51] , it is unlikely to qualitatively change the behaviour . In summary , when this recent model is applied to realistic mRNA sequences we find queueing behind slow sites or clusters of slow sites . The present model differs from previous ones in that the particular species of codon which becomes slow depends on the demand placed on aa-tRNAs and not just the overall tRNA abundances . We find that the behaviour depends on the dynamics of the system , and the same results cannot be produced with constant hopping rates; i . e . , including the full charging process in the model is crucial . We have shown that in larger systems , changes in the demand for tRNAs which occur during the cell's normal life cycle are sufficient to cause significant changes in protein production .
|
In this paper we show that the rate at which proteins are produced can be controlled at the elongation stage of mRNA translation . Regulation of translation initiation has been a focus of much study , but the subsequent effect of changes in the initiation rate on the overall translation rate , and the role of slow and fast codon usage in mRNA sequences is still not fully understood . We consider a model of elongation in which the dynamics of tRNA use and recharging are considered for real mRNA sequences . We find that the balance between the demand for , and supply of tRNAs is crucial in determining translation rates . Particularly interesting “competition” effects are observed when the simultaneous translation of multiple mRNA is considered . We show indeed that , via the choice of slow or fast codons , it is in principle possible to control how variation of the supply and demand for tRNA resources changes the rate of protein production from different mRNAs .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"What",
"is",
"the",
"Nature",
"of",
"the",
"Queueing?",
"Mixtures",
"of",
"Multiple",
"mRNA",
"Species",
"Discussion"
] |
[
"physics",
"rna",
"rna",
"processing",
"statistical",
"mechanics",
"nucleic",
"acids",
"biophysic",
"al",
"simulations",
"biology",
"computational",
"biology",
"biophysics",
"simulations",
"biophysics"
] |
2011
|
The Dynamics of Supply and Demand in mRNA Translation
|
An accurate diagnosis is essential for the control of infectious diseases . In the search for effective and efficient tests , biosensors have increasingly been exploited for the development of new and highly sensitive diagnostic methods . Here , we describe a new fluorescent based immunosensor comprising magnetic polymer microspheres coated with recombinant antigens to improve the detection of specific antibodies generated during an infectious disease . As a challenging model , we used canine leishmaniasis due to the unsatisfactory sensitivity associated with the detection of infection in asymptomatic animals where the levels of pathogen-specific antibodies are scarce . Ni-NTA magnetic microspheres with 1 , 7 µm and 8 , 07 µm were coated with the Leishmania recombinant proteins LicTXNPx and rK39 , respectively . A mixture of equal proportions of both recombinant protein-coated microspheres was used to recognize and specifically bind anti-rK39 and anti-LicTNXPx antibodies present in serum samples of infected dogs . The microspheres were recovered by magnetic separation and the percentage of fluorescent positive microspheres was quantified by flow cytometry . A clinical evaluation carried out with 129 dog serum samples using the antigen combination demonstrated a sensitivity of 98 , 8% with a specificity of 94 , 4% . rK39 antigen alone demonstrated a higher sensitivity for symptomatic dogs ( 96 , 9% ) , while LicTXNPx antigen showed a higher sensitivity for asymptomatic ( 94 , 4% ) . Overall , our results demonstrated the potential of a magnetic microsphere associated flow cytometry methodology as a viable tool for highly sensitive laboratorial serodiagnosis of both clinical and subclinical forms of canine leishmaniasis .
Efficient diagnostic tests capable of providing early and accurate diagnosis are essential in determining the choice of treatment and in the epidemiological surveillance of infectious diseases . Classically , the microscopic observation or isolation of the infectious agent was considered as the gold standard for laboratory confirmation of an infection . During the last decades , the development of molecular biology techniques capable of detecting and quantifying pathogen-specific DNA or RNA have emerged [1] . Despite their high sensitivity , these techniques often require specific and expensive equipment and highly trained personnel . On the other hand , serological approaches to detect specific antibodies against an infectious agent constitute a valuable alternative for early , rapid , and user-friendly diagnostic tests for both human and veterinary infections . The use of defined and well-characterized recombinant antigens has improved the performance of serodiagnosis in several infectious diseases by increasing overall sensitivity and specificity [2] , [3] , [4] . The last few years have positioned flow cytometry analysis as an emerging technology for the diagnosis of infectious diseases [5] . This technique possesses several advantages for immunoassays such as high throughput capacity , possibility of analyte quantification , reduced sample volume , high reproducibility and sensitivity , a wide dynamic range , and , the most exciting of all , the potential for multiplexing [5] . More recently , micro and nanotechnology have been applied in the development of biosensors that emerge as promising diagnostic methods [6] . Microsphere-based immunoassays with covalent binding between an antigen or antibody to magnetic microspheres have been considered promising alternatives for serological analysis [7] . Leishmaniasis is a zoonotic disease caused by protozoa of the genus Leishmania . Dogs are considered the main reservoir hosts for the zoonotic cycle of this parasite . Canine leishmaniasis ( CanL ) is a systemic chronic disease , ranging from asymptomatic subclinical to symptomatic infection . Nevertheless , actively infected animals , despite they did not show yet external signs of the disease [8] are already able to transmit the parasite to the vector , the phlebotomine sand flies [9] . As a consequence , CanL represents an important veterinary and public health problem since it contributes to the maintenance of the Leishmania life cycle and transmission to humans . As a result , the development of specific and efficient diagnostic methods capable of detecting both symptomatic and asymptomatic infected animals is essential for the control of this zoonosis , with special attention being paid to the unsatisfactory sensitivity associated with the detection of subclinical infections [10] . The present work describes a new method for the serodiagnosis of canine leishmaniasis . This method combines antigen-coated magnetic microspheres , immunomagnetic separation and flow cytometry for the detection of specific antibodies to Leishmania . An immunofluorescent assay was developed using a mixture of two magnetic microspheres with distinguishable size coated with the Leishmania recombinant proteins rK39 and LicTXNPx , which were recently proved to be a useful tool for the detection of both clinical and subclinical forms of canine Leishmania infection [11] . After magnetic separation , positive fluorescent microspheres were quantified by flow cytometry . A clinical evaluation of the method was done using a panel of serum samples from natural infected dogs .
This study observed Portuguese legislation for the protection of animals ( Law no . 92/1995 , from September 12th ) . According to the European Directive of 24 November 1986 , article 2 d , non experimental , agricultural or clinical veterinary were excluded . The Animal Ethics Committee of the Associate Laboratory IBMC-INEB approved the animal protocol used . Serum samples were collected during vaccination campaigns and informed consent was obtained from all dog owners before sample collection . 129 serum samples from domestic dogs were used in this work . Dogs were clinically classified as symptomatic , asymptomatic and healthy dogs . Sera from Leishmania-negative dogs presenting non-related pathologies were used as controls for cross-reactivity . Peripheral blood was collected from the cephalic vein and stored at −20°C . Serology for antibodies to Leishmania was performed by Direct Agglutination Test ( DAT ) according to the protocol described by Schallig et al [12] . For parasitological studies , bone marrow or lymph node aspirates were collected for microscopic examination . For PCR , DNA was extracted from blood . Based on the clinical , serological and parasitological examination , animals were divides into four groups: Two recombinant proteins , L . infantum cytosolic tryparedoxin peroxidase ( LicTXNPx ) and rK39 [13] were used in the present study . LicTXNPx was prepared as described by Cordeiro-da-Silva et al [14] . Both proteins contain a six-histidine residue at its N-terminal . Superparamagnetic silica microspheres coated with Ni-NTA as the functional group with two different sizes ( 8 . 07 µm and 1 . 7 µm ) were specifically synthesized for this study by Kisker-biotech ( Germany ) . Both microspheres ( 5×106 ) were coated with 5 µg of rk39 or LicTXNPx through binding of the recombinant protein histidine tail to the Ni-NTA groups present on the surface of the microspheres . Recombinant protein-coated microspheres were blocked for 1 h at 37°C with PBS containing one of the following blocking agents: 5% non-fat milk , 3% gelatin , 10% heat inactivated fetal bovine serum ( FBS ) or 3% bovine serum albumin ( BSA ) . The microspheres were separated using a neodymium magnet separation rack and washed twice in PBS . After blocking , the coated magnetic microspheres were mixed in an equal proportion ( 50%∶50% ) in a final volume of 100 µl . The mixture was incubated with 100 µl of dog serum sample dilutions ranging from 1∶100 to 1∶6400 in PBS and incubated for 30 minutes at room temperature with gentle mixing . After a washing step , the mixture was incubated with 100 µl FITC-conjugated sheep anti-dog IgG diluted at 1∶100 for 30 minutes at room temperature in the dark with gentle mixing . The microspheres were then recovered by magnetic separation and washed three times in PBS and resuspended in 500 µl of PBS . The microspheres were analyzed by flow cytometry in a FACSCalibur and analyzed with FlowJo software . Internal controls of the reaction were included in all experiments to monitor unspecific binding , in which the microspheres were incubated in the absence of dog serum , but in the presence of FITC conjugated goat anti-dog IgG . Also , in all batches of the experiments , positive and negative controls were included . Recombinant protein-coated microspheres were identified on the basis of forward/side scatter values . Gated cells ( excluding duplets ) were evaluated by FL-1 area versus forward scatter ( FSC ) pattern and a total of 10 , 000 events were acquired . Statistical analysis was performed using GraphPad Prism 5 software . Differences in immunoglobulin levels between groups were analyzed by means of the Mann–Whitney's test . A P-value<0 . 05 was considered as statistically significant .
Our initial hypothesis for the development of a fluorescent based immunosensor started for binding sensitive and specific defined antigens , already validated , to the magnetic microspheres . Nevertheless , unspecific adsorption of sera antibodies to the magnetic microspheres was observed when uncoated microspheres were incubated with a positive serum sample ( Figure 1A ) . To eliminate the unspecific binding of serum antibodies to the uncoated microspheres , several proteins were tested as reported in Methods . These proteins have been described as blocking agents of immunoassays such as ELISA and Western-blot with the optimal blocking agent for any particular assay to be determined by empirical testing . Uncoated magnetic microspheres coated with 3% gelatin still recognized positive serum samples , but the majority of the microspheres were lost during the assay ( data not shown ) . When using 5% non-fat milk or 3% BSA , 99% of the microspheres were found to have positive signal in the presence of a positive serum sample ( data not shown ) . FBS-coated microspheres , in the presence of positive or negative serum samples , showed similar results with very low percentage of positive microspheres ( Figure 1B ) . The next step was to determine the optimal serum dilution . For that , serial dilutions ranging from 1∶100 to 1∶6400 were tested in recombinant protein-coated microspheres blocked with 10% FBS ( Figure 1C ) . A good distinction between positive and negative serum samples was achieved up to 1∶3200 . Microspheres with two different sizes , each one coated with rk39 or LicTXNPx were used for the development of this immunofluorescent assay . This will allow a clear separation of these microspheres by flow cytometry and consequently specifically quantify anti-rK39 and anti-LicTXNPx antibodies . A better separation between positive and negative serum samples was obtained when larger magnetic microspheres ( PMSI-8 . 07Ni-NTA ) were coated with the recombinant protein rK39 and the smaller ones ( PMSI-1 . 7Ni-NTA ) coated with the recombinant protein LicTXNPx as shown in Figure 1D . It was observed that the magnetic microspheres when coated with these Leishmania-specific proteins formed two or more populations ( Figure 1E ) , which was found to be more evident for the low size magnetic microspheres ( Figure 1F ) . We believe that the larger populations are the result of microspheres aggregation , which lead us to exclude these smaller populations of the analysis . Based on these previous results , we have applied the same principle of combining these two proteins to develop this new method . In order to determine the optimal combination of rK39 and LicTXNPx , we determined the reactivity of randomly chosen 20 symptomatic and asymptomatic serum samples against different combinations of these two proteins . The LAM-ELISA described by Santarém et al . , [11] was described as the conjunction of 80% rK39 and 20% LicTXNPx . Thus , we have selected this proportion along with a 50% rK39: 50% LicTXNPx and 20% rK39: 80% LicTXNPx mixtures . As shown in Figure 2A and B , 50% rK39: 50% LicTXNPx achieved the higher percentage of fluorescent positive microspheres for both symptomatic and asymptomatic dogs . Negative dogs showed no significant reactivity with the three different proportions ( data not shown ) . Based on the results from Santarém et al [11] it was to be expected that the combination 80% rK39: 20% LicTXNPx would give the best results . During the immunoassay method , the magnetic microspheres are separated at several points of the protocol using a magnet . During these separating steps , a percentage of magnetic microspheres are lost , with higher incidence for the smaller microspheres . Since these smaller microspheres are coated with LicTXNPx , a higher amount of these microspheres must be used to compensate the LicTXNPx-coated microspheres lost during the process . On the basis of the histogram representing the binding of non-infected animals , an area was chosen in order to contain a maximum of 1% of fluorescent positive microspheres for each antigen in any negative sample . This area will be used to measure the percentage of fluorescent positive microspheres in all data . The cut-off , defined by the ROC curve for rK39 antigen , corresponded to 12 . 2% of fluorescent positive microspheres . The area under the curve was 0 . 9857 , 95% confidence interval: 0 . 9693–1 . 002 . The cut-off , defined by the ROC curve for LicTXNPx antigen , was 3 . 9% of fluorescent positive microspheres . The area under the curve was 0 . 9366 , 95% confidence interval: 0 . 8885–0 . 9847 [15] . The magnetic immunoassay method was applied to the diagnostic of CanL . Using the established optimal conditions , a panel of 129 serum samples was studied . Immunofluorescent assay was considered positive whenever at least one antigen was positive . With the defined cut-offs , a sensitivity of 91 . 4% was achieved for rK39 and a sensitivity of 88 . 9% for LicTXNPx with a specificity of 97 . 2% for both antigens ( Figure 2C and 2D ) . Comparing the results obtained with symptomatic and asymptomatic dogs , rK39 antigen demonstrated a higher sensitivity for symptomatic dogs ( 96 . 8% ) than for asymptomatic animals ( 93 . 5% for group 2 and 77 . 8% for group 3 ) , while LicTXNPx antigen showed a higher sensitivity for asymptomatic ( 90 . 3% for group 2 and 94 . 4% for group 3 ) than for symptomatic dogs ( 84 . 3% ) . Together these antigens increase the sensitivity of the immunofluorescent assay to 98 . 8% ( Table 1 ) . The use of Leishmania recombinant antigens is less prone to cross-reactivity , displaying lower false-positive reactions [16] . Cross-reactivity of magnetic microspheres flow cytometry was evaluated using 12 serum samples from dogs seronegative for Leishmania , but with other clinical conditions ( group 5 ) . Only two out of twelve serum samples cross-reacted with rK39-coated beads ( Figure 2C ) . A low level of cross-reactivity was also reported for LAM-ELISA [11] .
We have recently proposed a defined Leishmania antigen mixture , composed of the LicTXNPx and rK39 antigens , as an improvement to current ELISA-based serological techniques for the accurate detection of both clinical and subclinical forms of CanL [11] . The combined use of these two antigens achieved the highest score in both symptomatic and asymptomatic dogs among all antigens used . The aim of the present work was to develop a magnetic immunosensor incorporating these antigens that when associated with flow cytometry could be used as a valid approach for the serodiagnosis of CanL . Therefore , magnetic polymer microspheres were coated with the two recombinant antigens of Leishmania . Antibodies present in positive serum samples will recognize and interact with these antigen-modified microspheres . Finally , the complex antibody-antigen-magnetic microspheres will be captured by a neodymium magnet and positive microspheres will be quantified by flow cytometry . The principle of using magnetic microspheres for the development of diagnosis methods has been explored due to the ability of these scaffolds to easily adsorb biological materials such as proteins , antibodies or DNA [17] , [18] . We and others have already proposed the use of flow cytometry-based methods for the diagnosis of CanL using both promastigote as well as amastigote forms [19] , [20] . The development of fluorescent based immunosensors by coupling highly sensitive flow cytometry to protein-coated magnetic polymer microspheres capable of specifically retain the target antibodies was anticipated to increase overall performance without the problematic of using live or fixed parasites . In the absence of a gold standard to integrate the results obtained with magnetic microspheres coupled with flow cytometry , these were analyzed using ROC curves to determine the theoretical cut-off values . The antigens used in this technique enable good predictive values for the study cohort with a AUC of 0 , 9366 and 0 , 9857 for LicTXNPx and rK39 respectively . LAM-ELISA described by Santarém et al [11] had a AUC of 0 , 984 . This allowed the comparison of the performances between the two methods with acceptable confidence . LAM-ELISA described by Santarém et al [11] showed a specificity of 96 , 3% and a sensitivity of 90 , 7% . In the present study , magnetic microspheres flow cytometry reached similar specificity ( 94 , 4% ) but higher sensitivity ( 98 , 8% ) . Similarly to ELISA , this method showed a better performance when combining both antigens . Magnetic microspheres flow cytometry using rK39 as antigen showed a sensitivity of 91 , 4% and using LicTXNPx as antigen showed a performance of 88 , 9% . These results not only confirmed LicTXNPx as a good marker for the detection of asymptomatic infected dogs but , more importantly , allowed an increase in test performance in the detection of both asymptomatic and symptomatic dogs using the previous described antigens . With this immunofluorescent assay , we achieved to detect 48 out of 49 asymptomatic animals with high specificity ( 94 , 4% ) . Magnetic microspheres flow cytometry showed a sensitivity of 94 , 4% for the detection of infected animals seronegative by DAT ( group 3 ) . This method proved to be as good as other conventional serological methods to evaluate seropositive animals . More importantly , the developed method proved to be highly sensitive in detecting infected animals that are considered seronegative by conventional serological methods . Although being a serological method , magnetic microspheres flow cytometry cannot be considered a rapid and user friendly method . However , since this method allows the detection of infected animals that are seronegative by other conventional methods , we hypothesized that it can be a valuable alternative to conventional serological methods for the detection of Leishmania infected animals . In conclusion this study reports the development of a new tool for the laboratorial diagnosis of CanL . The method here described explores the potential of flow cytometry as a diagnostic method associated with antigen-modified magnetic microspheres . So far , all the approaches using flow cytometry used promastigotes or amastigotes as targets to detect Leishmania specific antibodies [19] , [20] , [21] . Here , we propose the use of recombinant antigens as a better target to detect specific anti-Leishmania antibodies . Two Leishmania specific antigens , previously described as highly sensitive for the detection of symptomatic and asymptomatic infected dogs were selected to coat magnetic microspheres with two distinct sizes . These antigen-coated microspheres mixed in the proportion 50% rK39: 50% LicTXNPx were used to separate anti-Leishmania specific antibodies present in the serum of infected dogs . Finally , flow cytometry allowed the specific quantification of the antibodies against anti-rK39 and anti-LicTXNPx . The magnetic microspheres associated flow cytometry clearly improved the performance of CanL serodiagnosis , detecting with high specificity and sensitivity both clinical and subclinical forms of CanL .
|
Dogs are the most important domestic reservoirs of the parasite Leishmania infantum . Some infected animals develop a subclinical infection , without the classical symptoms characteristics of this disease . One of the major challenges in the serodiagnosis of canine leishmaniasis is the detection of actively infected animals that are already able to transmit the parasite to the vector , despite the fact they did not yet show external signs of the disease . In the present work , we have developed a new tool for the laboratorial diagnosis of canine leishmaniasis that clearly improves the performance of canine leishmaniasis serodiagnosis . An immunofluorescence assay was developed combining Leishmania recombinant protein-coated magnetic microspheres and flow cytometry . The antigen-coated microspheres were used to separate anti-Leishmania specific antibodies present in the serum of infected dogs . Flow cytometry allowed the specific quantification of the antibodies . The clinical evaluation of a panel of serum samples from natural infected dogs clearly demonstrates that this method detects with high specificity and sensitivity both clinical and subclinical forms of the disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"veterinary",
"diseases",
"immunology",
"biology",
"microbiology",
"veterinary",
"science"
] |
2013
|
Development of a Fluorescent Based Immunosensor for the Serodiagnosis of Canine Leishmaniasis Combining Immunomagnetic Separation and Flow Cytometry
|
Infection with HIV-1 perturbs homeostasis of human T cell subsets , leading to accelerated immunologic deterioration . While studying changes in CD4+ memory and naïve T cells during HIV-1 infection , we found that a subset of CD4+ effector memory T cells that are CCR7−CD45RO−CD45RA+ ( referred to as TEMRA cells ) , was significantly increased in some HIV-infected individuals . This T cell subset displayed a differentiated phenotype and skewed Th1-type cytokine production . Despite expressing high levels of CCR5 , TEMRA cells were strikingly resistant to infection with CCR5 ( R5 ) –tropic HIV-1 , but remained highly susceptible to CXCR4 ( X4 ) –tropic HIV-1 . The resistance of TEMRA cells to R5-tropic viruses was determined to be post-entry of the virus and prior to early viral reverse transcription , suggesting a block at the uncoating stage . Remarkably , in a subset of the HIV-infected individuals , the relatively high proportion of TEMRA cells within effector T cells strongly correlated with higher CD4+ T cell numbers . These data provide compelling evidence for selection of an HIV-1–resistant CD4+ T cell population during the course of HIV-1 infection . Determining the host factors within TEMRA cells that restrict R5-tropic viruses and endow HIV-1–specific CD4+ T cells with this ability may result in novel therapeutic strategies against HIV-1 infection .
Chronic immune activation and homeostatic disturbance of T cell subsets that accompany viral replication are hallmarks of HIV-1 infection [1–4] . The cause and implications of these profound quantitative and qualitative changes in CD4+ memory T cell subsets during HIV-1 infection are still not well understood [2] . Elucidating the causal relationships between perturbed naïve and memory T cell compartments during the course of HIV-1 infection could be critical in understanding its pathogenesis . Human T cells are categorized as naïve ( TN ) and memory ( TM ) subsets based on expression of CD45RA and CD45RO isoforms , respectively [5–8] . It is now known that memory T cells are comprised of distinct subsets that can be identified based on other surface markers and effector functions [9] . Sallusto and colleagues defined two CD4+ memory T cell subsets , termed central memory ( TCM ) and effector memory ( TEM ) cells [8] . TEM cells have low expression levels of the chemokine receptor CCR7 and lymph node homing receptor CD62L , express receptors for migration to inflamed tissues , and display immediate effector functions [8 , 10] . In contrast , TCM cells express high levels of CCR7 and lack potent effector functions . It has been proposed that TCM cells are responsible for maintaining long-term memory , and upon re-exposure to antigens , differentiate into TEM cells with effector functions . Prior studies indicated that HIV-1 preferentially infects memory , rather than naïve CD4+ T cells [11–16] , possibly because of exclusive expression of the HIV-1 coreceptor CCR5 on memory T cells . Within the memory population , TEM cells are enriched for expression of CCR5 relative to other CD4 memory cells [17 , 18] , suggesting that they may be primary targets for CCR5-tropic ( R5-tropic ) viruses that predominate in most infected persons . Because chronic HIV-1 infection disrupts the balance between naïve and memory T cell subsets [19] , we characterized the distribution of these cells during HIV-1 infection . We found that a small subset of CD4+ TEM cells , which we called CD4+ TEMRA cells , were greatly increased in some HIV-infected individuals relative to uninfected individuals . Remarkably , CD4+ TEMRA cells displayed a specific post-entry block to R5-tropic HIV-1 , despite expressing high levels of CCR5 . Accumulation of this effector memory CD4+ T cell subset during chronic HIV infection could have important implications in understanding intrinsic resistance to the virus and perturbation of T cell compartments in infected individuals .
The dynamics of T cell changes were studied in HIV-infected and HIV-uninfected individuals by staining their peripheral blood mononuclear cells ( PBMCs ) with monoclonal antibodies against CD3 , CD4 , CCR7 , and CD45RO cell surface molecules . In most uninfected individuals , this analysis divides CD4+ T cells into three subsets that can be readily quantified: naïve T cells ( TN; CD45RO−CCR7+ ) , central memory T cells ( TCM; CD45RO+CCR7+ ) , and effector memory T cells ( TEM; CCR7− ) ( Figure 1A , left panel ) . However , in HIV-uninfected individuals , a fourth subset ( CD45RO−/dullCCR7− ) was also observed ( Figure 1A ) , albeit with a low frequency ( 0 . 5%–3% ) . This subset was greatly increased in some of the HIV-infected individuals ( Figure 1A , right panel ) . Because these cells resembled a previously defined CD8+ T cell subset ( called CD8+ TEMRA cells ) with effector functions that expressed CD45RA with effector functions [20] , we tentatively termed them CD4+ TEMRA cells ( referred to as TEMRA cells hereafter ) . Conversely , we denoted the CD45RO+CD45RA−CCR7− effector memory CD4+ T cell subset as TEMRO cells . The relationship between TEMRA cells and HIV-1 infection was studied in 33 HIV-infected and 30 HIV-uninfected individuals ( Figure 1B ) . The proportion of TEMRO and TEMRA subsets was significantly increased in HIV-infected individuals ( Figure 1B ) . On the other hand , the proportion of TN cells was significantly decreased in HIV-infected individuals , while the proportion of TCM cells remained similar in both groups ( Figure 1B ) . The high proportion of TEMRA cells found in HIV-infected individuals prompted further analysis of this subset . We hypothesized that TEMRA cells are a subset of effector memory CD4+ T cells , analogous to a subset recently described for CD8+ T cells with the same surface marker phenotype [20] . The four subsets of CD4+ T cells ( TN , TCM , TEMRO , and TEMRA ) obtained from HIV-infected and HIV-uninfected individuals were analyzed for expression of cell surface molecules known to be expressed differentially in naïve , memory , and effector T cells . All TN cells expressed CD28 , CD27 , CD7 , and CD62L , with progressively less expression on CD4+ TCM , TEMRO , and TEMRA cells ( Figure 2 ) . In contrast , expression of CD11b , CD57 , and HLA-DR were increased on TEMRO and TEMRA cells compared to CD4+ TN and TCM cells ( Figure 2 ) . In contrast to TCM and TEMRO cells , TEMRA cells also expressed high levels of CD45RA , similar to TN cells ( Figure 2 , top panel ) . This profile suggested that TEMRA cells are a subset of CD4+ effector memory T cells with a peculiar CD45RA+CD45RO−/dull phenotype . Differentiated effector memory T cells have a reduced proliferative capacity [8 , 20] . To assess the relative proliferative capacity of the different CD4+ T cell subsets , each subset was purified from an HIV-uninfected individual according to CCR7 and CD45RO expression as shown in Figure 1A , and stimulated with dendritic cells ( DCs ) pulsed with superantigen ( staphylococcal enterotoxin B [SEB] ) . Activated T cells were counted at day 12 ( Figure 3A ) . DC-mediated activation caused robust cell division of TN and TCM cells ( Figure 3A ) , whereas TEMRO cells and TEMRA cells divided fewer times ( Figure 3A ) . The reduced proliferative capacity of effector T cells correlates with a decrease in telomere length and with an increased propensity to undergo apoptosis [9] . To determine whether TEMRA cells undergo apoptosis similar to effector T cells , all T cell subsets were stained with a marker of apoptosis ( Annexin V ) before and after cells were stimulated through the T cell receptor ( TCR ) by anti-CD3 plus anti-CD28 antibodies for 18 h . A higher proportion of effector T cells underwent apoptosis compared to TN and TCM cells ( Figure 3B ) . Levels of apoptosis were comparable between TEMRO and TEMRA cells before and after TCR stimulation ( Figure 3B ) . A hallmark of TEM cells is secretion of greater quantities of cytokines when stimulated through the TCR , as compared to TN and TCM cells [8 , 10] . We therefore explored cytokine profiles of TEMRO and TEMRA subsets . As expected , [8 , 10] TEMRO cells secreted greater amounts of most cytokines assayed ( IL-4 , IL-5 , IL-10 , TNF-α , and IFN-γ ) compared to TN and TCM cells ( Figure 3C ) . TEMRA cells secreted high levels of IFN-γ , but much lower levels of IL-4 , IL-5 , or IL-10 compared to TEMRO cells ( Figure 3C ) . This cytokine profile suggested that the TEMRA subset is skewed towards a Th1 phenotype . Recently , a cell surface molecule called CRTH2 was shown to be highly expressed on Th2 but not on Th1 cells [21] . To confirm Th1 skewing of TEMRA cells , we analyzed the surface expression of CRTH2 . In agreement with the cytokine profile , significantly fewer TEMRA cells expressed CRTH2 compared to TEMRO or TCM subsets ( Figure S1 ) . Taken together , we conclude that TEMRA cells are differentiated effector memory T cells that are skewed toward a Th1 phenotype . Because TEMRA cells were proportionately increased in some HIV-infected individuals , we next investigated the susceptibility of these cells to HIV-1 infection . For these experiments , TN , TCM , TEMRO , and TEMRA cells purified from PBMCs of HIV-infected and HIV-uninfected individuals were activated through the TCR to render them susceptible to infection . The activated T cells were then infected with either R5-tropic replication-competent HIV ( R5 . HIV ) , CXCR4 ( X4 ) –tropic replication-competent HIV ( X4 . HIV ) , or replication-defective viruses that only undergo a single round of replication and are pseudotyped with vesicular stomatitis virus glycoprotein G ( VSV-G . HIV ) . Each virus used here encoded green fluorescent protein ( GFP ) that was used to quantify infection by flow cytometry at specific time points after inoculation [22] . Prior to the infectivity assay , we analyzed the expression of HIV-1 co-receptors CCR5 and CXCR4 on TN , TCM , TEMRO , and TEMRA cells isolated from an HIV-uninfected individual ( Figure 4A ) . TEMRA and TEMRO cells expressed the highest levels of CCR5 , while all four subsets expressed high levels of CXCR4 ( Figure 4A ) . In addition , the median CCR5 expression was quantitated from 20 HIV-infected individuals , and the similar subset expression trends were confirmed ( Figure S2 ) . When each T cell subset isolated from an HIV-uninfected individual was challenged with R5 . HIV , CD4 TCM and TEMRO cells were more susceptible to infection than TN cells ( Figure 4B ) , most likely reflecting high CCR5 surface expression levels on these memory T cells ( Figure 4A ) . In contrast , TEMRA cells were resistant to a high multiplicity challenge with R5 . HIV ( Figure 4B , top panel ) . This was an unexpected finding given the high cell surface CCR5 levels on TEMRA cells ( Figure 4A ) . At day 12 post-infection , R5 . HIV spread through the cultures , producing more infected TN , TCM , and TEMRO cells as compared to 5 d post-infection . Even at this late time point , TEMRA cells remained almost completely refractory to infection ( Figure 4B , second panel ) . In contrast , TEMRA cells were similarly susceptible to infection with X4 . HIV , as well as other T cell subsets ( Figure 4B , third panel ) . Surprisingly , TEMRA cells were also 5- to 10-fold less susceptible to VSV-G . HIV infection than other T cell subsets ( Figure 4B , bottom panel ) . We then sought to determine whether over time the TEMRA subset would progressively become more susceptible to infection post-activation , or whether these cells were being killed in culture by rapidly replicating virus . For this experiment , T cells were infected with R5 . HIV or X4 . HIV for 2 d at different multiplicities of infection ( MOIs ) and then washed to remove input virus . Infection was quantified based on GFP expression at different time points after inoculation ( Figure 5A ) , and viral replication was assessed by quantifying HIV p24 protein in culture supernatants . R5 . HIV replicated efficiently in TN , TCM , and TEMRO cells , but there was little or no replication in TEMRA cells ( Figure 5B ) . In contrast , X4 . HIV infected and replicated efficiently in all four subsets and rapidly killed most of the T cells ( Figure 5A and 5B , right panels; unpublished data ) . Similar results were observed when primary HIV-1 isolates , utilizing different R5-tropic , X4-tropic , and R5X4-dual tropic HIV-1 envelopes that also express nef , were used ( Figure 5C ) . The infectivity of TEMRA cells activated with SEB-pulsed DCs also remained identical ( unpublished data ) . The surface marker CD57 identifies terminally differentiated cells [23] , and expansion of CD57+ cells occurs in HIV-infected individuals [24] . Because TEMRA cells were enriched in CD57+ cells ( Figure 2 ) , we asked whether CD57+ T cells were differentially susceptible to R5-tropic or X4-tropic viruses . For this experiment , TEMRA and TEMRO cells were further subdivided into CD57+ and CD57− subsets by flow cytometry cell sorting . Both CD57+ and CD57− subsets of TEMRA cells were resistant to infection by R5-tropic virus , whereas both CD57+ and CD57− subsets of TEMRO cells remained susceptible to R5-tropic virus infection ( Figure 6 , top panel ) . However , the CD57+ and CD57− subsets of both TEMRO and TEMRA cells were similarly susceptible to X4-tropic viruses ( Figure 6 , bottom panel ) . Thus , the relative resistance of TEMRA cells to R5-tropic HIV was not attributable to enrichment with the CD57+ subset . We next investigated where in the HIV-1 life cycle R5-tropic infection of TEMRA cells was blocked . Because large numbers of cells were required for these experiments , we expanded TN , TCM , TEMRO , and TEMRA cells purified from PBMCs of HIV-uninfected individuals using SEB-pulsed DCs for 12 d in IL-2–containing medium . In order to verify that CCR5 expression levels were maintained on expanded T cell subsets and that TEMRA cells remained resistant to R5-tropic infection , CCR5 expression was determined post-activation and expansion ( Figure 7A , top panel ) . The expanded subsets were then reactivated with SEB-pulsed DCs and subsequently infected with R5 . HIV . Although the TEMRA cells maintained very high CCR5 expression , they remained resistant to R5-tropic infection ( Figure 7A , bottom panel ) . We first asked whether the block of R5-tropic infection was at the level of fusion . For this experiment , we employed a recently developed reporter assay to quantify HIV particle entry [25] . Expanded TCM , TEMRO , and TEMRA cells were infected with either R5 . HIV , X4 . HIV , or VSV-G . HIV . Fusion of these three viruses with TEMRA cells was similarly efficient , whereas fusion was inhibited in both TN and Jurkat cells , which do not express CCR5 , or when cells were pre-treated with T20 , a fusion inhibitor ( Figure 7B ) . Collectively , these data indicate that the R5 . HIV infection block in TEMRA cells is post-fusion . We next conducted analysis of the stage in the HIV-1 life cycle at which R5-tropic and VSV-G pseudotyped virus replication was blocked in the TEMRA subset . Late reverse transcripts in cells infected with VSV-G . HIV , R5 . HIV , and X4 . HIV were analyzed . Infection was blocked at the level of reverse transcription in TEMRA cells infected with R5 . HIV and VSV-G . HIV , suggesting an early block to infection in these cells that did not affect X4 . HIV ( Figure 7C ) . Because we did not see the accumulation of late reverse transcription products , we wanted to understand whether earlier steps in reverse transcription were impaired . Therefore , we investigated the initiation of reverse transcription of R5-tropic and VSV-G pseudotyped virus in TEMRA cells . Early reverse transcription was assessed by the presence of strong-stop , minus-strand viral DNA ( R/U5 DNA ) by quantitative real-time PCR . Early transcripts were not formed in TEMRA cells infected with R5 . HIV or VSV-G . HIV ( Figure 7D ) . These data suggest that the block in the HIV-1 life cycle occurs at or prior to the initiation of reverse transcription . Our findings that TEMRA cells are expanded in a portion of HIV-infected individuals and are highly resistant to R5-tropic infection prompted us to examine relationships between high TEMRA cells and CD4 numbers . Among HIV-infected individuals , the TN cell percentage correlated positively with absolute CD4+ T cell numbers ( Figure 1B ) . Conversely , the total TEM cell ( TEMRO + TEMRA ) percentage correlated negatively with CD4+ T cell numbers ( Figure 1B; unpublished data ) . To further delineate the association between TEMRO and TEMRA cell proportions and CD4 numbers , we subdivided infected individuals into three groups based on their total TEM cells ( Figure 8 ) . Infected individuals in whom the TEM percentage of their CD4+ T cells was similar to healthy individuals ( bottom group ) had high CD4+ cell numbers ( Figure 8; unpublished data ) . In contrast , the group with a very high TEM cell percentage had low CD4+ T cell numbers , and all of these individuals had high levels of TEMRO cells ( Figure 8 , top group ) . Importantly , however , when we subdivided the infected individuals with median levels of TEM cells ( Figure 8 , middle group ) , a highly significant association between higher TEMRA cell percentage and higher CD4+ T cell numbers and higher TN cells was established ( Figure 8 ) . These results imply that a greater proportion of TEMRA cells within the effector T cell subset may identify individuals with better preservation of CD4+ cell numbers , and possibly slow HIV-1 disease progression .
Our investigation of memory T cell subsets during HIV-1 infection led to the discovery of a unique subset of CD4+ T cells called CD4+ TEMRA cells . We found that these cells are highly susceptible to infection by X4-tropic HIV-1 but are almost completely resistant to R5-tropic HIV-1 despite high levels of cell surface CCR5 expression . These cells are also relatively resistant to infection by VSV-G pseudotyped HIV-1 . Our findings are consistent with a recent ex vivo analysis of T cell subsets from HIV-infected individuals , which demonstrated that CD4+CD57+ effector memory T cells were associated with approximately ten times fewer copies of viral DNA than TCM cells [23] . Although both CD57+ and CD57− subsets of TEMRA cells displayed the same R5-tropic HIV-1 infection ( Figure 5C ) , overall , CD57+ cells are more enriched within TEMRA cells ( Figure 2 ) . Thus , TEMRA cells represent the first unique subpopulation of CD4+ T cells that are uniquely resistant to HIV-1 infection and may emerge as a consequence selection during infection . Further studies are required to elucidate how TEMRA cells can be resistant to R5-tropic infection despite high levels of CCR5 expression , yet remain susceptible to X4-tropic viruses . In order to exclude that this restriction was at the level of post-entry and not because of downregulation or block of CCR5 by beta-chemokines , we showed that 1 ) TEMRA cells permitted entry of R5-tropic HIV-1 as measured by the BlaM-Vpr virion fusion assay , 2 ) TEMRA cells continued to express high levels of CCR5 at the time of infection , 3 ) and TEMRA cells were partly less susceptible to VSV-G pseudotyped viruses that bypass the coreceptor requirement . Taken together , these results indicate that the post-entry pathway followed by R5-tropic HIV-1 may differ in TEMRA cells compared to other CD4+CCR5+ T cell subsets and to X4-tropic HIV-1–infecting TEMRA cells . It is conceivable that either signaling or the entry pathway through the CXCR4 receptor elicits intracellular events needed for HIV replication or bypasses mechanisms that otherwise restrict HIV-1 in TEMRA cells . Elucidating cellular mechanisms that determine why some , but not all , CCR5-expressing CD4+ T cells are permissive to R5-tropic HIV-1 infection could provide clues to identify natural cellular HIV-1 barriers . Our findings suggest that at least one subset of primary human T cells display intrinsic restriction that limits HIV-1 infection . The presence of differentiated TEMRA cells in HIV-1 infected individuals and in uninfected individuals , albeit at lower frequency , suggests that these cells expand and survive during the course of the normal immune response . These findings also pose several important questions: How do TEMRA cells arise ? Are they repeatedly stimulated memory T cells ? What aspect of the TEMRA cell differentiation program renders them resistant to HIV-1 infection ? For example , TEMRA cells displayed a preferential Th1 phenotype and exhibited a reduced proliferative capacity as well as a cell surface marker and cytokine profile characteristic of highly differentiated T cells . A subset of CD8+ T cells that are CD45RA+CD27− ( CD8+ TEMRA cells ) has been shown to display similar phenotypic features to CD4+ TEMRA cells characterized here [8 , 20 , 26] . It is not yet clear whether CD4+ TEMRA cells are functionally similar to CD8+ TEMRA cells or what role these subsets play during chronic viral infections . The homeostatic mechanisms that induce and maintain CD4+ TEMRA cells also remains to be determined . Our finding that TEMRA cells correlate with higher CD4+ T cell numbers in a portion of HIV-infected individuals suggests that virus infection may positively drive selection for HIV-resistant cells in vivo , a phenomenon previously observed only in cell culture but usually involving loss of CD4 expression . Studies using animal models for HIV-1 infection may aid in determining whether there is a causal relationship between virus infection and selective enrichment of the TEMRA subset . Remarkably , HIV-infected individuals whose TEM cells were composed mostly of TEMRA cells were significantly associated with higher CD4+ T cell and TN cell levels . How TEMRA cells accumulate or expand in HIV patients , and whether they have a protective role against progression of disease , remains to be determined . Memory and effector T cells are enriched for CCR5 expression [17 , 18] , suggesting that they are targets for HIV-1 , especially T cells resident in the gut tissue [27–30] . It is conceivable that after continuous destruction of susceptible TEMRO cells , an HIV-resistant subset of TEMRA cells is selected . Alternatively , TEMRA cells may have a protective role against HIV-1 infection , perhaps because HIV-specific T cells are enriched in this subset . If TEMRA cells contain a high proportion of HIV-specific effector T cells , this would overcome a potential Achilles' heel of the immune response during HIV-1 infection; that is , CD4+ T cells that are activated by HIV-1 antigens themselves become highly susceptible targets for the virus [31] . Conferring an HIV-resistant ability to HIV-1–specific CD4+ T cells could lead to novel strategies aimed at potentiating a protective immune response against HIV-1 infection . During the primary and asymptomatic phases of HIV-1 infection , R5-tropic viruses predominate , whereas X4-tropic viruses are found in about 50% of infected individuals at late stages of HIV disease [32–34] . A more rapid decline in total CD4+ T cell counts is often associated with a switch from R5-tropic to X4-tropic HIV or R5/X4 HIV variants [35] . At present , it is unclear whether the switch to X4-tropic viruses is a cause or a consequence of the collapse of the immune system . Because TEMRA cells remain highly susceptible to X4-tropic viruses , it would be expected that these cells would also be rapidly depleted when an X4-tropic switch occurs . If TEMRA cells contain HIV-specific T cells or play some other protective role against infection , then elimination of these cells by X4-tropic viruses would further weaken the immune response against HIV-1 and facilitate immunological deterioration . In summary , our results demonstrate that CD4+ TEMRA cells are present at a higher frequency in HIV-infected than uninfected individuals and are resistant to R5-tropic HIV infection , but not to X4-tropic HIV-1 infection . Studies focused on emergence of these effector memory T cell subsets will contribute to a better understanding of HIV-1 pathogenesis and the role of these cells during normal immune responses . Decoding the precise molecular mechanism of the intrinsic resistance of TEMRA cells to R5-tropic infection may have significant implications for developing novel approaches to endow this unique phenotype on HIV-1–susceptible T cells .
PBMCs were separated from blood of HIV-uninfected and HIV-infected individuals through Ficoll-Hypaque ( Pharmacia , http://www . pfizer . com ) . Resting CD4+ T cells were purified as previously described [22] and were at least 99 . 5% pure as determined by post-purification FACS analysis . To purify naïve , central , and effector memory subsets , purified CD4+ cells were stained with CCR7 and CD45RO antibodies , and CD45RO−CCR7+ ( TN ) , CD45RO+CCR7+ ( TCM ) , CD45RO+CCR7− ( TEMRO ) , and CD45RO−CCR7− ( TEMRA ) subsets were sorted using the flow cytometer ( FACSAria; BD Biosciences , http://www . bdbiosciences . com ) . The culture medium used in all experiments was RPMI ( Cellgro , http://www . cellgro . com ) and prepared as described before [22] . All cytokines were purchased from R&D Systems ( http://www . rndsystems . com ) . In some experiments , TEMRO and TEMRA subsets were further subdivided into CD57+ and CD57− subsets by flow sorting by staining purified CD4+ T cells with CCR7 , CD45RO , and CD57 antibodies . Monocyte-derived DCs were generated as previously described [22] . The superantigen SEB ( Sigma , http://www . sigmaaldrich . com ) was used to stimulate resting T cells in the presence of DCs [36] . Uninfected individuals were adults ( ages 21–64 , mean age was 32 ) with no history of HIV infection . Whole blood samples from adult participants with HIV infection were obtained during routine primary care visits . Among the HIV-infected individuals , 76% were Caucasian , 82% were male , the median ( range ) age was 41 ( 28–59 ) years , and 79% were receiving potent antiretroviral therapy . Median ( IQR ) CD4+ T cell and log10 plasma HIV-1 RNA values were 380 ( 270–592 ) cells/mm3 and 2 . 7 ( 2 . 6–3 . 8 ) copies/ml plasma , respectively , and 50% had fewer than 400 HIV-1 RNA copies/ml in plasma . There were no selection criteria based on race or sex . All participants provided written informed consent that was approved by the Vanderbilt Institutional Review Board . VSV-G pseudotyped replication-incompetent HIV were generated as previously described [36] . R5-tropic and X4-tropic replication-competent viruses were prepared similarly by transfecting 293T cells with HIV that encodes either R5-tropic ( BaL ) or X4-tropic ( NL4–3 ) envelope and EGFP ( Clontech , http://www . clontech . com ) in place of the nef gene as previously described [37] . Wild-type virus ( NL4–3 ) with X4-tropic or with R5-tropic envelope ( BaL ) and virus ( R8 ) encoding heat stable antigen ( HSA ) in place of vpr [38] with intact nef gene were obtained from Chris Aiken ( Vanderbilt University ) . Additional viruses used in this study were as follows . NL4–3–based proviral constructs encoding Env genes from R5-tropic proviral 92MW965 . 26 , NL JRFL , NL YU2 , and dual-tropic NL89 . 6 were obtained from Paul Bieniasz ( Aaron Diamond AIDS Research Center ) and have been previously described [39] . R5-tropic virus JRCSF and X4-tropic virus R9 were obtained from Vineet KewalRamani ( National Cancer Institute [NCI] ) . Typically , viral titers ranged from 1 × 106 to 5 × 106 IFU/ml for replication-competent viruses and 10 × 106 to 30 × 106 IFU/ml for VSV-G pseudotyped HIV , as titered on CCR5-expressing Hut78 T cell lines ( gift of Vineet KewalRamani , NCI ) . Viral replication in T cell cultures was determined by measuring p24 levels within supernatants by an ELISA . To determine apoptosis , T cells were stimulated with α-CD3 ( OKT-3 , ATCC ) –coated plates in the presence of soluble α-CD28 ( 1 μg/ml; Pharmingen , http://www . bdbiosciences . com ) for 18 h . T cell apoptosis was measured by PE-conjugated Annexin V according to manufacturer's instructions ( BD Biosciences ) . Cytokines ( IL-2 , IL-4 , IL-5 , IL-10 , TNF-α , IFN- γ ) in the supernatants were assayed using a commercially available cytometric bead assay ( CBA ) ( BD Biosciences ) [40] , and analyzed using CBA 6-bead analysis software ( BD Biosciences ) . T cells were stained with the relevant antibody on ice for 30 min ( chemokine receptor staining performed at room temperature for 20 min ) in PBS buffer containing 2% FCS and 0 . 1% sodium azide . Cells were then washed twice , fixed with 1% paraformaldehyde , and analyzed with a FACSCalibur or FACSAria flow cytometer . Live cells were gated based on forward and side scatter properties , and analysis was performed using FlowJo software ( Tree Star , http://www . treestar . com ) . The following anti-human antibodies were used for staining: CD3 , CD4 , CD8 , CD45RO , CD45RA , CD28 , CD27 , CD11b , CD57 , CD7 , CD62L , HLA-DR , CCR5 ( all from BD Biosciences ) , CCR7 , and CCR4 ( R&D Systems ) . The CRTH2 antibody used for these experiments has been previously described [41] . Secondary goat-anti-mouse antibodies were conjugated with allophycocyanin or PE ( BD Biosciences ) . For the intracellular p24 stain , fixation and permeabilization was performed using a commercial kit ( BD Biosciences ) according to the manufacturer's instructions . Subsequently , cells were stained with anti-p24 for 1 h , followed by goat-anti-mouse conjugated to allophycocyanin for 30 min . HIV fusion assays were performed essentially as previously described [25] . Briefly , viruses carrying a β-lactamase reporter protein fused to the amino terminus of the virion protein Vpr ( BlaM-Vpr ) were added to expanded T cell subsets at 37 °C for 2 h to allow virus–cell fusion . CCF2/AM ( 20 μM; Aurora Biosciences Corporation , http://www . vrtx . com ) was added , and the cultures were incubated for 14 h at room temperature . Cells were pelleted and resuspended in PBS , and the fluorescence was measured at 447 and 520 nm with a microplate fluorometer after excitation at 409 nm . Uncleaved CCF2 fluoresces green , due to fluorescence resonance energy transfer between the coumarin and fluorescein groups; however , cleavage by BlaM results in the dissociation of these fluorophores , and the emission spectrum shifts to blue . Thus , the ratio of blue to green cellular fluorescence is proportional to the overall extent of virus–cell fusion . Fluorescence ratios were calculated after subtraction of the average background fluorescence of control cultures containing no virus ( blue values ) and wells containing PBS ( green values ) . Viral DNA was quantified by real-time PCR using an ABI 7700 instrument ( PE Biosystems , http://www . appliedbiosystems . com ) with SYBR Green chemistry . The reaction mixtures ( 25 μl total volume ) contained 2 . 5 μl of infected lysate , 12 . 5 μl of 2x SYBR Green PCR Master Mix ( PE Biosystems ) , and 50 nM of each primer . A standard curve was prepared from serial dilutions of HIV plasmid DNA . The reactions were amplified and analyzed as previously described [42] . The sequences of primers ( R and U5 ) specific for early products were 5′-GGCTAACTAGGGAACCCACTGCTT ( forward ) and 5′-CTGCTAGAGATTTTCCACACTGAC ( reverse ) . The late-product primer sequences ( R and 5NC ) were 5′-TGTGTGCCCGTCTGTTGTGT ( forward ) and 5′-GAGTCCTGCGTCGAGAGAGC ( reverse ) , as previously described . Statistical analyses were performed using Stata version 9 . 0 ( http://www . stata . com ) . T cell subset and clinical data are presented as means ( standard deviation ) . Statistical significance between groups was determined by Wilcoxon rank sum test . Differences were considered significant at p < 0 . 05 .
|
HIV-1 infection profoundly perturbs the immune system and is characterized by depletion of CD4+ T cells and chronic immune activation , which lead to AIDS . Although HIV-1 targets CD4+ T cells , it also requires a second receptor in order to infect the target cells . The majority of HIV-1 strains that are transmitted use a cell surface molecule called CCR5 , which is expressed on a portion of T cells . In this manuscript we identify a subset of human CD4+ T cells , which we termed TEMRA cells , that express CCR5 but still remain resistant to infection . We show that HIV-1 infection is blocked in TEMRA cells after entry of the virus , but before it has a chance to integrate into the cellular genome . TEMRA cells are present at low frequency in HIV-1 uninfected individuals but greatly increase in some HIV-infected individuals , which correlates with higher CD4+ T cell numbers . These findings provide the basis for future studies to understand the role of TEMRA cells during HIV-1 infection and identify the host factors that could restrict the virus . This knowledge may be used to endow susceptible T cells with the ability to resist infection and result in novel vaccine or therapeutic strategies against HIV-1 infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viruses",
"infectious",
"diseases",
"immunology",
"homo",
"(human)"
] |
2007
|
Identification of a CCR5-Expressing T Cell Subset That Is Resistant to R5-Tropic HIV Infection
|
Clustered Regularly Interspaced Short Palindromic Repeats ( CRISPR ) , together with associated genes ( cas ) , form the CRISPR–cas adaptive immune system , which can provide resistance to viruses and plasmids in bacteria and archaea . Here , we use mathematical models , population dynamic experiments , and DNA sequence analyses to investigate the host–phage interactions in a model CRISPR–cas system , Streptococcus thermophilus DGCC7710 and its virulent phage 2972 . At the molecular level , the bacteriophage-immune mutant bacteria ( BIMs ) and CRISPR–escape mutant phage ( CEMs ) obtained in this study are consistent with those anticipated from an iterative model of this adaptive immune system: resistance by the addition of novel spacers and phage evasion of resistance by mutation in matching sequences or flanking motifs . While CRISPR BIMs were readily isolated and CEMs generated at high rates ( frequencies in excess of 10−6 ) , our population studies indicate that there is more to the dynamics of phage–host interactions and the establishment of a BIM–CEM arms race than predicted from existing assumptions about phage infection and CRISPR–cas immunity . Among the unanticipated observations are: ( i ) the invasion of phage into populations of BIMs resistant by the acquisition of one ( but not two ) spacers , ( ii ) the survival of sensitive bacteria despite the presence of high densities of phage , and ( iii ) the maintenance of phage-limited communities due to the failure of even two-spacer BIMs to become established in populations with wild-type bacteria and phage . We attribute ( i ) to incomplete resistance of single-spacer BIMs . Based on the results of additional modeling and experiments , we postulate that ( ii ) and ( iii ) can be attributed to the phage infection-associated production of enzymes or other compounds that induce phenotypic phage resistance in sensitive bacteria and kill resistant BIMs . We present evidence in support of these hypotheses and discuss the implications of these results for the ecology and ( co ) evolution of bacteria and phage .
The experimental demonstrations , in 2007 and 2008 , that the Clustered Regularly Interspaced Short Palindromic Repeats that abound in the genomes of the vast majority of archaea and nearly half of bacteria can serve as part of an adaptive immune system ( CRISPR–cas ) that protects these prokaryotes against lytic phage [1] and conjugative plasmids [2] raised many intriguing questions and stimulated a great deal of research . Between 2007 and 2012 , according to PubMed , the number of articles with the acronym CRISPR in their title and/or body has been doubling every 20 months . Understandably , the majority of these articles are reports or reviews of the genetic , biochemical , and molecular mechanisms by which ( i ) the CRISPR–cas system acquires the 26–72 base pair sequences ( spacers ) of DNA from infecting phage , generating Bacteriophage Insensitive Mutants ( BIMs ) , or from invading plasmids , forming Plasmid Interfering Mutants ( PIMs ) ; ( ii ) the small RNAs coded for by these spacers abort subsequent infections by phage and plasmids with DNA sequences corresponding to those in these spacers ( protospacers ) [3] , [4] , [5] , [6]; ( iii ) the phage can evade this resistance and generate CRISPR Escape Mutants ( CEMs ) by modifying the DNA sequences of protospacers or protospacer adjacent motifs ( PAM ) [3] , [7] , [8] , [9]; ( iv ) the Cas proteins participate in these processes [1] , [8] , [9]; and ( v ) the existence , distribution and genetic diversity of CRISPR–cas systems in the archaea and bacteria came about [10] , [11] , [12] Some of this research has been devoted to the practical applications of CRISPR and the CRISPR–cas system . Long before the immunological role of CRISPR in these microorganisms was recognized , differences in the number of these palindromic repeats had been used as markers for studies of the genetic ( molecular ) epidemiology and forensics of pathogenic bacteria [13] , [14] , [15] , [16] . Isolates that would be classified as identical by standard typing procedures , like Serotyping , Multi-Locus Sequence Typing , and Pulse Field Gel Electrophoresis , can differ in their CRISPR regions , particularly in their spacer sequences [17] , [18] . Genes , and clusters of genes , borne on plasmids , phage , or chromosomal genes , and acquired by horizontal transfer , code for the virulence and antibiotic resistance of many pathogenic bacteria . And , as one might anticipate , strains of at least some species of pathogenic bacteria that lack functional CRISPR–cas systems are more likely to be virulent and resistant to antibiotics than those that bear these systems [19] . A number of biotechnological and industrial processes rely on the use of bacteria that are subject to infection by phage , and therefore risk disruption of production , low-quality products and economic loss . Can CRISPR–cas systems be harnessed to counter antibiotic resistance and virulence of pathogenic bacteria and/or prevent phage contamination of industrially important bacteria ( 8 ) ? Central to understanding these practical implications and potential applications of CRISPR–cas systems are the population and evolutionary dynamics of bacteria bearing these adaptive immune systems and those of the phage and plasmids that infect them . Prior to the experimental demonstration that CRISPR is part of an adaptive immune system , and motivating those experiments , were observations that the spacer sequences in the CRISPR regions of bacteria and archaea from natural communities were identical to sequences in the genomes of viruses in their communities [15] , [20] , [21] . These and more recent studies of bacteria and archaea and their viruses [10] , [11] , [12] , [22] , and metagenomic studies of microbiomes [23] , provided compelling support for the hypothesis that CRISPR–cas systems play an active role in the population dynamics and co-evolution of these organisms in natural communities . However , the inferences one can draw from these investigations are limited . They provide little of the necessary quantitative information about the nature and contribution of CRISPR–cas to these dynamics and co-evolution and its consequential role in determining the structure of their communities . Studies using mathematical models and computer simulations suggest that bacteria with CRISPR–cas systems can invade communities of phage and bacteria without this immune system and can also prevent phage from invading their communities [24] . Modeling studies also suggest that , in theory , a CRISPR–cas-mediated BIM-CEM co-evolutionary arms race can maintain considerable diversity in the interacting populations of bacteria and phage [25] and account for spacer changes in the CRISPR elements in the metagenomic structure of natural communities of archaea and bacteria and their phage [26] . But , as compelling as these mathematical models and computer simulation studies may be , their limitations are also apparent . The models used are based on simplifying assumptions about the phage infection process and CRISPR–cas adaptive immunity that have not been formally tested in the systems under study . The computer simulations and other numerical analyses of the properties of these models use parameter values for which there are few independent estimates . In this study we use mathematical models , computer simulations , parameter estimation , and population dynamic and other microbiological experiments with the Gram-positive bacteria Streptococcus thermophilus and its virulent phage 2972 ( Siphoviridae family ) to explore the nature and contribution of CRISPR–cas adaptive immunity to the population and evolutionary dynamics of bacteria and phage . The results of these experiments and the molecular characterization of the bacteria and phage employed demonstrate that the qualitative Lamarckian ( iterative acquisition of spacers ) [27] and Darwinian ( mutation in protospacers ) genetic conditions for a CRISPR–mediated arms race are met with this phage-host system . BIMs are readily formed and CEMs capable of growing on these BIMs are generated at high rates . On the other hand , our experimental results and theoretical analyses indicate that even at a qualitative level , models based on existing assumptions about phage infection and CRISPR–mediated resistance cannot account for the population and evolutionary dynamics of the interactions between S . thermophilus and phage 2972 . Findings contrary to what is anticipated from the analysis of the properties of these models include: ( i ) the invasion of phage into populations of BIMs resistant due to the acquisition of one ( but not two ) spacers; ( ii ) the survival of substantial numbers of sensitive bacteria despite the presence of high densities of phage; and ( iii ) the maintenance of phage-limited communities due to the failure of even two-spacer BIMs to become established in populations with wild type bacteria and phage . We postulate that the latter two results can be attributed to the phage infection-associated production of enzymes or other compounds that kill resistant BIMs and generate phenotypically phage-resistant “persister” populations of sensitive bacteria . By modifying the model to account for these chemical processes we can explain the observed dynamics . The predictions of this extended model are supported by longer-term population dynamic experiments .
Genetic analysis of the two active CRISPR loci of ten first-order BIMs revealed that they each had acquired a novel spacer in either the CRISPR1 or CRISPR3 locus . Five BIMs acquired one unique novel spacer in the CRISPR1 locus and 5 BIMs acquired one unique novel spacer in the CRISPR3 locus ( Figure 3 ) . Similar analysis of the five second-order BIMs revealed that they each had also acquired an additional novel spacer in the CRISPR1 or CRISPR3 locus . Two of these second-order BIMs acquired one additional unique novel spacer in the CRISPR1 locus , indicating iterative addition into the CRISPR1 locus ( BIM32 and BIM52 in Figure 3 ) . Likewise , three second-order BIMs acquired novel spacers in the CRISPR3 locus , including two variants with iterative build-up of the CRISPR3 locus ( BIM22 and BIM82 in Figure 3 ) . Interestingly , BIM72 acquired a CRISPR1 spacer to become a first-order CEM and a CRISPR3 spacer to become a second-order CEM , illustrating the duality of active CRISPR loci in Streptococcus thermophilus . Sequence analysis of all the CRISPR1 and CRISPR3 spacers acquired in the first and second rounds of phage challenges indicated perfect matches to the WT genome of phage 2972 used in the challenge ( Table S1 ) . Further , all new CRISPR1 and CRISPR3 spacers were associated with protospacer-adjacent motifs ( PAMs ) : NNAGAAW and NGGNG , respectively ( Table S1 ) . These findings are consistent with previous results indicating that spacer sequences are directly derived from phage DNA and are associated with PAMs [7] , [8] , [35] , [36] . The results of our sequence analysis of the CEM protospacer and PAM regions are fully consistent with previous findings that single mutations in the matching protospacer or PAM are sufficient to circumvent CRISPR–mediated immunity ( Figure 4 ) . Of the 10 CEMs that evaded the immunity of the first-order BIMs , one had a mutation in the protospacer region and the remaining nine CEMs had mutations in PAM sequences ( four in the CRISPR1 PAM and five in the CRISPR3 PAM ) . Each of the five second-order CEMs that evaded the immunity of the second-order BIMs had acquired a second mutation , as expected . One had acquired a second mutation in a protospacer , two had mutations in the PAM , one contained one mutation in the protospacer and another in the PAM , and the last one had an insertion in the PAM . Taken together , these sequencing data are consistent with previous results indicating that CEMs are the product of mutations in the protospacers and/or PAMs which enable these viruses to circumvent CRISPR–cas acquired immunity [7] , [8] . At best , the mathematical model and computer simulations employed here are simplistic caricatures of the population dynamic and other processes occurring in these experiments . They are not intended or anticipated to provide quantitatively precise predictions of the changes in density of bacteria and phage observed in our experiments . Some quantitative deviation between experiments and theory is expected . Not expected , however , are major qualitative deviations . There were two of these . One is the failure of the phage to eliminate the sensitive bacteria . The other is the failure of BIMs to ascend and dominate cultures with an abundance of resources . We postulate these failures can be attributed to either the phage or the products of phage replication generating phenotypic immunity among sensitive cells ( persistence ) [37] and killing emerging BIMs . One line of evidence in support of the hypothesis that something ( s ) in the phage lysate is preventing BIMs from ascending and also generating persisters comes from results obtained with 24-hour cell-free ( filtered , rather than chloroformed ) lysates of WT phage . S . thermophilus cells resistant to the phage ( BIM22 and SMQ-301 ) were added to these lysates and the optical and viable cell densities were followed with frequent sampling . In this experiment , 40 µl of BIM22 or SMQ-301 from overnight cultures were added to 4 ml of the phage lysate . As phage-free controls , these bacteria were added to 4 ml LM17Ca ( Figure 9 ) . Cells of SMQ-301 were able to grow in these WT phage lysates , thereby indicating that there were plenty of resources . However , the viable cell density of the BIM22 cells declined . At 24 and 31 hours , the optical density of the BIM22 culture in the phage lysate was less than 0 . 02 and the viable cell densities were , respectively , 4 . 7×105 cfu/ml and 1 . 2×106 cfu/ml . At 48 and 54 hours , these viable cell densities were , respectively , 5 . 6×105 cfu/ml and 2 . 1×105 cfu/ml . The sensitivity of BIM22 to the phage and/or molecules in the WT lysates was not shown by SMQ-301 . A second line of evidence in support of the hypothesis that phage or the products of their replication or lysis of infected cells prevent the ascent of BIMs is the results of our experiments with WT phage in cultures with first and second-order BIMs . Batch cultures inoculated with low densities of WT phage and bacteria that are resistant to those phages by one or two spacers become resource-limited . However , if we allow for considerable phage replication by adding WT cells , cultures with first- and second-order BIMs become phage-limited ( Figure S4 ) . We have yet to characterize the agent ( s ) responsible for killing and/or inhibiting the growth of infected BIMs . However , preliminary results from experiments with lysates prepared from French Press extracts suggest that this agent is likely to be present within the cells and need not be a product of phage infection . The growth rates of resistant BIMs are markedly reduced when cultured in mixtures of medium and French Press extracts of phage-free cultures of wild type cells ( Text S1 ) . In this interpretation , this agent is released as a product of lysis of the cells by the phage . To explore , in a more quantitative way , whether the mechanisms postulated above , where products of phage replication generate phenotypically resistant persisters and kill BIMs , can account for the observed dynamics , we use an extension of the basic model described in the Theoretical Framework . In this extension , we consider three bacterial populations with densities B0 , B1 , and B2 , and two populations of phage , P0 and P1 , respectively corresponding to WT , first- and second- order BIM bacteria , and WT and first-order CEM phage . For convenience , we assume there are agents of collective concentration LY µg/ml that kill resistant cells and provide transient immunity to sensitive bacteria ( persistence effect [38] , [39] ) . Since the effects of LY in killing resistant cells and generating persisters are governed by separate parameters , they can be at least two different compounds . These agents are produced at a rate proportional to the product of the densities of replicating phage and the corresponding sensitive bacteria:where vL is a rate parameter for the production of LY . We assume that the rates of growth of the bacteria now depend on the concentration of LY , as well as the resources , R . For example , the per capita growth of B0 is now where ν is the maximum growth rate of this strain , k the Monod constant , η the maximum rate of killing by LY , and KL the concentration of LY where killing is at its maximum rate . In the simulation LY/KL is set equal to 1 when LY exceeds KL . To account for phenotypically resistant B0 and B1 bacteria , we assume there are populations of non-dividing , persistent cells of densities BP0 and BP1 bacteria per ml that are produced from their respective dividing populations at a rate proportional to LY/KL and a constant ( g ) and return to the dividing state at a constant rate ( h ) . For example , the rate of change in the density of the wild type ( B0 ) population is nowThe Berkeley Madonna simulation used for this extended model can be obtained from www . eclf . net/programs . In Figure 10 , we present the results of simulation experiments initiated with wild type phage ( P0 ) , wild type bacteria ( B0 ) , and a second-order BIM ( B2 ) . The population growth , initial resource concentration and phage infection parameters used in this simulation are same as those used in earlier simulations and thus in the range estimated in these experiments . The parameters for the production of LY and its action are arbitrary and chosen to illustrate the principle rather than fit specific data in any but a qualitative way . In the absence of the killing effect , Figure 10A , the B2 population ascends and the phage are eliminated . The B0 population continues to be maintained , B1 bacteria and P1 phage are produced , and within short order the population becomes resource-limited . During the first transfer of the simulations with the killing effect ( Figure 10B ) , the P0 phage ascend and the densities of the B0 and B2 populations decline and the community becomes phage limited . While the second transfer ( Figure 10C ) also ends in a phage - limited state , the relative density of B2 is now greater . In subsequent transfers ( Figure 10D ) , B2 ascends to dominance , phage are eliminated , and the community becomes resource-limited . As a consequence of the serial passages , the molecule killing BIMs is diluted out and the BIMs ascend . For the arms race to continue , the phage would have to generate CEMs capable of replicating on the dominant population of BIMs . If the above postulated effect of dilution in serial passage is valid , in successive transfers of experiments initiated with WT cells and WT phage , BIMs should emerge . And , unless they are countered by CEM phage , they will ascend to dominance and the phage will be lost . To test this hypothesis , we mixed WT cells with phage and each day transferred the culture to fresh medium ( 40 µl into 4 ml ) . We initiated six separate cultures from the same mixture of approximately 2×106 cells and phage . As a control we serially transferred a phage-free WT culture . At the end of each transfer , we estimated optical densities of these cultures and , by serial dilution and plating , the viable densities of bacteria and free phage . The results of this experiment are presented in Figure 11 . We interpret the results of this experiment to be consistent with the hypothesis that as a consequence of successive dilutions of the compound killing BIMs , the resistant mutants ascend . Moreover , in 5 of the 6 cultures in which BIMs emerged , phage were lost and could not be recovered even with enrichment with WT cells ( i . e . the arms race was terminated ) . Additional serial transfer experiments should be performed with different starting conditions and possible different media ( perhaps milk ) to explore the generality of this observation about the limit to a BIM-CEM arms race .
The results of this jointly theoretical and experimental study make at least five predictions about the contribution of CRISPR–cas immunity to the population and community ecology of bacteria and phage and the ( co ) evolution of these organisms . Taken at large , this study predicts that the role of CRISPR–cas systems in the day-to-day ecology and evolution of natural communities of prokaryotes and their viruses is at best modest and more restrictive than anticipated from existing theory . As compelling as we may consider these predictions to be , at this juncture we only see them as hypotheses that have not been fully tested . We have restricted this study to the dynamics of the early phase of the interactions between single populations of phage and bacteria with very active CRISPR–cas immune systems . While our models and experiments are appropriate for this phase of the association between these organisms , they are not sufficient for making predictions about the longer-term population and ( co ) evolutionary dynamics of these interactions . Most importantly , our models and experiments do not formally consider the potential diversity of BIMs and CEMs that can be generated at each level and the interactions between them ( see , for example , [25] ) . How this diversity will play into the longer-term population and ( co ) evolutionary dynamics of these interactions and whether the bacterial populations will be limited by phage rather than resources remain to be seen . Particularly important in this regard is whether bacteria with other mechanisms of resistance to the phage will emerge and whether the phage will be able to generate mutants capable of replicating on these resistant cells ( see [42] but also [43] ) . It may well be that the failure to observe bacteria with other resistance mechanisms in these experiments is due to the relatively high rate at which CRISPR–mediated immunity is generated in S . thermophilus . Finally , in support of the prediction that CRISPR–mediated immunity plays a limited role in the day-to-day ( rather than historical ) ecology and evolution of prokaryotes and their viruses , is our choice of experimental system . S . thermophilus and the phage 2972 may well be an exceptional system for in vitro study of the population dynamics and evolution of phage and bacteria with CRISPR–mediated immunity . Not only is it arguably ( surely , to those who work with it ) the best-characterized naturally occurring CRISPR–phage system; with respect to the rate of acquisition of spacers and possibly the rate of generation of CEMs , it is the most active . It is an ideal system in which to observe CRISPR playing a prominent role in the ecology and evolution of prokaryotes and their viruses . To our knowledge , there is no evidence for naturally occurring CRISPR–cas systems of other organisms being as active in generating BIMs and CEMs as those of S . thermophilus and its phages . For these reasons , we conjecture that the CRISPR–cas systems of other bacteria , archaea and their phages would play an even less active role in the day-to-day ecology and evolution than anticipated from this study .
For our experimental model , we selected the strain S . thermophilus DGCC7710 because it contains arguably the best-characterized CRISPR–cas functional system . S . thermophilus DGCC7710 [1] , which bears four different CRISPR–cas systems , two of which are active ( CRISPR1 and CRISPR3 ) , was used as a host for the virulent phage 2972 [34] . We also used S . thermophilus SMQ-301 [44] , which is resistant to 2972 as a non-host strain . A single medium designated LM17Ca was used for all of the experiments . M17 ( Oxoid ) was supplemented with 0 . 5% lactose ( L ) and 0 . 05 M Calcium Borogluconate ( Ca ) . For the soft/top agar and hard/bottom agar , LM17Ca was supplemented with 0 . 6% and 1 . 5% agar , respectively . Cell densities were estimated by dilution in 0 . 85% saline and plating on LM17Ca . To estimate phage titers , serially diluted samples were added to 1 ml of a late-log culture of S . thermophilus , put into 3 ml soft agar and poured onto LM17Ca plates . About 5×108 S . thermophilus DGCC7710 cells were mixed with about 2 . 5×107 of wild type ( WT ) phage 2972 and held for about 10 minutes . These mixtures were then added to 3 ml of LM17Ca soft agar , which was then poured over LM17Ca plates . Ten experiments identical to the above were run in parallel . All the plates were incubated at 42°C for 30 hours . Single colonies were removed from each plate and streaked twice to purify the BIM clones and remove contaminating phages . Resistance to WT phage 2972 was confirmed by spotting 10 µl ( at least 5×105 pfu ) of the diluted lysate on soft agar lawns of the BIMs . Clear zones were scored as sensitive while turbid zones or few separate plaques were scored as resistant . From these experiments , we selected 10 BIMs ( one per experiment ) , designated BIM1 , BIM2 , … BIM9 , and BIM10 . A similar procedure was used to randomly isolate five second-order BIMs from five of these first-order BIMs by spotting CEMs capable of replicating on these first-order bacteria . These were designated BIM22 , BIM32 , BIM52 , BIM72 , and BIM82 . CEM phages were isolated by mixing 100 µl of phage 2972 lysate with 1 ml of overnight cultures of first- or second-order BIMs . After 10 minutes these mixtures were added into 3 ml soft agar , poured onto LM17Ca plates , and incubated overnight at 42°C . Single phage plaques were picked from the plates the following day and added to 3 or 10 ml of LM17Ca . A few drops of chloroform were added to each tube to kill the remaining bacteria . These lysates were then vortexed and centrifuged . These single plaque lysates were then filtered ( 0 . 45 µm ) and stored at 4°C . This single plaque isolation process was repeated two times to obtained purified phages . The CEM phages for the first-order BIMs ( BIM1 through BIM10 ) were designated CEM1 through CEM10 . The CEM phages for the second-order BIMs were designated CEM22 , CEM32 , CEM52 , CEM72 , and CEM82 . Two procedures were used to assay liquid cultures for the presence of WT phage and specific CEMs . One was a spot test: 10 µl of filtered or chloroform-treated cultures were either spotted directly or after serial dilution onto soft agar lawns of WT cells or first- or second-order BIMs . Either clear zones or turbid zones with specific plaques were considered to indicate the presence of phage capable of replicating on that bacterial cell line . In cases where negative results were obtained with this spot test , we used an enrichment procedure where 100 µl of the lysate was added to 4 ml of medium with 100 µl of overnight culture bacteria . The optical densities of these enrichment cultures were followed at 42°C and those that cleared were considered positive for phage capable of replicating on those host bacteria . Two procedures were also used to test for the presence of bacteria resistant to wild type or CEM phages . One was a spot test: single colonies were streaked onto fresh medium to purify them from the phage in the cultures from whence they came . Single colonies were grown overnight at 42°C in liquid medium ( 1 ml ) and then put into soft agar to be used as a lawn for spot testing with phage . For this we used the procedure and criteria for sensitivity/resistance described in the preceding paragraph . In some cases , the presence of resistant cells in samples was tested directly by adding 100 µl of phage and 100 µl of the cell culture to soft agar and looking for the presence of colonies . The active S . thermophilus CRISPR loci , namely CRISPR1 and CRISPR3 , were subjected to PCR , in order to test whether novel spacers had been acquired in BIMs following exposure to phage . Primers targeting the leader sequence and a unique WT spacer at the trailer end of CRISPR1 , as well as a primer pair targeting the leader and the trailer end of CRISPR3 , were used for PCR amplification . The names and sequences of these primers are , respectively: yc70 5′-TGCTGAGACAACCTAGTCTCTC-3′ and 89R6 5′-TCAGCAGATTGTCAAATCGG-3′ for CRISPR1 and F1 5′CTGAGATTAATAGTGCGATTACG-3′ and R2 5′-GCTGGATATTCGTATAACATGTC-3′ for CRISPR3 . PCR amplifications were carried out as previously described [36] , with Ambion Mastermix and the following PCR cycling: denaturation for 7 minutes at 95 C; followed by 35 cycles of 30 sec denaturation at 95C ; 30 sec hybridization at 53 C and 1 min extension at 72 C; followed by a final round of 5 min extension at 72 C . PCR amplicons were subjected to agarose gel electrophoresis for size analysis , purified using a Qiagen Kit , and subsequently subjected to Sanger sequencing using the same primer sets used for amplification . The sequencing results were visualized in EXCEL as previously described [36] in order to assess CRISPR repeat occurrence and visualize spacer content . In order to assess the protospacer sequences in the CEMs , targeted PCR amplifications followed by Sanger sequencing of the PCR amplicons were carried out for first and second generation CEMs as described previously [7] , [8] .
|
The evidence that the CRISPR regions of the genomes of archaea and bacteria play a role in the ecology and ( co ) evolution of these microbes and their viruses is overwhelming: ( i ) the spacers ( variable sequences of 26–72 bp of DNA between the repeats of this region ) of these prokaryotes are homologous to the DNA of viruses in their communities; ( ii ) experimentally , the acquisition and incorporation of spacers of viral DNA can protect these organisms from subsequent infection by these viruses; ( iii ) experimentally , viruses evade this immunity by mutation in homologous protospacers or protospacer-adjacent motifs ( PAMs ) . Not so clear are the nature and magnitude of the role CRISPR plays in this ecology and evolution . Here , we use mathematical models , experiments with Streptococcus thermophilus and the phage 2972 , and DNA sequence analyses to explore the contribution of CRISPR–cas immunity to the ecology and ( co ) evolution of bacteria and their viruses . The results of this study suggest that the contribution of CRISPR to the ecology of bacteria and phage is more modest and limited , and the conditions for a CRISPR–mediated coevolutionary arms race between these organisms more restrictive , than anticipated from models based on the canonical view of phage infection and CRISPR–cas immunity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"model",
"organisms",
"theoretical",
"biology",
"ecology",
"genetics",
"biology",
"microbiology",
"evolutionary",
"biology",
"population",
"biology",
"genetics",
"and",
"genomics"
] |
2013
|
The Population and Evolutionary Dynamics of Phage and Bacteria with CRISPR–Mediated Immunity
|
ZEBRA is a site-specific DNA binding protein that functions as a transcriptional activator and as an origin binding protein . Both activities require that ZEBRA recognizes DNA motifs that are scattered along the viral genome . The mechanism by which ZEBRA discriminates between the origin of lytic replication and promoters of EBV early genes is not well understood . We explored the hypothesis that activation of replication requires stronger association between ZEBRA and DNA than does transcription . A ZEBRA mutant , Z ( S173A ) , at a phosphorylation site and three point mutants in the DNA recognition domain of ZEBRA , namely Z ( Y180E ) , Z ( R187K ) and Z ( K188A ) , were similarly deficient at activating lytic DNA replication and expression of late gene expression but were competent to activate transcription of viral early lytic genes . These mutants all exhibited reduced capacity to interact with DNA as assessed by EMSA , ChIP and an in vivo biotinylated DNA pull-down assay . Over-expression of three virally encoded replication proteins , namely the primase ( BSLF1 ) , the single-stranded DNA-binding protein ( BALF2 ) and the DNA polymerase processivity factor ( BMRF1 ) , partially rescued the replication defect in these mutants and enhanced ZEBRA's interaction with oriLyt . The findings demonstrate a functional role of replication proteins in stabilizing the association of ZEBRA with viral DNA . Enhanced binding of ZEBRA to oriLyt is crucial for lytic viral DNA replication .
There are many gaps in our understanding of the process by which the Epstein-Barr virus ( EBV ) lytic replication machinery assemble on DNA sites present in the viral genome . EBV encodes an essential bZIP protein known as ZEBRA ( aka Zta , Z and BZLF1 ) that functions as a transcription activator of viral and cellular genes and as an origin binding protein during lytic DNA replication . An EB viral genome that lacks the open reading frame encoding ZEBRA , bzlf1 , loses its ability to activate lytic gene expression and DNA replication [1] . ZEBRA interacts both with promoters and with origins of lytic replication through DNA sequences known as ZEBRA response elements ( ZREs ) that are common to both types of DNA regulatory regions [2] , [3] , [4] . It is unknown how ZEBRA distinguishes between a replication site and a transcription activation site . The mechanism by which ZEBRA activates transcription relies on its capacity to bind DNA and to form physical contact with a number of cellular proteins . ZEBRA binds to a wide variety of ZREs located in target promoters . Some of these response elements contain methylated CpG motifs to which ZEBRA binds with high preference [5] . The protein also forms stable transcriptional initiation complexes with basic components of the transcription machinery such as TBP , TFIID , and the transcription co-activator CBP [6] , [7] , [8] . Since ZEBRA augments the histone acetyl transferase ( HAT ) activity of CBP , interaction of ZEBRA with CBP increases promoter accessibility [9] . Activation of viral DNA synthesis during the lytic phase of the EBV life cycle is dependent on the capacity of ZEBRA to efficiently recognize a large ( ∼1 kb ) complex intergenic region that serves as the origin of replication . This region , known as oriLyt , consists of essential and auxiliary segments [10] . The two essential components of oriLyt , the upstream and downstream elements , together constitute the minimal origin of DNA replication [2] , [11] , [12] . The auxiliary component serves as an enhancer element that augments DNA replication [13] , [14] . ZEBRA recognizes the origin of lytic DNA replication ( oriLyt ) by interacting with seven ZEBRA-binding sites [12] , [15] . Mutation of all seven binding motifs in the background of a recombinant virus drastically reduces production of infectious virus particles [16] . These ZEBRA binding elements are located in two non-contiguous regions of oriLyt . Four elements are present in the upstream core region of oriLyt and overlap with the promoter of the BHLF1 open reading frame [3] . Knocking out any of these four elements was deleterious for amplification of an oriLyt-containing plasmid in a transient replication assay [17] . Three additional ZEBRA binding elements located mainly in the enhancer region are dispensable for viral replication [17] . The current model for the role of ZEBRA in lytic DNA replication suggests that the protein serves as a physical link between oriLyt and core components of the replication machinery [18] , [19] . The six core replication factors encoded by EBV are the DNA polymerase ( BALF5 ) ; the polymerase processivity factor ( BMRF1 ) ; the helicase ( BBLF4 ) ; the primase ( BSLF1 ) ; the primase associated factor ( BBLF2/3 ) , and the single-stranded DNA binding protein ( BALF2 ) [4] . Corroboration for the proposed role of ZEBRA in replication is inferred from data showing that ZEBRA interacts with almost all components of the viral replication machinery , with the exception of the single-stranded DNA binding protein ( BALF2 ) [18] , [20] , [21] , [22] . The function of tethering replication proteins to oriLyt is not limited to ZEBRA; the transactivation domains of Sp1 and ZBP89 interact with BMRF1 and BALF5 and target them to the downstream region of oriLyt [18] , [23] . Similarly , ZBRK1 , a cellular DNA binding zinc finger protein , serves as a contact point for BBLF2/3 on oriLyt [19] . Deletion of the ZBRK1 binding site present in the downstream region of oriLyt reduced oriLyt-dependent replication of a transiently transfected plasmid . Binding of these cellular transcription factors is not essential but contributes to replication efficiency . ZEBRA mutants that activate transcription but not replication are valuable in furthering our understanding of the process of EBV lytic DNA replication . ZEBRA is phosphorylated in vivo at multiple sites [24] . Phosphorylation of ZEBRA at S173 regulates lytic viral replication [25] . Serine 173 is located in a region N-terminal to the DNA binding domain of ZEBRA . This region , known as the regulatory domain , regulates the DNA binding activity of the protein [25] , [26] , [27] . Alanine substitution of the phosphoacceptor site S173 reduced the capacity of ZEBRA to bind to DNA in vitro and in vivo [25] . Attenuation in DNA binding correlated with a defect in the capacity of ZEBRA to stimulate lytic viral replication . However , it had no effect on the ability of ZEBRA to activate transcription of downstream viral target genes . Thus phosphorylation of S173 segregates the two main functions of ZEBRA , namely activation of transcription and activation of viral replication . In addition , the S173A mutant demonstrates that activation of transcription is not sufficient to stimulate viral replication . Additional proof for the role of phosphorylation of S173 in replication was attained when a phosphomimetic substitution mutant Z ( S173D ) activated both transcription and replication and was competent to bind DNA to the same extent as wild-type ( wt ) ZEBRA . Therefore , phosphorylation of ZEBRA at S173 functionally mimics ATP binding in other origin binding proteins by enhancing the DNA binding activity of ZEBRA to all ZREs in general and not to a specific site [25] . In a comprehensive mutagenesis study of the DNA binding domain of ZEBRA we identified ZEBRA mutants that arrested the EBV lytic cycle at different stages [28] . Two of these mutants , Z ( Y180E ) and Z ( K188A ) , caused lytic cycle arrest prior to viral replication . They reproducibly activated expression of viral early genes but were defective in inducing amplification of EBV DNA and late gene expression [28] . These mutants did not affect the phosphorylation site in the regulatory domain , S173 , but changed specific residues within the DNA recognition domain . The availability of replication defective ( RD ) ZEBRA mutants prompted us to investigate the effect of alterations in the DNA binding activity of ZEBRA on viral replication . If replication is indeed less tolerant than transcription for weak interaction between ZEBRA and DNA , then stronger association with oriLyt is necessary and might play a critical role in origin activation . Augmentation of ZEBRA binding to oriLyt is likely to be mediated by factors specific for viral replication . For example in budding yeast , interaction of the ORC with Cdc6 enhances its interaction with the origin of replication [29] . Here we describe a new role for three components of the EBV replication complex , namely , the primase , the single-stranded DNA binding protein and the DNA processivity factor . We show that over-expression of these three replication proteins is sufficient to increase the association of ZEBRA with viral DNA . This augmentation in DNA binding suppressed the phenotype of ZEBRA replication defective mutants and partially restored viral genome amplification and late gene expression . Our findings represent the first indication that three replication proteins play a role in enhancing the interaction between ZEBRA and viral DNA thereby promoting origin recognition , a process that is exquisitely sensitive to the DNA binding activity of ZEBRA .
Previously we described three ZEBRA mutants which activated expression of early genes but failed to activate viral replication and late gene expression . The ZEBRA mutants that reproducibly exhibited replication defective phenotype were: Z ( S173A ) in the regulatory domain and Z ( Y180E ) and Z ( K188A ) in the DNA recognition domain [25] , [28] . In further exploration of this phenomenon we identified a fourth ZEBRA RD mutant with a conservative arginine to lysine substitution at position 187 . Fig . 1A , C and D compare the phenotype of Z ( R187K ) to wt ZEBRA , Z ( K188A ) and Z ( F193E ) . Z ( K188A ) served as a typical ZEBRA RD mutant; the mutant Z ( F193E ) was partially defective in induction of late genes and DNA replication . Expression of Z ( R187K ) in BZKO cells induced a pattern of lytic gene expression that mimicked Z ( K188A ) ; it fully activated expression of two early proteins , Rta and EA-D ( aka BMRF1 ) , encoded by brlf1 and bmrf1 , but failed to activate synthesis of two late proteins BFRF3 ( FR3 ) ( a component of the viral capsid ) and BLRF2 ( LR2 ) ( a tegument protein ) ( Fig . 1A ) . Rta and EA-D are direct targets of ZEBRA; their expression is governed by the ability of ZEBRA to bind to their corresponding promoters , Rp and BMRF1p , respectively [30] , [31] , [32] . Activation of expression of the two late proteins , FR3 and LR2 , is associated with the capacity of ZEBRA to induce lytic viral replication [33] . To demonstrate that the introduced point mutations were the sole cause for the observed defect in late gene expression , the mutant Z ( Y180E ) was reverted to its original amino acid composition , i . e . tyrosine . As expected , Z ( Y180E ) was impaired in activating late gene expression while the revertant mutant Z ( Y180E→Y ) was competent to activate late gene expression to the same level as wt ZEBRA ( Fig . 1B ) . To examine whether the defect in late gene expression was due to a failure in stimulating viral replication , we tested the capacity of Z ( R187K ) to induce viral genome amplification by probing for two different regions of viral DNA . First , we probed for a region upstream of the viral terminal repeats ( TRs ) . During lytic viral replication linear viral genomes are synthesized . These linear forms differ in their number of terminal repeats and are detected on a Southern blot as a ladder [34] . In Fig . 1C , wt ZEBRA induced the formation of a replication ladder . Z ( F193E ) was slightly impaired and resulted in a less intense ladder than wt ZEBRA . The two late mutants Z ( R187K ) and Z ( K188A ) failed to induce the replication ladder . Comparable results were observed when a Southern blot of a parallel experiment was probed for the reiterated BamH1 W sub-fragment of EBV DNA ( Fig . 1D ) . All the RD mutants were defective at amplifying viral DNA when assessed by qPCR ( Fig . S3 ) . Based on these results and our previous studies , we conclude that RD mutants Z ( S173A ) , Z ( Y180E ) , Z ( R187K ) , and Z ( K188A ) are competent to activate expression of early viral proteins but incompetent to activate lytic viral DNA replication and late gene expression ( see also Fig . S3 ) . Although the data in Fig . 1 showed that the four replication defective mutants activated expression of two early proteins , Rta and EA-D to the same level as wt ZEBRA , this result did not directly assess the capacity of the mutants to activate transcription from early promoters . The level of brlf1 transcripts is particularly important since activation of the brlf1 promoter by direct binding of ZEBRA is a crucial initial event in activation of the EBV lytic cycle [5] , [30] , [31] , [32] , [35] . Therefore , using quantitative RT-PCR we measured the level of endogenous brlf1 mRNA , encoding Rta , in BZKO cells expressing each of the four ZEBRA RD mutants . We found that wt ZEBRA induced expression of the brlf1 message by 776-fold relative to the background level of brlf1 mRNA detected in cells transfected with empty vector ( Fig . 2A ) . The level of brlf1 expression was corrected for the corresponding level of the gapdh transcript measured in each sample ( Fig . 2B ) . The ZEBRA RD mutants , Z ( S173A ) , Z ( Y180E ) , Z ( R187K ) and Z ( K188A ) , reproducibly activated expression of the brlf1 message to levels similar or higher than that of wt ZEBRA ( Fig . 2 , S4A and S5 ) . Therefore , despite a clear defect in the capacity of these ZEBRA mutants to activate viral replication , the mutants were fully competent to activate transcription of the early brlf1 gene . In addition to its role in replication , expression of ZEBRA leads to activation of transcription of early lytic cycle genes , six of which constitute core components of the viral lytic replication machinery [4] , [36] . The defect observed with the ZEBRA RD mutants could be attributed to failure to activate transcription of one or more genes encoding essential replication proteins . To investigate this possibility , we examined the capacity of the ZEBRA mutants to activate transcription of the different components of the viral replication machinery . Expression of balf2 , the gene encoding the single-stranded DNA binding protein was examined by expressing five ZEBRA mutants in BZKO cells . Three of these mutants , Z ( Y180E ) , Z ( K188A ) and Z ( R187K ) , are markedly defective in activating late gene expression and viral replication , Fig . 1 , Fig . S3 and [28] . The other two mutants , Z ( F193E ) and Z ( K194A ) , are slightly to moderately impaired in activating viral replication and late gene expression ( Fig . 1 , [28] and unpublished data ) . 48 h after transfection of BZKO cells , we compared the level of balf2 expression among the mutants using Northern blot analysis . All five mutants activated the balf2 message to a level equivalent to wt ZEBRA ( Fig . 3A ) . As a positive control for migration of the balf2 transcript we used RNA from HH514-16 cells induced into the lytic cycle with sodium butyrate . Using quantitative RT-PCR we assessed the level of transcripts encoding the heterotrimeric helicase-primase complex in cells expressing five RD mutants: the regulatory mutants , Z ( S173A ) and Z ( S167A/S173A ) and the three basic domain mutants , Z ( Y180E ) , Z ( R187K ) and Z ( K188A ) . We employed two different methods to prepare cDNA from purified RNA samples . In the experiment illustrated in Fig . 3B and 3C , we synthesized cDNA using gene specific primers that were complementary to viral helicase ( BBLF4 ) or viral primase ( BSLF1 ) . In Fig . 3D , 3E and 3F , we used a mixture of random hexamers and poly-dT to synthesize cDNA . It is important to note that each of the DNA fragments amplified by RT PCR acquired the same melting point and electrophoretic mobility on agarose gels as DNA fragments amplified by PCR from an expression vector containing a cloned version of the corresponding gene ( data not shown ) . To confirm that the purified RNA samples were not contaminated with genomic DNA we omitted the reverse transcriptase enzyme from the reaction mixture . As a result no DNA amplification was detected ( Fig . 3B ) . Regardless of the method used for cDNA preparation , we found that the levels of mRNAs for viral helicase , primase and primase-associated factor ( BBLF2/3 ) in cells expressing wt ZEBRA were several fold higher than in cells transfected with empty vector . All four RD mutants were competent to activate expression of the viral helicase and primase to levels comparable or higher than those activated by wt ZEBRA . The mutants , particularly the basic domain mutants , activated twice as much helicase and primase transcripts as the wild type protein . For example , Z ( K188A ) activated between 2 . 3 to 2 . 6-fold more bblf4 mRNA than wt ZEBRA ( Fig . 3B and 3D ) . The primase-associated-factor ( BBLF2/3 ) was the only gene that exhibited lower transcript levels in cells expressing RD mutants compared to those expressing wt ZEBRA ( Fig . 3F ) . However , the level of bblf2/3 mRNA was still 5–9-fold above background . To determine whether ectopic expression of BBLF2/3 could rescue the defect in these mutants , we co-expressed BBLF2/3 with two ZEBRA RD mutants , Z ( S173A ) and Z ( Y180E ) , in BZKO cells . Forty-eight hours after transfection , cells were harvested and analyzed for late gene expression and viral replication . We found that over-expression of BBLF2/3 had no effect on the level of the late protein , FR3 , induced by wt ZEBRA , Z ( S173A ) or Z ( Y180E ) ( Fig . 4A ) . Similarly , using quantitative PCR to determine the extent of viral genome amplification , we found the same levels of viral genome in cells expressing Z ( S173A ) or Z ( Y180E ) in the absence or presence of BBLF2/3 . The level of viral DNA present in cells transfected with the mutants was approximately equal to that in control cells transfected with empty vector ( Fig . 4B ) . These experiments showed that impairment of ZEBRA RD mutants to induce late gene expression and viral replication was not the result of the slightly reduced levels of the bblf2/3 transcript detected following expression of this class of ZEBRA mutants . Moreover , over-expression of BBLF2/3 protein could not rescue the late mutants . Previously , we showed that reduction in the DNA binding activity of ZEBRA , due to alanine substitution of the phosphorylation site S173 , correlated with a defect in the capacity of ZEBRA to induce viral replication . The same impairment of binding was detected between Z ( S173A ) and the Rta promoter , but Z ( S173A ) was competent to activate expression of Rta to the same extent as wt ZEBRA [25] . This finding provoked the hypothesis that the DNA binding affinity of ZEBRA was of relatively greater importance for activation of viral replication than for activation of transcription . To further investigate this correlation we used an electrophoretic mobility shift assay ( EMSA ) to assess the DNA binding activity of ZEBRA RD mutants located in the basic domain of the protein . Fig . 5 compares the DNA binding activity of Z ( Y180E ) and K ( 188A ) with that of wt ZEBRA and with Z ( K188R ) , a mutant with a conservative change that manifests a wild phenotype . An EMSA assay was performed using cell extracts obtained from EBV negative HKB5/B5 cells transfected with the indicated expression vectors . Four ZEBRA response elements , ZIIIB and ZREs 1 to 3 , were used as probes . ZIIIB represents the highest affinity binding site for ZEBRA; it mediates auto-stimulation of the ZEBRA promoter [37] , [38] . ZREs 1–3 represent a cluster of sites present in the upstream essential region of oriLyt . Both Z ( Y180E ) and Z ( K188A ) were markedly impaired in binding to each of the four probes relative to wt ZEBRA . The efficiency of binding was calculated as the percentage of probe shifted by each mutant protein . Z ( Y180E ) shifted between 0 . 1% and 0 . 7% depending on the probe used in the shift assay; Z ( K188A ) , 1% to 9 . 2% , and wt ZEBRA , 23 . 4% to 46% ( Fig . 5A ) . The ZEBRA mutant , Z ( K188R ) , which is fully competent to activate the lytic cycle [28] , shifted the same set of ZEBRA specific DNA probes to percentages that were markedly higher than those observed with the ZEBRA RD mutants , namely 12 . 3% and 38 . 8% of the total probe ( Fig . 5A ) . These in vitro DNA binding studies clearly indicated that Z ( Y180E ) and Z ( K188A ) are both significantly impaired in their capacity to bind to ZEBRA response elements present in regulatory sites for transcription or replication . The differences in DNA binding between wt ZEBRA and the mutants were not due to variable protein levels . Western blot analysis with an antibody against ZEBRA demonstrated that all EMSA extracts contained similar levels of ZEBRA protein ( Fig . 5B ) . To analyze the ability of the ZEBRA RD mutants to associate with the viral origin of lytic replication ( oriLyt ) in vivo , we employed chromatin immunoprecipitation ( ChIP ) . To study the associations of the three basic domain ZEBRA RD mutants with oriLyt we transfected BZKO cells with expression vectors encoding each of the ZEBRA RD mutants , wt ZEBRA and a non-DNA binding form of ZEBRA , Z ( R183E ) , which does not activate transcription or replication . In this experiment , the wild type protein was the only form of ZEBRA that was capable of inducing viral replication . To compare the amount of oriLyt immunoprecipitated by each ZEBRA protein we maintained equivalent levels of viral DNA by blocking viral replication with phosphonoacetic acid ( PAA ) . We found that all ZEBRA RD mutants were more efficient than the non-DNA binding mutant Z ( R183E ) , but less competent than wt ZEBRA in precipitating the upstream region of oriLyt . 3 . 7-fold less oriLyt was immunoprecipitated from cells expressing Z ( Y180E ) compared to those expressing wt ZEBRA ( Fig . 6A ) . Similarly , Z ( R187K ) and Z ( K188A ) pulled down 2 . 9 and 8 . 3-fold less DNA than wt ZEBRA . The extent of association of each mutant with oriLyt was corrected for the total amount of oriLyt detected in the corresponding input sample . Fig . 6B shows that the level of input oriLyt was approximately the same in cells transfected with wild type and all three mutants . These results suggest that amino acid changes introduced in the three ZEBRA RD mutants did not completely abolish interaction of ZEBRA with oriLyt as was observed with the non DNA binding mutation R183E . Nonetheless , the ability of the RD mutants to bind to oriLyt in cells was 3- to 8-fold impaired compared with wt ZEBRA . The Rta promoter ( Rp ) is a direct target for activation by ZEBRA . In Fig . 1 , 2 , S4 and S5 , we showed that all ZEBRA RD mutants were fully competent to induce wild type levels of brlf1 ( Rta ) mRNA and protein . However , EMSA experiments showed that the ZEBRA RD mutants were similarly defective in binding to ZEBRA response elements regardless of their presence in transcription or replication regulatory regions ( Fig . 5 and [25] ) . To investigate whether the ZEBRA RD mutants are impaired in their capacity to associate with Rp , in a separate experiment we carried out ChIP experiments to compare directly the capacity of two RD mutants to precipitate oriLyt and Rp DNA relative to wt ZEBRA . We found that Z ( Y180E ) and Z ( K188A ) were two-to three-fold defective in interacting both with Rp and with oriLyt when compared to the wild type protein ( Fig . 6C and D ) . However , these RD mutants displayed higher efficiency to interact with oriLyt and Rp than the non-DNA binding ZEBRA mutant Z ( R183E ) . The Z ( R183E ) mutant pulled down amounts of oriLyt and Rp that were equivalent to those detected in ChIP experiments performed with cells transfected with empty vector or precipitated with pre-immune serum ( Fig . 6 ) . The finding that RD mutants are equally impaired in binding to Rp and oriLyt suggests that activation of brlf1 transcription is more tolerant of weaker interaction between ZEBRA and its response elements than is stimulation of replication . The ChIP assay measures the amount of DNA associated with ZEBRA , but does not measure how much ZEBRA interacts with DNA . Therefore , we employed a different approach to assay for the capacity of ZEBRA to bind DNA in cells ( Fig . 7 ) . The assay relied on co-transfecting vectors encoding wild type ZEBRA or ZEBRA mutants together with biotin-conjugated probes . The BUR probe is 167 bp long and encompasses the four ZREs in the upstream region of oriLyt that are crucial for lytic replication . BRpS and BRpL represent short ( 156 bp ) and long ( 277 bp ) segments of Rp . BRpS contains the ZIIIA site , while the BRpL has all three identified ZREs present in Rp . After 48 h , BZKO cells were harvested and biotinylated probes were captured using avidin coated beads . The level of ZEBRA protein bound to each probe was determined by western blot . The relative binding of ZEBRA to each probe was corrected for the total amount of ZEBRA protein present in each sample . In cells transfected with ZEBRA RD mutants , all three biotinylated probes pulled down less ZEBRA protein compared to cells transfected with wt ZEBRA . The defect in binding relative to wt ZEBRA averaged between 75% to 89% for the oriLyt probe ( Fig . 7A ) ; 57% to 93% for the short Rp probe ( Fig . 7B ) , and 66% to 95% for long Rp probe ( Fig . 7C ) . Our results with the transfected biotinylated probe assay confirm the EMSA and ChIP experiments . These three different assays show that replication defective mutants of ZEBRA are markedly impaired in binding to DNA . This defect in DNA binding can be seen with probes for oriLyt and Rp . Our findings suggest that weak association of ZEBRA with oriLyt has significant ramifications for subsequent events that lead to lytic viral DNA replication . These events might involve a specific protein-protein interaction between ZEBRA and one or more of the replication proteins . In an attempt to restore this interaction we over-expressed the six components of the EBV replication machinery together with each of the ZEBRA RD mutants in BZKO cells . Over-expression of replication proteins partially rescued late gene expression by all four ZEBRA RD mutants . The extent of rescue ranged between 3- to 4-fold regardless of the level of late gene expression induced by each mutant in the absence of replication proteins ( Fig . 8A , C and D ) . For example , in case of Z ( S173A ) , expression of replication proteins reproducibly increased FR3 expression by 3 . 2-fold reaching 55% that of wt ZEBRA alone . This effect on late gene expression was not an anomalous feature of these mutants; a similar increase was detected with the wild type ZEBRA protein and ranged between 1 . 6- and 2 . 5-fold ( Fig . 8C and D ) . While expression of the late FR3 protein can be used as an indirect marker for viral replication , we also examined the effect of over-expressing replication proteins on the capacity of wt ZEBRA and ZEBRA RD mutants to induce viral genome amplification . Expression of high levels of replication proteins reproducibly augmented the capacity of wt ZEBRA and Z ( S173A ) to stimulate EBV lytic replication by 1 . 9- and 3 . 4-fold respectively . In this experiment no similar effect of the complete mixture of replication proteins on DNA amplification was observed with the other ZEBRA RD mutants ( Fig . 8B ) . However , subsequent experiments defined a subset of replication proteins that was capable of rescuing replication by all the RD mutants ( Fig . S3 ) . In experiments illustrated in Figs . 5 to 7 and previously published [25] we found a direct correlation between strong association of ZEBRA with oriLyt and viral replication . To explore the possibility that replication proteins enhance interaction of ZEBRA with oriLyt , thereby partially restoring EBV lytic replication , we carried out ChIP experiments combined with quantitative PCR . In Fig . 9A , BZKO cells were transfected with empty vector ( CMV ) , Z ( S173A ) or wt ZEBRA in the presence and absence of the six EBV replication proteins . In ChIP experiments , we found that BZKO cells transfected with Z ( S173A ) or wt ZEBRA yielded more oriLyt when replication proteins were co-expressed , 1 . 58-fold and 1 . 72-fold , respectively ( Fig . 9A ) . This increase was independent of the level of ZEBRA expressed or immunoprecipitated . Western blot analysis showed that similar levels of ZEBRA protein were present in each immune-precipitate ( Fig . 9B ) . Expression of the six replication proteins had no effect on the amount of oriLyt immunoprecipitated from cells transfected with empty vector . A biological replicate experiment was performed and included two additional ZEBRA RD mutants , Z ( Y180E ) and Z ( S167A/S173A ) [25] . Wild type and mutant ZEBRA were expressed in BZKO cells minus and plus replication proteins . Co-expression of replication proteins enhanced the ability of wild type and mutant forms of ZEBRA protein to associate with oriLyt in vivo . A 1 . 8-fold increase in association with oriLyt was detected with wt ZEBRA; 2 . 2-fold with Z ( S173A ) ; 3-fold with Z ( Y180E ) , and 4 . 24-fold with Z ( S167A/S173A ) ( Fig . 9C ) . A compilation of several chromatin immunoprecipitation experiments showed that replication defective ZEBRA mutants weakly associated with oriLyt ( Fig . S2A ) . Z ( S173A ) was the least defective while Z ( K188A ) was the most impaired . For wild type ZEBRA and three of the mutants , Z ( S173A ) , Z ( Y180E ) and Z ( S167A/S173A ) , we demonstrated an increase in their association with oriLyt as a result of overexpressing the EBV replication proteins . The effect of replication proteins on association of ZEBRA with oriLyt was greatest with the mutant Z ( S167A/S173A ) , 6 . 87-fold . Z ( Y180E ) precipitated 3 times more oriLyt in the presence replication proteins; Z ( S173A ) , 1 . 8-fold , and wild type ZEBRA 1 . 6-fold ( Fig . S2A ) . The two ZEBRA RD mutants which were most defective in binding to oriLyt , namely Z ( R187K ) and Z ( K188A ) were the least affected by replication proteins . To investigate further the effect of replication proteins on interaction of ZEBRA with oriLyt we transfected BZKO cells with Biotin-conjugated oriLyt Full length ( BOF ) and expression vectors encoding wild type and the RD ZEBRA mutants with and without replication proteins . Cells were harvested 48 h after transfection; ZEBRA bound to oriLyt was purified using avidin coated beads . Both input and BoF-captured ZEBRA proteins were analyzed by Western blot . The effect of replication proteins on binding of ZEBRA to oriLyt was calculated after correcting for the amount of ZEBRA present in the corresponding input samples . We found that co-expression of the six core components of the replication machinery enhanced binding of wt ZEBRA , Z ( Y180E ) , Z ( R187K ) and Z ( S173A ) to oriLyt by 2 . 1- , 6 . 0- , 16 . 4- and 5 . 0-fold , respectively . In summary the ChIP and iBDAA experiments demonstrate that the core components of the EBV replication machinery augment the interaction between ZEBRA and oriLyt . To determine whether the effects of replication proteins were specific for ZEBRA's association with oriLyt , we examined the effect of replication proteins on association of ZEBRA with other lytic viral regulatory sites by studying interaction of ZEBRA and RD mutants with Rp and two other ZEBRA responsive promoters , BZLF1p ( Zp ) and BMRF1p ( EAp ) . Zp is auto-stimulated by ZEBRA while BMRF1p is activated by synergy between ZEBRA and Rta . Over-expression of the six EBV replication proteins increased the relative amount of Rp , Zp and BMRF1p DNA precipitated by wt ZEBRA , Z ( S167A/S173A ) and Z ( S173A ) ( Fig . S2B and Supplementary Table S1 ) . The effect of replication proteins on the amount of viral DNA pulled down by Z ( Y180E ) was more pronounced on Rp ( 2 . 2-fold ) . The amount of Z ( Y180E ) bound to Zp and BMRF1p was minimally enhanced by replication factors , 1 . 3-fold and 1 . 25-fold , respectively . No difference was detected by ChIP for the effect of replication proteins on the relative binding capacity of Z ( R187K ) and Z ( K188A ) to Rp . This could be attributed to the marked defect in the DNA binding capacity of these two mutants or limitations in the ChIP technique to detect small changes in association with a particular site . Our results show that replication proteins enhance the interaction of ZEBRA and the phosphorylation site mutants with oriLyt , and with at least three transcription regulatory sites , Rp , Zp and EAp . To delineate the contribution of each replication protein in restoring lytic viral DNA synthesis , Z ( S173A ) was co-expressed with different mixtures of replication proteins . In each mixture one of the six components was omitted . After 48 h , DNA was purified from BZKO cells and analyzed for its viral DNA content using quantitative PCR ( Fig . S1 ) . Elimination of individual components of the mixture of replication proteins led to several distinct outcomes . Exclusion of BBLF2/3 had no significant effect . Omission of BALF2 and BBLF4 reduced the efficacy of the replication proteins complex to rescue replication by Z ( S173A ) . Eliminating BSLF1 or BMRF1 from the mixture of replication proteins abolished its activity . In contrast , omitting the expression vector of BALF5 augmented the capacity of the other five replication proteins to restore viral replication by Z ( S173A ) . These results suggest that over-expression of different mixtures of replication proteins can stimulate , inhibit or have no effect on viral replication . To select the minimum subset of replication proteins sufficient to suppress the phenotype of these RD ZEBRA mutants , we examined the effect of expressing the primase individually or together with various combinations of replication proteins excluding the polymerase ( BALF5 ) that had been shown to be inhibitory ( Fig . S1 ) . After 48 h , transfected BZKO cells were analyzed by Western blot for the level of the FR3 protein as a marker for late gene expression . While co-expression of all six replication proteins with Z ( S173A ) induced late gene expression to 33 . 4 and 35 . 4% that of wt ZEBRA ( Fig . 10A compare lane 3 to 4 and 13 to 14 ) , addition of the primase alone had no significant effect on the level of the FR3 protein as compared to cells transfected with the S173A mutant in absence of RP . However , combining the primase with either the viral single-stranded DNA binding protein ( BALF2 ) or the viral DNA polymerase processivity factor ( BMRF1 ) enhanced late gene expression to 21 . 8 and 27 . 2% of wild type , respectively . A mixture containing all three proteins , the primase , the ssDNA-binding protein and the DNA polymerase processivity factor , restored late gene expression to 49 . 1% , a level higher than that induced by all six replication proteins ( Fig . 10A lane 17 ) . Addition of the viral helicase and/or the primase associated factor was either inhibitory or had no effect on the level of FR3 . To assess the effect of the different combinations of replication proteins on viral replication , we purified DNA from the same group of cells and analyzed it using quantitative PCR . The findings obtained by qPCR were similar to those seen by analyzing late gene expression . A mixture of the primase , the single-stranded DNA binding protein and the DNA polymerase processivity factor suppressed the defect in Z ( S173A ) and restored replication to approximately 44% that of the level activated by the wild type protein ( Fig . 10A ) . To determine if the same tripartite mixture of replication proteins could complement the defect in viral genome amplification observed in ZEBRA mutants in the DNA recognition domain , we repeated the same experiment using Z ( R187K ) . Addition of all replication proteins induced viral replication 2 . 2-fold above that induced by Z ( R187K ) alone . Transfection of the primase and the DNA polymerase processivity factor together with Z ( R187K ) had no effect on late gene expression or viral DNA synthesis ( Fig . 10B ) . However , addition of the single-stranded DNA binding protein to this mixture resulted in the highest impact on viral genome amplification , a 4 . 2-fold increase compared to replication induced by Z ( R187K ) alone ( Fig . 10B ) . Similar results were observed for the effect of these three replication proteins on late gene expression ( Fig . 10B ) . The capacity of BALF2 , BMRF1 and BSLF1 to rescue viral genome amplification by all five identified ZEBRA RD mutants was examined . BZKO cells were transfected with expression vectors encoding Z ( S167A/S173A ) , Z ( S173A ) , Z ( Y180E ) , Z ( R187K ) , Z ( K188A ) and wild type ZEBRA in the absence and presence of plasmids encoding the tripartite mixture of replication proteins . Cells were harvested at 48 h and 72 h and DNA was purified . The amount of EBV lytic replication induced by each condition was assessed by qPCR . At both time points , over-expression of the three components of the replication machinery enhanced activation of EBV lytic DNA replication by all five replication defective mutants as well as wt ZEBRA ( Fig . S3 ) . Our findings stress the importance of the primase , the DNA polymerase processivity factor and the single-stranded DNA binding protein on suppressing the effect of ZEBRA mutations that render the protein incompetent to activate lytic DNA replication . The upstream region of oriLyt encompasses four ZEBRA binding sites that are essential for oriLyt replication . Therefore it was important to assess directly the effect of the three replication proteins that rescued the function of the RD mutants on the capacity of ZEBRA to interact with the upstream region of oriLyt . In an iBDA assay , we transfected BZKO cells with a biotinylated upstream region of oriLyt ( BUR ) together with expression vectors for wt ZEBRA or Z ( S173A ) in the absence and presence of the tripartite replication mixture . BUR-bound ZEBRA was captured on avidin coated beads and the amount of ZEBRA bound was analyzed by western blot . We found that over-expression of the primase , the ssDNA-binding protein and the polymerase associated factor resulted in a 2 . 5- to 3 . 7-fold increase in the amount of ZEBRA that interacted with BUR ( Fig . 10C ) . This finding supports a role for the tripartite mixture of replication proteins in lytic origin recognition by ZEBRA . The results presented in supplemental Fig . S2B show that over-expression of replication proteins enhanced the capacity of wt ZEBRA , the phosphorylation site mutants and Z ( Y180E ) to interact with Rp , the BRLF1 promoter . The functional significance of expressing this subset of replication proteins on transcriptional activation of brlf1 by wt ZEBRA or mutant ZEBRA was studied in BZKO cells . To maintain an equal number of viral genome templates in each group , viral replication was blocked by phosphonoacetic acid ( PAA ) and the cells were harvested after 24 hours . Total RNA was purified and the level of brlf1 transcript was assessed using quantitative RT-PCR . Fig . S4A represents the average of two biological replicate experiments in which each value is the mean of three distinct RT-PCR reactions . As previously demonstrated in Fig . 2 , expression of ZEBRA replication defective mutants induced the synthesis of up to 2 . 4-fold more brlf1 mRNA than did wt ZEBRA . Over-expression of the tripartite mixture of replication proteins co-stimulated synthesis of the brlf1 transcript to various levels depending on the form of the ZEBRA protein being expressed . Replication proteins had a modest effect on the capacity of wt ZEBRA , Z ( S173A ) and Z ( S167A/S173A ) to activate transcription of brlf1 ( 1 . 3 to 1 . 5- fold ) . A significant 2 . 4-fold to 3 . 6-fold increase in the level of the brlf1 transcript was detected when BALF2 , BMRF1 and BSLF1 were co-expressed with each of the three DNA binding domain ZEBRA mutants , Z ( Y180E ) , Z ( R187K ) and Z ( K188A ) . These results show that despite the defect in activating DNA lytic replication , all ZEBRA RD mutants were capable of activating transcription to levels equal to or higher than that of wt ZEBRA . In addition , replication proteins enhanced the capacity of wt ZEBRA and ZEBRA RD mutants to activate transcription of brlf1 . This effect was more prominent when the BALF2-BMRF1-BSLF1 mixture was co-expressed with any of the three mutant forms of ZEBRA proteins containing single point mutations in the DNA recognition domain . The tripartite mixture of replication factors also enhanced the level of Rta protein activated by wild-type and mutant ZEBRA proteins . The enhancement was most marked for RD mutants Z ( Y180E ) and Z ( R187K ) ( Fig . S4B ) .
ZEBRA RD mutants with compromised DNA binding activity can be divided into two subclasses: the phosphorylation site mutants , Z ( S173A ) and Z ( S167A/S173A ) , and the basic domain mutants , Z ( Y180E ) , Z ( R187K ) and Z ( K188A ) [25] , [28] . The defect in DNA binding was demonstrated using three different DNA binding assays: EMSA , ChIP and in vivo biotin-conjugated DNA affinity assay ( iBDAA ) . Each of these assays addressed a different aspect of the DNA binding activity of ZEBRA . EMSA compared the capacity of the ZEBRA RD mutants to bind to individual ZREs in vitro . Four ZREs were tested , three present in oriLyt ( ZRE1–3 ) and a fourth ( ZIIIB ) in Zp . The defect in binding to these sites by the ZEBRA RD mutants was severe relative to the wild type protein . However , examining the ability of the mutants to associate with oriLyt in vivo using ChIP revealed a milder defect ( 2- to 8-fold ) ( Fig . 6 ) . This difference could be attributed to several factors; for example , ZEBRA binds to ZREs present in oriLyt in a cooperative manner [39] , other viral proteins affect ZEBRA association to oriLyt ( Fig . 9 ) , and formation of pre-replication foci increases the local concentration of ZEBRA [40] . To directly assess the level of ZEBRA protein bound to oriLyt or Rp , we examined interaction of ZEBRA with biotinylated probes in BZKO cells . Using this in vivo biotinylated DNA affinity assay ( iBDAA ) , we showed that all the mutants were impaired in their capacity to associate with both the oriLyt and Rp probes ( Fig . 7 ) . Our studies on the DNA binding activity of ZEBRA revealed important correlations between strong interaction of ZEBRA with oriLyt and its capacity to activate viral replication . These correlations are: 1 ) all five ZEBRA mutants defective in activating viral replication exhibited a 2- to 8-fold defect in interacting with oriLyt ( Fig . S2A ) . 2 ) The level of reduction in the DNA binding of each mutant correlated with its defect in stimulating viral replication ( Fig . S5 ) . 3 ) Replication proteins that enhanced interaction of ZEBRA with oriLyt restored viral replication ( Fig . 9 and S2A ) . 4 ) At position S173 , a mutation that disrupts DNA binding , e . g . Z ( S173A ) , also abolishes viral replication , while another substitution that maintains DNA binding , e . g . Z ( S173D ) , has no effect on viral replication [25] . All together these correlations point to the importance of strong interaction between ZEBRA and oriLyt to stimulate viral replication . Specific DNA binding by a protein that regulates different processes is not sufficient to confer specificity; additional levels of regulation likely play an important role beyond the initial step of DNA recognition . Consistent with the notion that initial recognition of the origin by the origin binding protein per se is not sufficient to induce replication , in Saccharomyces cervisiae , interaction with Cdc6p increased sequence-specific binding of ORC to the origin by altering its structure [29] . Also , the herpes simplex virus polymerase processivity factor ( UL42 ) facilitated loading of the origin binding protein ( UL9 ) to single-stranded or partially duplex DNA [41] . This study was done in vitro and the effect of replication proteins on the process of replication was not directly assessed in infected cells . Here , we showed that expression of replication proteins enhanced interaction of ZEBRA with both oriLyt and Rp . This enhancement in binding is likely to have more impact on replication than on transcription of brlf1 for the following reasons: 1 ) the ZEBRA RD mutants were fully competent to activate transcription of Rta and other lytic products . 2 ) Replication , and not transcription , was dependent on the capacity of ZEBRA to strongly bind to the corresponding viral DNA regulatory sites . 3 ) The replication proteins not only enhanced oriLyt recognition by the ZEBRA RD mutants but also restored their capacity to activate viral replication and late gene expression . Over-expression of the tripartite mixture of replication factors did not rescue viral replication by the ZEBRA RD mutants to wild type level . This could be due to the presence of additional defects , other than DNA binding , in the ZEBRA RD mutants . Alternatively , over-expression of other viral or cellular proteins might be necessary to completely suppress the phenotype of these mutants in replication . However , a complete rescue of the mutants might be technically challenging since it is unlikely that all the cells will obtain and express the transfected plasmids . Two findings suggest that replication proteins exert their effects early , during the assembly of the pre-replication complex or in the initial stages of replication rather than in extension . First , omission of the viral DNA polymerase ( BALF5 ) expression vector markedly enhanced viral replication ( Fig . S1 ) . Second , addition of phosphonoacetic acid ( PAA ) , an inhibitor of the viral DNA polymerase , had no effect on the ability of replication proteins to enhance ZEBRA association with oriLyt ( Fig . 9 ) . This effect of replication proteins is specific to three of the six replication proteins and is unlikely to be due to over-expression . In other EBV-infected cell lines , such as the Burkitt lymphoma derived cell line HH514-16 , replication proteins are expressed at much higher levels than in transfected BZKO cells ( Fig . 3A and [25] ) . Origin recognition is a complex process that is regulated at multiple levels . In addition to the role of replication proteins in enhancing association of ZEBRA with oriLyt , other mechanisms must also be involved . For example , interaction of BBLF2/3 with ZBRK1 serves as a tethering point on oriLyt for other replication proteins [19] . The involvement of multiple mechanisms in regulation of origin recognition reflects the complexity of such an initial but essential step for activation of EBV replication . At the initial stage of the EBV lytic cycle , stimulation of Rta expression by ZEBRA is independent of the presence of any replication proteins . As the lytic cycle proceeds into the early phase , ZEBRA and Rta , solely or synergistically , activate transcription of genes encoding the different components of the replication machinery . Our data shows that expression of three replication proteins , BALF2 , BMRF1 and BSLF1 positively modulate the capacity of ZEBRA to stimulate expression of Rta ( Fig . 2 , S2B and S4 ) . The co-stimulatory effect of this subset of replication proteins on Rta expression is likely to be a secondary event that occurs later during the pre-replicative phase of the lytic cycle . Our findings suggest that replication proteins trigger a positive feedback mechanism prior to viral replication that increases the level of Rta and replication proteins ( Fig . 11 ) . Upsurge in expression of replication proteins is likely to play a significant role in origin recognition , assembly of the replication complex and the process of viral DNA synthesis . Evidence supporting the possible role of replication proteins in a positive feedback loop comes from a recent report suggesting that the DNA polymerase processivity factor , BMRF1 , enhances the capacity of ZEBRA to activate the BALF2 promoter [42] . BMRF1 has also been shown to modulate the ability of Sp1 and ZBP-89 to activate the early viral BHLF1 promoter and the cellular gastrin promoter [43] , [44] . The mechanism responsible for the transcriptional co-activation function of BMRF1 is still unknown . It is possible that the effect of replication proteins in augmenting the capacity of ZEBRA to activate transcription is mediated by BMRF1 only . The following models might account for the role of replication proteins in origin recognition . First , ZEBRA interacts with sub-complex ( es ) containing the three replication proteins , the primase , the ssDNA-binding protein and the DNA polymerase processivity factor , off DNA . This interaction results in the formation of a high affinity quaternary origin recognition complex . Second , ZEBRA binds independently to oriLyt and interacts with replication proteins that are already tethered to oriLyt through other cellular transcription factors , e . g . Sp1 and ZBRK1 [18] , [19] , [23] . The formation of this network of protein-protein interactions with multiple contacts among replication proteins , ZEBRA and oriLyt is likely to have a synergistic effect on the stability of this protein-DNA complex and to facilitate recruitment of other replication proteins [45] . Third , formation of the ZEBRA-oriLyt complex results in a specific DNA-protein architecture that functions as a landing pad for the three replication proteins which in turn augment and stabilize the interaction between ZEBRA and oriLyt . One possible function for the three replication proteins is to enhance the capacity of ZEBRA to occupy all the ZREs present in oriLyt ( Fig . 9D and Fig . 10C ) . ZEBRA-oriLyt complexes that are not recognized by these three proteins are likely to become unstable and will fail to assemble a functional replication complex . These models do not yet account for the precise role of individual proteins . For example , it is possible that only one of these proteins , such as the ssDNA-binding protein , alters the origin binding capacity of ZEBRA while the two other proteins are important in subsequent events . Based on our results , we propose that a tripartite mixture of replication proteins plays a role in EBV lytic origin recognition . This is a novel role for replication proteins . Additional experiments will be necessary to investigate the mechanism ( s ) by which each of these three replication proteins modulate the binding activity of ZEBRA to oriLyt and other ZEBRA response elements and enhance viral replication and transcription .
The plasmids pHD1013/Z , pHD1013/Z ( S173A ) , pHD1013/Z ( R187K ) , pHD1013/Z ( Y180E ) , pHD1013/Z ( K188A ) , pHD1013/Z ( K188R ) , pHD1013/Z ( F193E ) and pHD1013/Z ( K194A ) were prepared as described previously [28] , [46] . Expression vectors for the viral open reading frames encoding BALF5 , BBLF4 , BBLF2/3 and BSLF1 were a kind gift from Dr . Diane Hayward [4] . The full length coding sequences for BALF2 and BMRF1 were amplified from EBV genomes purified from HH514-16 cells by PCR . The amplified fragments were cloned in pFLAG-CMV2 using EcoR1 and Xba1 restriction sites . BZKO cells were previously described [1] . HKB5/B5 cells represent an EBV negative subclone that was initially generated by hybridizing HEK293 cells with the EBV positive cell line HH514-16 [47] . All transient transfection experiments were performed in 25 cm2 flasks using 3 µg of ZEBRA expression vector and 2 µg of each construct encoding a replication protein . The DMRIE-C reagent ( Invitrogen ) was used for transfection according to the manufacturer's protocol . Immunoblotting was performed as previously described [28] . The following antibodies were used: anti-ZEBRA , anti-FR3 and anti-LR2 are polyclonal rabbit sera raised to TrpE-fusion proteins expressed in E . coli . The anti-Rta antibody was generated by expressing the C-terminal 320 a . a of Rta using the pET-expression system . The fragment was purified on a nickel column and used for rabbit immunization . The monoclonal antibody against BMRF1 ( EA-D ) ( R3 . 1 ) was a kind gift from G . Pearson . Anti-FLAG is a mouse monoclonal antibody ( Sigma ) . ChIP experiments were performed as previously detailed [25] . Sequences for the primers used are available upon request . RNA was purified from 8×106 BZKO or HH514-16 cells using RNeasy kits ( Qiagen ) according to the manufacturer's protocol . All RNA samples were treated with 30 U DNase1 ( Qiagen ) . Twenty micrograms of RNA was separated on 1% agarose gel and transferred by capillarity to a Hybond N+ filter ( Amersham ) . The membrane was hybridized to two 32P-labeled probes detecting the H1 component of RNase P ( a loading control ) and BALF2 . The probes were generated from a 370-bp NcoI-Pst1 fragment of RNase P and full length BALF2 DNA using random primers . DNA was isolated from 107 BZKO cells as detailed previously [28] . Ten micrograms of DNA was digested with 40 units BamH1 ( New England Biolabs ) for 3 h at 37°C . DNA fragments were separated by electrophoresis in a 0 . 8% agarose gel and transferred to a Zeta probe GT genomic membrane ( Bio-Rad ) . Formation of a replication ladder was detected using a probe complementary to a 336-bp sequence in the unique Xho 1 . 9-kb sequence upstream of the viral terminal repeats [34] . The template for the 336-bp probe was generated by PCR using the following primers 5′-CTCACGAGCAGGTGG-3′ and 5′-CGCAGTCTTAGGTATCTGG-3′ . An excised EBV BamH1 W fragment was used as a template to generate a corresponding probe [48] . Radioactive probes were synthesized using 10 units of the Klenow fragment of DNA polymerase ( New England Biolabs ) , [α-32P]dCTP and 1 ng random primers . The probes were purified using a Sephadex-G50 column . RNA samples were prepared 48 h after transfection of BZKO cells . Phosphonoacetic acid ( PAA ) was added to inhibit viral replication . RT-PCR was performed on 100 ng of total RNA using reagents and instructions described in the manual for the SuperScript One-Step RT-PCR with platinum Taq kit ( Invitrogen ) . In reactions where the reverse transcriptase was omitted , 2 units of platinum Taq was added . Random hexamers or gene specific primers were used to generate cDNA . A 131-bp fragment was amplified by the BBLF4 primers; a 121-bp fragment by the BSLF1 primers , and 211-bp by the BBLF2/3 primers . The sequences for the primers used are available upon request . Incorporation of Sybr green into DNA was detected using Cepheid Smart Cycler II or a Bio-Rad MyiQ real time PCR machines . Standard curves were generated using 10-fold serial dilutions of expression vectors encoding each of the three open reading frames . Quantitative PCR for detection of viral genome amplification was previously described [25] . Preparation of supernatants of HKB5/B5 cell extracts expressing wt ZEBRA or mutants as well as the DNA binding reactions were previously described [28] . Super-shifts were performed using BZ1 , a monoclonal antibody against the dimerization domain of ZEBRA . The percent probe shifted is calculated as previously described [28] , [49] . Full length oriLyt was cloned into pBSKII+ from HH514-16 cells using primers containing EcoR1 and BamH1 sites 5′-GCGCGAATTCTGGGGTCTCTGTGTAATACTTTAAG-3′ and 5′-GCGCGGATCCGTTA ATAAGGAGCC GTCCTTATTC-3′ . Biotin-labeled full length oriLyt ( BoF ) was prepared by PCR using primers that were conjugated to biotin at their 5′ends . BZKO cells were co-transfected with 150 ng BoF and the indicated expression vectors . Cells were harvested after 60 h and re-suspended in lysis buffer containing 15 mM Tris-HCl pH 8 . 1 , 167 mM NaCl , 1 . 2 mM EDTA , 3 mM MgCl2 , 0 . 01% SDS and 1 . 1% Triton X-100 . Cell extracts were briefly sonicated and supernatants were collected . The amounts of total protein were assessed using the Bradford reagent ( Bio-Rad ) and equalized . ZEBRA bound to BoF was captured using Avidin coated beads . The beads were washed and heated in SDS-PAGE sample buffer . The amount of captured ZEBRA protein was determined using Western blot analysis .
|
Epstein-Barr virus encodes a protein , ZEBRA , which plays an essential role in the switch between viral latency and the viral lytic cycle . ZEBRA activates transcription of early viral genes and also promotes lytic viral DNA replication . It is not understood how these two functions are discriminated . We studied five ZEBRA mutants that are impaired in activation of replication but are wild-type in the capacity to induce transcription of early viral genes . We demonstrate that these five mutants are impaired in binding to viral DNA regulatory sites . Therefore , replication required stronger interactions between ZEBRA and viral DNA than did transcription . Three components of the EBV-encoded replication machinery , including the single-stranded DNA binding protein , the polymerase processivity factor and the primase markedly enhanced the interaction of ZEBRA with viral DNA . These three components partially rescued the defect in ZEBRA mutants that were impaired in replication . The results suggest that through protein-protein interaction , replication proteins play a role in enhancing ZEBRA's association with the origin of DNA replication and other regulatory sites .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/dna",
"replication",
"virology/viral",
"replication",
"and",
"gene",
"regulation",
"virology/viruses",
"and",
"cancer",
"virology"
] |
2010
|
A Subset of Replication Proteins Enhances Origin Recognition and Lytic Replication by the Epstein-Barr Virus ZEBRA Protein
|
Phenotypic mutations are errors that occur during protein synthesis . These errors lead to amino acid substitutions that give rise to abnormal proteins . Experiments suggest that such errors are quite common . We present a model to study the effect of phenotypic mutation rates on the amount of abnormal proteins in a cell . In our model , genes are regulated to synthesize a certain number of functional proteins . During this process , depending on the phenotypic mutation rate , abnormal proteins are generated . We use data on protein length and abundance in Saccharomyces cerevisiae to parametrize our model . We calculate that for small phenotypic mutation rates most abnormal proteins originate from highly expressed genes that are on average nearly twice as large as the average yeast protein . For phenotypic mutation rates much above 5 × 10−4 , the error-free synthesis of large proteins is nearly impossible and lowly expressed , very large proteins contribute more and more to the amount of abnormal proteins in a cell . This fact leads to a steep increase of the amount of abnormal proteins for phenotypic mutation rates above 5 × 10−4 . Simulations show that this property leads to an upper limit for the phenotypic mutation rate of approximately 2 × 10−3 even if the costs for abnormal proteins are extremely low . We also consider the adaptation of individual proteins . Individual genes/proteins can decrease their phenotypic mutation rate by using preferred codons or by increasing their robustness against amino acid substitutions . We discuss the similarities and differences between the two mechanisms and show that they can only slow down but not prevent the rapid increase of the amount of abnormal proteins . Our work allows us to estimate the phenotypic mutation rate based on data on the fraction of abnormal proteins . For S . cerevisiae , we predict that the value for the phenotypic mutation rate is between 2 × 10−4 and 6 × 10−4 .
Every biological organism is built according to information stored in its genome . Genomes composed of billions of base pairs are not unusual . This information has to be duplicated during cell replication . Since replication errors can have devastating effects , DNA replication needs to be very accurate . Estimates for error rates in Eukaryotes are as low as 5 × 10−4 errors per base pair per replication [1] . But even flawless genetic information is useless if the cell is not able to synthesize functional proteins . Transcription and translation , the two processes involved in decoding DNA , have to be sufficiently accurate to allow a cell to build a reliable protein machinery . We refer to errors that occur during transcription and translation as phenotypic errors , and to errors that occur during DNA replication as genotypic errors . Most phenotypic errors are introduced during translation when ribosomes translate RNA sequences into amino acid sequences [2 , 3] . The accuracy of translation depends on the considered codon and context . In Escherichia coli it can range from 5 × 10−4 to 1 × 10−4 ( see Table 1 for some examples ) , with 5 × 10−4 as a commonly used estimate for the average frequency of errors per codon [4 , 5] . In comparison , Blank et al . [2] measured an E . coli error rate during transcription of 5 × 10−6 . Measuring the genotypic mutation rate is easier than measuring the phenotypic mutation rate . Estimates of genotypic mutation rates exist for many organisms . The data show that the number of mutations per genome per replication is constant for a wide range of organisms [1] . This is in agreement with theoretical results that suggest that the number of errors per replication per genome have to be below a certain error threshold to avoid an error catastrophe at which the propagation of genetic information becomes impossible [6–9] . There are many theoretical approaches for studying the evolution of genotypic mutation rates [10–14] , and one can tentatively claim that we have a basic understanding of what governs the evolution of genotypic mutation rates . This is not the case for phenotypic mutation rates . Apparently , very little theoretical work has been devoted to this topic . A notable exception is Wilke and Drummond [15] , who study translational robustness and the evolution of gene-specific phenotypic mutation rates . Their work predicts selection for proteins that fold properly despite mistranslation and provides an explanation for the fact that highly expressed genes evolve slower . A closely related study , and the starting point for this investigation , is Bürger et al . [16] . They studied a model in which the total number of synthesis attempts to produce sufficiently many functional proteins is limited and showed that the selection pressure to reduce the phenotypic mutation rate below a certain threshold vanishes . In addition , empirical information is scarce . Most measurements of phenotypic mutation rates are limited to E . coli [5] . For fast-growing E . coli laboratory strains , a correlation was found between ribosomal accuracy and ribosomal kinetics [3 , 17] . This suggests that the ( high ) phenotypic mutation rates are a result of a cost–benefit tradeoff . More accurate ribosomes reduce speed of translation and are hence disadvantageous . Natural isolates , however , do not show such a correlation between ribosomal accuracy and ribosomal kinetics . They display a wide diversity of ribosomal kinetic properties and growth rates which suggests that the tradeoff between accuracy and kinetics is not limiting in natural populations [17–19] . Apparently , natural populations are not so obsessed with optimizing translation kinetics for fast growth under laboratory conditions . This is not surprising considering that the estimated doubling time of , for example , intestinal E . coli ( 40 h ) is substantially longer than the doubling time of laboratory strains ( 0 . 5 h ) [20] . Hence , it is not clear if the optimization of translation kinetics is governing the evolution of phenotypic mutation rates . In this paper we analyse phenotypic mutations from a genomic/proteomic viewpoint . In particular , we derive and analyze a model that allows us to calculate the amount of abnormal proteins in a cell as a function of the phenotypic mutation rate . We also evolve genotypic and phenotypic mutation rates in computer simulations that are based on properties of the Saccharomyces cerevisiae genome/proteome . We discover that the current estimate for global phenotypic mutation rates of 5 × 10−4 is at a value where the amount of erroneous proteins begins to increase exponentially with the mutation rate . Further , at this value we observe a change in the kind of genes that contribute the most to erroneous proteins . For phenotypic mutation rates below 5 × 10−4 , erroneous proteins from highly expressed genes are frequent . Above 5 × 10−4 , however , erroneous proteins from large genes begin to dominate . Finally , we study models in which individual proteins can decrease their phenotypic mutation rate by using preferred codons or evolve robustness against amino acid substitutions . We point out the similarities and differences between the two mechanisms and show how an increase of the amino acid substitution rate above 5 × 10−4 affects the adaptation of highly expressed proteins .
In the following , we develop and analyze a model regarding the evolution of phenotypic mutation rates . We use data from S . cerevisiae to parameterize our model and calculate here relevant properties of the available yeast data . The genotypic mutation rate in S . cerevisiae is approximately 2 . 2 × 10−10 mutations per base pair per replication [1] . Our model requires the number of deleterious mutations per codon per replication as the unit for the genotypic mutation rate . Since each codon is composed of three nucleotide acids and 438/576 ≈ 3/4 single site mutations are nonsynonymous [21] , the mutation rate per codon is given by 3 × 3/4 × 2 . 2 × 10−10 = 4 . 95 × 10−10 . Of these nonsynonymous mutations , about 10% to 60% [22] are deleterious , placing the genotypic mutation rate somewhere between 4 . 95 × 10−11 , and 2 . 97 × 10−10 deleterious mutations per codon per replication . For simplicity , we use 1 × 10−10 . The phenotypic mutation rate in yeast appears to be similar to the mutation rates measured in E . coli [23] . The mutation rate is therefore likely to range from 1 × 10−5 to 5 × 10−3 , with 5 × 10−4 as an estimate for the global phenotypic mutation rate [5] . To parameterize our model , we need the length ni and abundance yi of each protein in yeast . Complete genomic sequences [24] provide the length , ni , of each protein of an organism . We only consider reading frames from the Saccharomyces Genome Database [24] that have been classified as nonspurious by Ghaemmaghami et al . [25] . This leaves us with 5 , 675 open reading frames ( proteins ) that have an average length of 496 amino acids . The effective genome is residues long . Information about the abundance , yi , of proteins is provided by Ghaemmaghami et al . [25] . From their data we calculate that the total amount ( measured in number of amino acids ) of functional proteins is given by and that ( this expression is relevant for Equation 8 ) .
We consider a large population of asexual single-cell organisms ( cells , for short ) , each with one DNA chromosome and K genes of ( possibly ) different length and expression level . Gene i ( i = 1 , … , K ) is ni amino acids long . During one cell cycle , yi , functional proteins have to be synthesized from gene i . We assume that regulation of gene expression guarantees that the gene is expressed until yi functional proteins are present . A cell with error-free transcription and translation will therefore synthesize exactly yi proteins of gene i . Since we are interested in variation within a population , we can normalize the fitness of such a cell to 1 and consider only relative fitnesses . Any cost of synthesizing all the functional proteins is accounted for in the fitness value of 1 . Cells , however , have a phenotypic mutation rate u > 0 , which denotes the probability ( per codon ) that protein synthesis is erroneous and produces a nonfunctional protein . Hence , a phenotypic mutation is a deleterious amino acid substitution that occurs during protein synthesis . We assume that phenotypic mutations are independent of each other . Hence , the probability of synthesizing a nonfunctional protein from gene i is given by . Let xi denote the number of nonfunctional proteins that have been synthesized until yi functional proteins were made . Then a cell synthesizes functional and nonfunctional proteins . It uses amino acids to synthesize functional proteins and amino acids to synthesize nonfunctional proteins . We are interested in the cost of erroneous protein synthesis in natural populations . Natural populations grow much slower than laboratory cultures . The growth of bacteria is limited by the rate of protein synthesis per ribosome . In slow-growing bacteria , the rate of protein synthesis per ribosome is , because of limiting amounts of charged tRNAs , almost 50% lower than in fast-growing bacteria [26] . We will therefore base our cost function on the effects of the phenotypic mutation rate on the availability of charged tRNAs . Most erroneous proteins are identified as abnormal and are degraded rapidly [27] . In this case , the amino acids that have been used to synthesize the erroneous proteins can be recycled to charge new tRNAs . This constant turnover , however , will diminish the pool of charged tRNAs by an amount depending on x . Hence , the cost of phenotypic mutations is a function of x . We will use η ( x ) to denote the cost of erroneous protein synthesis . Even though we have motivated η ( x ) by the cost of depleting the tRNA pool , it can account for other possible costs of erroneous proteins synthesis as well . Examples include toxic effects of aggregates of misfolded proteins , the waste of metabolic energy ( ATP/GTP ) , or usage of the ribosomal machinery to synthesize nonfunctional instead of functional proteins . Overall , the fitness of a cell that uses x amino acids to synthesize nonfunctional proteins is given by 1 − η ( x ) . It is unnecessary to explicitly consider the cost of protein synthesis of functional proteins , cy , because y is constant and , hence , , where denotes the costs if the costs of synthesis of functional proteins are not included . Besides phenotypic , we also take genotypic mutations into account . This allows us to directly compare the cost and evolution of phenotypic and genotypic mutation rates . Genotypic mutations introduce deleterious mutations at rate μ per codon , that is , gene i replicates successfully with probability , and all genes are replicated successfully with probability ( 1 − μ ) n , where can be interpreted as the effective genome size , i . e . , the total number of amino acids encoded by all K genes . We assume that the population is large enough so that deleterious mutations cannot spread . We ignore mutations that recover the wild type . In the following , the average fitness of a population with phenotypic and genotypic mutation rates u and μ per codon will play a central role . In the absence of genotypic mutations , the mean fitness is 1 − , where , and pu ( x ) denotes the probability that a cell with a phenotypic mutation rate u uses x amino acids to synthesize abnormal proteins . Genotypic mutations reduce this mean fitness . Because we ignore back mutations , at mutation–selection balance the mean fitness is reduced by the factor ( 1 − μ ) n , and this factor is independent of the actual fitness of the mutants . This is a special case of Haldane's principle for the mutation load ( see the section Mutation Rates at Equilibrium . The mean fitness at mutation–selection balance is given by As a consequence , we get the following formula for the cost Cu of phenotypic mutations , which is defined as the difference between the expected mean fitness of a population with and without phenotypic mutations: Similarly , the cost Cμ of genotypic mutations is We allow the mutation rates μ and u to evolve . Our aim is to derive approximations for the mutation rates that evolve in the long run . For this purpose , it is convenient to consider μ and u as quantitative traits with values between 0 and 1 . Both traits have the potential to evolve due to new mutations which are assumed to occur at constant rates πμ and πu , respectively . These mutations will primarily increase the genotypic and phenotypic mutation rates , but some will decrease them . Because the precise form of these mutation distributions does not enter our approximate formulas given here , we introduce them only in the context of our computer simulation model below . Mutation is counteracted by selection because , on average , cells with a higher mutation rate have a lower fitness . Applying Haldane's principle , it can be shown ( see the section Mutation Rates at Equilibrium ) that the evolved genotypic mutation rate , μ̂ , can be approximated as Similarly , the phenotypic mutation rate equilibrates to a value , û , which is obtained ( approximately ) by solving the equation for u . If , as is likely the case , is monotone increasing , this solutionû is uniquely determined . By taking the ratio of Equation 4 and Equation 5 , and performing a simple rearrangement , we obtain The term gives the number of mutations per genome per replication and is surprisingly constant for a wide range of organisms [1] . The rates πμ and πu at which the genotypic and phenotypic mutation rates are changed will primarily depend on the number of genes ( and their length ) involved in DNA replication and protein synthesis , respectively . In our model , for a given set of parameters , genotypic and phenotypic mutation rates evolve independently . Although we focus on the evolution of phenotypic mutation rates , for the analysis of our simulations it proved useful to also keep track of the genotypic mutation rate . Its equilibrium value is independent of the cost function η and can be used to estimate the effectiveness of selection ( i . e . , the drift–selection equilibrium value off̄ ) for given population size and given values of πu and πμ . A more detailed expression for the evolved phenotypic mutation rate can be obtained by approximating the cost function η ( x ) , presumably concave , by a linear one . Thus , let us assume henceforth η ( x ) = cx , so that fitness decreases linearly with the number of amino acids used to synthesize erroneous proteins . Here , c measures the costs per codon to synthesize an abnormal protein . As a consequence , we can write . In the Discussion we will address how nonlinear cost functions might affect our results . Since , according to our model , a cell produces proteins until exactly yi functional proteins of gene i are synthesized , the number xi of nonfunctional proteins produced during the process follows the negative binomial distribution NB ( yi , 1 − ui ) . Here , 1 − ui gives the probability of a successful protein synthesis . The expected value of xi is . Hence , x̄ , the expected value of x , is given by For uni < 1 , we obtain and . Therefore , Equation 6 can be rearranged to This illustrates nicely the factors that determine the ratio of the evolved genotypic and phenotypic mutation rates . This ratio depends on ( i ) the effective genome size , ∑ini , ( ii ) the average total cost of abnormal protein synthesis , , and ( ii ) the ratio at which mutations of the two mutation rates occur . Below , we will use computer simulations to complement these analytical considerations . We simulate the evolution of phenotypic and genotypic mutation rates based on the model introduced above . The main difference is that we use a finite ( effective ) population of size N = 104 and that we specify our mutation distributions for the mutation rates . Each generation , N organisms are selected ( with replacement ) for reproduction with probabilities proportional to their fitness ( Wright-Fisher model of drift and selection ) . Fitness is calculated as , wherex̄ gives the expected amount ( in amino acids ) of abnormal proteins . Protein length and abundances are taken from S . cerevisiae ( see Materials and Methods ) . The number of expected abnormal proteins is calculated according to Equation 7 . To avoid the fixation of genotypic mutants for high genotypic mutations rates at the beginning of the simulations , we set the fitness of genotypic mutants to zero . As predicted by the theory , the fitness of the genotypic mutants does not affect the equilibrium mutation rates ( see the section Effect of Initial Values and Parameters on the Simulation Results ) . The initial population is homogeneous with equal phenotypic and genotypic mutation rate . To allow the evolution of mutation rates , we change ( mutate ) u and μ with probabilities πu and πμ , respectively . Unless otherwise mentioned , we assume πμ = πu = 10−4 . In the section Effect of Initial Values and Parameters on the Simulation Results , we show that changes in the initial values of u and μ do not affect the results of our simulations and changes in πu and πμ affect them as predicted by the theory . Since beneficial mutations , that is , mutations decreasing the mutation rate , are generally rare , we increase the mutation rate with a probability of 0 . 99 ( conditional on a mutation event ) . In this case , we draw a new phenotypic mutation rate from a beta distribution B ( a = 1 , b = 9 ) on [u , 2u] ( or on [μ , 2μ] for genotypic mutation rates ) . Hence , the average increase is 10% and small changes are more frequent than large ones . Similarly , in case of a decrease , we draw the new mutation rate from a reflected beta distribution B ( a = 1 , b = 9 ) on [0 , u] ( or [0 , μ] ) . Hence , small changes are again more likely than large ones . During a simulation run , we kept track of the ancestry of each individual . After 4 × 106 generations , we calculate the most recent common ancestor of the population and determine its line of descent . We can use this line of descent to observe the evolution of phenotypic and genotypic mutation rates . For each parameter combination , we conducted ten runs that differ only with respect to the seed for the random number generator . Based on the ten trajectories , we compute an expected evolutionary trajectory for each parameter combination by calculating the geometric average of μ and u . Figure 1 shows these average trajectories of u and μ for seven sets of simulations that use different values for c , ranging from 1 × 10−16 to 1 × 10−10 , with increments of one order of magnitude . For all seven cost values , the genotypic mutation rates ( lower lines ) show essentially identical behavior . They decrease from the initial value of 1 × 10−7 to about 1 . 01 × 10−9 which leaves the cost of genotypic mutations at about Cμ = 2 . 84 × 10−3 . The equilibrium value of the phenotypic mutation rate depends strongly on the cost of abnormal proteins . For the given values of n = 2 . 81 × 106 and ( see Materials and Methods ) and because we assume πμ = πu , Equation 9 predicts . For c = 10−10 , this equals 2 . 10 × 103 and is indeed very close to the observed value of ( see black curves in Figure 1 ) . For this set of simulations , the phenotypic mutation rate evolves to a level at which the cost of phenotypic mutations is Cu = 2 . 67 × 10−3 , which is very close to Cμ . As expected , a decrease in the cost of abnormal proteins , c , results in an increase of the phenotypic mutation rate . But , apparently , there is an upper limit for phenotypic mutation rates above which a further decrease of c does not increaseμ̂ much more ( compare brown and grey curves in Figure 1 ) . Also , for very small c , Equation 9 becomes inaccurate . For example , if c = 10−13 , we get instead of . We can explain both observations by considering the average numberx̄ of amino acids required to synthesize nonfunctional proteins ( Equation 7 ) and the total number of nonfunctional proteins . For brevity and in distinction to the number of nonfunctional proteins , , we henceforth refer to as the amount of abnormal proteins . ( We use the number of amino acids as the unit for the amount of proteins . ) Figure 2 displays these quantities as functions of u ( solid line forx̄ , dash-dotted line for ) as well as the linear approximation ( Equation 8 ) tox̄ ( dashed line ) . As one can see , the linear approximation becomes inaccurate if u > 5 × 10−4 , andx̄ begins to grow exponentially with u . Consequently , even a small increase in u will cause a tremendous increase inx̄ . Even if abnormal proteins are not very costly , the rapid increase inx̄ prevents a further increase of u . We used to linearizex̄ . This approximation is only accurate if uni is sufficiently small . For a protein length of about 890 amino acids ( see below for an explanation why we chose 890 ) and a phenotypic mutation rate of 5 × 10−4 , we have uni = 0 . 445 , which is apparently too large for the approximation to be accurate . For ni = 890 and u = 5 × 10−4 , the exact value for is 0 . 56; this illustrates the difference between the true value ofx̄ and its linear approximation at u = 5 × 10−4 . It is important to emphasize that the observed upper bound for u is a consequence of the protein length distribution and the expression profile of the organism , as we will see below . It would be interesting to know the length and expression level of the genes that contribute most to the amount of abnormal proteins in a cell . For a given phenotypic mutation rate , it is easy to calculate , the amount ( in amino acids ) of erroneous proteins that were produced from gene i . To determine which gene lengths and expression levels are most important forx̄ , we calculate weighted averages of ni and yi . As weights , we use the amount of erroneous proteins that stem from gene i , that is , . Hence , we have and as indicators of the average protein length and expression level , respectively , that are most important for the amount of abnormal proteins in the cell . Since the expected number of abnormal proteins , , depends on the phenotypic mutation rate , the weighted averages are functions of u as well . How they change as a function of u is shown in Figure 3 . Interestingly , the weighted average of ni for very small u is about 890 and nearly twice as large as 496 , the average protein length in yeast . Even more interestingly , this value increases suddenly as u increases beyond 5 × 10−4 . For high values of u , phenotypic mutations are so frequent that it becomes essentially impossible to synthesize large proteins accurately . These large proteins dominate the cost of phenotypic errors . This can also be seen in the change of the weighted average of the expression level . For low mutation rates , the amount of erroneous proteins is dominated by highly expressed genes with a weighted average expression level of 2 . 67 × 105 proteins per cell . This value begins to decrease at u = 5 × 10−4 because large proteins , instead of highly expressed proteins , begin to increasingly contribute to the amount of abnormal proteins in the cell . For u = 5 × 10−4 , the bulk of the amount of erroneous proteins comes from proteins that are noticeably larger than the average protein and are highly expressed . How much these proteins contribute tox̄ compared with the rest of the genome can be seen in Figure 4 . The solid line shows for , that is , the cumulative contribution of each gene tox̄ . Genes are sorted decreasingly by their contribution tox̄ . It is obvious that only few genes contribute to most of the abnormal proteins in the cell . In fact , 5% ( 10% ) of the genes contribute to 78 . 6% ( 87 . 5% ) of the abnormal proteins in a yeast cell . The average length of these proteins is 927 ( 818 ) which confirms the conclusion from above that genes that contribute most to the amount of abnormal proteins are much larger than the average gene . From Equation 7 we know that nix̄i = niyi ( 1− ( 1−u ) ni ) / ( 1−u ) ni and can distinguish three components: ( i ) the protein length , ni , ( ii ) the expression level , yi , and ( iii ) the expected number of erroneous proteins that have to be synthesized to get one error-free protein , , with . Which of these components is primarily responsible for the fact that only few genes contribute to most of the abnormal proteins in a cell ? To answer this question , we can compare the dashed , dotted , and dash-dotted lines in Figure 4 which show how unevenly genes contribute to each of the three components ( for u = 5 × 10−4 ) . For example , the dotted line shows for . From the three components , only the expression level ( dotted line ) shows a curvature similar to the solid line . Hence , the fact that few genes contribute to most of the abnormal proteins in a cell is due to differences in expression levels rather than differences in protein length . If most of the abnormal proteins in a cell are synthesized by few , highly expressed proteins , the cell could reduce the cost of phenotypic mutation rates considerably by decreasing the phenotpyic mutation rate for these few genes . In fact , highly expressed genes are special in many ways . They use preferred codons more frequently than “normal” genes [28] and evolve more slowly [29] . The usage of preferred codons conveys several advantages , among them is a more efficient [30–32] and accurate [33–35] translation . As argued by Drummond et al . [29] , the slow rate of evolution of highly expressed genes might be the result of selection for translational robustness , that is , the ability of proteins to work properly despite amino acid substitutions [15 , 29] . The effect of preferred codon usage and translational robustness are conceptually very different . The usage of preferred codons reduces the phenotypic mutation rate by reducing the amino acid substitution rate , while an increase in translational robustness reduces the phenotypic mutation rate by improving a protein's ability to withstand the effect of amino acid substitutions . Let us first consider translational robustness . Let uaa denote the amino acid substitution rate . Together with the robustness of a protein against amino acid substitutions it determines the protein's phenotypic mutation rate ui . According to Bloom et al . [36] , the probability that a protein retains its wild-type structure after m amino acid substitutions is given by where v denotes the average neutrality to amino acid substitutions ( m-neutrality ) , that is , the average probability that a protein will be unaffected by ( “neutral” to ) an additional amino acid substitution . In the following we make the conservative assumption that a protein is functional if it is able to fold into its wild-type structure and that the wild-type sequence always folds into its wild-type structure , i . e . , that . Consequently , is the probability that a protein exposed to m amino acid substitutions is functional . A protein contains m amino acid substitutions with probability and is therefore functional with probability The probability to synthesize a nonfunctional protein from gene i is given by For protein i , the average number of nonfunctional proteins is given by , and the overall average amount of nonfunctional proteins in a cell is given by In comparison with Equation 7 , we note that , the term that is responsible for the rapid , nonlinear increase ofx̄ , has been replaced by a sum over the number of amino acid substitutions . Since ni is usually much larger than the number of amino acid substitutions , we have ni ≈ ni − m for relevant m values and can approximate the sum very accurately by We see that the term that caused the dramatic increase in the previous model also appears in this model , which considers translational robustness . The phenotypic mutation rate u is replaced by uaa and the term multiplied by a factor that depends on the protein's m-neutrality vi . Analogous to our previous observations , we can expect a nonlinear increase ofx̄ for uaa > 5 × 10−4 . In theory , but not in practice , it is possible to reduce the phenotypic mutation rate to zero by increasing vi to 1 for all proteins . In practice , an upper limit for vi is given by the function and stability of the protein . In our simulations , this upper limit is set by a prior distribution for the values of vi . Very high values for vi will be possible but unlikely . Given this ( soft ) upper limit for m-neutralities , we can , for a given amino acid substitution rate , ask which proteins will be selected for translational robustness ( large m-neutralities ) and what amount of abnormal proteins can be expected . We present simulations in which m-neutralities are drawn from a beta distribution , B ( v|a , b ) ∝ va−1 ( 1 − v ) b−1 with a = 16 . 95 and b = 20 . 72 , which has variance 0 . 0064 and mean 0 . 45 , reflecting the mean and variance of m-neutralities of seven proteins estimated by Bloom et al . [36] . We want to emphasis that the quantitative results , in particular the relative changes as a function of uaa , are not affected when other ( reasonable ) prior distributions are used . We obtained similar results for a beta distribution with mean 0 . 5 and variance 0 . 02 and for corresponding normal distributions ( truncated to the interval [0 , 1] ) . Even though the absolute value ofx̄ is smaller for prior distributions that allow larger m-neutralities , the relative changes remain the same . Mutations generate m-neutralities vi for each protein from the prior distribution . Selection determines if an m-neutrality reaches fixation and subsequently the eventual distribution of vi after many generations of selection . By how much selection has caused a protein's m-neutrality to deviate from the prior distribution can be expressed in terms of the log likelihood ( LL ) of the vi values under the prior distribution , log ( B ( vi|a , b ) ) ∝ ( a − 1 ) log ( vi ) + ( b − 1 ) log ( 1 − vi ) . If selection leads to a significant increase of a protein's m-neutrality , then the LL of this m-neutrality will be very low . The average LL for a cell's proteins is given by and can be used to quantify the extent of selection for translational robustness that the proteins of a cell were exposed to . As we will see , the LL of the m-neutralities ( after selection ) decreases substantially for uaa > 5 × 10−4 and indicates the intensified selection for translational robustness . We use a drift-selection model based on the fixation probability in a Moran process to determine the vi values at mutation–selection balance ( the post-selection vi distribution ) . For a given amino acid substitution rate , we initialize vi for all proteins by setting it equal to the mean of the prior distribution and calculatex̄ . For every protein ( gene ) i , we draw a new vi from the prior distribution and calculate the newx̄ that reflects this change , . For computational reasons and since the binomial distribution has most of its weight at small values of m , we truncate the summation over m in Equation 14 to the smallest integer larger than ( i . e . , m-values that are more than four standard deviations away from the mean are ignored; the probability of getting m's above this threshold is less than 2 × 10−3 ) . We accept the new vi with probability ( 1–1/r ) / ( 1–1/rN ) , which corresponds to the fixation probability in a Moran process of a mutant with relative fitness r = fnew/f in a population of size N . We use N = 10 , 000 and , with c = 10−9 . Here , we cannot use the fitness function as before , because it might lead to negative fitness values . In the previous section , we chose because of its analytical tractability and its similarity to the cost of genotypic mutations ( see Equation 1 ) . We did not have to worry about negative values for since u could evolve freely and selection caused u to converge to levels where < 1 . In this section , uaa is constant and the adaptation of individual proteins cannot reducex̄ arbitrarily ( there are upper limits to vi ) . Note that for small . Hence , our results from the previous section , where we used as cost function also hold for the cost function . For each uaa , we report the average of 20 simulations , which differ only with respect to the seed for the random number generator . For each simulation , we sequentially conducted 500 , 000 updates of each protein as described above . We then analyzed which proteins were selected for translational robustness by calculating the LL . We also analyzed to which extentx̄ is reduced by increasing vi and how an increase in uaa affects the selection for translational robustness . Figure 5 summarizes the results of our simulations . The top and the middle panels showx̄ and the average LL ( 18 ) after selection as a function of uaa . Similar to the previous section , we notice a dramatic increase ofx̄ for uaa > 5 × 10−4 . This is not surprising , considering the mentioned analytical similarities between Equations 7 and 16 . For amino acid substitution rates above 5 × 10−4 , the cell has difficulties to prevent the increase ofx̄ . This is also evidenced by the decline of the LL . The lower panel in Figure 5 shows the change in the average LL of three sets of 100 proteins . The three sets of proteins are given by the 100 proteins with the largest ni , yi , and , respectively . As expected , proteins with large values are more effectively selected for large m-neutralities than large or highly expressed proteins and , accordingly , have the smallest LL . With increasing uaa , large proteins contribute more to the amount of abnormal proteins and the corresponding LL decreases more rapidly than for the other two groups of proteins . In Figure 6 we show the m-neutralities of individual proteins for three amino acid substitution rates . It illustrates the intensified selection for large m-neutralities in large proteins . The simulations conducted in this section implicitly assume that the population is homogeneous and that one mutant appears at a time and either goes extinct or gives rise to another homogeneous population . Hence , every organism in these simulations represents a homogeneous population of size N . This organism is of course also the MRCA of this population . We can compare its fitness , , with the fitness , ( for ) , of the MRCA in our previous simulations to speculate on what would happen if we allowed uaa to change here as well . Here , where uaa was held constant , the fitness of the organism converged to fairly small values compared to the equilibrium values of f ≈ 0 . 995 in our previous simulations which allowed changes in u . For uaa = 1 × 10−5 , where selection for higher m-neutralities is insignificant , f = 0 . 938 , and f is much lower for larger phenotypic mutation rates ( e . g . , f = 0 . 090 for uaa = 5 × 10−4 ) . This suggests that if uaa is able to evolve freely to equilibrium values of f ≈ 0 . 995 , then selection for translational robustness will be insignificant . Simulations in which we mutated uaa as described in the previous section confirmed this expectation . No significantly elevated m-neutralities evolved ( unpublished data ) . This was not the case in simulations with few ( e . g . , ten ) genes , where the LL of highly expressed proteins converged to significantly lower values . Apparently , if there are many genes and if uaa is in mutation–selection balance , the m-neutralities of individual proteins do not contribute enough to allow selection for higher m-neutralities . But as we have seen above , if uaa is constant , selection for larger m-neutralities can reducex̄ to some extent . Hence , if uaa is above its mutation–selection-balance value , then significantly higher m-neutralities will evolve and decrease the phenotypic mutation rate by decreasing the effect of amino acid substitutions . Besides increasing the translational robustness of certain proteins , a cell can also use preferred codons to decrease the phenotypic mutation rate ui . This would actually decrease the amino acid substitution rate and is therefore conceptually different from translational robustness , which reduces the effect of amino acid substitutions but not their occurrence . Considering codon usage , the amino acid substitution rate uaa has two components , a ribosomal component ur and a codon-based component , uc . We assume that uaa = uruc for preferred codons and that uaa = ur for nonpreferred codons . Preferred codons are more accurate than nonpreferred codons , hence , uc < 1 . In this section we ignore translational robustness , i . e . , u = uaa . A protein of length ni that uses preferred codons synthesizes a functional protein with probability . The average amount of abnormal proteins is given by Again , we notice similarities between Equations 7 and 19 and can expect a rapid increase ofx̄ for ur increasing above 5 × 10−4 . We conducted simulations analogous to those investigating the effect of translational robustness . We calculatex̄ according to Equation 19 with uc = 0 . 1 . Each time we mutate the number of preferred codons , we increase by one with probability ( the fraction of nonpreferred codons in the gene ) . We decrease by one with probability . After changing , we calculate and accept the new as described in the section Selection for Translational Robustness . We report the average of 20 simulations for each ur . Each simulation was terminated after 2 × 107 sequential mutations ( not necessarily fixation ) of . Figure 7 shows the results of our simulations . Analogous to Figure 6 , we plot the equilibrium fraction of preferred codons , , for each gene for three ribosomal amino acid substitution rates ur . For u = 1 . 37 × 10−4 , only few genes evolve a major codon bias of pi > 0 . 6 . The gene with the largest codon bias of about 0 . 8 encodes for the protein with the largest and contributes 5 . 7% to the total amount of functional proteins and 7 . 9% to . For ur > 5 × 10−4 , large proteins begin to contribute more to the amount of abnormal proteins and selection increases the codon bias of large proteins . Similar to our observation in Figure 2 , the codon bias cannot prevent the drastic increase ofx̄ for ur > 5 × 10−4 . As before , no significant codon bias evolved if we allowed ur to change as well . Comparing Figure 6 with Figure 7 , we notice that selection for translational robustness results in a more distinct bias in vi than what we observe for pi after selection for preferred codons . In the next section we compare the two mechanisms to identify the source of this difference . In our simulations , the two mechanisms differ in the way pF , i , the probability of synthesizing a functional protein , is calculated and in the way it is mutated . For the translational robustness model , we calculate pF , i as The approximations are accurate as long as proteins with many amino acid substitutions are rare . For the preferred codon model , we have where we used 1 + ur/ ( 1 − ur ) ≈1 + ur and [1 + ur ( 1 − uc ) ]pi≈1 + urpi ( 1 − uc ) , which are reasonable approximations if ur is small . The analogy between Equation 21 and Equation 22 is obvious . In theory , the m-neutralities , vi , can range from 0 to 1 . In our simulations , for the chosen prior distribution , vi's larger than 0 . 8 are rare . The m-neutralities vi are analogous to the term pi ( 1 – uc ) in the preferred codon model . Since pi can range from 0 to 1 , the two mechanisms can reduce the amount of abnormal proteins equally well if vmax = 1 – uc , where vmax denotes the upper limit for m-neutralities . In our simulations , we have vmax ≈ 0 . 8 < 1 – uc = 0 . 9 . Hence , we would expect lowerx̄ values in the preferred codon model . This is not the case . For example , for uaa = 10−5 , x̄ converged to 6 . 4 × 107 in the translational robustness model , whereasx̄ converged to 7 . 3 × 107 in the preferred codon model . Hence , we have to consider the way in which the pF , i's are mutated to understand this result . In the translational robustness model , vi is sampled from a prior distribution . The new vi value is independent of the previous one . Hence , large changes of vi and , consequently , of pF , i are possible . In the preferred codon model , the number of preferred codons can only change in increments of one , and corresponding changes of pF , i andx̄ are small . The small changes in pi and , therefore , in pF , i allow the evolution of noticeable codon biases only in genes that produce large amounts of abnormal proteins . In the translational robustness model , large changes in the pF , i's are possible , and they have a higher fixation probability . Take , for example , the protein with the largest value of . It is 918 amino acids long , and a change of from 459 to 460 increases pi from 0 . 5 to 0 . 501 ( by 0 . 2% ) . This small change reaches fixation only if the costs of abnormal proteins from this gene are very large . Changes larger than this are frequent in the translational robustness model and have a higher probability of fixation .
A functional protein machinery , built from genetic information , is central to every living organism . Surprisingly , the decoding of genes into amino acid sequences is fairly inaccurate . Errors ( phenotypic mutations ) occur several orders of magnitude more frequently than during DNA replication . The frequency of errors depends on the codon and its context ( see Table 1 ) . In this paper , we have explored the evolution of phenotypic mutation rates . In our model , a cell maintains protein synthesis until a certain number of functional proteins are present . Depending on the phenotypic mutation rate , u , a certain number of amino acids , x , are “wasted” in erroneous proteins and reduce the fitness of the organism by η ( x ) . For simplicity , we used a linear cost function η ( x ) = cx . With genomic and proteomic data from S . cerevisiae [24 , 25] , we discover ( a ) an effective upper bound for the phenotypic mutation rate , ( b ) that most of the abnormal proteins stem from genes that are highly expressed and substantially larger than the average yeast protein , ( c ) that an average phenotypic mutation rate of u = 5 × 10−4 is at a value where x begins to increase dramatically as a function of u and large , lowly expressed genes begin to contribute substantially to the amount of abnormal proteins , and ( d ) that an increased codon bias or translational robustness in highly expressed genes can reduce the amount of abnormal proteins but cannot stop the dramatic increase for amino acid substitution rates above 5 × 10−4 . To what extent do our results depend on the assumption that η ( x ) is linear and that gene expression is maintained until a certain number of functional proteins are present ? Dekel and Alon [37] found a convex increase of the cost of protein synthesis with the amount of proteins synthesized . Considering this and that aggregates of misfolded proteins are , in a concentration-dependent way , toxic to cells [27 , 38] , we can expect the cost of erroneous proteins to increase faster than linear with the amount of erroneous proteins produced . A nonlinear η ( x ) , however , would only affect the position of the upper bound for u , which we observed in our simulations ( see Figure 1 ) . For a nonlinear cost function , we would expect this upper bound to be lower than what we have observed here , because of the nonlinear increase of x on top of the nonlinear increase of the costs of x . Results ( b ) – ( d ) are not affected by the shape of the cost function . Let us now consider our assumption about the regulation of gene expression . The largest protein in the yeast genome is Mdn1p , a dynein-related AAA-type ATPase [24 , 39] . It is 4 , 910 amino acids long . For u = 5 × 10−4 , only 12 . 8% of the synthesized proteins are error-free . To get the required number of 0 . 538 × 103 error-free proteins [25] , the cell has to synthesize 6 × 103 proteins . This is not a tremendous burden considering that about 46 , 600 × 103 functional proteins are synthesized in total . However , this number increases rapidly if u increases . Doubling or quadrupling u would require the synthesis of 72 . 7 × 103 or 10 , 000 × 103 proteins , respectively . It is unrealistic to assume that a cell will synthesize 107 proteins to get 538 functional ones . But we can consider this rapid increase as an indication for the inability of the cell to synthesize this protein and would have to rephrase result ( c ) to account for our assumption about gene expression: ( c′ ) a phenotypic mutation rate of u = 5 × 10−4 is at a value where it is still feasible to synthesize large proteins . Higher phenotypic mutation rates would make it impossible to synthesize large proteins . Interestingly , if one considers the ability of the cell to synthesize a certain number of functional proteins after a certain number of synthesis attempts , an upper bound for u is also encountered . In this situation , however , this upper bound is not due to the increase in abnormal proteins and the associated cost but due to the inability of the cell to synthesize enough functional proteins . In such a situation the cost of abnormal proteins is largely irrelevant and the upper bound for u primarily a result of the protein-length distribution and not of the cost of abnormal proteins . Furthermore , in such a situation there is little selection pressure to reduce the phenotypic mutation rate much below this upper bound [16] . If the synthesis of large proteins is such a problem , why does the cell not synthesize many smaller proteins and assemble them after successful production ? An intermediate check for proper folding ( which equals proper function for most amino acid substitutions ) would prevent the incorporation of nonfunctional subunits and reduce the probability of assembling a nonfunctional complex . In yeast , proteins with a length of about 1 , 000 amino acids are quite common . This suggests that the complexation of proteins much smaller than 1 , 000 amino acids constitutes a considerable challenge . For so many large proteins , it might be impossible to get the same biological function from a complex of smaller proteins . According to our model , an upper bound of 1 , 000 for the yeast protein length does not reduce the drastic increase by much . If we calculatex̄ after removing all proteins from the dataset that are larger than 1 , 000 amino acids , we can still observe a rapid increase inx̄ at u = 5 × 10−4; doubling ( quadrupling ) u would lead to a 2 . 4 ( 7 . 3 ) -fold increase in the amount of abnormal proteins . Therefore , partitioning extremely large proteins into protein complexes is not sufficient to avoid the negative effects of an increasing phenotypic mutation rate . Instead of complexing large proteins , evolution could reduce the phenotypic mutation rate of individual proteins . The phenotypic mutation rate of individual proteins could be reduced by using preferred codons [33–35] or by increasing the translational robustness of proteins [15 , 29 , 40] . Our analysis shows that these two mechanisms have nearly the same potential to minimizex̄ if uc is sufficiently small ( i . e . , if preferred codons are sufficiently more accurate than nonpreferred codons ) . One big difference between preferred codons and translational robustness is the way in which the trait is mutated . For preferred codon usage , it seems reasonable to assume that the number of preferred codons changes in increments of one , which leads to very small changes in the amount of abnormal proteins . Considering translational robustness , little is known about how mutations change the translational robustness of a protein . In our simulations , we mutate the translational robustness of a protein by sampling it from a prior distribution , which allows for large changes . Alternatively , one can use models that allow only small changes in a protein's translational robustness . More empirical data on the translational robustness spectrum of proteins is necessary to develop a satisfying model . The effect of incremental changes of the number of preferred codons on the amount of abnormal proteins is fairly small . An increase in the number of preferred codons by one increases the probability of synthesizing a functional protein only by a factor of ( 1 – uruc ) / ( 1 – ur ) . For u = 5 × 10−4 and uc = 0 . 1 , this equals 1 . 00045 . Since only few genes contribute much to the amount and number of abnormal proteins , this will lead to very small changes ofx̄ for most proteins . As mentioned previously , preferred codons are also able to increase the rate of translation . Selection for faster translation ( or higher expression level ) could be responsible for the observed codon biases . Since the time it takes to synthesize yi functional proteins is proportional to yini and the amount of erroneous proteins is approximately proportional to , it is possible to distinguish between the two sources of codon bias by comparing the observed codon bias in yeast with the predicted codon bias if selective forces were proportional to yini or . Further , a refined version of our preferred codon model that considers the genetic code and the actual amino acid sequence of each yeast protein could be used to estimate the cost of abnormal proteins and the amino acid substitution rate . For a given amino acid substitution rate , ur , an increase of the cost of abnormal proteins , c , increases the extent of codon bias but does not affect its distribution with respect to the protein length ( the points in the top panel of Figure 7 would all move upward by an amount that is independent of ni since remains unchanged for constant ur ) . For given c , an increase of ur changes the extent of codon bias as well as the codon bias distribution with respect to the protein length ( as seen in Figure 7 , if ur increases , the codon bias of large proteins changes to a greater extent than the codon bias of small proteins since will increase more for genes with large ni ) . Hence , by choosing different values for c and ur and by comparing the resulting extent and distribution ( with respect to ni ) of codon biases with the extent and distribution of codon bias found in yeast , one can estimate the two parameters . To experimentally measure the rate of amino acid substitutions during protein synthesis is notoriously difficult . Abnormal proteins are difficult to detect and usually degraded within minutes [27] . Experiments are usually limited to measuring the rate of specific substitutions at specific sites ( see Table 1 ) . One exception is work by Ellis and Gallant [4] , who measured the rate of substitution of charged amino acids by uncharged amino acids . For many proteins such substitutions are detectable as satellite spots after 2-D gel electrophoresis . However , their method might fail to detect rapidly degraded abnormal proteins and is dependent on the number of codons at which charge substitutions can occur [4] . It would be highly desirable to be able to calculate the actual frequency of phenotypic mutations , that is , the frequency of deleterious amino acid substitutions during protein synthesis as opposed to the frequency of all ( detrimental or not ) amino acid substitutions . We can use our model together with data on the fraction of proteins that are abnormal and degraded rapidly [41 , 42] to calculate this . Schubert et al . [41] and Princiotta et al . [42] measured that in human cells about 33% and 25% , respectively , of newly synthesized proteins are rapidly degraded . The proteins are degraded mainly because of their inability to achieve a functional state [27] . Since these are values for human cells and might also include proteins that could not achieve a functional state despite error-free protein synthesis , we will use 15%–35% as the range for the fraction of proteins that are nonfunctional due to phenotypic mutations . In our model , y andx̄ give the amount of functional and nonfunctional proteins synthesized , respectively . Hence the fraction of nonfunctional proteins synthesized due to phenotypic errors is given by . According to our model ( Equation 7 ) and the data from yeast ( see Materials and Methods ) , 2 . 4 × 10−4 to 6 . 1 × 10−4 deleterious amino acid substitutions per codon would result in the synthesis of 15% to 35% nonfunctional proteins . Better estimates of the fraction of abnormal proteins in yeast would allow a narrowing of the calculated range .
Here , we derive our main analytical results on the magnitude of the genotypic and phenotypic mutation rates stated in the section The Model . We start by recalling Haldane's principle for an asexually reproducing population . This population is assumed to be sufficiently large so that random genetic drift can be ignored . The only evolutionary forces considered are selection and mutation . We assume that there is an optimal type ( wild type ) in this population . Its fitness is denoted by W0 , the rate at which mutations to other types occurs is denoted by U , and back mutations are ignored . Then the mean fitness of the population at mutation–selection balance is given by . This is obtained immediately from the recursion relation for the frequency p0 of the optimal type . The important , but simple point , first made by Haldane , is that the mean fitness is independent of the fitnesses of the deleterious types ( [43] , pp . 106–107 ) . This principle can be generalized to a large class of mutation patterns among possible types , and even to a continuum of possible types . It then states that in mutation–selection balance mean fitness satisfies , where every type in the population is assumed to have the same mutation rate U . In addition , becomes asymptotically equal to if the mutation rate U becomes sufficiently small . Detailed formulations as well as proofs can be found in ( [43] , pp . 127 , 143–148 ) . Again , the equilibrium mean fitness is , to first order in U , independent of the precise mutation pattern and of the fitnesses of the deleterious types . Now we derive approximations forμ̂ andû in our model . We assume that the cost function η is linear , i . e . , . Because of its complexity , we need a simplified model to make analytical progress . We identify all cells that have the same pair of mutation rates , ( μ , u ) , and assign to them the average fitness ( see Equation 1 ) of a population of cells with these mutation rates . For given μ and applying Haldane's generalized principle to the trait “phenotypic mutation rate , ” we get Rearrangement and use of Equation 8 yields the following approximation for the evolved phenotypic mutation rate at equilibrium: For the evolved genotypic mutation rate , we already have derived the approximation ( Equation 4 ) . The general theory [43] , as well as numerical results ( unpublished data ) , show that the above approximations forμ̂ andû are slight overestimates of the true values . Taking the ratio of Equation 24 and Equation 4 , we obtain Equation 9 .
To show the robustness of our results with respect to the initial conditions and the parameters , we conducted additional simulations analogous to the simulations presented in Figure 1 . For Figure 1 , we used πu = πμ = 10−4 and 10−7 as initial values of u and μ; genotypic mutations were lethal . The blue and violet lines in Figure 8 show that the initial values for u and μ and the fitness of the genotypic mutant do not change the equilibrium mutation rates at mutation–selection balance . The genotypic and phenotypic mutation rates will converge to the same equilibrium mutation rates as long as ( a ) the initial value for u is low enough so that , and ( b ) the initial value for μ is low enough ( or genotypic mutations deleterious enough ) so that a fixation of genotypic mutants does not occur . We conducted simulations with different values for πu and πμ . The green and cyan lines in Figure 8 show the evolution of u and μ for πu = πμ = 10−3 and πu = πμ = 10−5 , respectively . As expected , higher ( lower ) πu and πμ lead to faster ( slower ) evolution of u and μ and to increased ( decreased ) equilibrium values . The magnitude of this change is smaller than predicted by theory , e . g . , Equation 9 . This can be attributed to the finite population size , N = 104 . In finite populations , selection is inefficient for costs ( C u , Cμ ) below a certain threshold . Note that from Equations 23 and 2 we have and , respectively .
|
A functional protein machinery , built from genetic information , is central to every living organism . Surprisingly , the decoding of genes into amino acid sequences is fairly inaccurate . Errors in this process ( phenotypic mutations ) are several orders of magnitude more frequent than errors during DNA replication ( genotypic mutations ) . Many researchers have explored the evolution of genotypic mutation rates , but there are as yet few investigations into the evolutionary dynamics of phenotypic mutation rates . Here we present a mathematical model that describes the effect of phenotypic mutation on the amount of abnormal proteins in cells . We parameterize our model using data from yeast ( Saccharomyces cerevisiae ) . We show that for phenotypic mutation rates above 5 × 10−4 per amino acid , the error-free synthesis of large proteins becomes nearly impossible . We estimate the phenotypic mutation rate of S . cerevisiae to be between 2 × 10−4 and 6 × 10−4 per amino acid .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion",
"Mutation",
"Rates",
"at",
"Equilibrium",
"Effect",
"of",
"Initial",
"Values",
"and",
"Parameters",
"on",
"the",
"Simulation",
"Results"
] |
[
"computational",
"biology",
"evolutionary",
"biology",
"saccharomyces"
] |
2007
|
Phenotypic Mutation Rates and the Abundance of Abnormal Proteins in Yeast
|
Cyclic nucleotide signalling is a major regulator of malaria parasite differentiation . Phosphodiesterase ( PDE ) enzymes are known to control cyclic GMP ( cGMP ) levels in the parasite , but the mechanisms by which cyclic AMP ( cAMP ) is regulated remain enigmatic . Here , we demonstrate that Plasmodium falciparum phosphodiesterase β ( PDEβ ) hydrolyses both cAMP and cGMP and is essential for blood stage viability . Conditional gene disruption causes a profound reduction in invasion of erythrocytes and rapid death of those merozoites that invade . We show that this dual phenotype results from elevated cAMP levels and hyperactivation of the cAMP-dependent protein kinase ( PKA ) . Phosphoproteomic analysis of PDEβ-null parasites reveals a >2-fold increase in phosphorylation at over 200 phosphosites , more than half of which conform to a PKA substrate consensus sequence . We conclude that PDEβ plays a critical role in governing correct temporal activation of PKA required for erythrocyte invasion , whilst suppressing untimely PKA activation during early intra-erythrocytic development .
The malaria parasite life cycle comprises extended phases in both a human host and a mosquito vector , but little is known of the control mechanisms that orchestrate progression of parasite development and transmission . Asexually replicating blood stage forms cause all the symptoms and pathology associated with malaria , whereas sexual stage parasites called gametocytes are required to mediate transmission to mosquitoes . Cyclic nucleotide signalling is important at most of the key stages of the parasite life cycle in both the host and vector . A role for cyclic GMP ( cGMP ) –dependent protein kinase ( PKG ) has been demonstrated in blood stage egress [1 , 2] and invasion [3] , gametogenesis [4] , ookinete motility [5 , 6] , and sporozoite motility required for invasion of mosquito vector salivary glands and host hepatocytes [7–9] . Available evidence suggests a role for cyclic AMP ( cAMP ) –dependent protein kinase ( PKA ) in blood stage invasion [10–12] , cell cycle progression [13 , 14] , anion conductance , and gametocyte deformability [15 , 16] as well as regulated exocytosis of sporozoite apical organelles and hepatocyte infectivity [17] . Two recent studies on cAMP signalling in Toxoplasma gondii have shown that absence of one of the three PKA catalytic subunits ( PKAc1 ) leads to premature egress of tachyzoites [18 , 19] . These studies also revealed roles for PKAc1 in cross talk with cGMP signalling at this stage of the life cycle . An earlier study established a role for PKAc3 in negative regulation of bradyzoite differentiation [20] . Additional key players in cyclic nucleotide signalling are purine nucleotide cyclases , which synthesise cAMP and cGMP from adenosine triphosphate ( ATP ) and guanosine triphosphate ( GTP ) , respectively , and cyclic nucleotide phosphodiesterases ( PDEs ) , which break down these messenger molecules by hydrolysis . Cyclic nucleotide levels in the cell are balanced by the opposing action of these two enzyme classes and , upon reaching a concentration threshold , activate their respective cyclic nucleotide-dependent protein kinases , PKA and PKG . The P . falciparum genome encodes four PDEs ( PlasmoDB identifiers: PDEα , PF3D7_1209500; PDEβ , PF3D7_1321500; PDEγ , PF3D7_1321600; and PDEδ , PF3D7_1470500 ) . Reverse genetic approaches have demonstrated that PDEα , PDEγ , and PDEδ are all associated with cGMP hydrolysis but are not essential for blood stage replication [6 , 9 , 21–23] . In contrast , previous attempts to delete PDEβ in P . falciparum were unsuccessful , suggesting that the enzyme might be essential for asexual blood stage development . Consistent with this , the P . berghei PlasmoGEM global gene knockout project and a recent P . falciparum global transposon mutagenesis project defined P . berghei PDEβ ( PbPDEβ , PBANKA_141980 ) and P . falciparum PDEβ ( PfPDEβ ) as likely essential based on an extremely low relative growth rate of gene knockout parasites ( http://plasmogem . sanger . ac . uk/ ) [24] and the absence of transposon insertion [25] , respectively . Collectively , these data suggest that PDEβ is the only essential PDE in the clinically relevant asexual blood stages of the parasite life cycle . Attempts to express recombinant PDEβ have also been unsuccessful . As a result , its substrate specificity and molecular function in the parasite are unknown . Here , we have used a conditional genetic approach to investigate the essentiality and role of PDEβ in P . falciparum blood stage development . This has revealed a critical role in blood stage growth that is likely the result of dysregulated PKA activity .
PfPDEβ is expressed during asexual blood stage development with mRNA levels increasing in the second half of the approximately 48-hour cycle and peaking in mature schizonts ( http://plasmodb . org/ ) . The presence of six putative transmembrane domains distinguishes PfPDEβ from all but one ( hPDE3 ) of the 11 human PDE families that are otherwise soluble [26] . It is currently not possible to predict the substrate specificity of a PDE from its sequence , as this is thought to be defined by multiple components of the binding pocket [27] , but sequence comparisons of the catalytic domain of PfPDEβ with selected mammalian PDEs show that 14 of the 15 residues that are invariant amongst all human PDEs are conserved in PfPDEβ ( S1 Fig ) . This suggests that it is a bona fide enzymatically active PDE . Using a transgenic P . falciparum line in which PfPDEβ was tagged with a triple haemagglutinin ( 3×HA ) tag ( PfPDEβHA , S2A and S2B Fig ) , expression was detectable by immunofluorescence ( IFA ) throughout blood stage development ( S2C Fig ) . In a western blot time course , a band at around the expected size of the tagged protein ( about 136 kDa ) was most intense at the late schizont stage ( Fig 1A ) . Full-length PfPDEβ protein was also detected in early and late ring stages ( S2D Fig ) . Further IFA experiments showed that in early schizonts , PDEβ colocalises with the endoplasmic reticulum ( ER ) –resident protein plasmepsin V ( Fig 1B ) . We then used the PKG inhibitor Compound 2 and the cysteine protease inhibitor E64 in parallel to examine the localisation by IFA of PDEβ at two stages of late schizont development . Compound 2 blocks development of fully segmented schizonts with all the surrounding membranes intact . E64 blocks schizont development at a slightly later stage when the parasitophorous vacuole membrane ( PVM ) has ruptured ( Fig 1C ) . This approach revealed a dual localisation for PDEβ consistent with a distinct apical signal predominant in Compound 2–arrested schizonts and a pattern reminiscent of a plasma membrane or inner membrane complex ( IMC ) localisation in E64-arrested schizonts and in free merozoites ( Fig 1B ) . Both localisation patterns were also observed in unblocked schizonts ( S2E Fig ) . These data , combined with the prediction that PDEβ is an integral membrane protein , suggest that the PDEβ is transported via the ER to an apical location ( presumably a secretory apical organelle ) and then subsequently discharged to the plasma membrane of individual merozoites within mature schizonts . Previously reported work using recombinant PfPDEα [22 , 23] and gene knockout studies on PfPDEδ [21] as well as P . berghei PDEδ [6] and P . yoelii PDEγ [9] detected only cGMP hydrolytic activity associated with these three isoforms , with no evidence attributing cAMP hydrolytic activity to any of these malaria parasite PDEs . To establish whether PDEβ is capable of hydrolysing cAMP , we immunoprecipitated the protein from PfPDEβHA schizont extracts via its epitope tag , followed by a PDE activity assay . This clearly demonstrates that PDEβ is a dual-specific PDE that is able to hydrolyse both cAMP and cGMP in vitro ( Fig 1D ) . We conclude that PDEβ is likely the only blood stage PDE with cAMP-hydrolysing activity . To examine the function and essentiality of PDEβ , we used a conditional system employing a rapamycin ( RAP ) –inducible , dimerisable Cre recombinase ( DiCre [28] ) to disrupt the PfPDEβ gene . We first modified the gene by homologous recombination to introduce loxP sites flanking exons 7 to 9 , encoding the catalytic domain of the enzyme to produce the conditional knockout line PfPDEβΔcatHA . PCR analysis of PfPDEβΔcatHA confirmed the desired gene modification and also demonstrated RAP-induced excision of the floxed PfPDEβ sequence ( Fig 2A and 2B ) . Ablation of expression of the haemagglutinin ( HA ) –tagged PDEβ catalytic domain following RAP treatment of synchronous ring-stage cultures was confirmed at the protein level by western blot ( Fig 2C ) and IFA ( Fig 2D ) . Quantification of anti-HA positive schizonts was used to determine the excision rate , which was 95 . 5% ( ±3% ) ( Fig 2E ) . The morphology of PfPDEβΔcatHA parasites prior to treatment with RAP was indistinguishable on microscopic analysis of Giemsa-stained blood films from that of the 1G5DC parental line parasites . Similarly , no discernible differences in morphology were detected between RAP- and mock-treated PfPDEβΔcatHA parasites up to and including the fully segmented schizont stage in the excision cycle ( cycle 0 ) ( Fig 3A ) . Consistent with this , there were no detectable differences in the DNA content of schizonts or the numbers of nuclei per schizont ( Fig 3B and 3C ) . Taken together , these data clearly show that truncation of PfPDEβ at the ring stage does not affect intracellular parasite development or schizont maturation in cycle 0 . Monitoring of parasite DNA replication and growth ( Fig 3D ) for more prolonged periods of approximately 8 days ( 4 erythrocytic cycles ) showed a significant reduction in parasite growth in the RAP-treated cultures . PCR analysis of these cultures showed that PfPDEβ-null parasites did not survive and were quickly outgrown by nonexcised parasites ( Fig 3E ) . Furthermore , viable parasites cloned from these RAP-treated cultures never displayed an excised PfPDEβ locus , indicating that these derived from a minor population of nonexcised parasites ( S3 Fig ) . Collectively , these results show that PDEβ plays an essential role in asexual parasite growth . To more precisely define the developmental stage in the erythrocytic cycle at which loss of PDEβ exerted its effect , we next compared egress of mature RAP- or mock-treated PfPDEβΔcatHA schizonts at the end of cycle 0 . Analysis by western blot of release of the parasitophorous vacuole ( PV ) protein serine repeat antigen 5 ( SERA5 ) into the culture supernatant ( Fig 4A ) , or flow cytometry ( S4A Fig ) and time-lapse video microscopy ( S1 and S2 Videos; wild type and knockout , respectively ) , showed that PDEβ deletion had no effect on merozoite egress . We have previously shown that addition of the PDE inhibitor , zaprinast , to mature P . falciparum schizonts leads to elevated cGMP levels , which activates PKG and triggers merozoite egress , and that this is blocked by addition of a PKG inhibitor ( Compound 2 [1] ) . We have also used this approach previously to show that addition of a PDE inhibitor to mature schizonts triggers elevated cytosolic calcium levels and that , again , this is blocked by PKG inhibition [5] . We used this approach ( with zaprinast or 5-Benzyl-3-isopropyl-1H-pyrazolo[4 , 3-d]pyrimidin-7 ( 6H ) -one [BIPPO] , a more potent PfPDE inhibitor [29] ) to test whether there is any difference in calcium release in PDEβ knockout and wild-type parasites . Levels of cytosolic calcium release were equivalent in PDE inhibitor-treated wild type and PDEβ null schizonts and were PKG dependent ( S4C Fig ) . These results indicate that deletion of PDEβ has no effect on calcium mobilisation , which is required for merozoite egress . In contrast , flow cytometry of SYBR Green–labelled parasites showed a substantial reduction in invasion efficiency ( 71% ± 2 . 78%; n = 5 ) in the RAP-treated PfPDEβΔcatHA parasites ( Fig 4B and S4B Fig ) . Merozoites emerging from individual RAP- and mock-treated PfPDEβΔcatHA schizonts were followed by video microscopy to assess their competence to induce red cell deformation , echinocytosis , and to conclude successful invasion . Rupture events from RAP-treated schizonts showed a highly significant reduction in all three steps , suggesting that invasion by PDEβ-null merozoites is impaired upstream of tight junction formation ( Fig 4C and S3–S7 Videos; the first two are wild type and the last three are knockout ) . The subpopulation ( 29% ) of PfPDEβ-null parasites that were able to invade consistently gave rise to small , apparently intracellular , merozoite-sized parasites with little or no development of a vacuole or cytoplasm , suggesting a block in development immediately following invasion ( Fig 4D ) . Analysis of the morphology of these dysmorphic intracellular parasites over time revealed that pyknotic parasites were present from the first hour after invasion , whilst some parasites that initially developed a vacuole appeared to rapidly shrink to condensed , dysmorphic forms ( Fig 4D and S6A Fig ) . IFA using two different monoclonal antibodies reactive with distinct proteolytic fragments of merozoite surface protein 1 ( MSP1 ) showed that the majority of newly appearing parasites were intracellular in mock- and RAP-treated cultures ( S4D Fig ) , confirming that a proportion of PfPDEβ-null parasites were able to successfully enter erythrocytes . Together , these results indicate that disruption of PDEβ function leads to approximately a 70% reduction in merozoite invasion and that in the subpopulation of merozoites that successfully invade an erythrocyte , subsequent early post-invasion development is prevented , leading to parasite death prior to ring stage formation . To evaluate the impact of PfPDEβ ablation on overall PDE activity in the parasite , schizont extracts were assayed for levels of cAMP and cGMP hydrolytic activity . Cyclic AMP-PDE activity was reduced by approximately 11-fold in extracts of the PfPDEβ-null parasites ( Fig 4E ) , with the small amount of residual cAMP-PDE activity likely being attributed to parasites in which gene excision had not taken place . In contrast , cGMP-PDE activity was reduced by only approximately 3 . 5-fold , with significant residual activity . These results confirmed that PfPDEβ disruption leads to ablation of enzyme activity , and importantly were also consistent with the analysis of immunoprecipitated PDEβ-HA described above in showing that PDEβ is a dual-specific PDE enzyme capable of hydrolysing both cAMP and cGMP . The results also confirmed that there is at least one other PDE expressed in schizonts possessing cGMP-PDE activity , probably PDEα [22] . The results strongly suggest that there is no other asexual blood stage PDE capable of hydrolysing cAMP . In support of this conclusion , whilst PDEβ disruption had no significant effect on cGMP levels in parasite extracts , it resulted in a 3-fold increase in intracellular cAMP levels ( Fig 4F ) . This result provides further confirmation that there is no other PDE capable of regulating cAMP levels in P . falciparum schizonts , whereas cGMP levels can still be regulated in the absence of PDEβ . The unchanged cGMP levels are consistent with the absence of an egress phenotype in the PDEβ knockout line , as egress is known to be regulated by PKG . We reasoned that the deleterious effects of PfPDEβ disruption on parasite viability might be due to the elevated levels of intracellular cAMP leading to increased phosphorylation of parasite proteins by the parasite PKA . To address this , we first examined extracts of mock- and RAP-treated PfPDEβΔcatHA schizonts by western blot with an antibody specific to phosphorylated PKA consensus motifs R , R/K , X , pS/pT ( where R is arginine , K is lysine , X is any amino acid and pS or pT denote phosphorylated serine or threonine ) . PDEβ disruption resulted in an increased number and intensity of antibody-reactive polypeptides , suggesting that the phosphorylation was a result of cAMP-induced ‘hyperactivation’ of PKA following PDEβ disruption ( Fig 5A ) . To gain insights into candidate proteins underpinning the PfPDEβ-null phenotype and to identify the full complement of putative PKA substrates that become phosphorylated upon PfPDEβ disruption , we carried out quantitative mass spectrometric global phosphoproteome analysis of mock- and RAP-treated PfPDEβΔcatHA schizonts . Our strategy incorporated PKG inhibition ( with Compound 2 ) to ensure that all the schizonts used for the analysis were synchronised precisely at the point when PKG activity is required for merozoite egress . PKG inhibition also allowed us to distinguish between sites phosphorylated by PKA and PKG , because in other species their consensus substrate sequences are very similar [30] . A total of 5 , 374 phosphosites were identified , distributed over 1 , 326 proteins ( 1 , 192 P . falciparum and 134 Homo sapiens proteins ) . Of these , 893 sites were significantly different ( Welch unpaired t test ) between the RAP- and mock-treated samples ( Fig 5B left panel and S1 Table ) , with 341 sites being reduced and 537 sites increased in the knockout . A total of 255 sites were changed by >2-fold , with 239 exhibiting a >2-fold increase but only 16 being decreased by >2-fold in the PDEβ knockout , 7 of which were from the PDEβ N-terminal domain , strongly suggesting that excision of the catalytic domain results in expression of an unstable truncated form of PfPDEβ . Unphosphorylated peptides present in the phosphopeptide-enriched sample were quantified to show that the vast majority of the 3 , 170 ( 2 , 953 P . falciparum and 217 H . sapiens ) identified proteins were unchanged in abundance in RAP- and mock-treated PfPDEβΔcatHA schizonts ( Fig 5B right panel and S2 Table ) . Only eight proteins were significantly less abundant ( Welch unpaired t test ) in the PfPDEβ-null sample , with PfPDEβ itself ( 2 . 7-fold ) showing the greatest change . The abundance of human dihydrofoate reductase ( hDHFR ) , used as a drug-resistance selection marker during modification of the PfPDEβ locus , was also significantly reduced in RAP-treated samples; this was as expected because the gene is excised together with the PfPDEβ catalytic domain upon activation of DiCre ( Fig 2A ) . Two of the few proteins showing a significant increase in abundance in the PfPDEβ-null sample were human proteins , FK506-binding protein 1A ( FKBP1A ) ( 1 . 96-fold ) and mechanistic target of rapamycin ( mTOR ) ( 2 . 93-fold ) . These correspond to the RAP-binding fusion partners used in the DiCre system [28] , so this finding is consistent with RAP binding enhancing their stability . Among the phosphosites significantly increased in the PDEβ knockout , we found a highly significant enrichment in motifs with R or K in the -2 position or -2 and -3 positions relative to the phosphorylation site , resembling mammalian consensus PKA substrate sequences ( Fig 5C , S3 Table , and S5 Fig ) . Approximately 52% ( 279/537 ) of the significantly up-regulated phosphosites and 63% ( 151/239 ) of phosphosites increased by >2-fold conform to the minimal PKA consensus motif R/K , x , pS/pT , consistent with hyperactivation of the enzyme in the absence of PfPDEβ ( Fig 5B and S5 Fig ) . It is therefore likely that these data define the P . falciparum consensus PKA substrate sequence motif ( Fig 5C ) . A 1D rank-based annotation analysis found different variations of K/R , K/R , x , S/T to be the most highly enriched motifs in the PDEβ knockout sample , with RRxS ( where R is arginine , x is any amino acid and S is serine ) occupying the top rank ( Fig 5D and S3 Table ) . Although the canonical R in position -2 seems to be slightly more enriched , phosphosites with a K in the -2 position , if paired with any basic residue in the -3 position , were also highly enriched . Phosphosites with a K in the -2 position were generally more frequent , which may be a consequence of the A/T-rich P . falciparum genome . The P . falciparum PKA may have evolved to better accommodate such K-rich substrates . Gene ontology ( GO ) analysis revealed that PfPDEβ disruption led to dysregulated phosphorylation of proteins involved in a range of cellular processes , including chromatin remodelling , transcription , RNA metabolism , translation , and ubiquitination . A number of proteases , ATPases , ion transporters , and signalling components also showed significant changes in phosphorylation . Furthermore , significant changes were found in components of the parasite-specific glideosome as well as rhoptry- and microneme-associated proteins . PKA consensus motifs were overrepresented in the phosphosites up-regulated in the PfPDEβ knockout in these functional groups ( Fig 5E and S4 Table ) . Examination of GO term enrichment , using Gene Ontologizer software , identified significant enrichment of only two GO terms in the significantly down-regulated phosphosites . These were the Biological Process , ‘Ion transport’ , and the Cellular Component , ‘Inner membrane pellicle complex’ . This reflects a detectable reduction in phosphorylation of significant numbers of proteins within just these two categories in the presence of elevated cAMP levels . One possible explanation for this is that PKA may selectively activate a protein phosphatase to dephosphorylate a restricted number of proteins . A previous global phosphoproteome of P . falciparum schizonts showed that 425 of the 2 , 541 unique phosphosites resembled a consensus PKA sequence , suggesting an important role for this kinase at this life cycle stage [31] . We previously identified 98 P . falciparum schizont phosphosites that were regulated in a PKG-dependent manner [3] . Interestingly , 46% of these sites were also differentially regulated in the PDEβ knockout ( in the presence of a PKG inhibitor ) , supporting a functional link between the two pathways ( S1 Table ) . Both the P . falciparum adenylyl cyclase β ( PfACβ ) and PfPDEβ had a single PKG-dependent phosphorylation site ( S1572 and S156 , respectively [3] ) , which points to a potential mechanism for the regulation of cAMP levels by PKG . Calcium-dependent protein kinase 1 ( CDPK1 ) was identified as a likely direct PKG substrate in the previous study , in which it was phosphorylated at position S64 . However , in the PDEβ knockout schizonts , an alternative CDPK1 phosphosite ( Y44 ) was up-regulated >4-fold ( Fig 5B ) , although which kinase performs this tyrosine phosphorylation event is not known . This also raises the question of whether a second spike of calcium release is required post-egress , as previously implied [32–34] . To seek biological validation of the list of potential PKA substrates , we focused on the phosphosite identified in P . falciparum myosin A ( PF3D7_1342600 ) , MyoA S19 ( increased by 2 . 6-fold in the PDEβ knockout ) . MyoA is a component of the so-called glideosome , a complex of parasite proteins involved in actinomyosin-based motility and host cell invasion [35] . We have previously shown that phosphorylation of MyoA S19 in mature wild-type P . falciparum schizonts is dependent on PKG activity , as treatment with the PKG inhibitor Compound 2 drastically reduces phosphorylation of this residue [3] . However , another study [36] suggested that phosphorylation of MyoA S19 is carried out by PKA . To investigate whether MyoA S19 is phosphorylated by PKA or by PKG , we blocked PKG activity ( and schizont rupture ) in PfPDEβ knockout and control parasites with Compound 2 . MyoA S19 phosphorylation was not detected in Compound 2–blocked control schizonts , consistent with this being a PKG-dependent event . Surprisingly , however , MyoA S19 phosphorylation was abundant in Compound 2–treated PfPDEβ-null schizonts ( Fig 6A ) , strongly arguing for this phosphorylation event being PKG independent in the absence of PfPDEβ . In contrast , MyoA S19 phosphorylation accumulated in both control and PfPDEβ-null schizonts incubated with the cysteine protease inhibitor E64 , which prevents schizont rupture downstream of PKG activation , confirming that MyoA S19 phosphorylation occurs just prior to or at egress ( Fig 6A ) . Levels of MyoA S19 phosphorylation in E64-blocked wild-type schizonts were sensitive to the PKA inhibitor H89 in a dose-dependent manner ( Fig 6B ) , consistent with this phosphorylation event being mediated by PKA . In contrast , treatment of Compound 2–blocked wild-type schizonts with the PDE inhibitor BIPPO [29] resulted in concentration-dependent enhancement of MyoA S19 phosphorylation , phenocopying the PfPDEβ-null mutant ( Fig 6C ) . PKG regulates egress upstream of calcium release [1 , 37] . To address the sequence of events and relationship of cGMP , cAMP , and calcium signalling with respect to phosphorylation of MyoA , we investigated the effect of the membrane-permeable calcium chelator BAPTA-AM ( 1 , 2-bis ( o-aminophenoxy ) ethane-N , N , N′ , N′-tetraacetic acid-acetoxymethyl ester ) on MyoA S19 phosphorylation . BAPTA-AM severely reduced MyoA S19 phosphorylation in Compound 2–treated PfPDEβ knockout schizonts as well as in E64-blocked wild-type and PfPDEβ knockout schizonts . This indicates that calcium signalling is also required for this phosphorylation event ( Fig 6D ) . It is therefore possible that MyoA19 could be phosphorylated by a calcium-dependent protein kinase . Collectively , our results suggest that , although phosphorylation of MyoA S19 is PKG dependent , ablation of PDEβ can bypass the need for PKG activity because of the resulting elevated cAMP levels . Further work will be needed to determine whether MyoA S19 can be phosphorylated directly by PKA and/or a CDPK in vivo . Interestingly , in the related parasite T . gondii , MyoA S21 , which may be functionally equivalent to P . falciparum MyoA S19 , has been linked to parasite motility and host cell egress and invasion [38 , 39] . Our results therefore suggest that PKG governs cAMP levels as well as PKA activation and that premature phosphorylation of MyoA S19 by PKA could lead to premature activation of the actomyosin motor . This may contribute to the severe invasion phenotype observed in PDEβ-null parasites . A second phosphosite that was significantly up-regulated ( 2 . 3-fold ) in the PfPDEβ-null schizont phosphoproteome and that had previously been linked to PKA activity is S610 of apical membrane antigen-1 ( AMA1 ) , a merozoite integral membrane protein that plays an essential role in host cell invasion [40] and that is released onto the merozoite surface from apical secretory organelles called micronemes in a strictly PKG-dependent manner [1] . The S610 phosphosite lies within the short cytoplasmic domain of AMA1 . Although the exact function of AMA1 phosphorylation remains unknown , it has been shown that the S610 modification is one of a series of AMA1 cytoplasmic tail phosphorylation events needed for efficient AMA1 function during invasion [11 , 12] . It has also been reported that this event is a prerequisite for subsequent phosphorylation at T613 by the parasite glycogen synthase kinase ( GSK3 ) and that both events are required for invasion [12] . In the present study , we did not detect phosphorylation of AMA1 T613 in PfPDEβ-null schizonts in which S610 phosphorylation is stimulated , although it is possible that the PKG blockade included in our phosphoproteome analysis protocol prevented this subsequent phosphorylation step . Comparative IFA analysis of mock- and RAP-treated PfPDEβΔcatHA schizonts showed that AMA1 release remained PKG dependent in PfPDEβ-null schizonts ( Compound 2 block ) and there was no significant difference in the proportion of parasites exhibiting surface-localised AMA1 in the presence of E64 , which blocks the red blood cell membrane rupture step of egress but is permissive for AMA1 release ( Fig 6E ) . However , examination of supernatants from schizont cultures rupturing in the absence of red blood cells detected significantly more shed AMA1 in the PfPDEβ-null samples than in the control , indicating increased levels of AMA1 cleavage ( Fig 6F ) . We also detected increased amounts of shed forms of another micronemal invasion-related protein , erythrocyte-binding antigen 175 ( EBA175 ) , in the PfPDEβ-null supernatant sample , whilst there was no difference in levels of the PV protein SERA5 between the two samples , confirming that PfPDEβ disruption does not affect schizont rupture . Confirmation of enhanced AMA1 shedding in the PfPDEβ-null mutant was obtained by IFA analysis of released merozoites , revealing a significant decrease in the proportion of free PfPDEβ-null merozoites reactive with an antibody against the AMA1 ectodomain ( Fig 6G ) . These results suggest that proteolytic cleavage of AMA1 may be increased as a result of enhanced PKA activity in the PfPDEβ-null mutant . Shedding of AMA1 from the merozoite surface is predominantly mediated by the micronemal membrane-bound subtilisin-like protease 2 ( SUB2 ) , and to a lesser extent through intramembrane cleavage by the rhomboid-like protease 4 ( ROM4 ) [41–43] . The enhanced shedding of AMA1 may result from enhanced SUB2 activity . Intriguingly , our global phosphoproteomic analysis identified seven phosphosites in ROM4 elevated by greater than 2-fold following RAP treatment , four of which conform with the PKA consensus motif , and two adjacent residues in SUB2 were also significantly changed . It remains to be shown whether any of these phosphorylation events regulate the activity of these proteases . Activation of PKA at or around egress is thought to be essential for merozoite invasion [10 , 11 , 31] . Our results show that the requisite increase in cAMP levels is governed by PfPDEβ . However , nothing is known regarding the activity status of PKA following erythrocyte invasion . As described above ( Fig 4D ) , those PfPDEβ-null parasites that successfully invaded underwent immediate developmental arrest , suggesting that PfPDEβ activity is also essential at this stage of the life cycle . To further explore this finding , we examined the effects of treating early ring stage wild-type parasites with the PDE inhibitor BIPPO . As shown in Fig 7A , BIPPO treatment phenocopied the PfPDEβ-null post-invasion phenotype . Previous studies using BIPPO have primarily investigated its effects on cGMP hydrolysis in apicomplexan parasites [18 , 29] . Our new results demonstrating that this inhibitor is able to target PDEβ show that it has the potential to affect both cAMP and cGMP hydrolysis in Plasmodium blood stages . Reasoning that both the PfPDEβ-null and BIPPO-mediated phenotype are likely caused through elevated cAMP or cGMP levels and inappropriate activation of the respective effector kinases PKA or PKG , we examined whether we could rescue the PfPDEβ knockout and PDE inhibitor phenotype by treating early ring stages ( 0–2 h post-invasion ) with PKG or PKA inhibitors . Survival of PfPDEβ-null parasites was not significantly extended by the addition of either kinase inhibitor ( S6C Fig ) , presumably because they had been exposed to elevated cAMP levels over a long period , causing extensive dysregulation and irreversible damage on multiple levels . In contrast , treatment of wild-type ring stage parasites with the PKA inhibitor H89 partially reversed the effects of BIPPO , resulting in normal ring stage development ( Fig 7B and S6B Fig ) . Treatment with the PKG inhibitor Compound 2 did not reverse BIPPO-mediated killing , clearly attributing the observed phenotype to elevated cAMP levels and untimely PKA activity ( Fig 7B ) . To further link the PfPDEβ-null and PDE inhibitor phenotypes to untimely PKA activation , we treated ring stage parasites with BIPPO alone or in combination with kinase inhibitors . Total parasite lysates analysed by western blot reveal a clear increase in reactivity , with an antibody against phosphorylated PKA substrate motif in the BIPPO-treated sample . Addition of the PKA inhibitor H89 reduced BIPPO-induced antibody reactivity to control levels , whereas the PKG inhibitor Compound 2 did not , confirming that the post-invasion phenotype is likely due to untimely PKA rather than PKG activity ( Fig 7C ) . Future work will be needed to understand the dynamics of PDEβ function in blood stage malaria parasites , how its translocation from an apical to a peripheral location impacts on the regulation of local cyclic nucleotide levels , and how this might relate to the differential hydrolysis of cGMP and cAMP during merozoite egress and invasion . In conclusion , we have shown that PfPDEβ is a dual-function enzyme that is the only PDE responsible for regulation of cAMP levels in blood stage malaria parasites . Ablation of PfPDEβ results in hyperactivity of PKA , with the resulting dysregulated phosphorylation leading to either complete loss of merozoite invasive capacity or lethal defects in parasite development immediately post-invasion . We also provide the first direct genetic evidence for PKA dependence of MyoA S19 and AMA1 S610 phosphorylation in P . falciparum . Several licensed drugs ( e . g . , Roflumilast , Sildenafil , and Pentoxifylline ) that target human PDEs are widely used to treat a range of disorders [26 , 44] . Our results revealing that PfPDEβ is essential for blood stage P . falciparum replication suggest that PDE inhibitors targeting this enzyme could be developed as new antimalarial drugs , particularly if they also displayed activity against the parasite PDEs expressed in gametocytes and pre-erythrocytic stages of the parasite life cycle .
WR99210 was a kind gift from Jacobus Pharmaceuticals ( New Jersey ) , RAP and the cysteine protease inhibitor E64 were purchased from Sigma , the PKG inhibitor Compound 2 was synthesised by MRC Technology ( London , United Kingdom ) , the PKA inhibitor H89 was obtained from TOCRIS Biosciences , the PDE inhibitor BIPPO [29] was a kind gift from Philip E . Thompson ( Monash University , Australia ) . Calcium chelators BAPTA-AM and Fluo-4 AM were purchased from Thermo Fisher Scientific , and the calcium ionophore A23187 was from Sigma-Aldrich . Rat monoclonal anti-HA tag antibody ( clone 3F10 ) and the same antibody conjugated to agarose beads were purchased from Roche LifeScience . Rabbit anti-AMA1 antibody raised against the ectodomain was described previously [45] . Rabbit anti-EBA175 is described in [43] . Rabbit anti-GAP45 [46] and rat anti-MyoA antisera [3] were kind gifts from Judith Green ( The Francis Crick Institute , London , UK ) . Monoclonal mouse antibodies to MSP1-19 ( 2F10 ) and MSP1-83 ( 89 . 1 ) have been described previously [47 , 48] . A mouse monoclonal antibody to Plasmepsin V was a kind gift from Daniel E . Goldberg ( Washington University School of Medicine in St . Louis , USA ) . A mouse monoclonal antibody against PfGAPDH was a kind gift from Claudia Daubenberger ( SwissTPH , Basel , Switzerland ) , rabbit anti-SERA5 is described in [49] , rabbit anti-PKG antibody was from ENZO life sciences ( New York ) , rabbit anti pS19-MyoA phospho-antibody was raised against the phosphopeptide ‘N’-RRV[pS]NVEAFDKC conjugated to KLH and double purified on the phosphopeptide , followed by passing through its nonphosphorylated counterpart [3] . A rabbit monoclonal antibody specifically reacting with phosphorylated PKA substrate consensus motif ( R , K/R , X , pS/pT ) was purchased from Cell Signaling Technology . All fluorescently labelled secondary antibodies used were highly cross-adsorbed and either conjugated to Alexa 488 ( green ) or Alexa 594 ( red ) ( Molecular Probes ) . A construct based on the pHH1_SERA5del3DC vector [28] was generated to C-terminally tag the endogenous Pfpdeβ locus with a 3×HA tag . The construct contained a 0 . 9-kb 3′ fragment of the PfPDEβ gene , to facilitate single crossover recombination , fused to the HA tag . A loxP site was placed downstream and the 3′UTR of P . berghei DT . The construct also contained a second loxP site and a hDHFR gene , which confers resistance to the antifolate WR99210 . The resulting plasmid pPfPDEβ-HA was originally generated with the intention of conditionally ablating PfPDEβ function by excision of the 3′UTR flanked by loxP sites . To create the PfPDEβ conditional knockout plasmid , a synthetic , partially recodonised ( Spodoptera frugiperda codon usage ) PfPDEβ sequence was synthesised by GenScript . The sequence comprised 1 kb of native P . falciparum PfPDEβ targeting sequence containing native PDEβ sequence to drive recombination by single crossover , a loxP site inserted into intron 6 ( of 8 ) , and the remaining 819 bp of the PfPDEβ exonic sequence were recodonised to prevent recombination downstream of the loxP site . A triple HA tag was added to the 3′ end of the sequence , followed by a stop codon . The synthetic PDEβ gene containing the internal loxP site and the recodonised sequence was cloned into pPfPDEβ-HA , replacing the 0 . 9-kb fragment to yield pPfPDEβΔcatHA . P . falciparum erythrocytic stages were cultured in human A+ erythrocytes ( National Blood Transfusion Service , London , United Kingdom ) and RPMI 1640 medium ( Lifetech ) supplemented with 0 . 5% AlbuMAX type II ( Gibco ) , 50 uM hypoxanthine , and 2 mM L-glutamine according to standard procedures [50] . Tightly synchronised parasites were obtained by purification of segmented schizonts on a 70% Percoll ( GE Healthcare ) cushion , addition of fresh erythrocytes to allow invasion for 1 to 3 hours shaking , followed by another Percoll purification to remove unruptured schizonts and sorbitol lysis of the pellet to obtain highly pure and synchronous ring stages . Genetic manipulation of P . falciparum parasites was carried out as previously described [4] . A total of 80 μg of precipitated plasmid DNA was resuspended in 400 μL cytomix ( 120 mM KCl , 0 . 15 mM CaCl2 , 2 mM EGTA , 5 mM MgCl2 , 10 mM K2HPO4/KH2PO4 , and 25 mM HEPES [pH 7] ) . The DNA-cytomix solution was added to 250 μL of the packed ring stage cultures at 5% to 10% parasitaemia and the sample electroporated at 950 μF capacitance and a voltage of 0 . 31 kV using a GenePulser Xcell ( Bio-Rad ) . Twenty-four hours later , the selection drug WR99210 was added at 5 nM concentration . Once parasites were visible , the cultures were cycled off the drug for 3 weeks , then on the drug until parasites reached 1% parasitaemia for one to four cycles . Ring stage cultures were counted using a haemocytometer and diluted to give 0 . 25 parasites per well in a 96-well plate at 2% haematocrit . Culture media was replaced every 3 days . After 14–21 days , positive wells were identified using a lactate dehydrogenase assay [51] . Integration of the HA-tagging plasmid pPfPDEβ-HA into the Pfpdeβ ( PF3D7_1321500 ) locus was verified using primers Int F 5′ GTTGAAAAGCAGTACAATAATGTTCCTTATC 3′ and Int R 5′ CGGGATCATAAACCTCGATTG 3′ , and the following primers were used to detect the WT locus and the absence of integration: WT R 5′ GCCAAGTCGAATGGAAAGATATTG 3′ and WT F 5′ GTTGAAAAGCAGTACAATAATGTTCCTTATC 3′ . Integration of the PDEβ loxP plasmid pPfPDEβΔcatHA into the same locus to create PfPDEβΔcatHA was confirmed by PCR using primers specific for the integrated locus: int-F 5′ GTTCTTCAAATGGTTGTGTAAAATTAT 3′ and int-R 5′ GGCCAATGTCGTGGCAGATG 3′ . Cre recombinase–mediated excision of the PDEβ catalytic domain ( exons 7 to 9 ) was monitored using primers specific for the excised locus: exc-F 5′ GTAATAAGAATGAATAGGCATATATGT 3′ and exc-R 5′ TGAACATTGAAATTTGTATCCGTCT 3′ . The 3′ end of the PDEβ coding region unaffected by plasmid integration or Cre recombinase–mediated excision served as DNA quality and loading control: 3′end-F 5′ CAACTAAACCAATGTAATATTTT 3′ and 3′end-R 5′ CGGGATCATAAACCTCGATTG 3′ . Primers int-F and 3′end-R were combined to specifically amplify the wild-type locus . Synchronous ring stage cultures were adjusted to 0 . 5% parasitaemia , set up in 96-well flat-bottom plates at 1% haematocrit , and triplicate samples frozen down daily for the duration of the assay . Eventually , cells were lysed in 20 mM Tris , 5 mM EDTA , 0 . 008% saponin , 0 . 08% Triton X-100 , 1× SYBR Green I ( Molecular Probes ) , pH 7 . 5 [52] , and read in a fluorescent plate reader at 485-nm excitation and 535-nm emission . Relative fluorescence units were normalised based on the day 0 sample and plotted . To determine EC50 concentrations for inhibitors used in this study , the same assay was conducted with 2-fold serial dilutions of the test compounds in triplicate wells and termination of the assay after 72 hours . Dried blood films were fixed in 4% formaldehyde and permeabilised with 0 . 1% Triton X-100 in PBS . Blocking and antibody reactions were carried out in 3% bovine serum albumin in PBS and washed with PBS . Slides were air-dried and mounted with ProLong Gold Antifade Mountant containing DAPI ( Thermo Fisher Scientific ) . Images were acquired on a NIKON Eclipse Ti fluorescence microscope fitted with a Hamamatsu C11440 digital camera and overlaid in ICY bioimage analysis software ( icy . bioimageanalysis . org ) . Pure merozoites were obtained by dual MACS ( Miltenyi Biotec ) purification . Rupturing schizont cultures were isolated on the magnet , put back into culture for 45 minutes , then run through the MACS column again , and the flowthrough containing merozoites was centrifuged at 3 , 500g for 5 minutes . Merozoite preparations were smeared on glass slides , air-dried , and fixed with cold methanol . Blocking and antibody reactions were carried out as described above . Segmented schizonts were purified from RAP- or mock-treated PfPDEβΔcatHA cultures as described above , introduced into custom-made viewing chambers [28] , and imaged on a Nikon Eclipse Ni-E widefield microscope with a Hamamatsu C11440 camera and a Nikon N Plan Apo λ 100x/1 . 45NA oil immersion objective . For egress videos , images were taken at 5-second intervals over a total of 30 minutes . Individual egress events were cropped , trimmed , and converted to video file format in ICY bioimage analysis software . For invasion videos , images were taken every 150 ms for 8 minutes following schizont rupture and processed using the Nikon NIS elements AR software . Merozoites from each rupture event were followed up and scored for their ability to deform the host cell , induce echinocytosis , and complete invasion . Thin blood films were air-dried , methanol fixed , and stained with Giemsa’s azure-eosin-methylene blue ( Merck ) and imaged on an Olympus BX51 microscope fitted with an Olympus SC30 digital colour camera through a 100× oil immersion objective . RAP-mediated excision was performed on PfPDEβΔcatHA as described above , but treating 3/4 of the whole culture and leaving 1/4 for the control to adjust for the reduced invasion expected in the PDEβ KO . Segmented schizonts from RAP- and DMSO-treated cultures were purified and fresh erythrocytes added to allow invasion for 4 hours and obtain a ring stage parasitaemia of 8% to 10% for both conditions . Giemsa-stained thin blood films taken at five different time points spanning the whole intra-erythrocytic cycle were assessed blind by two different researchers and parasites assigned to either of three morphological categories: normal morphology , delayed , or pyknotic/condensed . More than 300 parasites were scored per time point and condition . Wild-type parasites ( 3D7 ) were synchronised to a 2-hour invasion window as described above to obtain a pure culture with 7% to 10% ring stage parasitaemia . Kinase inhibitor treatments were started at 2–4 hours post-invasion , 1 hour before addition of the PDE inhibitor BIPPO . The PKA inhibitor H89 was used at 16 . 3 μM ( approximately 1×EC50 ) , the PKG inhibitor Compound 2 at 1 . 5 μM ( approximately 3×EC50 ) , and the PDE inhibitor BIPPO at 1 . 2 μM ( approximately 3×EC50 ) . Giemsa-stained thin blood films taken at 19–21 hours post-invasion were scored for their viability . More than 100 parasites per condition and experiment were counted blind by three researchers each . Parasite cultures were set up in triplicate wells per condition and fixed with 4% formaldehyde/0 . 1% glutaraldehyde in PBS containing 1× SYBR Green I ( Molecular Probes ) for 30 minutes at room temperature . Fixative was washed out with PBS and SYBR Green fluorescence read on a BD LSR II Flow Cytometer ( Becton Dickinson ) . Data were analysed using FlowJo 7 analysis software ( Becton Dickinson ) . For schizont DNA content analysis , the distribution of SYBR Green fluorescence was displayed as a histogram . To analyse schizont rupture and ring stage formation over time , schizontaemia and ring stage parasitaemia were calculated using high or low SYBR Green fluorescence , respectively . Parasites were released from host erythrocytes by saponin lysis and PBS-washed pellets resuspended in 2–3-pellet volumes of NP-40 lysis buffer ( 10 mM Tris , 150 mM NaCl , 0 . 5 mM EDTA , 0 . 5% NP-40 , pH 7 . 5 , + cOmplete protease inhibitors [Roche] ) , incubated on ice for 10 minutes , and supernatants collected after centrifugation at 15 , 000g for 15 minutes at 4 °C . Reducing SDS sample buffer was added and proteins separated on 4%–15% Mini-PROTEAN TGX Stain-Free Protein Gels ( Bio-Rad ) . Proteins were transferred to nitrocellulose membranes in a Trans-Blot Turbo Transfer System ( Bio-Rad ) and blocked with 10% skimmed milk in PBS/0 . 1% Tween-20 . Antibody reactions were carried out in 1% skimmed milk in PBS/0 . 1% Tween-20 and washed in PBS/0 . 1% Tween-20 . Washed membranes were incubated with Clarity Western ECL substrate ( Bio-Rad ) and exposed to X-ray film . Schizonts were Percoll purified from synchronised PfPDEβΔcatHA cultures containing mainly segmented schizonts and some young ring stages . Purified schizonts were resuspended in RPMI , 100 uL aliquots distributed in 96-well plates , and assay started immediately . At different time points , culture supernatants were separated from parasite material by centrifugation followed by purification through 0 . 22-μm Costar Spin-X centrifuge filters ( Corning ) . Presence of SERA5 in culture supernatants was quantified by western blot and used as a measure of schizont rupture . Western blots were probed with anti-AMA1 and anti-EBA175 antibodies to detect differences in adhesin shedding dynamics . Changes in the levels of intracellular free Ca2+ in response to PDE inhibitors were measured in purified late schizonts . Schizonts were resuspended in warm Ringer buffer ( 122 . 5 mM NaCl , 5 . 4 mM KCl , 0 . 8 mM MgCl2 , 11 mM HEPES , 10 mM D-Glucose , 1 mM NaH2PO4 , pH 7 . 4 ) to 1–2 × 108 parasites/mL ( 25 μL packed cell volume per 1 mL Ringer buffer ) . A total of 2 μL of 5 mM Fluo-4 AM ( Thermo Fisher ) was added per 1 mL of parasite preparation . Cells were incubated in the dark with Fluo-4 AM at 37 °C for 45 minutes . Cells were then washed twice in warm Ringer buffer and incubated for 20 minutes to allow for de-esterification of the AM ester . This was followed by a further two washes . The pellet was resuspended in Ringer buffer at 1–2 × 108 parasites/mL and plated out on a 96-well plate . Baseline Fluo-4 fluorescence in each well was read at 488-nm excitation and 525 emission using a SPECTRAmax M3 microplate fluorimeter ( Molecular Devices ) preheated to 37 degrees Celcius at 20-second intervals for a period of 3 minutes . The plate was removed from the reader onto a heat block prewarmed to 37 °C , and cell suspensions were transferred to wells containing test compounds to give the desired final concentrations ( ionophore A23187 [20 μM] , BIPPO [2 μM] , zaprinast [100 μM] , and Compound 2 [2 μM] ) . The plate was placed back in the plate reader and read for a further 5 minutes at 20-second intervals . Relative fluorescence units from individual reads were averaged and averaged baseline reads subtracted . Results are presented as percentage of ionophore control . Packed P . falciparum schizonts were obtained by saponin lysis and resuspended in ice-cold 5 mM Tris-HCl ( with EDTA-free protease inhibitors ) , centrifuged repeatedly at 16 , 000g for 10 minutes at 4 °C , and the supernatant aspirated to remove residual RBC material until the supernatant was clear . The pellet was then resuspended in 250 μL PDE lysis buffer ( 10 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 0 . 5% Nonidet P-40 , and EDTA-free protease inhibitors ) per 50 μL of sample , incubated on ice for 30 minutes and centrifuged at 16 , 000g for 20 minutes at 4 °C . Supernatants were added to PDE assay . Pull-downs of the transgenic HA epitope–tagged PDEβ-HA were performed using the anti-HA Affinity Matrix ( Roche , 11815016001 ) that incorporates immobilised rat monoclonal antibody ( clone 3F10 ) . Packed parasite pellets were obtained by saponin lysis . The pellet was resuspended in 250 μL ice-cold PDE lysis buffer per 50 μL of sample , incubated on ice for 30 minutes with occasional mixing , and centrifuged as described above . The supernatant was adjusted to 500 μL with PDE dilution buffer ( 10 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , and EDTA-free protease inhibitors ) to give a final detergent concentration of less than 0 . 2% , and the pellet discarded . A total of 20 μL of the matrix was washed twice with dilution buffer ( centrifuged at 30 seconds 13 , 000g to pellet the matrix ) to equilibrate the anti-HA affinity matrix . The lysate sample was then added to the equilibrated anti-HA affinity matrix , and incubated at RT for 2 hours with constant mixing . After incubation , the matrix was pelleted at 13 , 000g and the supernatant removed . The beads were washed twice with ice-cold dilution buffer and were added to the PDE assay . PDE activity in P . falciparum particulate fractions and pull-down assays was measured by a scintillation proximity assay ( SPA ) using yttrium silicate–based SPA beads ( Perkin Elmer , RPNQ0150 ) . Scintillant is incorporated into beads , which bind to the primary phosphate groups of noncyclic 5′AMP or GMP , and the assay relies on the fact that cAMP and cGMP are unable to bind . Assays were carried out in flexible 96-well plates ( Perkin Elmer , 1450–401 ) in a 100-μL volume . A total of 90 μL of protein sample diluted in PDE assay buffer ( 50 mM Tris-HCl , 8 . 3 mM MgCl2 , and 1 . 7 mM EGTA ) was added to each well , and 10 μL of a cNMP dilution ( 5 μL of [3H] cNMP tracer [Perkin Elmer cAMP-NET275250UC , cGMP-NET337250UC] in 995 μL PDE assay buffer ) was added to start the reaction . Plates were incubated at 37 °C for 1 hour . Reactions were terminated by addition of 50 μL of resuspended PDE SPA beads ( reconstituted to 20 mg/mL in distilled H2O ) . Plates were sealed with Plateseal ( Perkin Elmer ) briefly shaken and then incubated for 20 minutes at RT to allow the beads to settle . Scintillation was measured using a Wallac 1450 Microbeta Counter ( Perkin Elmer ) for 30 seconds . An initial dose-response assay was performed with doubling dilutions of the sample to ensure the substrate was not depleted during the course of the assay . The initial sample was diluted to give roughly 30% hydrolysis of the cyclic nucleotide . Relative intracellular cAMP and cGMP in mature schizonts were measured using ELISA-based FluoProbes high-sensitivity chemiluminescent assay kits ( Interchim ) . Mature schizonts were Percoll purified from RAP- or DMSO-treated PfPDEβΔcatHA cultures followed by saponin lysis and two PBS washes . Parasite pellets were directly lysed in sample diluent for 10 minutes at room temperature , centrifuged at 20 , 000g for 15 minutes , and the supernatant collected and diluted 1:5 in sample diluent . Samples and cyclic nucleotide standards were acetylated according to the manufacturer’s high sensitivity protocol . Standards and samples were run in triplicates on the same plate and luminescence read with a Spectramax M3 plate reader . The standard was fitted to a sigmoidal curve and used to determine cyclic nucleotide concentrations in parasite samples . The PfPDEβΔcatHA line was synchronised to a 2-hour invasion window , as described above , to obtain 5×108 ring stages . The culture was split into two , and one half was treated with 100 nM RAP and the other with vehicle ( DMSO ) . RAP and DMSO were washed out 3 hours later . Schizonts were Percoll purified 40 hours post-invasion and grown for a further 8 hours in the presence of 1 . 5 μM PKG inhibitor Compound 2 . Fully segmented schizonts were then harvested and host erythrocytes lysed with 0 . 15% saponin ( Sigma ) in the presence of complete protease inhibitors ( Roche ) and washed twice in PBS plus protease inhibitors , snap-frozen and stored at −80 °C . Parasite proteins were extracted with 10 volumes of 9 M urea in 50 mM HEPES , pH 8 . 5 , containing benzonase ( Sigma ) at 100 units/mL . Lysates were sonicated with a probe sonicator ( three bursts of 15 seconds on ice ) , centrifuged at 15 , 000g for 30 minutes at 4 °C , and protein content determined by a Bradford protein assay . A total of 1 . 05 mg of each protein sample was reduced by 5 mM dithiothreitol , alkylated with 10 mM iodoacetamide , and quenched with 7 . 5 mM dithiothreitol . Samples were diluted with 50 mM HEPES to reduce the urea concentration to <2 M prior to trypsin digestion . Peptides were desalted using a C18 Sep-Pak cartridge under vacuum , each sample divided into three ( 3 × 350 μg ) , and dried . Samples were resuspended in 50 mM HEPES and 30% ( v/v ) acetonitrile , and the corresponding TMTsixplex ( 0 . 8 mg ) label ( resuspended in anhydrous acetonitrile ) was added ( TMT6-126 , -127 , -128 to +RAP; TMT6-129 , -130 , -131 to DMSO ) . Hydroxylamine was added to quench the reaction , and the samples were mixed and desalted using a C18 Sep-Pak cartridge . Dried peptide mixtures were resuspended in 1 M glycolic acid + 80% acetonitrile + 5% trifluoroacetic acid and added to titanium dioxide beads ( 5:1 [w/w] beads:protein ) , washed under acidic pH , and eluted from the beads by adding 1% ammonium hydroxide followed by 5% ammonium hydroxide , and dried by vacuum centrifugation . One third of the material was desalted with the use of a C18 Stage Tip , and the other two thirds was fractionated and desalted by the use of a Pierce High pH Reversed-Phase Peptide Fractionation Kit . An Orbitrap Fusion Lumos was used for data acquisition . Desalted phosphopeptide mixtures were resuspended in 25 μL 0 . 1% trifluoroacetic acid and injected twice ( 10 μL per injection ) ; high-pH fractionated phosphopeptide mixtures were resuspended in 15 μL 0 . 1% trifluoroacetic acid and injected once ( 10 μL ) . Each run consisted of a 3-hour gradient elution ( 75 μm × 50 cm C18 column ) , with higher-energy collision dissociation ( HCD ) being the selected activation method . MaxQuant [53] ( version 1 . 5 . 2 . 8 ) was used for all data processing . The data were searched against UniProt extracted H . sapiens and P . falciparum proteome FASTA files . A decoy database containing reverse sequences was used to estimate false discovery rates and the false discovery rate was set at 1% . Default MaxQuant parameters were used with the following adjustments: reporter ion MS2 with the sixplex TMT isobaric labels was selected , Phospho ( STY ) was added as a variable modification , and ‘Filter labeled amino acids’ was deselected . Protein levels ( MaxQuant ProteinGroups ) were calculated from nonphosphorylated material quantified in the phosphopeptide-enriched sample . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [54] partner repository with the dataset identifier PXD009157 . CDPK1 and CRK4 substrate motifs used as controls were combinations of motifs proposed previously [55 , 56] . Individual motifs are shown in S5 Fig . All graphs were created using GraphPad Prism7 and statistical significance tests performed in the same software . Statistical significance tests on phosphoproteome data ( Welch t test with S0 = 0 . 2 , permutation-based FDR set to 0 . 05 and 250 randomisations ) , motif analyses , 1D rank-based annotation enrichment [57] , scatterplots , and all matrices were created in Perseus 1 . 4 . 0 . 2 . Sequence logo was created using IceLogo ( https://iomics . ugent . be/icelogoserver/ ) using all 5 , 374 phosphosites ( 31 amino acid sequence windows ) identified in this study as the reference dataset and all phosphosites significantly ( Welch t test ) increased in the PfPDEβ KO sample as the experimental set . GO enrichment analysis was performed on Gene Ontologizer ( http://ontologizer . de/ ) using the latest ontology and P . falciparum gene association files downloaded from http://www . geneontology . org . Parent-Child-Union was used as the calculation method and p-values adjusted using the Bonferroni correction . All gene IDs present in the phosphoproteome dataset served as reference against gene IDs representing significantly changed phosphosites in the PfPDEβ KO sample .
|
Cyclic nucleotide signalling pathways are ubiquitous in eukaryotes and regulate a plethora of cellular processes . Pathway components include cyclases and phosphodiesterases that synthesise and break down the intracellular second messengers cyclic AMP ( cAMP ) and cyclic GMP ( cGMP ) ; the signal is translated into a cellular response by effector kinases activated by elevated cyclic nucleotide levels . Malaria parasites deploy cyclic nucleotide signalling to regulate virtually every stage of their complex life cycle . Using a conditional gene knockout approach , we investigate the function of phosphodiesterase β ( PDEβ ) in the disease-causing blood stage parasites . PDEβ disruption causes a severe reduction in erythrocyte invasion and rapid post-invasion death . Although we show that PDEβ can hydrolyse cAMP and cGMP , both parts of the phenotype are linked to elevated cAMP levels and hyperactivation of PKA . Quantitative phosphoproteomic analysis identified sites that are differentially phosphorylated in the PDEβ knockout , revealing a role for cAMP signalling in cellular processes ranging from chromatin organisation to protein synthesis , as well as the regulation of parasite-specific components of the erythrocyte invasion machinery . In summary , PDEβ disruption causes a profound dysregulation of key events during blood stage replication that could be exploited for the development of new antimalarial drugs .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Materials",
"and",
"methods"
] |
[
"phosphorylation",
"parasite",
"groups",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"plasmodium",
"enzymes",
"enzymology",
"plasmodium",
"falciparum",
"parasitic",
"protozoans",
"parasitology",
"apicomplexa",
"protozoans",
"sequence",
"motif",
"analysis",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"malarial",
"parasites",
"bioinformatics",
"proteins",
"merozoites",
"phosphodiesterases",
"biochemistry",
"eukaryota",
"blood",
"anatomy",
"post-translational",
"modification",
"database",
"and",
"informatics",
"methods",
"physiology",
"biology",
"and",
"life",
"sciences",
"organisms"
] |
2019
|
Phosphodiesterase beta is the master regulator of cAMP signalling during malaria parasite invasion
|
In most settings , the diagnosis of scabies is reliant on time-consuming and potentially intrusive clinical examination of all accesible regions of skin . With the recent recognition of scabies as a neglected tropical disease by the World Health Organization there is a need for standardised approaches to disease mapping to define populations likely to benefit from intervention , and to measure the impact of interventions . Development and validation of simplified approaches to diagnose scabies would facilitate these efforts . We utilised data from three population-based surveys of scabies . We classified each individual as having scabies absent or present overall , based on whole body assessment , and in each of 9 regions of the body . We calculated the sensitivity of diagnosing the presence of scabies based on each individual body region compared to the reference standard based on whole body examination and identified combinations of regions which provided greater than 90% sensitivity . We assessed the sensitivity according to gender , age group , severity of scabies and the presence or absence of impetigo . We included 1 , 373 individuals with scabies . The body regions with highest yield were the hands ( sensitivity compared to whole body examination 51 . 2% ) , feet ( 49 . 7% ) , and lower legs ( 48 . 3% ) . Examination of the exposed components of both limbs provided a sensitivity of 93 . 2% ( 95% CI 91 . 2–94 . 4% ) . The sensitivity of this more limited examination was greater than 90% regardless of scabies severity or the presence or absence of secondary impetigo . We found that examination limited to hands , feet and lower legs was close to 90% for detecting scabies compared to a full body examination . A simplified and less intrusive diagnostic process for scabies will allow expansion of mapping and improved decision-making about public health interventions . Further studies in other settings are needed to prospectively validate this simplified approach .
Scabies , a skin condition due to the microscopic mite Sarcoptes scabiei [1] , is a major public health problem worldwide , particularly in low and middle income tropical settings , and has recently been adopted as a neglected tropical disease by the World Health Organization ( WHO ) [2] . This designation arose because of the increasing recognition of the importance of scabies , as well as the emerging evidence that effective control can be achieved by the strategy of mass drug administration ( MDA ) . Evidence from studies using permethrin and ivermectin have demonstrated that MDA has a substantial impact on the prevalence of both scabies and secondary bacterial skin infections ( impetigo ) at the community level [3–6] . The scabies mite can only be directly visualised with a microscope , and there is currently no laboratory test for infestation . Therefore , the diagnosis of scabies is most often reliant on detection of characteristic signs on clinical examination , with only a limited role for direct visualisation in high resource settings . Scaling up MDA for scabies control will require a substantial effort in disease mapping to define populations and communities likely to benefit from intervention and then determining the impact of interventions . Development and validation of a simplified approach to diagnosis of scabies would facilitate these efforts . In the clinical setting , the purpose of scabies diagnosis is to support optimal decision-making about individual patient management . As such , a thorough examination , that generally covers the entire skin surface , is required to minimise errors in diagnosis . However , for public health decisions , such as whether or not to initiate MDA , the aim is to assess the community prevalence of scabies , so a simplified examination might be appropriate , as has been used for other NTDs [7] . Such a protocol , if valid at the community level , could speed up data collection and reduce the imposition on survey participants that comes with full body examination which can be a barrier to accepting examination in some settings . Therefore , we aimed to evaluate the accuracy of a simplified examination for the diagnosis of scabies that could be used to guide public health decision making .
We utilised data from three recent , large population-based surveys of scabies , two conducted in the Solomon Islands , in Western and Choiseul provinces respectively , and one in Fiji . [6 , 8] . These studies were all population-based prevalence surveys using similar diagnostic and data collection tools . The surveys in Choiseul and Fiji were conducted as baseline for intervention trials . In all three studies examination involved examination of arms , legs , face and torso ( excluding the breasts in women ) . Patients were asked if they had itch in the groin , buttocks or breasts and , if so , these areas were also examined . The whole body was examined in children aged under 1 year . Examination for all surveys was performed by individuals with experience in the diagnosis of scabies in low-resource settings . From each survey’s primary database , we extracted patient-level data , including demographic characteristics , the presence or absence of both scabies and impetigo ( bacterial infection ) lesions and , if present , their number and distribution . Each study had used similar diagnostic criteria for scabies based on the finding of typical lesions ( burrows , papules , nodules , vesicles ) in a classical distribution[9] . In both the original studies and the combined analysis presented here , scabies severity was defined by the number of lesion detected as mild ( ≤10 lesions over all areas examined ) , moderate ( 11–49 lesions ) or severe ( ≥ 50 lesions or crusted scabies ) [9] . In each study , lesions which were moist , purulent or crusted were considered to indicate the presence of impetigo . In each of the original studies , examination findings were recorded for each of nine body regions ( Fig 1 ) . To obtain a reference diagnosis , we classified each individual as having scabies or not , according to whether scabies had been detected at any body site , using the standard examination conducted in the original studies . We further classified those with scabies , based on the presence or absence of scabies at each of the nine regions . We defined body regions as “exposed” if they could be routinely examined without removing clothes . The face , the upper arm ( including the elbow ) and lower arm ( including the wrist ) , hands and the lower leg ( including the ankles ) and feet met this definition . The torso , upper legs ( including the knee ) , buttocks and groin were the “unexposed” regions . We then evaluated simplified diagnostic algorithms based on body region-specific findings: A person would be classified as having scabies if it was detected at a particular region or grouping of regions . For these algorithms , we calculated the sensitivity compared to the reference standard based on whole-body examination . We then identified groupings which provided greater than 90% sensitivity in comparison to the reference standard . We assessed sensitivity across subgroups defined by gender , age group , severity of scabies and the presence or absence of impetigo . We calculated the prevalence of scabies that would have been measured in each of the three original studies using optimal combinations based on simplified examination . We used a one-sided test to compare the proportion of individuals diagnosed with scabies based on the standard examination with the proportion diagnosed based on an examination of ‘exposed’ body regions . We considered a p-value of <0 . 05 to be consistent with a statistically significant difference . Statistical analysis was performed in R 3 . 4 . 3 ( The R Foundation for Statistical Computing ) .
The combined sample size of the three study datasets was 5 , 358 , with similar numbers contributed from each of the three surveys ( 1908 , 1399 and 2051 from Western and Choiseul provinces of the Solomon Islands , and Fiji ) . Overall 2 , 801 ( 52 . 3% ) of study participants were female and the median age was 14 years ( IQR 7–36 years ) ( Table 1 ) . In the original studies 1 , 373 individuals ( 25 . 6% ) were diagnosed with scabies ( 18 . 1% , 18 . 7% and 36 . 4% across the surveys ) at any body location . Scabies was present in a median of 2 body regions ( IQR 1–3 ) . Of the 1373 cases of scabies , the disease was classified as mild in 684 ( 49 . 7% ) participants , moderate in 513 ( 37 . 5% ) and severe in 176 ( 12 . 8% ) . Data on scabies severity was missing for two participants , so they were excluded from subgroup analyses . Overall the proportion of individuals with impetigo in the original studies was 26 . 6% ( n = 1 , 425 ) and was significantly higher among individuals with scabies ( 45 . 1% vs 20 . 2% , OR 3 . 24 , p <0 . 001 ) . The highest diagnostic yield was through examination of the hands ( sensitivity compared to whole body examination 51 . 2% ) , feet ( 49 . 7% ) , and lower legs ( 48 . 3% ) . As shown in Table 2 , examination of the whole of the upper limb ( upper arm , lower arm and hand ) had a sensitivity of 67 . 4% ( 95% CI 64 . 8–69 . 8% ) compared to the reference standard examination . Examination of the exposed part of the lower limbs ( lower leg and feet ) had a sensitivity of 55 . 8% ( 95% CI 53 . 2–68 . 5% ) compared to the reference standard examination . Examination of the exposed components of both limbs had sensitivity of 93 . 2% ( 95% CI 91 . 2–94 . 4% ) . The sensitivity of the algorithm based on exposed regions was above 90% across all subgroups defined by sex and age group except people over 50 years , in whom it was 88 . 0% ( 95% CI 81 . 3–92 . 7 ) . It was greater than 90% in mild , moderate and severe scabies and individuals with or without impetigo ( Table 3 ) . Excluding the upper arms from the examination significantly reduced the sensitivity in a number of subgroups while examining the remaining exposed site , the face , did not significantly increase sensitivity ( Table 3 ) . The prevalence estimates derived from simplified examination did not differ significantly from those obtained in any of the original surveys . Excluding the upper arm resulted in a statistically significant difference in the prevalence estimate in a single survey ( Table 4 ) .
Based on analysis of primary data from three large , population-based surveys of scabies prevalence , we found that restriction to particular body regions defined as exposed had close to 90% sensitivity for detecting scabies , compared to a whole-body examination . Use of a restricted examination would have generated prevalence estimates very similar to those obtained from full body examination . Importantly , this finding was not dependent on severity of scabies or the presence or absence of impetigo . With scabies newly recognised as a neglected tropical disease by WHO , efforts are underway to identify and implement intervention strategies , potentially including MDA . In order to scale up interventions , it will be necessary to have standardised means of classifying geographic areas in regard to scabies prevalence . Best practice methods of assessment reported from recent prevalence surveys and trials have generally depended on whole body examination by experts in dermatology . This method has a number of limitations , including the need for private examination rooms , the time required , and participant sensitivities about examination . Defining and validating a simplified form of examination will facilitate mapping , especially in resource-limited settings . The approach of seeking simplifications in diagnostic processes has been used in the context of other NTDs such as the WHO grading criteria for trachoma [7] , and allowed the large-scale mapping of disease prevalence [10] . Even more limited examination , such as the hands alone , had a sensitivity of only 51 . 2% . It might be argued that for public health decision-making , it is more important to provide a broad ranking of prevalence than to accurately estimate the absolute level , but the markedly reduced sensitivity of examining the hands alone would substantially increase the likelihood of making the wrong decision . More feasibly , an examination of both arms and both lower legs had greater than 90% sensitivity , providing an option that balances accuracy for public health purposes , while being practical in the field . In all three studies that were the source of data for the analyses presented here , the diagnosis of scabies was made by an individual experienced in the diagnosis of scabies . We used current best available diagnostic criteria which have previously been validated in both the Pacific and Africa [9 , 11] . Potentially , the sensitivity of a more limited examination might be reduced if conducted by a person with less training or experience . It will be important to conduct further validation of simplified examination performed by those with less experience . Validation of the simplified criteria could be conducted alongside prospective validation of recently published consensus diagnostic criteria for scabies [12] . A crucial step in preparing for such validations will be the development of standardised training materials so that a much larger number of assessors can be engaged in evaluations of scabies prevalence , as has been done for trachoma grading [10] . A limitation of the data sources analysed is that the breasts and groin were only examined in the underlying surveys if participants reported itch . It is therefore possible that some people had scabies in these regions but were classified as not having scabies . The consequence of this would be an over-estimate of the sensitivity of a more limited examination . However , these differences would be unlikely to alter the prevalence estimates sufficiently to be of public health importance . Our data are derived entirely from studies conducted in the Pacific region . Evaluation of the proposed simplified diagnostic approach will need to be conducted in a wider range of demographic and geographic settings to ensure the findings are broadly applicable . The extent to which areas of the skin are exposed and may be examined is to a large extent culturally dependent , and will therefore vary by region . For example , in some regions lifting up a sleeve to examine the lower portion of the upper arm may therefore be considered unobtrusive but lifting up a shirt to expose the abdomen less so . Further studies are necessary to evaluate our proposed simplified algorithm in a variety of epidemiologic settings , using prospective methodology . Further validation of the simplified assessment will need to consider both the accuracy , and acceptability of different levels of examination . Other issues , such as the gender of the examiner and setting of the examination , will also be relevant in ensuring that culturally appropriate methods of prevalence assessment are widely available in scabies-endemic areas . The adoption of scabies as a neglected tropical disease by the WHO has provided fresh impetus to the development of tools to control scabies as a public health problem . Our study adds valuable data to the development of a simplified diagnostic process for scabies that may be applied to guide decisions about future public health interventions .
|
Scabies , caused by infestation with the microscopic mite Sarcoptes scabiei , is a major public health problem worldwide , particularly in low- and middle-income tropical settings . The diagnosis of scabies is reliant on detection of characteristic signs on clinical examination . Examination of the whole body is time-consuming and intrusive , whereas a more limited examination might be sufficient to guide public health decisions . We analysed data from several large scabies prevalence surveys to see if a more limited examination of the body provided acceptable sensitivity . We found that limiting examination to the exposed components of both limbs had high sensitivity compared to full body examination . Further studies are needed to prospectively validate simplified diagnostic approaches and aid scale up of scabies control programmes .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"legs",
"feet",
"decision",
"making",
"tropical",
"diseases",
"social",
"sciences",
"parasitic",
"diseases",
"neuroscience",
"cognitive",
"psychology",
"ectoparasitic",
"infections",
"sexually",
"transmitted",
"diseases",
"cognition",
"neglected",
"tropical",
"diseases",
"body",
"limbs",
"public",
"and",
"occupational",
"health",
"infectious",
"diseases",
"musculoskeletal",
"system",
"hands",
"scabies",
"arms",
"psychology",
"anatomy",
"biology",
"and",
"life",
"sciences",
"cognitive",
"science"
] |
2018
|
Exploration of a simplified clinical examination for scabies to support public health decision-making
|
A facile and efficient method for the precise editing of large viral genomes is required for the selection of attenuated vaccine strains and the construction of gene therapy vectors . The type II prokaryotic CRISPR-Cas ( clustered regularly interspaced short palindromic repeats ( CRISPR ) -associated ( Cas ) ) RNA-guided nuclease system can be introduced into host cells during viral replication . The CRISPR-Cas9 system robustly stimulates targeted double-stranded breaks in the genomes of DNA viruses , where the non-homologous end joining ( NHEJ ) and homology-directed repair ( HDR ) pathways can be exploited to introduce site-specific indels or insert heterologous genes with high frequency . Furthermore , CRISPR-Cas9 can specifically inhibit the replication of the original virus , thereby significantly increasing the abundance of the recombinant virus among progeny virus . As a result , purified recombinant virus can be obtained with only a single round of selection . In this study , we used recombinant adenovirus and type I herpes simplex virus as examples to demonstrate that the CRISPR-Cas9 system is a valuable tool for editing the genomes of large DNA viruses .
With the rapid development of biotechnology , site-specific genome editing approaches allow researchers to target any gene within any organism . Two well-known genome-editing technologies include zinc-finger nucleases ( ZFNs ) and transcription activator-like effector nucleases ( TALENs ) . These approaches function by using a nuclease to specifically target a gene and cleave its DNA to induce double-stranded breaks ( DSBs ) at the target site . The breakage then triggers the cellular DNA repair mechanisms , including error-prone non-homologous end joining ( NHEJ ) and homology-directed repair ( HDR ) [1] . However , customizing gene disruption using either ZFNs or TALENs requires the design of specific proteins to target each dsDNA site [2] , [3] . Clustered regularly interspaced short palindromic repeats ( CRISPR ) - CRISPR-associated 9 ( Cas9 ) is a recently discovered , site-specific genome editing system that is part of the CRISPR-Cas bacterial acquired immune system , which cleaves foreign DNA [4] , [5] , [6] . The Cas9 protein belongs to the type II CRISPR-Cas system that , with the guidance of a CRISPR RNA ( crRNA ) and trans-activating crRNA ( tracrRNA ) , cleaves DNA matching the crRNA in a sequence-specific manner [7] , [8] . Moreover , a recent study by Jinek et al . demonstrated that the crRNA-tracrRNA complex can be fused to form guide RNAs ( gRNAs ) that function in specific DNA recognition and Cas9 protein binding [8] . The CRISPR-Cas9 system requires the design of only a single guide sequence that matches the DNA targeted for cleavage . This property greatly increases the system's ease of use compared with ZFN and TALEN-based genome editing . Since the first report of the use of CRISPR-Cas9 for genome editing in human cells in early 2013 [9] , [10] , [11] , [12] , this technology has been used in vivo in human cells and other organisms , including Streptococcus pneumoniae , Escherichia coli , Saccharomyces cerevisiae , crop plants , Arabidopsis , Nicotiana benthamiana , Danio rerio ( zebrafish ) , mice , and rats [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] . Nevertheless , as mentioned in several recent publications , Cas9 tolerates mismatches between the guide RNA and target DNA in a sequence-dependent manner and potentially tolerates up to five target mismatches in human cells [12] , [24] , [25] , [26] , [27] , thereby promoting undesired off-target mutations . To improve the specificity of Cas9-mediated genome editing , a strategy that combines a mutant nickase version of Cas9 ( Cas9n ) with a pair of offset gRNAs that bind to opposite DNA strands at the target locus was developed . In cell lines , this strategy reduces off-target activity by 50- to 1500-fold without sacrificing cleavage efficiency [28] . Non-cellular microorganisms such as viruses replicate within the living cells of other organisms and can infect all forms of life , thus causing diseases . These organisms are also potential gene therapy vectors . The sizes of viral genomes vary greatly , and their genetic information can range from 1 Kb to 2 . 47 Mb [29] . Based on the accessibility of genetic manipulation handling , those viruses with genome sizes less than 30 Kb , including DNA viruses such as Hepadnaviridae , Polyomaviridae , and Papillomaviridae and all RNA viruses , are categorized as small viral genomes . Normally , several monoclonal restriction enzyme digestion sites exist on a viral genome or cDNA , which makes mutant construction and fragment substitution easier . However , for those viruses with large viral genomes , including Adenoviridae , Herpesviridae , and Poxviridae , which have genome sizes larger than 30 Kb , monoclonal restriction enzyme digestion sites are not readily available , making the enzyme digestion and ligation processes more difficult . Currently available genome editing approaches use large fragment cloning procedures , such as two-step counter-selection and bacterial artificial chromosome ( BAC ) system construction . These methods require multiple steps , including continuous growth of transformants and the selection and construction of a BAC vector , which are time-consuming and labor-intensive [30] , [31] . Therefore , it would be beneficial to establish a more efficient and straightforward genome editing technology for constructing mutant or recombinant large DNA viruses . Ebina et al . used the CRISPR-Cas9 system to disrupt the latent HIV-1 provirus [32] . However , use of the CRISPR-Cas9 system to induce mutations in non-integrating viral genomes has not been reported , likely due to the genomic differences between viruses and cells . The genomes of viruses are much smaller than those of mammalian cells; therefore , cleavage of a DNA sequence in a virus is more likely to affect viral replication than mammalian cell replication . Furthermore , unlike mammalian cells [33] , the genomes of DNA viruses are in a dissociated form , so DSBs may terminate viral replication . We first confirmed the effectiveness of the CRISPR-Cas9 system at altering large-genome DNA viruses by introducing site-specific indels and inserting a foreign gene into an adenoviral vector ( ADV ) and type 1 herpes simplex virus ( HSV1 ) in a single step using the CRISPR-Cas9 system . We found that this system interfered with viral replication and that the efficiency of genome mutation and recombination increased significantly . Our work demonstrates the versatility of the CRISPR-Cas9 platform for non-cellular microorganisms in an episomal form .
We chose the commonly used ADV to study whether DNA viruses could be repaired and mutated after nuclease cleavage . To exclude the effects of viral gene inactivation on viral replication , we selected the nonessential heterologous gene Enhanced Green Fluorescent Protein ( EGFP ) carried by a recombinant adenoviral vector ( ADV-EGFP ) as the target gene . Three guide RNAs ( gRNA-173 , gRNA-174 , and gRNA-175 ) were designed to target the coding sequence of the EGFP gene ( Figure 1A ) . When the vector DNA carrying the CRISPR-Cas9 system ( pcw173 , pcw174 , and pcw175 ) was transfected into 293FT cells ( Figure 1B ) , Cas9 protein expression was detected ( Figure S1A ) . At 24 hours post-transfection , the cells were infected with ADV-EGFP at a multiplicity of infection ( m . o . i . ) of 1 PFU/cell , and the progeny viral genomes ( P1 ) were harvested when cytopathic effects ( CPEs ) occurred . A SURVEYOR nuclease assay was then performed to detect site-specific mutations . All three gRNAs could induce site-specific mutations in the adenoviral genome ( Figure 1C ) ; gRNA-175 was the most efficient ( 47 . 4% ) , followed by gRNA-174 ( 37 . 3% ) , whereas gRNA-173 was the least efficient ( 32 . 7% ) . We further analyzed gRNA-175 by cloning the PCR products from the gRNA-175-cleaved viral genome into T-vectors for sequencing to determine the types of mutations that were present ( Figure 1D ) . Nine of the twenty clones were wild type ( containing no mutations ) , and eleven clones had indels in the region complementary to the gRNA-175 . Five of these eleven clones had a GCG sequence missing , and three had a G missing , which are both believed to be dominant mutations . Additionally , each other type of mutation was found in one or two samples ( Figure 1E ) . We next examined whether such mutations could be passed down to progeny viruses by infecting cells with the P1 virus and extracting the progeny viral genomes ( P2 ) when CPEs occurred . A mutated genome was observed among the infectious P2 viruses that were analyzed using the SURVEYOR assay ( Figure 1C ) . Our data indicate that DNA viral genomes can be repaired through NHEJ after site-specific cleavage by the CRISPR-Cas9 system and that the mutant genomes can be effectively transferred to progeny viruses . Moreover , the more active Cas9 resulted in more mutations in the progeny virus contained . To evaluate the advantages of the CRISPR-Cas9 system , we compared the efficiency of gene editing between CRISPR-Cas9 and a pair of TALENs targeting the same adenoviral locus ( Figure 2A ) . The CRISPR-Cas9 system performed gene editing at a higher efficiency than the TALENs ( Figure 2B ) . Furthermore , the ratio of mutant to total viral genomes decreased dramatically from P1 to P2 during infectious viral particle packaging . Therefore , Cas9-mediated indel-mutated genomes were less likely to form infectious viral particles than wild-type viral genomes . A more significant example of this phenomenon was demonstrated by co-expressing the Cas9 protein with gRNA-174 and gRNA-175 in cells to induce simultaneous double site-specific cleavages within the viral genome ( Figure 3A ) . When the viral genome was amplified by PCR , this double cleavage resulted in the formation of a DNA fragment that was approximately 100 bp shorter ( Figure 3B ) than the product of single site-specific cleavages by gRNA-174 or gRNA-175 . Further sequencing confirmed that the viral genomes were repaired at the exact gRNA-174 and gRNA-175 cleavage sites ( Figure 3C ) . However , few of the viral genomes that resulted from double site-specific cleavages could form infectious viral particles , and the mutations in these genomes were rarely passed to the viral progeny when progeny viruses were used to infect fresh cells . Because EGFP is a nonessential protein in the adenoviral replication cycle , our preliminary results revealed that viral genomes could be repaired by cellular DNA repair systems after Cas9 cleavage . Moreover , indels were formed and could be transferred to the progeny virus . Because of the off-target activity of Cas9 , we aligned the gRNA-175 sequence with ADV-EGFP genome . Based on previous reports that non-specific cleavage of CRISPR-Cas9 is sensitive to the position and number of mismatches , two methods were used to find homologous sequences . One approach was to find the longest concatenated sequence that matched the protospacer-adjacent motif ( PAM ) -proximal region . Until the PAM-upstream gRNA sequence was decreased to 7 nt , only one homologous sequence other than the target sequence was found . The number of mismatches between gRNA-175 and this sequence was ten ( OTC175-A1 ) ( Table S3 ) . The other method was searched for a homologous sequence with the closest match to the gRNA-175 base pairs , for which the most similar homologous sequence contained five mismatched base pairs and included 2 gaps . However , this sequence contained no PAM motif that Streptococcus pyogenes Cas9 could recognize ( OTC175-B1 ) . Among those containing the PAM homologous sequence , the fewest number of mismatched base pairs was seven ( OTC175-B4 ) ( Table S4 ) . Using RGN-treated ( gRNA-175 ) P1 ADV-EGFP progeny viral genomes as the object , deep sequencing was performed on these three highly homologous regions , and no off-target mutations were detected ( Table 1 ) . Furthermore , an RGN ( gRNA-175 ) -induced ADV-EGFP mutant without green fluorescence was purified ( ADV-EGFPdG ) , and whole-genome sequencing revealed only one guanine deletion in the gRNA-175 target site compared with ADV-EGFP genome ( Text S2 ) . These results suggest that the CRISPR-Cas9 system avoids off-target activity on adenoviral genomes . Because of the convenience of downstream recombinant virus isolation , factors affecting viral genome editing were further evaluated . We first examined the effect of post-transfection infection time on the efficiency of mutant formation when introducing the CRISPR-Cas9 system into cells . The mutation efficiency increased with the increase in Cas9-gRNA ( pcw175 ) expression and reached its peak between 24 and 36 hours post-transfection ( Figure 4A ) , which is the peak time for the expression of heterologous genes post-plasmid transfection and the time when Cas9 accumulates in the nucleus ( Figure S1B ) . We next studied the effect of various viral m . o . i . on the efficiency of mutant formation ( Figure 4B ) . Mutant viral genome formation was the most efficient at an m . o . i . of between 1 and 10 , and the efficiency of Cas9 cleavage and NHEJ repair decreased at a viral m . o . i . of 100 , likely due to excessive quantities of viral genomes entering the cells . Consequently , the viral genome may be completely cleaved and repaired at a low m . o . i . Surprisingly , at a viral m . o . i . of 0 . 1 , the mutation efficiency declined , and at this relatively low m . o . i . , no significant CPE was observed , even after a long incubation period post-CRISPR-Cas9 transfection ( up to 6 days ) ; this result indicated that at a low m . o . i . , viral genomes cannot effectively replicate in cells transfected with the CRISPR-Cas9 system . In the few cells that were not transfected with CRISPR-Cas9 ( approximately 5% ) , the wild-type virus replicated normally; therefore , the percentage of mutants decreased . If a sufficient m . o . i . was maintained , the proportion of mutant virus among viral progeny could be further improved after two rounds of Cas9 cleavage ( Figure S2 ) . Finally , we evaluated the proportion of mutant progeny viral genomes among the total viral genomes at various harvest times . Viral genomes mutated by Cas9 cleavage and subsequent repair were detectable at 12 hours post-infection . As time progressed , the percentage of mutant progeny genomes reached a maximum and was maintained at 47%-52% of the total genomes at 36 hours ( Figure 4C ) . We also detected a change in viral titer during sampling . In control cells , the viral logarithmic growth phase occurred at 24–36 hours post-infection , although in RGN ( gRNA-175 ) -treated cells , the logarithmic growth phase was delayed to 36–48 hours , and the peak titer was 4–5-times lower than the viral titer of control cells ( Figure 4D ) . When the guide RNA was substituted with gRNA-174 , the replication of ADV-EGFP was also inhibited . Moreover , the viral replication was further reduced when gRNA175 and gRNA174 were expressed . However , when the virus was substituted with ADV-DsRed harboring a red fluorescent protein from Discosoma coral ( DsRed ) , specific gRNA-175-induced RGN did not interfere with the viral replication process ( Figure 4D ) . These results suggest that RGN can specifically inhibit the replication of viral genomes carrying a sequence complementary to the gRNA . To investigate the mechanism by which RGN inhibits viral replication , we examined the repair efficiency after ADV-EGFP was cleaved by Cas9:gRNA-175 . This experiment quantitatively detected three sequence regions ( F1–F3 ) in the ADV-EGFP genome ( Figure 4E ) . The cleavage site that gRNA-175 recognizes is within the F2 region , and the complete , but not the cleaved , genome can be amplified to obtain the F2 PCR product . F1 and F3 lie up- and down-stream of the gRNA-175 complementary sequence , respectively , and F3 was used to normalize the quantities of F1 and F2 in the two samples . In RGN ( gRNA-175 ) -expressing cells , the quantity of F1 was equal to that of the control , whereas F2 was significantly lower than that in the control ( Figure 4F ) . These results suggest that , in RGN-expressing cells , most of the viral genomes are in a cleaved form . At 24 hours post-viral infection , only 6 . 4±1 . 1% of the viral genomes were intact . Considering that the percentage of indels was 19 . 2% among the total F2 genomes ( Figure 4C ) , we predicted that at 24 hours post-infection , more than 90% of the viral genomes were cleaved , and between 1% and 7% of the viral genomes were repaired by the host DNA repair machinery . With an increase in viral replication , the proportion of the F2 fragment increased , and at 48 hours post-infection , the proportion of complete viral genomes among the total viral genomes within cells reached 23 . 8±3 . 0% . Our results indicate that the cellular expression level of the RGN complex , viral m . o . i . , and viral harvest time all affect the formation of mutant viral progeny . In addition , cleavage by the CRISPR-Cas9 system significantly inhibits viral replication . Homologous recombination is induced to repair DNA in the presence of donor DNA , DSBs caused by wild-type Cas9 cleavage , and nicks caused by Cas9n nickase mutant cleavage [8] . Therefore , this endogenous mechanism can be exploited to introduce heterologous genes that are carried on a viral vector . To analyze the efficiency and feasibility of this approach , we introduced donor DNA encoding DsRed ( pcw167 ) while transfecting the CRISPR-Cas9 system ( pcw175 ) into cells ( Figure 5A ) . The titer of the viral progeny obtained from cells infected with recombinant adenovirus was detected using a 10-fold serial dilution plaque assay . Viral titers decreased by 0 . 7–1 . 1 orders of magnitude ( approximately 4–10-fold ) in the presence of gRNA compared with a control that contained wild-type Cas9 protein alone . Fluorescence microscopy demonstrated no expression of red fluorescent protein in fresh cells infected with control progeny virus , which suggests that the efficiency of homologous recombination was lower than 10−7 . 4 . However , red fluorescent protein expression was observed at multiple dilution factors of fresh cells that were infected with progeny virus and were obtained from the RGN system , and the expression of red fluorescent protein was still observed in progeny virus , even at dilutions of 105 . The efficiency of homologous recombination increased to 2 . 6±0 . 57% ( Figure 5B ) , which was a significant increase compared with naturally occurring homologous recombination . Individual red plaques could be observed and isolated after 4–6 days of incubation due to the high recombination efficiency . A plaque was selected and examined after viral amplification by PCR and sequencing to further confirm that the progeny viruses were single ADV-DsRed ( Figure 5C , 5D ) . Moreover , whole-genome sequencing of ADV-DsRed confirmed that no other mutations were induced ( Text S2 ) . To specifically control for the types of Cas9-mediated mutations , the D10A nickase mutant of Cas9 was used to site-specifically edit the adenoviral genome [28] . High-concentration Cas9n could still introduce indels into the adenoviral genome ( Figure 6A ) and is different from that used to site-specifically edit cellular genomes [28] . Deep sequencing results indicated that by controlling the concentration of Cas9n that was introduced into cells in the presence of donor DNA ( pcw270 ) ( Figure 6B ) , an appropriate amount of Cas9n:gRNA ( 1 . 5 µg pcw180 or pcw178 ) could induce homologous recombination with donor DNA in viral genomes . The efficiency of homologous recombination by Cas9n was slightly lower than that of the wild-type Cas9 protein but could prevent the formation of other indels . By choosing a pair of Cas9n:gRNAs , even with an offset of up to 140 bp , several other types of indels could be introduced into the viral genome ( Figure 6C ) . These results indicate that unlike cells , a single-site nickase combined with donor DNA can generate more precise viral mutants for relatively small viral genomes . We performed gene knockouts and rapid reporter gene knock-ins in wild-type HSV1 to further establish the CRISPR-Cas9 system as a universal tool for large mutant viral genome construction . For convenient mutant efficiency validation , the UL23 gene encoding thymidine kinase ( TK ) , which has enzymatic activity , was chosen as the target for Cas9 . We selected gRNA-206 on UL23 as the target sequence , and the cleavage site that was induced by gRNA-206 was within the cleavage site of the restriction enzyme BsiWI ( Figure 7A ) . In the absence of RGN ( pcw206 ) , the PCR product was completely digested by BsiWI; and two fragments , 462 bp and 204 bp , were produced . However , in the presence of RGN , more than 50% of the fragments within the HSV1 genome could not be cleaved ( Figure 7B ) . These results suggest that the HSV1 genome was efficiently cleaved by Cas9 and that the BsiWI restriction cleavage site was destroyed through cellular NHEJ , which repaired the indels . We then used endpoint dilution assays to measure the titers of the progeny viruses collected at different time points . The viral growth curves showed inhibition of the HSV1 replication by Cas9:gRNA-206 ( Figure S3 ) . At 36 hours post infection , the titer for control viral progeny lacking RGN was 3 . 2×108 TCID50/ml , and the titer decreased to 6 . 31×107 TCID50/ml for the viral progeny with RGN . A known treatment for herpes infection is acyclovir ( ACV ) , a drug that targets TK . Drug-resistant , but not wild-type , viral strains can survive in the presence of 100 µg/ml ACV during titration . No ACV-resistant viruses were isolated from the viral progeny produced by the control cells; however , in the presence of RGN , ACV-resistant viral progeny reached titers of 3 . 16×107 TCID50/ml ( Figure 7C ) , which was 50 . 1% of the total virus . PCR and sequencing were performed on virus extracted from eight wells of cells with CPE at the highest dilutions ( 10−6 and 10−7 ) ( Figure S4A ) . Sequencing indicated that seven of the eight wells contained single viral mutant ( Figure 7D ) , all of which included indels at the gRNA-206 site within the UL23 sequence . Of these eight mutants , one was missing a cytosine ( D1 ) , three harbored a guanine insertion ( +1a ) , two contained a cytosine insertion ( +1b ) , one had an insertion of a 115-bp sequence ( +115 , see Figure S4B for sequence ) , and one was a mixture of two mutant viruses ( Figure S4C ) . All ACV-resistant progeny viruses underwent site-directed mutagenesis within the UL23 gene . The homologous sequence of gRNA-206 was aligned with the HSV1 viral genome , and the most similar PAM-proximal sequence in the genome contained nine concatenated identical base pairs and nine mismatches ( OTC206-A1 ) ( Table S5 ) . Moreover , the most similar homologous sequence to gRNA-206 in the HSV1 genome contained five mismatches and included a gap or six mismatches within the PAM sequence ( Table S6 ) . Deep sequencing was performed on these regions after RGN cleavage , and no mutations were detected ( Table 2 ) . These results further demonstrate that , when using the CRISPR-Cas9 system to edit viral genomes , off-target effects can be avoided . To construct HSV1 carrying an EGFP reporter gene , we introduced homologous donor DNA ( pcw209 ) and used RGN to cleave the viral genome ( Figure 7E ) . The progeny virus was assessed using a plaque assay and fluorescence microscopy . The efficiency of homologous recombination was less than 1 . 45×10−8 in control cells without RGN . In cells treated with RGN and donor DNA , ( 5 . 8±1 . 7 ) ×105 pfu/ml green plaques were obtained among ( 6 . 9±1 . 8 ) ×106 pfu/ml total plaques ( Figure 7F ) , and the efficiency of homologous recombination increased to 8 . 41% . A single HSV1-EGFP viral plaque was isolated from a 10−5 dilution and verified by PCR as pure virus with no wild-type HSV1 contamination ( Figure 7G , 7H ) . Therefore , the purified virus could be used directly for further amplification and incubation .
To the best of our knowledge , the use of the CRISPR-Cas system for editing a non-integrating viral genome has not been previously reported . HSV1 was used to exemplify the current genome editing approach , which requires amplification and purification to obtain viral genomic DNA . The genomic DNA is then co-transfected with homologous DNA sequences into cells followed by two-step counter-selection and multiple plaque isolation [30] or by inserting the viral genome into a BAC system and performing several in vitro molecular biological procedures [31] . Both processes require several weeks and are extremely laborious and time-consuming . However , in our study , by simply transfecting the CRISPR-Cas system into cells , infecting with virus , and performing an ordinary progeny virus isolation procedure , a purified viral mutant with a specific gene deletion , insertion , or sequence substitution can be obtained within a short period . Compared with well-known , site-specific genome editing technologies , such as ZFNs and TALENs , the new CRISPR-Cas approach is more convenient and efficient [10] , [12] . Because the sequence specificity of Streptococcus pyogenes Cas9 tolerates 4 mismatches between the gRNA and complementary sequence , non-specific cleavage may occur in the cellular genome [12] , [24] , [25] , [26] , [27] . Sequence alignment indicated that 76 gRNA-175 off-target sites are present in the human genome , in which 19 are located in the exons of protein-coding genes ( Table S7 ) . The highest homology contains only 2 mismatches . At this site , the indel mutation frequency that is induced by Cas9:gRNA-175 was found to be 2 . 27±0 . 18% using deep sequencing . The off-target sites for gRNA-174 , gRNA-173 , and gRNA-206 that are present in the human genome are 123 , 34 , and 8 , respectively . Because progeny viruses are able to infect new cells after cell lysis , the effect of host-cell mutations on progeny viral replication is limited . By contrast , no off-target sites containing fewer than 5 mismatches for those gRNAs exist in the ADV or HSV genomes . Therefore , for large viral genomes sized 40–1000 kb , non-specific cleavage should occur only rarely ( Tables 1 , 2 , S3 , S4 , S5 , S6 ) . The guide RNAs that were used in the present study can specifically recognize a single target in the viral genome . However , it is noteworthy that , due to the compact structure of the viral genome , gene overlapping occurs; therefore , multiple genes might sometimes be affected by the single cleavage of a viral genome . The CRISPR-Cas9 system accurately controls the site of mutation and is a highly efficient strategy for the construction of large recombinant viral genomes . Based on our experiments , high proportions of site-specific viral mutants and recombinant viruses were obtained using CRISPR-Cas9 through at least two mechanisms . First , the high cleavage efficiency of the system can significantly inhibit the replication of the wild-type virus , and second , breakage at the cleavage site can efficiently induce NHEJ or HDR . CRISPR-Cas9-induced cellular genome repair efficiency is not a major concern for eukaryotic cells , but for viruses , it could be an issue . Our study demonstrates that large quantities of viral genomes cannot be efficiently repaired after cleavage , which is likely the major reason for the inhibition of viral replication . Because viral genomes are much smaller than cellular genomes , which are restricted to the nucleus , once the relatively dissociated viral genome is cleaved by a nuclease , gene breakage and separation may be more likely to occur . As a result , even high concentrations of Cas9n nickase in complex with gRNA can induce indels . Alternatively , due to the presence of hundreds of viral genomes within a single infected cell , the limited efficiency of the DNA repair systems within cells may not be able to repair the fragmented viral genomes in time . Therefore , only some cleaved viral genomes are repaired during the early stages of infection . However , mutant viruses possess replication priority under the cleavage pressure of the CRISPR-Cas9 system . As viral replication ensues , the proportion of viral mutants gradually increases , eventually reaching a high percentage of the total amount of virus . During the process , the formation of a high proportion of viral mutants is affected by factors , including infection time after Cas9 transfection , m . o . i . , and time of harvest of viral progeny . High cleavage efficiency and low cellular repair efficiency may limit the use of the CRISPR-Cas9 system to perform multiple-site editing of viral genomes within a single replication cycle . Moreover , the mutant virus that is formed in P1 cannot be passed to the P2 virus in equal proportions , which illustrates the steric hindrance caused by the association of the Cas9 protein with specific DNA sequences [34] , and the time that elapses during cleavage and repair may affect viral genome packaging . Nevertheless , due to the short replication period and high progeny productivity of the virus , several rounds of CRISPR-Cas9 selection pressure could gradually increase the proportion of recombinant virus among total virus . This strategy could thus meet most of the genome reconstruction requirements of various types of large DNA viruses . The high capacity of large DNA viruses allows them to be more sophisticated gene vectors , but the complexity of DNA virus life cycles and the difficulty of attenuated vaccine selection make it challenging to develop vaccines against them . The highly effective , site-specific CRISPR-Cas9 genome editing system will promote more rapid development of large-genome viral vectors , attenuated vaccines against large DNA viruses , and an understanding of viral life cycles .
The human embryonic kidney ( HEK ) cell lines 293FT ( Life Technologies , Carlsbad , CA , USA ) and AD293 ( Clontech , Palo Alto , CA , USA ) and the African green monkey kidney cell line Vero ( ATCC , Manassas , VA , USA ) were maintained in Dulbecco's modified Eagle's Medium ( DMEM; Corning , New York , USA ) supplemented with 10% fetal bovine serum ( FBS; Life Technologies ) , 100 U/ml penicillin , and 100 µg/ml streptomycin at 37°C with 5% CO2 . The culture medium was changed to DMEM supplemented with 2% FBS after viral infection of 293FT , AD293 , and Vero cells . ADV-EGFP [35] and HSV1 strain 8F [36] were cultured and titered on AD293 cells and Vero cells , respectively . Virus from infected 293FT cells was harvested at 48 hours post-infection , and viral genomic DNA was extracted using the Takara miniBEST Viral RNA/DNA Extraction Kit Ver . 4 . 0 ( Takara Bio Inc . , Dalian , China ) . The genomic region surrounding the CRISPR target site of each gene was PCR amplified using Phusion high-fidelity DNA polymerase ( New England Biolabs , Beverly , MA , USA ) with primers oligo 1 and oligo 2 for ADV-EGFP and oligo 8 and oligo 9 for HSV1 . The PCR products were purified using the Universal DNA Purification Kit ( Tiangen , Beijing , China ) . Purified ADV PCR products ( 400 ng ) amplified from the genomic DNA extraction were re-annealed and treated with SURVEYOR nuclease ( Transgenomics , Omaha , NE , USA ) according to the manufacturer's recommended protocol . The products were analyzed on 10% TBE polyacrylamide gels , which were stained with SYBR Gold DNA stain ( Life Technologies ) and imaged using a Bio-Rad Gel Doc gel imaging system ( Richmond , CA , USA ) . Quantification was based on the relative band intensities , as described by Cong et al . ( 2013 ) [12] . The PCR products were ligated into the pMD20-T vector ( Takara Bio Inc . ) and submitted for sequencing using universal primers ( BGI , Guangzhou , China ) . Purified HSV1 DNA products from the genomic DNA extraction and PCR amplification were digested with BsiWI ( New England Biolabs ) for 16 hours at 55°C and analyzed on 0 . 5 µg/ml ethidium bromide-stained agarose gels ( 1% ) . Quantification was based on relative band intensities . To detect homologous sequences in the viral genome , gRNA-175 and gRNA-206 were aligned to the ADV-EGFP and HSV1 whole genomes , respectively , using two methods . gRNA sequences were sequentially deleted ( from PAM-distal to PAM-proximal ) and used to search for identical sequences in the viral genome using Vector NTI ( Life Technologies ) . In addition , gRNA sequences were randomly aligned to viral genomic DNA using the EBI online sequence alignment tool ( http://www . ebi . ac . uk/Tools/psa/lalign/nucleotide . html ) . Amplicon deep sequencing was performed on a 454/Roche GS Junior platform ( Roche , 454 Life Sciences , Branford , CT , USA ) according to the manufacturer's instructions . Each sample was amplified independently with different primers , including the 454 primer keys , and a different multiple identifier ( MID ) was used for each sample . The PCR products were resolved by 2% agarose gel electrophoresis and purified . The purified amplicons were quantified using the QuantiFluor dsDNA System ( Promega , Madison , WI , USA ) , diluted , and subjected to emulsion PCR ( emPCR ) . The enriched DNA beads were loaded onto a picotiter plate , and pyrosequencing was performed using titanium chemistry . Amplicon Variant Analyzer version 2 . 7 was used for the analysis . Quantitative PCR ( qPCR ) was performed to analyze the repair efficiency after Cas9 cleavage of the viral genome . Viral genomic DNA from infected cells was extracted as described above , and qPCR was performed on an ABI 7500 ( Life Technologies ) using SuperReal PreMix Plus ( SYBR Green ) ( Tiangen , Beijing , China ) . Three sets of sequences for ADV-EGFP were amplified separately ( F1–F3 ) . F1 was amplified using primers oligo 3 and oligo 4 , F2 was amplified using primers oligo 1 and oligo 5 , and F3 was amplified using primers oligo 6 and oligo 7 . Each experiment was performed in parallel , and triplicate samples were included in each reaction .
|
The clustered regularly interspaced short palindromic repeats ( CRISPR ) -associated ( Cas ) system was discovered as a component of the bacterial acquired immune system that cleaves foreign DNA . This system is now used for site-specific genome editing in a wide range of organisms , including bacteria , yeasts , plants , and animals . However , the use of this approach in non-cell organisms , such as non-integrating viruses , has not been reported . Because multiple steps are required to construct mutant or recombinant DNA viruses with large genomes using the current approaches , we used the CRISPR-Cas9 system to introduce site-specific indels and insert a foreign gene into an adenoviral vector and wild-type herpes simplex virus . The high efficiency of CRISPR-Cas9 editing allowed for simple construction and purification of recombinant progeny virus . We believe that this new technique will have broad practical significance for selecting attenuated vaccine strains and antiviral drugs , constructing gene therapy vectors , and establishing efficient methods for viral biological studies .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"medicine",
"and",
"health",
"sciences",
"viral",
"transmission",
"and",
"infection",
"virology",
"epidemiology",
"vector",
"biology",
"biology",
"and",
"life",
"sciences",
"microbiology",
"disease",
"vectors",
"genetic",
"engineering",
"viral",
"vectors"
] |
2014
|
High-Efficiency Targeted Editing of Large Viral Genomes by RNA-Guided Nucleases
|
For two decades , onchocerciasis control has been based on mass treatment with ivermectin ( IVM ) , repeated annually or six-monthly . This drug kills Onchocerca volvulus microfilariae ( mf ) present in the skin and the eyes ( microfilaricidal effect ) and prevents for 3–4 months the release of new mf by adult female worms ( embryostatic effect ) . In some Ghanaian communities , the long-term use of IVM was associated with a more rapid than expected skin repopulation by mf after treatment . Here , we assessed whether the embryostatic effect of IVM on O . volvulus has been altered following frequent treatment in Cameroonian patients . Onchocercal nodules were surgically removed just before ( D0 ) and 80 days ( D80 ) after a standard dose of IVM in two cohorts with different treatment histories: a group who had received repeated doses of IVM over 13 years , and a control group with no history of large-scale treatments . Excised nodules were digested with collagenase to isolate adult worms . Embryograms were prepared with females for the evaluation of their reproductive capacities . Oocyte production was not affected by IVM . The mean number of intermediate embryos ( morulae and coiled mf ) decreased similarly in the two groups between D0 and D80 . In contrast , an accumulation of stretched mf , either viable or degenerating , was observed at D80 . However , it was observed that the increase in number of degenerating mf between D0 and D80 was much lower in the frequently treated group than in the control one ( Incidence Rate Ratio: 0 . 25; 95% CI: 0 . 10–0 . 63; p = 0 . 003 ) , which may indicate a reduced sequestration of mf in the worms from the frequently treated group . IVM still had an embryostatic effect on O . volvulus , but the effect was reduced in the frequently treated cohort compared with the control population .
The macrocyclic lactone drug ivermectin ( IVM ) has a broad spectrum of applications against arthropods and nematodes . In human medicine , one of the major indications for IVM is the treatment of onchocerciasis or river blindness [1] . IVM targets both the microfilariae ( mf ) and adult stages of Onchocerca volvulus , the filarial nematode causing river blindness . By binding to glutamate-gated chloride ( GluCl ) channels , IVM may provoke pharyngeal and/or somatic paralysis of nematode parasites [2]–[5] . In Brugia malayi , a filarial nematode closely related to O . volvulus , it has been postulated that IVM may paralyze the muscle associated with the excretory vesicle , leading to a reduction in the release of immunomodulators from the parasite that enable evasion of the host immune system [6] . In synergy with the host immune response , this paralyzing effect possibly leads to the elimination of O . volvulus skin mf . Following a standard therapeutic dose ( 150 µg/kg of bodyweight ) , this so-called microfilaricidal effect of IVM leads to a 98% clearance of the skin mf within 2–3 weeks [7] . However , a standard dose of IVM is not adulticidal for O . volvulus even though repeated treatments at short intervals ( ≤3 months ) have a significant effect on the viability of a proportion of adult worms [8] . The effect of IVM on adult male worms is not very well known but multiple doses may reduce their ability to re-inseminate the females [9] . In female worms , the drug prevents , temporarily , the release of mf from the uteri . Apparently , IVM has no effect on the embryogenesis per se , but the newly produced mf accumulate in the uteri and degenerate in situ . This is the so-called embryostatic effect of IVM . This inhibition of release of viable mf for some months is important , together with the initial microfilaricidal effect , for reducing the transmission of the parasite . Because of the longevity of adult worms , IVM distribution programs need to be sustained for 15–20 years , with a high level of suppression of parasite transmission , if one wants to reach elimination of the parasite in the population [10]–[13] . Unfortunately , suboptimal responses to IVM have been reported from some Ghanaian communities that had been subjected to 10–19 rounds of annual community treatments [14] , [15] . In those poorly responding communities , repopulation of the skin by mf after IVM was unexpectedly rapid in a fraction of the population , and the response to IVM by O . volvulus was considered atypical [16] . Indeed , the joint analysis of skin microfilarial dynamics after treatment and of female adult worms' reproductive capacity suggests that in these Ghanaian communities , the strength of the embryostatic effect of IVM has been reduced in some parasites that had been previously exposed repeatedly to this drug . In a previous paper , we compared the dynamics of O . volvulus skin microfilarial densities after IVM treatment in two cohorts with contrasting exposure to this drug: one which had received repeated treatment for 13 years and one which had no history of large-scale treatments . We observed that the repopulation rate was significantly higher in the frequently treated group than in the controls between 15 and 80 days post-IVM , which suggests that the worms from the frequently treated area had resumed their capacity to release mf earlier [17] . In the present paper , we analyzed the reproductive status of O . volvulus female worms collected and the composition of the different embryonic stages found in utero , in the same cohorts before ( D0 ) and 80 days ( D80 ) after IVM , to assess whether embryo production , development and viability in the females are consistent with our previous findings .
The objective of this study was to assess whether the embryo production and development , and/or the embryostatic effect of IVM on O . volvulus have been altered after several years of drug pressure . To do this , we composed two cohorts of patients and defined the exposure factor as the area of residence ( frequently treated area or area naïve to mass IVM administration ) . The group from the IVM-naïve area was recruited in 10 neighboring communities of the Nkam valley ( Bayon , Ekom-Nkam , Mboue , Mpaka , Mbarembeng , Bakem 1 , Bakem 2 , Lonze , Manjibo and Mounko ) , a forested area located in the Littoral Region of Cameroon . These villages were known to be endemic for onchocerciasis but had not benefitted from any mass IVM treatment at the outset of the study . Since the IVM-naïve region was also known to be endemic for loiasis , Loa loa microfilaremia was assessed and those few subjects presenting with more than 30 , 000 L . loa mf per milliliter of blood were excluded from the study to prevent the occurrence of a Loa-related post-IVM encephalopathy . This IVM-naïve area will be referred , throughout the text , as the control area . The group of patients subjected to multiple IVM treatments was recruited in 22 communities of the Mbam valley ( Babetta , Balamba 1 , Balamba 2 , Bayomen , Bialanguena , Biamo , Biatsotta , Boalondo , Bombatto , Botatango , Boura 1 , Diodaré , Gah-Bapé , Kalong , Kon , Lablé , Lakpang , Ngomo , Ngongol , Nyamanga , Nyamsong and Yébékolo ) . In these communities , annual large-scale treatments with IVM have been conducted since 1994 . In addition , these patients had taken part in a clinical trial conducted between 1994 and 1997 aimed at evaluating the macrofilaricidal potential of IVM [8] . During this clinical trial , eligible patients were randomly allocated to one of the four following IVM treatment groups: 150 µg/kg body weight annually ( standard group or group 1 ) ; 150 µg/kg three-monthly ( group 2 ) ; high doses ( one dose of 400 µg/kg and then two doses of 800 µg/kg ) annually ( group 3 ) ; and high doses ( two doses of 400 µg/kg and then 10 doses of 800 µg/kg ) three-monthly ( group 4 ) . A “clearing dose” of IVM ( 150 µg/kg ) was given to all volunteers in May 1994 to avoid the possibility of severe reactions developing in any patients subsequently taking their first dose on the high-dose regimen and the first “trial” treatment was given three months later . Thus , over the four-year study period ( 1994–1997 ) and depending on their treatment group during the trial , they received 4 to 13 doses of IVM under the direct observation of the investigators . To date , no vector control has ever been implemented in either study area . Patients eligible for the present study , either from the control or the frequently treated area , were males aged 25 years and over carrying at least two palpable onchocercal nodules , but otherwise in a good state of health . All eligible subjects , including those from the IVM-naïve area , were questioned about their history of IVM treatment . A small number of patients from the IVM-naïve area declared they had occasionally received the drug during distribution campaigns organized in communities located 10–20 km away , in the West Region where large scale treatments with IVM had been ongoing for more than 10 years . Assuming that the effect of IVM on adult worm reproduction gradually disappears after 9 months [18] , all individuals who had taken IVM during the previous 9 months were discarded from the analysis . Consequently , the effect of a single dose of IVM , and not the potential cumulative effect of two doses of IVM given within a short time frame was assessed . A total of 15 individuals from the frequently treated population declared having taken IVM more recently than the previous Community-Directed Treatment with IVM which had taken place about 9 months before the first nodulectomy planned for the present study , and were thus excluded from the analyses . To assess whether the embryostatic effect of IVM had been reduced in the frequently treated population , the reproductive activity of O . volvulus adult female worms was evaluated in both populations before and 80 days after the administration of IVM . The study received ethical clearance from the National Ethics Committee of Cameroon and was approved by the Cameroonian Ministry of Public Health . The objectives and schedule of the study were explained to all eligible individuals , and those who agreed to participate signed a consent form and kept a copy of the latter . The diagnosis and extirpation of onchocercal nodules were performed as previously described [19] , with only slight modifications . Briefly , subcutaneous nodules were sought , at the outset of the study , by visual inspection of subjects , then by careful palpation in a closed but well illuminated room . The locations of all palpated nodules were recorded on a body chart . Two of these locations were randomly selected for subsequent surgical removal , the first one just before the administration of IVM , and the second one 80 days after treatment . Nodulectomies were performed under optimal aseptic conditions . All nodules present in the randomly chosen anatomical sites were collected and each was placed individually in a Petri dish containing RPMI-1640 medium ( GIBCO , Life Technologies Inc . , Burlington , ON , Canada ) in which they were cleaned of remaining human tissue . The nodules were then stored in liquid nitrogen until use . In order to isolate the adult worms contained in the nodules , the latter were digested using the collagenase technique [20]–[22] , blinded as to their origin ( frequently treated or IVM-naïve area ) , or date of nodulectomy ( pre- or post-treatment ) . After thawing , each nodule was incubated for 12–19 hours at 35°C or 37°C ( time and temperature of digestion depending on the nodule's weight ) in five milliliters of the culture medium 199 ( GIBCO , Life Technologies Inc . , Burlington , ON , Canada ) containing type I collagenase ( SIGMA , Aldrich Co . , Oakville , ON , Canada ) at a final concentration of 2 . 25 mg/ml . Details on the process of the nodule digestion are given as supplementary information ( Text S1 ) . The product of digestion ( the worm mass and digested human tissues constituting the nodule ) was placed in a Petri dish containing 15 ml of medium 199 enriched with Earle's salts ( E199 ) , L-glutamine , sodium bicarbonate ( GIBCO , Life Technologies Inc . , Burlington , ON , Canada ) , and supplemented with gentamicin sulfate ( SIGMA , Aldrich Co . , Oakville , ON , Canada ) at a final concentration of 2 mg/ml . Individual worms were isolated under a dissecting microscope using entomological and Dumont #5 forceps ( Fine Science Tools GmbH , Heidelberg , Germany ) . Each entire and live worm ( dead or calcified and incomplete or broken worms were counted but discarded from the further process ) was then spread on a labeled slide and examined under a light microscope ( magnification ×40 ) to confirm the sex of the worm . Entire male worms were individually frozen for subsequent genotyping . In the case of female worms , the head and the tail were localized , and the whole worm examined to determine whether it had been broken during the isolation process . A 15 mm-long section was then removed with a scalpel from the tail end , of each complete and unbroken female , for subsequent genotyping . The rest of the body of the female worm was used to prepare embryograms: it was cut in 1 mm thin slices and crushed in a porcelain mortar containing 1 ml of medium 199 . To avoid the shells of embryos breaking during the crushing process , the mortar was placed on a 3 cm thick wet sponge to absorb shocks between the pestle ( also in porcelain ) and the mortar . Fifteen microliters of the homogenized resulting suspension was then transferred into a 0 . 2 mm deep Malassez counting chamber and the embryograms were examined under a light microscope ( magnification ×100 or ×400 ) . All embryonic stages were identified and counted according to the following classification: viable stretched mf , degenerating stretched mf , viable coiled mf , degenerating coiled mf , viable morulae and degenerating morulae [21] . The density of oocytes was assessed in a semi-quantitative manner using four categories: absence , rare ( less than one oocyte per square of the counting chamber or PSC ) , few ( 1–10 oocytes PSC ) and numerous ( more than 10 oocytes PSC ) . The suspensions with embryos were examined by two experienced and independent investigators and when any discrepancy was found , the preparation was re-examined by both investigators . The evaluation of the uterine content was made from 15 µl of the homogenized suspension resulting from the crushing of each female worm; for a matter of simplicity , we shall express the numbers of embryos using this volume ( 15 µl ) as arbitrary unit . The reproductive status of the female worms was analyzed using one qualitative and three quantitative criteria .
In the control group , 190 individuals underwent nodulectomies before receiving IVM , and 171 ( 90 . 0% ) of them were present for the second round of nodulectomy , 80 days later . One hundred and eighty eight frequently treated individuals underwent nodulectomies before receiving IVM , and 159 ( 84 . 6% ) of them took part in the second round of nodulectomy . Overall , the 708 surgical interventions led to the collection of 1110 nodules , of which 1069 were examined and contained 1230 male and 2036 female worms ( Table 1 ) . Details on the composition of the nodules for each study site and time of examination are given in Table 1 . At D0 , embryograms were performed on 469 and 471 female worms from the control and the frequently treated groups , respectively , and at D80 , embryograms were done on 396 and 320 worms from the control and the frequently treated groups , respectively . Thus embryograms were available for 1656 of the 2036 females isolated , the difference consisting of incomplete or broken worms and of dead or calcified worms ( Table 1 ) . The distribution of female worms according to the contents of their uteri is summarized in Table 1 . Before IVM , most worms contained oocytes ( 87 . 8% in the control group vs 90 . 0% in the frequently treated ) . In the frequently treated group , female worms appeared to have a slightly higher oocyte production than in the control group , with higher proportions of females containing oocytes density of 1–10 oocytes PSC and >10 oocytes PSC ( Chi-squared: 18 . 509; 3 degrees of freedom ( df ) ; p = 0 . 0003 ) ( Figure 1 ) . This difference remained at D80 ( Chi-squared: 11 . 544; p = 0 . 0091 ) . However , in both the control and frequently treated groups , the distribution of worms according to their oocyte production did not change between D0 and D80 ( Chi-squared: 2 . 396; 3 df; p = 0 . 4943 and Chi-squared: 2 . 226; 3 df; p = 0 . 5268 , respectively ) , meaning that oocyte production remained unchanged after the IVM dose given as part of the study . A reduction in the mean number of viable morulae per female worm was observed between D0 and D80 in the two groups ( Table 1 , Figure 2a ) . This reduction was less marked in the frequently treated group ( 38 . 4% decrease ) than in the control group ( 67 . 6% decrease ) . A very similar pattern was observed for the mean number of viable coiled mf per female worm , with an average decrease of 32 . 4% in the frequently treated group and 68 . 7% in the control group ( Table 1 , Figure 2a ) . The mean numbers of embryos per worm are summarized in Figure 2a for each group and for each time of observation . Similar numbers of embryos per worm were observed in the two groups both before IVM ( mean ( standard deviation , sd ) : 54 . 9 ( 97 . 0 ) in the control group vs 54 . 3 ( 90 . 3 ) in the frequently treated group ) and 80 days after IVM ( mean ( sd ) : 72 . 4 ( 185 . 7 ) in the control group vs 72 . 5 ( 160 . 9 ) in the frequently treated group ) ( Table 1 ) . Multilevel Poisson regression confirmed a similar evolution in the total number of embryos per worm in the two groups between D0 and D80 ( incidence rate ratio , IRR: 0 . 67; 95% Confidence Interval ( 95% CI ) : 0 . 28–1 . 61; p = 0 . 37 ) ( Table S1 ) . The number of males present in the nodule was the only covariate associated with the number of embryos per worm ( IRR: 2 . 10; 95% CI: 1 . 77–2 . 49; p = 0 . 001 ) . The mean numbers of embryos per productive worm are summarized in Figure 2b for each group and for each time of observation . At D0 , 49 . 3% of the worms from the control group were productive with an average of 102 . 2 ( sd: 116 . 8 ) embryos ( viable or degenerating ) per productive worm ( Table 1 ) . In the frequently treated group , we observed similar values , with 45 . 3% of productive worms and an average of 103 . 0 ( sd: 106 . 5 ) embryos ( viable or degenerating ) per productive worm . At D80 , the proportion of productive females decreased slightly in the two groups to reach 43 . 2% in the control group and to 41 . 5% in the frequently treated group . On average , at D80 , the productive females from the control and frequently treated groups contained 156 . 5 ( sd: 252 . 5 ) and 154 . 1 ( sd: 220 . 4 ) viable or degenerate embryos per worm , respectively ( Table 1 ) . Multilevel logistic regression of the productive status of female worms showed an absence of significant difference between the two groups at each nodulectomy round and that changes in the proportion of productive worms between D0 and D80 were similar in the two groups ( OR: 0 . 97; 95% CI: 0 . 50–1 . 26; p = 0 . 339 ) ( Table S2 ) . The number of male worms in the nodule was the only covariate significantly associated with the productive status of female worms . At D0 , the proportion of female worms with viable stretched mf was significantly higher in the control group than in the frequently treated group ( 45 . 2% vs 38 . 7% , respectively , p = 0 . 044 ) ( Table 1 ) . At D80 , these proportions had slightly decreased and were not anymore significantly different between the two groups ( 37 . 4% vs 34 . 7% in the control and frequently treated group , respectively , p = 0 . 45 ) . Similarly , before treatment , the number of viable stretched mf per worm ( all worms ) was slightly higher in the controls than in the frequently treated group ( mean ( sd ) : 12 . 6 ( 24 . 9 ) vs 8 . 5 ( 19 . 7 ) , respectively , p = 0 . 002 ) . The number of viable stretched mf per worm increased by about 35% in both groups at D80 ( mean ( sd ) : 17 . 1 ( 80 . 0 ) vs 11 . 4 ( 44 . 6 ) in the control and frequently treated group , respectively , p = 0 . 127 ) ( Figure 2a ) . Multilevel Poisson regression did not show a significant difference between the two groups in the evolution of number of viable stretched mf per worm from D0 to D80 ( IRR: 1 . 03; 95% CI: 0 . 37–2 . 89; p = 0 . 949 ) ( Table S3 ) . The numbers of male and of female worms in the nodule were positively and significantly associated with the number of viable stretched mf per worm ( p = 0 . 001 and 0 . 015 , respectively ) . At D0 , the proportion of females with degenerating stretched mf was significantly higher in the frequently treated than in the control group ( 48 . 7% vs 31 . 6% , respectively , p<0 . 001 ) ( Table 1 ) . This was associated with a higher number of degenerating stretched mf per worm ( all worms ) in the frequently treated group ( mean ( sd ) : 16 . 4 ( 45 . 6 ) ) than in the control group ( mean ( sd ) : 5 . 4 ( 22 . 7 ) ) ( Figures 2a and 3 ) . At D80 , the proportion of females with degenerating stretched mf was still higher in the frequently treated group ( 58 . 1% vs 47 . 9% in the control group ) but the mean number of degenerating stretched mf per worm was the same in the two groups ( mean ( sd ) : 42 . 2 ( 110 . 7 ) vs 42 . 2 ( 134 . 6 ) in the frequently treated and the control group , respectively ) ( Figures 2a and 3 ) . However , the multilevel Poisson regression indicated that the increase in number of degenerating stretched mf per worm between D0 and D80 was much lower in the frequently treated group than in controls ( IRR: 0 . 25; 95% CI: 0 . 10–0 . 63; p = 0 . 003 ) ( Table S4 ) . Moreover , it showed that age ( IRR: 1 . 02; 95% CI: 1 . 00–1 . 04; p = 0 . 038 ) and the number of male worms in the nodules ( IRR: 2 . 08; 95% CI: 1 . 75–2 . 48; p = 0 . 001 ) were positively associated with the number of degenerating stretched mf . Oocyte production was unchanged after IVM treatment in both groups . The proportion of productive females was slightly reduced after IVM in both groups but the uteri of those productive females contained about 50% more embryos , all stages considered together , than before treatment . Whereas the numbers of morulae and coiled mf both decreased after IVM , especially in the control group , the number of viable mf increased significantly ( by about 35% ) in both groups . The number of degenerating mf in the uteri of the worms also increased after IVM in both groups , but this accumulation was more marked in the worms from the control group .
The present study was carried out in a context where many controversies about possible resistance of O . volvulus to IVM still subsist [25]–[28] . As a chapter of a detailed study conducted in Cameroon to address this issue , this investigation aimed at assessing whether the strength of the embryostatic effect of IVM against the parasite has been modified after repeated treatments . To this end , we compared the embryonic populations , before and 80 days after a standard dose of IVM , between worms collected from naïve and frequently treated cohorts of Cameroonians . In the design of the study , we tried to match the two groups as much as possible , except for the history of drug administration , on all other factors related to the epidemiology of onchocerciasis ( age , sex , level of endemicity of river blindness , Simulium species , human activities , individual level of infection ) . Yet , to account for residual differences between the groups , these individual host factors were included as adjustment covariates in the regression models ( either Poisson or logistic ) while comparing the effect of IVM on embryonic populations between the two groups . Embryograms revealed that the worms from the repeatedly treated cohort had a higher oocyte production compared to the naïve worms , suggesting that the former may have a higher capacity of reproduction than the latter . Nonetheless , at D80 , the oocyte production was similar to its level at D0 in the two groups . These results confirm that oocyte production is not affected by IVM [29] . Morulae and coiled mf were also found at D80 , which confirms that IVM does not interrupt the embryogenesis of O . volvulus [18] , [30] . However , despite the unchanged production of oocytes after IVM treatment ( Figure 1 ) , we observed a reduction in the mean number of viable morulae and coiled mf per female worm between D0 and D80 ( Figure 2a and 2b ) . Such a reduction has been previously described in O . volvulus [30] and Dirofilaria immitis ( dog heartworm ) [31] . Maintenance of oocyte production associated with a reduction of morulae and coiled mf suggests that the oocytes were likely not fertilized after treatment , probably due to a lack of female re-insemination [9] , [32] . It has been hypothesized that IVM interferes with mate-finding by reducing the number of male worms in the nodules [33] . Migration of male worms away from the nodules might be due to the fact that IVM concentration is higher in the latter than in other human host tissues [34]–[36] . In view of the probable effect of IVM on release of substances from the excretory pore of filariae [6] , one could alternatively hypothesize that IVM may block the release of sex pheromones from the female worms which normally attract male worms to the nodule and to mate with the female worms . Investigating the effects of multiple monthly doses of IVM on adult O . volvulus , Duke et al . [37] also provided histological evidences that , after IVM , sperm of male worms can be stuck in the mass of degenerating mf in the anterior parts of the uteri of re-inseminated female worms . This suggests that , despite re-insemination , the sperm would be unable to reach the seminal receptacle of a proportion of female worms . In the present study , an accumulation of stretched mf ( either viable or degenerating ) in female worms uteri was observed in both groups after IVM treatment . This indicates that the embryostatic effect of IVM was still operating in the worms from the frequently treated population . However , and this is probably the most interesting finding of our study , we observed a much lower increase in the mean number of degenerating stretched mf between D0 and D80 in the frequently treated cohort compared to the control group . The physiological mechanisms associated with degenerative changes of O . volvulus mf in utero have not been elucidated . In the skin , degeneration of mf results from immunological process induced or facilitated by IVM [38]–[40] . However , in the uteri , mf are not in contact with the host immune cells . As suggested by recent observations on B . malayi , IVM might prevent the release of mature mf by interacting with glutamate-gated chloride channels localized in the uterine wall [5] . A prolonged stay in the uterus may not be suitable to mf survival , especially when they are densely packed and , as an indirect consequence of IVM , sequestrated mf may degenerate quicker than those living in their natural environment , the dermis . The lower increase in the number of degenerating mf in those worms repeatedly exposed to the drug might thus reflect an earlier than expected weakening of the embryostatic effect of IVM , allowing viable mf to move from the uteri . The genetic characterization of the worms collected as part of this study , using genes associated with the mode of action of IVM such as the avr-14 gene coding for GluCl [5] , [41] , are warranted to confirm possible selection towards resistance . Precisely , correlation between embryogram results and genetic profile of these worms will be particularly informative to assess whether some worms have become less sensitive to IVM , and in which proportion . A limitation of our study may be related to the observation that , despite matching the two study groups on a number of criteria , a higher mean number of degenerating stretched mf was observed at D0 , i . e . about 9 months after the last distribution of IVM in the frequently treated population , in the worms from the latter group as compared to the control group . This might be explained by a cumulative effect of repeated IVM treatments on the uteri wall . This could also be the consequence of a different age structure in the worm population between the two areas . It has been shown that , in areas of the former Onchocerciasis Control Programme in West Africa , a sustained decrease in transmission brings about an ageing of the worm population [42] , associated with an increase in the proportion of old female worms harboring degenerating stretched mf [18] . The mean age of the parasites in the frequently treated population is probably higher following the decrease in transmission in this area where large-scale IVM treatments have been ongoing for more than 10 years [43] . However , since we did not score the adult worms for age , we cannot assess the respective roles of previous IVM distributions and of a possible ageing of the worm population on the excess of degenerating mf in the frequently treated group at D0 . This being said , we do not think that this difference at D0 may have influenced the effect of the IVM dose given during the study . As an ancillary result of our analyses , a positive association was observed between the number of female worms in a nodule and the number of viable stretched mf observed in their uteri . This might be explained by a stronger effect of grouped female worms to attract male worms for mating and insemination . In Nippostrongylus brasiliensis and Trichinella spiralis , a strong dosage-dependency to female pheromone was observed in male worms [44]–[47] . This means that the higher the number of female worms , the higher the number of male worms attracted and consequently the higher the chance of mating . The influence of pheromone produced by female worms in the attractiveness of male worms was considered in O . volvulus [29] . In the present study , the number of male worms in a nodule was also positively associated with the number of viable stretched mf observed in the female worms' uteri , indicating that the oocyte fertilization succeeded for a proportion of female worms in those nodules with higher number of female and male worms . The present study demonstrated that the embryostatic effect of IVM on O . volvulus was still present even after multiple treatments . Nevertheless , this effect appears to weaken earlier after treatment in the frequently treated cohort . The higher repopulation rate of the skin by mf after IVM treatment in the individuals from the frequently treated area is consistent with an earlier recovery of mf productivity of their worms [17] . Genetic selection has been described in worm populations submitted to a high drug pressure , including worms collected from individuals of the frequently treated group of the present study [48]–[50] . The analysis of the genetic profile of the adult worms , mf and infective larvae collected as part of this study would constitute the last piece of the puzzle to complete these investigations .
|
Onchocerciasis , also known as river blindness , is a parasitic disease due to the filarial nematode Onchocerca volvulus . It affects more than 37 million people worldwide , most of them ( 99% ) living in Africa . The control of river blindness is , up to now , based on annual or six-monthly mass treatment with ivermectin . This drug kills O . volvulus microfilariae ( mf ) present in the skin and the eyes and prevents for 3–4 months the release of new mf by female worms ( embryostatic effect ) . In Ghana , after 10–19 years of repeated treatments , the emergence of adult parasite populations not responding as expected to ivermectin was postulated . In this study , the reproductive status of female worms was compared , just before and 80 days after ivermectin treatment , between frequently treated and ivermectin-naïve cohorts from Cameroon . In both groups , embryogenesis of O . volvulus was not affected by ivermectin . However , the accumulation of microfilariae ( mf ) in the females uteri expected after ivermectin was less marked in the frequently treated population , suggesting that the temporary sequestration of mf following treatment may have been weakened in this group . After 13 years of repeated annual treatments , the embryostatic effect of ivermectin on O . volvulus still occurs but the present findings , associated with observations of higher rates of skin repopulation by mf in the same individuals , suggest that this effect has been decreased .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"helminth",
"infections",
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"neglected",
"tropical",
"diseases",
"tropical",
"diseases",
"infectious",
"disease",
"control",
"onchocerciasis",
"parasitic",
"diseases"
] |
2014
|
Reproductive Status of Onchocerca volvulus after Ivermectin Treatment in an Ivermectin-Naïve and a Frequently Treated Population from Cameroon
|
Most humans harbor both CD177neg and CD177pos neutrophils but 1–10% of people are CD177null , placing them at risk for formation of anti-neutrophil antibodies that can cause transfusion-related acute lung injury and neonatal alloimmune neutropenia . By deep sequencing the CD177 locus , we catalogued CD177 single nucleotide variants and identified a novel stop codon in CD177null individuals arising from a single base substitution in exon 7 . This is not a mutation in CD177 itself , rather the CD177null phenotype arises when exon 7 of CD177 is supplied entirely by the CD177 pseudogene ( CD177P1 ) , which appears to have resulted from allelic gene conversion . In CD177 expressing individuals the CD177 locus contains both CD177P1 and CD177 sequences . The proportion of CD177hi neutrophils in the blood is a heritable trait . Abundance of CD177hi neutrophils correlates with homozygosity for CD177 reference allele , while heterozygosity for ectopic CD177P1 gene conversion correlates with increased CD177neg neutrophils , in which both CD177P1 partially incorporated allele and paired intact CD177 allele are transcribed . Human neutrophil heterogeneity for CD177 expression arises by ectopic allelic conversion . Resolution of the genetic basis of CD177null phenotype identifies a method for screening for individuals at risk of CD177 isoimmunisation .
CD177 ( also known as neutrophil specific antigen B1 [NB1] , human neutrophil antigen 2a [HNA-2a] , and polycythemia rubra vera 1 [PRV1] ) is a 56–64 kDa protein belonging to the Ly-6 family [1 , 2] , and is expressed exclusively on neutrophils by glycosylphosphatidylinositol ( GPI ) -linkage [3 , 4] . CD177 expression is heterogeneous within the human population . 1–10% of people are CD177null , while the remainder harbor neutrophils that are bi- or tri-modal for CD177 expression [4–6] . Heterogeneity for neutrophil surface antigen expression within the population results in susceptibility to alloantibody formation due to loss of acquired immunological tolerance . Transplacental passage or transfusion of neutrophil antibodies have been implicated in severe neonatal alloimmune neutropenia ( NAN ) [7] and transfusion related acute lung injury ( TRALI ) [8 , 9] . Polymorphic human neutrophil antigens ( HNA ) include CD16 or FcγRIIIb ( HNA-1a , -1b , -1c and -1d , encoded by FCGR3B ) [10] , CD177 ( HNA-2a , encoded by CD177 ) [11 , 12] , choline transporter-like protein 2 ( HNA-3a ) [13–15] , CD11b ( HNA-4a ) and CD11a ( HNA-5a ) [16–19] . In some cases , the genetic polymorphisms that account for alloantigenicity have been resolved . For example , three separate alleles of FCGR3B ( designated HNA-1a , -1b , and -1c ) appear to account for CD16 alloantigenicity , and the different epitopes of each defined isoform are specified by variations of five amino acid exchanges [10 , 20–22] . Similarly , single amino acid substitutions account for HNA-3a antigenicity [13 , 14 , 23] . Absence of HNA-1a , -1b , -1c , -1d , HNA-3a , HNA-4a and HNA-5a has been associated with formation of maternal alloantibodies [24 , 25] . CD177 deficiency has also been shown to result in development of maternal alloantibodies that cause neonatal alloimmune neutropenia [11] . In addition , CD177 is pertinent to systemic vasculitis , since one of the principal autoantigens , proteinase 3 , is a constituent of primary granules , but is exposed on the neutrophil surface in association with CD177 [26 , 27] . Complete elucidation of the genetic basis of neutrophil alloantigenic variation is an important goal , since testing for neutrophil antibodies is technically challenging and limited in current clinical practice [28] . By contrast , identification of the genetic basis of antigen expression , particularly for absence of antigen , permits screening individuals at risk of generating specific antibodies in these disease settings [29] . Variation in CD177 expression has been the subject of previous investigations ( S1 Table ) . Analysis of mRNA amplified from neutrophils of two CD177null donors showed two separate RNA insertions . In one case , there was an intronic fragment inserted into exon 6 , and in the other an alternative 5’ end splicing donor of exon 4 . Both were postulated to cause loss of CD177 expression by introducing an in-frame premature stop codon [30 , 31] ( S1 Fig ) . Further investigation revealed an association between certain CD177 single nucleotide variations ( SNVs ) and different CD177 phenotypes , although the mechanism to account for these effects was not elucidated , and the association was insufficient to permit diagnostic testing [32 , 33] . More recently , a SNV of cDNA829A>T mutation that introduces a stop codon was identified in CD177 as a cause for loss of CD177 expression [34] . We set out to determine the genetic basis of inter- and intra-individual CD177 phenotypes , by deep sequencing neutrophil-derived genomic DNA across the CD177 locus . Taking this approach , we confirmed the stop codon identified by Li et al in CD177null individuals , but discovered that variation arises when exon 7 of CD177 gene is supplied entirely by allelic conversion with the CD177 pseudogene ( CD177P1 ) , which comprises sequences homolog of CD177 exons 4–9 on the minus strand . This variant is present within the germline rather than somatically acquired within neutrophils . Individuals who are homozygous for CD177 have higher CD177 expression , whereas individuals with ectopic CD177P1 exon 7 conversion have larger proportions of CD177neg neutrophils in the blood . We demonstrated that CD177 is a heritable trait determined by the ratio of CD177/CD177P1 alleles , and uncovered distinctive CD177 transcription in CD177neg and CD177hi neutrophils within the same individual . These findings resolve the basis of interindividual ( CD177null versus CD177 expressing ) and intra-individual ( CD177neg versus CD177hi subsets ) CD177 expression , and identify a method for screening for individuals at risk of CD177 isoimmunisation .
Our discovery cohort comprised 40 patients with systemic vasculitis ( cohort 1 ) , and emerged as part of investigation of expression of vasculitis-associated autoantigens ( proteinase 3 ( PR3 ) , myeloperoxidase , and the associated alloantigen CD177 ) . Our study population was made up of individuals of European , Asian and Australian self-reported ethnicity ( S2A Fig ) . Consistent with previous reports [33 , 35] , we identified three neutrophil populations according to CD177 expression: negative ( neg ) , intermediate ( int ) and high ( hi ) . The majority of individuals were bi-modal for CD177 ( CD177hi and CD177neg ) ( Fig 1A ) , while some individuals harbor a substantial proportion ( >20% ) of neutrophils expressing CD177 at intermediate levels ( Fig 1B ) . In a larger cohort ( n = 535 ) of healthy donors ( cohort 2 ) , 65 . 4% of the subjects’ neutrophils were predominantly CD177hi ( Fig 1C ) , while in 24 . 7% of the cohort , the distribution of CD177hi and CD177neg were similar ( CD177hi/neg ) . 2 . 6% ( n = 14 ) were found to be CD177null . A similar prevalence of CD177 phenotypes were observed in both cohorts ( S2B Fig ) . We found that CD177 phenotypes are stable within individuals over six months ( S2C , S2D and S2E Fig ) . Flow cytometric analysis using two different CD177 monoclonal antibodies ( MEM-166 and REA258 ) yielded similar results , indicating absence of CD177 expression rather than modification of a CD177 epitope in CD177null ( Fig 1D ) . We deep sequenced CD177 in cohort 1 . To ensure that we did not miss somatic mutations we isolated genomic DNA using a custom capture array specifically from neutrophils purified from each subject . Loci containing all nine CD177 exons were isolated and deep sequenced ( >9 , 000x ) ( Fig 2A ) . We identified 41 SNVs , including 17 in the coding regions ( Fig 2A and 2B and Table 1 ) . These included common non-synonymous SNVs in exon 5 ( rs12981714 , rs12980412 and rs12981771 ) in 39/40 subjects . We also identified three non-synonymous SNVs in exon 7 ( rs200145410 , rs200006364 and rs201266439 ) and three novel variants located within five nucleotides of each other in all 40 subjects . One of these novel variants ( genomic location 19:43 , 361 , 169 , c . 787A>T , g . 7497A>T in hg38 ) changes a lysine codon ( AAA ) to a stop codon ( TAA ) . This variant was present in 100% of reads from two individuals with CD177null phenotype . We designed a high throughput assay using two-tailed allele specific primers for universal energy-transfer amplification ( Amplifluor PCR ) , which identifies reference g . 7497A and variant T alleles [36] . This correctly identified all genotypes defined by deep sequencing , and confirmed that the variant allele occurs with frequencies of 100 , 75 and 50% in our test cohort ( S3D and S3E Fig ) . Next , we genotyped the 535 healthy subjects in cohort 2 ( S3F Fig ) and found a similar distribution of genotypes as in cohort 1 ( S3G Fig ) . Analysis of population frequencies of CD177 g . 7497 genotypes was similar in individuals from each ethnic group ( S3H Fig ) . SNVs identified by deep sequencing were confirmed by Sanger sequencing ( Fig 2C ) . In addition , we compared sequences obtained from genomic DNA isolated from saliva and neutrophils , to determine whether CD177 exon 7 variations were transmitted in the germline or arose spontaneously by somatic mutation in neutrophils , and whether variant alleles were represented at different frequencies in individuals with different neutrophil phenotypes . Results were perfectly concordant with those obtained by deep sequencing , and confirmed all CD177 sequence variants , including the novel stop codon CD177 g . 7497T ( K263X ) , in DNA from both neutrophils and saliva , and consistent with allelic ratios derived from deep sequencing in neutrophil-derived DNA ( Fig 2A and 2C ) . We also examined all variants by in silico prediction algorithms , as our recent analysis on de novo or low-frequency missense mutations revealed that deleterious effects might be over-estimated in animal models [37] . Although only the stop gain K263X variation segregates with altered CD177 expression , 5 out 17 ( 35% ) coding variations in CD177 are predicted to be damaging with high scores of PolyPhen2 , CADD and SIFT ( S2 Table ) [38 , 39 , 40] . A mutation significance cutoff ( MSC ) study demonstrated that with a 99% confidence interval ( CI ) , CD177-specific cutoff for PolyPhen2 and CADD are 0 . 523 and 5 . 946 respectively , predicting high impact of five PolyPhen2 predicted and three CADD predicted damaging variations [41] . We examined CD177 g . 7497A allele frequency in individuals with different CD177 phenotypes in both cohorts . This analysis included a total of sixteen CD177null individuals . We observed a strong correlation between expression of CD177 and CD177 g . 7497A allele frequency in cohort 2 ( Fig 3A ) . In particular , all individuals homozygous for CD177 g . 7497T were CD177null . Individuals with a g . 7497A allele read frequency of 50% have larger proportions of CD177hi and fewer circulating CD177neg neutrophils than individuals with 25% A alleles ( Fig 3B and 3C ) . These data are consistent with the proposition that presence of the reference g . 7497A allele determines neutrophil CD177 expression , whilst the CD177 g . 7497T allele specifies the abundance of CD177neg neutrophils . Exclusive presence of T at g . 7497 accounts for the CD177null phenotype . We expressed genotype-phenotype data according to all nucleotide variants identified by deep sequencing , and by reference to CD177 surface phenotypes in cohort 1 ( Fig 3D ) . This makes obvious the concordance for read frequencies of each exon 7 variant . There is a correlation between each haplotype encompassing exon 7 and CD177 phenotype . We found 100% variant exon 7 reads in all CD177null individuals , 75% variant exon 7 reads in 7/9 of the individuals with the next lowest ratios of CD177hi to CD177neg cells , and 50% variant exon 7 reads in 26/28 individuals expressing the highest proportion of CD177hi cells . According to Human Genome Assembly 106 ( build 38 ) , human chromosome 19 contains CD177 ( CD_00019 . 10 ) separated by 10kb from the CD177P1 pseudogene , which comprises sequences homologous with CD177 exons 4–9 on the minus strand ( NC_000019 . 10 ) ( Fig 4A ) . Analysis of these reference sequences , and alternative sequences deposited in GenBank reveals uncertainty over the provenance of the variants we identified ( Fig 4B ) . Comparison of human reference CD177 gene sequences with those from other mammalian species is informative for resolving this uncertainty ( S4A , S4B and S4C Fig ) . The g . 1991C>G variant identified in exon 4 ( 43 , 355 , 663 , c . 381C>G ) , which causes a proline to alanine ( P128A ) substitution , has not been reported in any of the reference sequences , and is not annotated in dbSNP . The exon 5 variants we identified as heterozygous in 39/40 subjects appear as discrepancies in reference sequences and probably reflect differences between CD177 gene and CD177P1 . Most significantly , the exon 7 variant haplotype containing g . 7497T appears to arise from CD177P1 ( Fig 4B ) . We postulated that the CD177null phenotype arises when exon 7 reads are derived exclusively from CD177P1 , while CD177 exon7 sequence is not detected in the genome of these individuals . The sequence homolog between CD177 and CD177P1 , along with detection of two bi-alleles of CD177 exon 4 , 5 and 7 as described above , suggested that variations in these regions may reflect sequence divergence from CD177P1 . To explore this proposition further , we examined the relation between variation g . 1991C at 43 , 355 , 663 locus in exon 4 and g . 7497A ( 43 , 361 , 169 ) in exon 7 across cohort 1 ( Fig 4C and 4D ) . We recorded read frequencies of 25 , 50 and 75% for g . 1991C , but read frequencies of 0 , 25 and 50% for g . 7497A . Our observation of a maximum of 50% g . 7497A reads is consistent with homozygosity for the T allele at the CD177P1 locus in the population . This was also most frequently observed in 29/40 subjects , in which 15/40 subjects were 50:50 heterozygous at both g . 1991 in exon 4 and g . 7497 in exon 7 , suggesting homozygous reference alleles of C/C and A/A in the two loci of CD177 gene and ‘variant’ alleles G/G and T/T in CD177P1 . Other possibilities included homozygosity at one locus but heterozygosity at the other , or heterozygosity at both loci . Possible haplotypes are shown in Fig 4D . Analysis of haplotypes between exon 4 and exon 5 showed similar results . Interestingly , linkage disequilibrium ( LD ) analysis with Genome1000 data are consistent with a haplotype block encompassing CD177P1 and only the 3’ region of CD177 ( S5 Fig ) . Phylogenetic analysis reveals CD177-like sequence in orang-utan and pygmy chimpanzee ( S6 Fig ) . However , the exonic structure of CD177 varies considerably between mammalian species ( S7 Fig ) , with evidence of gene duplication giving rise either to CD177-like genes ( S8 Fig ) or to CD177 itself . Thus , mouse Cd177 comprises 17 exons , and with significant nucleotide and protein sequence homology between the first and second halves of the molecule ( S9 Fig ) . Both halves of mouse CD177 exhibit approximately 50% amino acid homology with human CD177 ( S10 Fig ) . As another approach to evaluate the association between CD177 and CD177P1 genotypes and CD177 phenotypes , we examined CD177 expression according to the genotypes of parents and their offspring in families where parents exhibit different ratios of A and T at CD177 . g7497 ( Fig 5A–5D ) . In pedigree 1 , both parents exhibit CD177hi phenotypes , and are sequenced for CD177 . g7497A/T at 50:50 ratio according to electropherogram , from which we infer homozygous g . 7497A in CD177 , since CD177P1 is homozygous T . Consistent with this , their offspring shares the same genotype and phenotype ( Fig 5A ) . In pedigree 2 , the maternal phenotype is CD177hi and 50:50 CD177 . g7497A/T , while the paternal phenotype is CD177hi/neg , with 25:75 CD177 . g7497A/T , from which we infer A/T heterozygosity for CD177 . The offspring is genotyped as 50:50 g . 7497A/T ( inheriting a reference CD177 allele from each parent ) , and exhibits a CD177hi phenotype ( Fig 5B ) . By contrast , pedigree 3 illustrates similar parental genotypes and phenotypes to pedigree 2 , but the offspring is 25:75 CD177 . g7497A/T according to the electropherogram and exhibits a CD177hi/neg phenotype , suggesting a CD177 variant allele from the father ( Fig 5C ) . Finally , in pedigree 4 , both parents have 25:75 g . 7497A/T genotype and CD177hi/neg phenotypes , and both offspring inherit similar phenotypes and genotypes ( Fig 5D ) . In order to resolve the uncertainty of CD177 reference sequence , we compared sequences of CD177 gDNA and mRNA isolated from individuals who exhibited different CD177 phenotypes and harboured putative CD177 polymorphisms . We sorted CD177hi and CD177neg subsets from a subject whose 88% of neutrophils in the blood were CD177hi , amplified full-length CD177 cDNA from both subsets , and compared them with genomic DNA sequences determined by deep sequencing ( Fig 6A ) . In individuals whose neutrophils are predominantly CD177hi , CD177 transcripts are homozygous c . 787A . Variant c . 787T transcripts were not detected , consistent with prediction of nonsense mediated decay ( NMD ) of CD177P1 transcripts . We identified 11 CD177 polymorphisms in exons 2 , 4 , 5 , 7 and 8 , all apparently heterozygous ( approximately 50% of reads ) . Exon 2 is absent from CD177P1 , therefore , we inferred that g . 242G>A is a SNP in CD177 , which was confirmed by analysis of cDNA sequences ( Fig 6B ) . Similarly , g . 7968G>A in exon 8 appears to be a CD177 SNP , whereas other putative polymorphisms in exons 4 , 5 and 7 were not found in CD177 transcripts ( Fig 6A ) , suggesting that they represent divergence between CD177 and CD177P1 rather than CD177 SNPs ( Fig 6B ) . In summary , g . 1991C ( exon 4 ) , g . 2368G , g . 2427A and g . 2431G ( exon 5 ) , and g . 7492G , g . 7496A , g . 7497A , g . 7500G , and g . 7509A ( exon 7 ) are CD177 gene reference sequences , and variants at these loci are actually derived from CD177P1 . Next , we investigated the CD177 sequences in individuals with 25:75 ratio of g . 7497A/T genotype , which confers CD177hi/neg neutrophil phenotypes . Once again , we compared genomic and transcript sequences but this time from a subject with approximately equal distributions of CD177hi and CD177neg neutrophils in peripheral blood . gDNA sequences revealed similar abundance of variations and references bases for seven SNPs in exons 2 , 4 , 5 and 6 . By contrast , we observed a 25:75 ratio ( reference to variant allele ) for five SNPs in exon 7 ( Fig 7A ) , consistent with presence of one copy of CD177 exon 7 and three copies of CD177P1 pseudogene derived sequence . A possible explanation is that one CD177 allele was partially replaced with CD177P1 homolog via ectopic gene conversion , yielding a chimeric CD177 allele containing a pre-mature stop codon ( g . 7497T ) in exon 7 ( Fig 7A and 7B ) . This hypothesis was lent additional support by the presence of distinctive CD177 transcripts in CD177neg and CD177hi cells within the same individual ( Fig 7A ) . Monomorphic CD177 mRNA transcripts from the reference CD177 allele were recovered from CD177hi neutrophils . By contrast , two different transcripts were recovered from CD177neg neutrophils . Besides a same copy of CD177 reference transcript , a variant transcript was also recovered containing eight SNPs: c . 92A/T , c . 114G/A , c . 751C/A , c . 782G/C , c . 786A/C , c . 787A/T , c . 790G/A and c . 798A/G in CD177neg neutrophils . These SNPs corresponded exactly to genomic heterozygosity of g . 220A/T and g . 242G/A ( exons 2 ) , g . 6724C/A ( exon 6 ) and g . 7492G/C , g . 7496A/C , g . 7497A/T , g . 7500G/A and g . 7509A/G ( exon 7 ) . Again , SNPs identified in exon 2 ( g . 220A>T and g . 242G>A ) and exon 6 ( g . 6724C>A ) represented common variations in CD177 gene , whereas SNPs in exon 7 arose from CD177P1 . CD177P1 derived exon 7 sequences were recovered in the cDNA . This demonstrated the expression of chimeric CD177 transcripts and supported the proposition of CD177P1 exon 7 incorporation in CD177 locus ( Fig 7A and 7B ) . This finding proved that both intact and converted CD177 alleles are transcribed in CD177neg neutrophils , whereas CD177hi neutrophils express only reference allele . A mechanism of ectopic gene conversion also explains 25:75 ( ref/var ) ratio of polymorphisms in exon 7 of the gene . To confirm this structural change , we performed MLPA using probes specific to different regions of CD177 and CD177P1 genes in relation to individuals with normal copy numbers of both genes . This analysis confirmed the presence of an additional copy of CD177P1 exon 7 ( Fig 7G , sample 1 in blue ) , in concordance with deep sequencing data ( Fig 7A ) . These results indicate ectopic gene conversion of CD177P1 exon 7 into the CD177 locus , resulting in 25:75 ratio of CD177 g . 7497 A/T alleles . All CD177null subjects were homozygous for CD177P1 derived exon 7 sequence , suggesting an allelic gene conversion in the region ( Figs 2A–2C , 7C–7F , S2D and S2F ) . Analysis of upstream variations implied different homologous recombination events among CD177null individuals . One subject harboured g . 1991C>G polymorphism at 50:50 ratio ( Fig 7C ) , indicating co-existence of two alleles of CD177 exon 4 ( homozygous g . 1991C ) and two alleles of CD177P1 ( homozygous G ) . By contrast , only CD177P1 derived sequence was found from exon 5 to exon 7 in the same individual , suggesting replacement of CD177 exon 5 to 7 by CD177P1 homolog in both alleles , and chromosomal crossover occurred between exon 4 and 5 ( Fig 7C and 7D ) . Similarly , presence of both CD177 and CD177P1 sequences in exon 5 but exclusive CD177P1 sequence in exon 7 in another CD177null subject indicated homologous recombination between exon 5 and 7 ( Fig 7E and 7F ) . MLPA confirmed the presence of 4 copies of CD177P1 exon 7 in both CD177null subjects , demonstrating allelic CD177 gene conversion ( Fig 7G ) . Furthermore , duplication of CD177P1 exon 5 in one subject ( red ) but not in another ( orange ) confirmed various homologous recombination occurred in CD177null subjects ( Fig 7G ) . Our data from both deep sequencing and MLPA support an allelic gene conversion by CD177P1 exon 7 in CD177null subjects . CD177 exon 7 had been mistakenly annotated as a polymorphic pseudogene in GRCh37 . It should be noted that current understanding for exon 5 sequence was incorrect too . Our data suggested that “reference” g . 2368T , g . 2427G and g . 2431T according database were actually linked with other CD177P1 elements and “variant” genotypes of g . 2368G , g . 2427A and g . 2431G should be annotated as CD177 sequence ( Figs 6 and 7 ) . Complete CD177 sequence in alignment with CD177P1 is shown in S11 Fig Taken together , these findings indicate that the stop codon responsible for the CD177null phenotype is derived from CD177P1 . This chimeric CD177 gene has arisen by gene conversion . This would be consistent with both the allelic frequencies observed for exon 7 haplotypes , with the transcriptional analysis , and with gene structural analysis by MLPA . The allelic frequencies are in Hardy-Weinberg equilibrium ( = 0 . 9998 ) based on the genotype frequencies in our large cohort 2 ( Fig 8A and 8B ) .
We report evidence for the genetic specification of heterogeneous CD177 phenotypes by ectopic and allelic conversion . We identified a novel polymorphism in exon 7 ( g . 7497A>T ) , encoding a stop codon in place of a lysine codon . Based on analysis of more than 9000 nucleotide reads from each individual and a large cohort over 500 subjects , we determined with a high level of confidence that this codon is present in all individuals . Most individuals have both lysine and stop codons detected , whereas in CD177null individuals , only the stop codon is detected which is derived from CD177P1 exon 7 conversion . Previous studies have identified variations in the transcript of CD177 deficient individuals with no satisfactory explanation for the origin of the splicing error [30 , 31] . Other investigators have identified SNVs within CD177 gene in association with expression [32 , 33] , and although this resulted in some progress , were ultimately inconclusive either because of the absence of full length genomic sequence of CD177 , or the absence of CD177null subjects ( S1 Table ) . As a result , the mechanisms that account or the CD177null phenotype have not been resolved . Recently , Li and colleagues independently identified the same putative nonsense mutation in CD177 gene reported here , and demonstrated that this variation account for lack of CD177 expression in transfected cells [34] . Our finding that the null allele arises by conversion from a pseudogene with close sequence homology helps to explain why previous results have been inconclusive . Indeed , we show that the CD177 reference sequences lodged in GenBank contain inconsistencies , and have resulted from assembly of sequences from the gene and pseudogene . CD177P1 comprises orthologs of exons 4–9 of CD177 gene . Previous studies have unsuccessfully attempted to resolve the contributions of the CD177P1 [42 , 43] . Resolution of this uncertainty has been achieved here with deep sequencing of captured CD177 alleles . We have identified portions of the CD177 gene that harbour variants with strict Mendelian inheritance , whereas exon 7 exhibits variant frequencies that could not be accounted for by the presence of just two alleles . Our findings demonstrated that CD177 is sometimes a chimeric gene resulting from incorporation of a gene segment derived from CD177P1 ( including exon 7 ) . We provide evidence in this study that this chimeric gene has arisen by gene conversion . Gene conversion is a process of homologous recombination involving unidirectional transfer of genetic material to duplicated gene from its ancestor such as pseudogene [44] . A similar mechanism of gene conversion from pseudogenes resulting in insertion of a nonsense codon has been reported in chronic granulomatous disease , polycystic kidney disease , and B cell immune deficiency [45–47] . The presence of the homologous sequences in CD177 and CD177P1 has resulted in misannotation of CD177 , and thwarted efforts to identify the genetic basis for the CD177null phenotype . The explanation we propose for CD177null alleles within the population also appears to account for phenotypic heterogeneity . We observed a marked concordance between levels of CD177 expression and the number of CD177 exon 7 alleles . Thus , one allele ( g . 7497A read frequency of 25% , by ectopic CD177P1 conversion ) is associated with lower levels of CD177 expression than two alleles ( g . 7497 read frequency 50% ) . Allelic frequency is supported by analysis of transcripts within neutrophils of subjects having various CD177 phenotypes . CD177 expression not only varies across the population , but also within individuals . CD177neg cells appear to be distinguished from CD177hi neutrophils . In individuals heterozygous for CD177 exon 7 , CD177neg cells harbor two CD177 transcripts containing CD177P1-derived exon 7 sequences , whereas CD177hi cells express only CD177-exon 7 containing transcripts . We have identified allelic and ectopic gene conversion as driving forces for CD177null and CD177neg expression respectively . Additional investigations are merited to explore mechanism of atypical CD177 expression ( i . e . , CD177int subset ) within individuals , which might include epigenetic changes and posttranscriptional regulation . Absence of self-antigen predispose to a breakdown in immunological tolerance upon exposure to self-antigen . Failure to acquire self-tolerance to neutrophil antigens places an individual at risk of developing antibodies to these antigens , either as natural antibodies , or after immunisation . For neutrophil antigens , this is likely to occur after exposure to fetal antigens , or less likely , after blood transfusion or allotransplantation . The consequences of passive transfer of neutrophil antibodies include TRALI , and neonatal immune neutropenia . Indeed , the first description of neonatal alloimmune neutropenia arose in the offspring of CD177nul mothers [11] . Despite the pathogenic role for anti-CD177 antibodies in TRALI and NAN , the lack of a method to genotype CD177 has prevented CD177 deficiency from being investigated in TRALI causing donors , TRALI patients [48] , and pregnant women . For the first time , our study established a method to genotype CD177nul individuals with risk of anti-CD177 antibody development after pregnancies and infusions . Prospective studies will be necessary to characterise in more detail the requirements for alloimmunisation and antibody production in CD177null individuals . In summary , we have identified the genetic variant that accounts for the CD177null phenotype and heterogeneous CD177 expression . The mechanism appears to result from insertion of a pseudogene derived sequence into the CD177 locus . Nevertheless , this event can be identified as apparent homozygosity for g . 7497 of CD177 gene . This discovery makes it possible to screen individuals at risk of CD177 isoimmunisation .
Study subjects consisted of 40 patients with anti-neutrophil cytoplasmic autoantibodies ( ANCA ) associated vasculitis ( AAV ) ( cohort 1 ) and 535 healthy subjects ( cohort 2 ) were examined for CD177 genotypes and phenotypes , in an effort to elucidate neutrophil mediated autoimmunity . All research described was approved by the ACT Health Human Research Ethics Committee , under protocols ETH . 11 . 11 . 269 and ETH . 1 . 15 . 15 . Participating subjects provided written informed consent . Anticoagulant citrate dextrose solution-treated fresh blood was layered on Ficoll-Paque Plus separation medium ( GE Healthcare Life Science ) at room temperature for 45 minutes to allow erythrocytes to sediment . Leukocytes with minimal residue erythrocytes after sedimentation were carefully layered on the top of 10 ml Ficoll-Paque Plus separation medium and centrifuged at 400g for 40 minutes without brake at room temperature . Neutrophils and peripheral blood mononuclear cells ( PBMC ) were then recovered to separate tubes [49] . Leukocytes ( approximately 1x106 ) were stained with fluorescent conjugated antibodies for CD16 ( 3G8 ) , CD66b ( G10F5 ) from Biolegend , CD177 ( MEM-166 ) from Abcam and CD177 ( REA258 ) from Miltenyl Biotec in Ca++Mg++ free HBSS . Data were acquired on a FACSCanto II flow cytometer ( BD Bioscience ) and analysed using FlowJo software ( TriStar ) . 1–2 million of CD177neg and CD177hi subsets were sorted on a BD FACS Aria II from CD66b+ neutrophils for sequencing and RNA analysis . Genomic DNA was extracted from neutrophils and saliva using DNeasy Blood kit ( Qiagen ) and Oragen-DNA OG-500 kits ( DNAgenoTec ) respectively . CD177 exon 5 and 7 sequence was amplified with primers ( CD177E5F: CAGCATCACTGACTCTCCC TC; CD177E5R: ATGCCCCATGTGTCATCGTG; CD177E7F: AGCTTTCCCTCTCACCCTC AG; CD177E7R: TCTGGGCCTCATTTCTCCACG ) , and examined in the Bioscience Research Facility . Two allele specific forward primers and a single common reverse primer were designed to amplify across the polymorphisms at 19:43 , 361 , 164 ( GRCh38 ) ( CD177 . g7492G/C ) . GAAGGTGACCAAGTTCATGCTGACTCACATCAACCCTGGTGGG ( CD177F-1 ) and CD177F-2 ( GAAGGTCGGAGTCAACGGATTGACTCACATCAACCCTGGTGGC ) both had a 5’ tail corresponding to fluorophores FAM and HEX respectively . The common reverse primer was 86bp downstream within exon 7 ( CD177-R: CGAGGAGCAGAAGTGGGTAT ) . Amplification cocktails were prepared with KASP Master Mix ( LGC Group ) . Fluorescences were measured after amplification . Allelic frequencies were discriminated using FLUOstar OPTIMA ( BMG Labtech ) . Neutrophil RNAs were extracted with TRIzol reagent ( Invitrogen ) and reverse transcribed into cDNA using Qiagen Reverse Transcription kit . Full length of CD177 transcript was amplified and sequenced with a pair of primers ( CD177F: CTGGGGTTCATCCTCCCACT; CD177R: TTAGCAGGAAGGGCAAACCA ) . Multiplex ligation-dependent probe amplification ( MLPA ) were performed using the MRC-Holland Salsa MLPA EK1 FAM reagent kit [50] . Probes were designed based on either homology or discrepancy between CD177 and CD177P1 genes following previously described criteria [51] . Oligonucleotides from Sigma-Aldrich are listed in Table 2 . Probe mixes were prepared in water with each oligonucleotide at a final concentration of 4 fmol/ul and MLPAs were performed using 100ng gDNA . The products were separated by an ABI 3730 DNA Analyzer ( Applied Biosystems ) . Trace data were analyzed using GeneMarker ( Softgenetics ) . Peak heights were normalized to the average peak height of the control probes followed by normalization to the average peak height of the control samples in cohort 1 whose sequences indicated to have one copy of CD177 and CD177P1 gene respectively per chromosome . Threshold values for deletion were set at 0 . 75 and 1 . 25 for duplication . TruSeq Custom Amplicon libraries were prepared according to the manufacturer’s instruction ( Illumina ) . 76 pairs of primers were designed for amplicons , covering the entire coding regions of three neutrophil antigen genes including CD177 . High through-put paired-end sequencing was performed on the Illuminia MiSeq platform for 500 cycles in the Bioscience Research Facility . Primary processed FATSO files were analysed with MiSeq reporter and a homemade pipeline developed by the Immunogenomics Bioinformatics team . BAM files were viewed with integrative genomics viewer ( the Broad Institute ) , comparing to reference human genome hg19 and converted to GRCh38 ( hg38 ) .
|
Expression of the neutrophil-specific antigen CD177 varies across the population . 1–10% of humans are CD177null . CD177pos neonates born to CD177null mothers are susceptible to alloimmune neutropenia . Interestingly , CD177pos and CD177neg populations of neutrophils often exist together within individuals . The reasons for heterogeneous CD177 expression are not well understood . We deep sequenced the CD177 locus in individuals with different levels of CD177 expression , catalogued CD177 single nucleotide variants , and identified a premature stop codon that causes lack of CD177 expression . Comparison of messenger RNA from neutrophils with genomic CD177 DNA identified significant sequence similarity with CD177P1 pseudogene , which probably explains existing misannotation in public databases , but also explains susceptibility to cross-over errors . Indeed , we report that the stop codon responsible for the CD177null phenotype arises when exon 7 of CD177 gene is supplied entirely by CD177P1 by gene conversion . We also show that the proportion of CD177hi neutrophil numbers within individuals is a heritable trait , determined by the proportion of intact CD177 and converted CD177 alleles . Furthermore , within individuals , CD177 gene is differentially transcribed in CD177neg and CD177hi neutrophils . Our work resolves the genetic basis of CD177 phenotype and identifies a method for screening individuals at risk of CD177 isoimmunisation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"blood",
"cells",
"sequencing",
"techniques",
"pseudogenes",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immunology",
"variant",
"genotypes",
"alleles",
"genetic",
"mapping",
"gene",
"types",
"molecular",
"biology",
"techniques",
"dna",
"gene",
"conversion",
"neutrophils",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"genome",
"complexity",
"white",
"blood",
"cells",
"genomics",
"sequence",
"alignment",
"animal",
"cells",
"molecular",
"biology",
"genetic",
"loci",
"nucleotide",
"sequencing",
"biochemistry",
"cell",
"biology",
"nucleic",
"acids",
"heredity",
"genetics",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"dna",
"recombination",
"computational",
"biology"
] |
2016
|
Heterogeneity of Human Neutrophil CD177 Expression Results from CD177P1 Pseudogene Conversion
|
In most bacteria , Clp protease is a conserved , non-essential serine protease that regulates the response to various stresses . Mycobacteria , including Mycobacterium tuberculosis ( Mtb ) and Mycobacterium smegmatis , unlike most well studied prokaryotes , encode two ClpP homologs , ClpP1 and ClpP2 , in a single operon . Here we demonstrate that the two proteins form a mixed complex ( ClpP1P2 ) in mycobacteria . Using two different approaches , promoter replacement , and a novel system of inducible protein degradation , leading to inducible expression of clpP1 and clpP2 , we demonstrate that both genes are essential for growth and that a marked depletion of either one results in rapid bacterial death . ClpP1P2 protease appears important in degrading missense and prematurely terminated peptides , as partial depletion of ClpP2 reduced growth specifically in the presence of antibiotics that increase errors in translation . We further show that the ClpP1P2 protease is required for the degradation of proteins tagged with the SsrA motif , a tag co-translationally added to incomplete protein products . Using active site mutants of ClpP1 and ClpP2 , we show that the activity of each subunit is required for proteolysis , for normal growth of Mtb in vitro and during infection of mice . These observations suggest that the Clp protease plays an unusual and essential role in Mtb and may serve as an ideal target for antimycobacterial therapy .
Intracellular protein degradation is critical for maintaining cellular homeostasis through protein quality control and regulation of numerous biological pathways [1] , [2] . In eukaryotes , the ubiquitin-proteasome pathway constitutes the predominant degradation pathway [3] . Most prokaryotes , however , possess a variety of ATP-dependent serine protease complexes , such as Lon and Clp protease [4] , and some actinomycetes and archaea contain proteasomes , which are threonine proteases . Interestingly , Mycobacterium tuberculosis ( Mtb ) encodes both a proteasome and Clp protease . While recent work has explored the role of the Mtb proteasome [5]–[7] , little is known about mycobacterial Clp protease . This serine protease was first discovered and is best characterized in Escherichia coli [8] , [9] . The Clp proteolytic complex is formed by the association of proteolytic subunits , ClpP , with ATPase adapters , ClpX or ClpA in Gram-negative organisms and ClpX or ClpC in Gram-positive organisms . E . coli ClpP is a tetradecamer composed of two stacked heptameric rings of identical ClpP subunits that form an internal proteolytic chamber [10] . This core associates with distinct hexameric ATPase adapters , ClpX and ClpC1 in mycobacteria , which provide substrate specificity and catalyze ATP-dependent unfolding of globular proteins [11] , [12] . In E . coli , the ClpXP protease is involved in the regulation of the DNA damage response and degradation of SsrA-tagged peptides stalled on the ribosome [13] , [14] . Clp proteolytic enzymes are also required for full virulence in several pathogenic organisms , including Listeria monocytogenes where the protease is required for the production of α-listeriolysin [15] , [16] . In most bacteria including E . coli , Clp protease is dispensable for normal growth , and in fact , until recently , the only organism in which clpP has been found to be essential is Caulobacter crescentus , where Clp degrades CtrA , an inhibitor of cell cycle progression [17] . Unlike most bacteria , which have a single ClpP subunit , the genome of Mtb encodes two closely related ClpP homologs , clpP1 and clpP2 , in a single operon . A transposon-based mutagenesis screen for essential genes in Mtb predicted that ClpP2 and the ATPase adapters ClpC1 and ClpX , were required for normal growth [18] while a recent publication has shown that ClpP1 is essential [19] . Here , we show that both ClpP1 and ClpP2 are required for growth , and that their activity is important for the removal of abnormal proteins . Our data suggest that ClpP1 and ClpP2 assemble to form a single proteolytic complex , referred to as ClpP1P2 , that is required for normal growth in vitro and during infection . In related studies , we have found that although pure ClpP1 and ClpP2 by themselves form tetradecamers , they are inactive . However , in the presence of low molecular weight activators they reassociate to form a mixed tetradecamer , ClpP1P2 , which is capable of proteolysis ( Akopian et . al . , manuscript submitted ) . The unusual properties of this heteromeric complex , the absence of such an enzyme in the eukaryotic cytoplasm , and the essentiality of both subunits make ClpP1P2 protease an attractive target for novel therapeutic development for the treatment of tuberculosis .
Mycobacterial genomes contain two homologous ClpP protease genes , clpP1 and clpP2 , arranged in a putative operon . To investigate whether the two proteins may function together in a complex , we co-expressed Mtb clpP1 and clpP2 , each containing a different C terminal epitope tag , in Mycobacterium smegmatis ( Msm ) . We used affinity chromatography with nickel resin to isolate 6×-His tagged Mtb ClpP2 together with associated proteins from the Msm cell lysate . As shown in Figure 1A , a fraction of the c-myc tagged ClpP1 bound to the Ni column and co-eluted with ClpP2 . To verify that ClpP1 and ClpP2 co-eluted from the Ni column may be associated in a complex , we applied the fraction from the Ni column containing both proteins to an anti-c-myc agarose column and analyzed by SDS PAGE . Figure 1B shows that a large fraction of the ClpP2 was associated with ClpP1 . Incidentally , expression of the Mtb proteins in Msm also led to the co-isolation of Msm ClpP1 and ClpP2 , as shown by tandem mass spectrometry of the purified complex . In each case , peptides present uniquely in Mtb or Msm ClpP1 and ClpP2 were detected ( Figure 1C ) . If ClpP1 and ClpP2 do in fact associate to form a single proteolytic core , we reasoned that mutations blocking the catalytic activity of one subunit might reduce that activity of the enzyme . We identified likely active site residues of ClpP1 and ClpP2 by mapping the Mtb proteins onto E . coli ClpP and locating the catalytic triad of Asp-His-Ser , which is characteristic of serine proteases . In both cases , the serine likely to be responsible for nucleophillic attack was replaced by an alanine ( ClpP1 S98A and ClpP2 S110A ) . To analyze the effects of these mutations , we expressed and purified 6×His-tagged forms of each protein , and assayed their effect on the enzymatic activity of the wild type ClpP1P2 in an in vitro peptidase assay ( Akopian et . al . , manuscript submitted ) . Enzyme activity of the reconstituted ClpP1P2 complex was quantified using cleavage of the fluorescence reporter , Z-Gly-Gly-Leu-AMC . As seen in Figure 1D , addition of an excess of mutated ClpP1 or ClpP2 to the active wild type ClpP1P2 complex inhibited proteolytic cleavage of a fluorescent peptide substrate , presumably by replacing the wild type subunits . These results suggest that the ClpP1 and ClpP2 subunits interact to form a single proteolytic complex in vitro , that each active site is important for activity , and that these mutations can be used as dominant negative inhibitors . We employed three complementary strategies to determine if ClpP1 and ClpP2 are required for normal growth in mycobacteria . First , using mycobacterial recombineering [20] , we replaced the endogenous promoter of clpP1 and clpP2 in Msm with a tetracycline-inducible promoter ( Figure 2A , Figure S1 ) . Introduction of a tetracycline repressor resulted in a strain ( ptet_clpP1P2 ) that could only be maintained in the presence of the inducer anhydrotetracycline ( ATc ) ( Figure 2B ) . In the absence of this compound , growth did not occur , but could be restored by the presence of an episomal plasmid containing both clpP1 and clpP2 . Plasmids expressing only clpP1 or clpP2 alone could not rescue growth and depletion of either subunit resulted in bacterial death ( Figure 2C ) . Since complementation was conducted with Mtb homologs and subunits from different species associate into a functional tetradecamer , the ClpP1P2 complex is likely very similar in Msm and Mtb . Furthermore , active site mutants of either ClpP1 or ClpP2 were unable to complement ptet_clpP1P2 in the absence of ATc , suggesting that the activity of both subunits were required for normal growth ( data not shown ) . Second , we inserted a tetracycline inducible promoter upstream of the clpP1P2 operon via homologous recombination in Msm creating a strain in which clpP2 was inducibly expressed ( Figure 2D ) , and clpP1 was under the control of its native promoter ( ptet_clpP2 ) . In accord with the previous findings , the growth of this strain was dramatically inhibited in the absence of ATc ( Figure 2E ) . Third , we used a system of inducible protein degradation recently developed in Msm ( Figure 3A ) [21] . Briefly , we employed mycobacterial recombineering to add an inducible degradation ( ID ) tag to the C-terminus of ClpP2 ( clpP2_ID ) . Upon cleavage of the tag by a tetracycline inducible HIV-2 protease , an SsrA sequence is revealed on the substrate that directs degradation of the protein . By inserting epitope tags C-terminally to the HIV-2 protease recognition motif ( FLAG ) and N-terminally to the SsrA tag ( c-myc ) , we were able to monitor the amount of ClpP2 by immunoblotting . As shown in Figure 3B , induction of HIV-2 protease resulted in degradation of the majority of ClpP2 and inhibited bacterial growth ( Figure 3C ) . Using this system , we did not observe cell death , perhaps due to incomplete inhibition , as would be expected for a system where the protease targets itself . Loss of ClpP2 , as measured by immunoblotting , was rapid and reached near completion within hours . Furthermore , the growth defect was complemented by expression of Mtb clpP2 using a constitutively active promoter . A similar approach with ClpP1 was unsuccessful as extended C-terminal tagging was not tolerated , and the ID tag was indiscriminately cleaved . Collectively , these results confirm that both ClpP1 and ClpP2 are required for normal growth in mycobacteria , presumably because they function together in the ClpP1P2 complex . In other bacteria , Clp plays a role in degrading abnormal proteins such as SsrA-tagged peptides that stall on the ribosome [22] . To determine the importance of ClpP1P2 protease in the degradation of misfolded proteins , we used antibiotics that alter protein synthesis in distinct ways including chloramphenicol , which blocks protein elongation without increasing mistranslation rates [23] , and streptomycin and amikacin , which induce translational errors resulting in missesnse or prematurely-terminated polypeptides [24] . We found that the strain ptet_ClpP2 , in which clpP2 expression is regulated by anhydrotetracycline , grows well in low or high concentrations of ATc , 1 to 100 ng/mL ( Figure 4A ) . Treatment with sublethal concentrations of chloramphenicol resulted in no difference in viability between bacteria maintained on low or high concentrations of ATc ( Figure 4A , bottom ) . In contrast , sub-MIC concentrations of the aminoglycosides streptomycin and amikacin significantly inhibited the growth of strains incubated in low concentrations of ATc , while they had no effect on growth of the strain maintained in high concentrations of ATc ( Figure 4A , top ) . Together , these results suggest that ClpP1P2 protease protects against error-prone translation by catalyzing the degradation of misfolded proteins . To specifically assess whether ClpP1P2 is responsible for the removal of SsrA-tagged proteins in mycobacteria , we fused the mycobacterial SsrA-tag to the C-terminus of GFP ( GFP-SsrA ) and expressed the construct constitutively on an episomal plasmid . This construct was introduced into the strain clpP2_ID , in which ClpP2 degradation was regulated . In the presence of ClpP2 ( and in wild type cells ) , there was no detectable GFP-SsrA . However , upon depletion of ClpP2 , there was substantial accumulation of GFP-SsrA , as measured by both fluorescence and immunoblot analysis after four hours ( Figure 4B , 4C ) . Quantitative PCR showed that the rise of GFP-SsrA was not due to transcriptional activation of the gene ( Figure S2 ) . GFP lacking the SsrA tag is present at similar levels in all strains ( data not shown ) . Because we cannot detect GFP-SsrA in the presence of Clp activity , we were unable to accurately measure changes in protein stability . However , the rate of accumulation of GFP-SsrA was consistent with the time course of ClpP2 depletion , which occurred over six hours , as shown by immunoblotting . Thus , functional ClpP1P2 protease is vital for the rapid clearance of SsrA-tagged substrates in mycobacteria . As shown above , catalytically inactive forms of ClpP1 and ClpP2 inhibit proteolysis by the wild type enzyme , possibly by replacement of wild type subunits with inactive ones . To assess whether ClpP1P2 activity is required for the growth of Mtb , we expressed a catalytically inactive form of Mtb clpP1 , clpP1 S98A , on a tetracycline-inducible plasmid in wild type Mtb . Addition of ATc led to expression of the catalytically inactive mutant protein and resulted in a significant inhibition of growth ( Figure 5A ) while overexpression of wild type Mtb clpP1 had no effect . To determine if the dominant negative mutant of ClpP1 affected ClpP1P2 function during infection , we infected mice with a 3∶1 mixture of Mtb expressing clpP1 S98A on a hygromycin-resistant doxycycline inducible plasmid and wild type Mtb ( containing a kanamycin-resistant control vector ) . Mice were fed either normal chow or chow infused with the inducer doxycycline . Growth of Mtb was monitored by assessing CFU in lung tissue at day 30 post-infection . While there were no differences in the growth of wild type Mtb between treated and untreated mice , expression of the active site mutant significantly inhibited growth ( Figure 5B ) . Our results suggest that functional ClpP1P2 protease is required for the growth of Mtb both in vitro and during infection .
We find that the mycobacterial ClpP1P2 protease has two unusual properties that distinguishes it from other members of the prokaryotic ClpP family . First , the protease consists of distinct types of subunits , each of which is required for full activity of a single proteolytic complex . While other species do encode multiple ClpP subunits , two different proteolytic subunits forming a single protease has not been documented . Second , unlike in most bacteria that have been studied , ClpP1P2 activity is absolutely required for normal growth . This requirement is particularly striking as mycobacteria contain several cytoplasmic ATP-dependent proteolytic complexes , including FtsH , and the proteasome [7] , [25] , [26] . Clearly , the mycobacterial ClpP1P2 proteolytic core has unique roles that are important for viability . The ClpP proteases that have been characterized biochemically in other bacteria and mitochondria are tetradecameric complexes containing a single type of proteolytic subunit . In mycobacteria , however , two different protein species contribute to protease activity . Although Mtb ClpP1 forms a tetradecameric complex , a crystal structure of Mtb ClpP1 lacks appropriate active site geometry to support proteolysis [27] . The presence of two ClpP subunits with distinct substrate preferences may facilitate an expansion of the peptide specificity of the complex , much like the eukaryotic proteasome . Interestingly , the Mtb proteasome is composed of a single type of subunit , and the presence of distinct subunits comprising a single proteolytic core is rare among prokaryotes . There is at least one example of an essential role for ClpP . In Caulobacter crescentus , ClpXP degrades CtrA , a protein that normally inhibits cell cycle progression during cellular replication [28] . In this case , a single protein target is responsible for the essentiality of the enzyme . ClpP1P2 protease might play a similar role in mycobacteria . While this may be true , screens for essential proteins in mycobacteria suggest that , in addition to the clpP1 and clpP2 , multiple Clp-associated ATPase adapters ( clpX and clpC1 ) are also essential [18] . The requirement of multiple adapters makes it possible that accumulation of multiple protein substrates contribute to the poor growth phenotype observed on depleting the ClpP1 and ClpP2 subunits in mycobacteria . ClpP1P2 might be important for other reasons . As shown here , ClpP1P2 protease is required for the clearance of SsrA-tagged proteins . These tagged polypeptides are generated under conditions when protein synthesis is stalled and are required for ribosome release . In the absence of ClpP1P2-mediated proteolysis , protein synthesis might eventually be inhibited . In addition , ClpP1P2 protease is necessary for degrading abnormal proteins , such as those produced in the presence of certain antibiotics . Accumulation of such non-functional misfolded proteins might result in cellular stress in the absence of an effective system for their removal [29] . Clearance of damaged proteins might be particularly important in Mtb during infection , when cells are exposed to multiple oxidative and nitrosoative radicals that can induce protein damage . In fact , a transcriptional activator of the clpP1P2 operon , clgR , is critically activated upon reaeration of hypoxic Mtb and during Mtb growth within the macrophage [30] , [31] . Degradation of pre-existing proteins during such stressful transitions may be the initial event that triggers adaptation and facilitates the bacterium's ability to handle a wide array of environmental challenges . Using a dominant negative overexpression mutant in Mtb , we have confirmed that optimal Clp proteolytic activity is required for growth during infection . The essential nature of ClpP1P2 protease makes it an attractive target for antibiotic development , particularly because the proteases as a class are druggable enzymes and have already been validated as therapeutic targets in the treatment of HIV , hepatitis , and cancer [32] . In organisms where ClpP is not essential , uncontrolled activation of ClpP activity can be toxic . For example , in E . coli , acyldepsipeptide compounds reorganize the ClpP proteolytic core , causing dissociation from ATPase adapters , and indiscriminate protein degradation [33] . Compounds that produce a similar effect should result in toxicity in a broad range of organisms . In fact , it was recently discovered that the natural product cyclomarin kills Mtb by targeting the ClpC1 ATPase and presumably increasing Clp-mediated proteolysis , as demonstrated in a whole cell fluorescence-based assay [34] . In mycobacteria , where ClpP1P2 protease activity is required and depletion of either subunit is bactericidal , either non-specific activation or inhibition could effectively limit bacterial growth . An example of a ClpP inhibitor with potential therapeutic activity already exists . In S . aureus , beta-lactones have been found to inhibit Clp protease activity and decrease the virulence of the organism [35] . Additionally , the synergistic nature of ClpP1P2 protease depletion with aminoglycosides , a class of drugs already used to treat tuberculosis , points to a potential combination therapy against Mtb . As ClpP1P2 protease is most likely involved in preventing the accumulation of misfolded proteins and the degradation of critical endogenous regulatory proteins , small molecule modulators of ClpP1P2 activity would target a critical aspect of Mtb physiology , and might prove useful in the face of growing multi-drug resistance in one of the world's most successful pathogens .
The animal experiments were preformed with protocols approved by the Harvard Medical School Animal Management Program , which is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care , International ( AAALAC ) and meets National Institute of Health standards as set forth in the Guide for the Care and Use of Laboratory Animals ( Revised , 2010 ) . The institution also accepts as mandatory the PHS Policy on Humane Care and Use of Laboratory Animals by Awardee Institutions and NIH Principles for the Utilization and Care of Vertebrate Animals Testing , Research , and Training . An Animal Welfare Assurance of Compliance is on file with the Office of Laboratory Animal Welfare ( OLAW ) ( #A3431-01 ) . Msm mc2155 ( Msm ) or Mtb H37Rv were grown at 37°C in Middlebrook 7H9 broth with 0 . 05% Tween 80 and ADC ( 0 . 5% BSA , 0 . 2% dextrose , 0 . 085% NaCl , 0 . 003 g catalase/1L media ) . Mtb was additionally supplemented with oleic acid ( 0 . 006% ) . For growth curves , overnight cultures were diluted into the appropriate media and growth was either measured by OD600 or colony forming units per mL . A summary of all strains , plasmids , and primers used in this study as well as a summary of the construction of conditional mutants can be found in the supporting information ( Text S1 ) . The C-terminally 6× His-tagged wild type clpP1 , wild type clpP2 , clpP1Ser98Ala , and clpP2Ser110Ala subunits were overexpressed in Msm using an anhydrotetracycline ( ATc ) inducible expression system . After overnight induction with ATc ( 100 ng/mL ) , cells were lysed by French press , and lysates were centrifuged for 1 h at 100 , 000 g . The subunits were purified from the supernatant by Ni-NTA affinity chromatography ( Qiagen ) . Eluted fractions containing ClpP proteins were pooled and further purified by size exclusion chromatography on Sephacryl S-300 column . Equal amounts of ClpP1 and ClpP2 ( 1 µg each ) were mixed in the reaction buffer ( 50 mM K-phosphate buffer pH 7 , 5 , 100 mM KCl , 5% glycerol , 2 mM BME , 5 mM Z-Leu-Leu ) and peptidase activity was measured by a rise in fluorescence at 460 nm ( Ex at 340 nm ) with 0 . 1 mM Z-Gly-Gly-Leu-AMC as a substrate . To measure dominant negative effect of active site mutants , same reaction was carried out in the presence of 5 µg of the mutant proteins . Six to eight week old C57BL/6 mice ( Jackson Laboratory ) were used for animal infections . Mice were infected via aerosolization with 5×106 CFU each of a 3∶1 mixture of Mtb pTet::ClpP1 S98A and Mtb pTet::GFP ( wild type Mtb transformed with a control pTet plasmid containing GFP ) . Mice were fed with chow with or without inducer doxycycline . At 30 days after infection , mice were sacrificed , lungs were homogenized and appropriate dilutions were plated on 7H10 plates containing hygromycin or kanamycin to select for the Clp mutant or the control respectively .
|
Due to the significant and rapid rise in multidrug resistant Mycobacterium tuberculosis ( Mtb ) , there is an urgent need to validate novel drug targets for the treatment of tuberculosis . Here , we show that Clp protease is an ideal potential target . Mtb encodes two ClpP genes , ClpP1 and ClpP2 , which associate together to form a single proteolytic complex , referred to as ClpP1P2 . Both proteins are required for growth in vitro and in a mouse model of infection . Depletion of either protein results in rapid death of the bacteria . Interestingly , this is rare among bacteria , most of which have only one ClpP gene that is dispensable for normal growth . We also show that Clp protease plays an important quality control role by clearing abnormally produced proteins . As known antimycobacterial therapeutics increase errors in protein synthesis , inhibitors of ClpP1P2 protease in Mtb may prove synergistic with already existing agents .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biology"
] |
2012
|
Mycobacterium tuberculosis ClpP1 and ClpP2 Function Together in Protein Degradation and Are Required for Viability in vitro and During Infection
|
The larval salivary gland of Drosophila melanogaster synthesizes and secretes glue glycoproteins that cement developing animals to a solid surface during metamorphosis . The steroid hormone 20-hydroxyecdysone ( 20E ) is an essential signaling molecule that modulates most of the physiological functions of the larval gland . At the end of larval development , it is known that 20E—signaling through a nuclear receptor heterodimer consisting of EcR and USP—induces the early and late puffing cascade of the polytene chromosomes and causes the exocytosis of stored glue granules into the lumen of the gland . It has also been reported that an earlier pulse of hormone induces the temporally and spatially specific transcriptional activation of the glue genes; however , the receptor responsible for triggering this response has not been characterized . Here we show that the coordinated expression of the glue genes midway through the third instar is mediated by 20E acting to induce genes of the Broad Complex ( BRC ) through a receptor that is not an EcR/USP heterodimer . This result is novel because it demonstrates for the first time that at least some 20E-mediated , mid-larval , developmental responses are controlled by an uncharacterized receptor that does not contain an RXR-like component .
During metamorphosis in Drosophila melanogaster , pulses of the 20E steroid hormone , stimulate diverse tissue-specific responses such as the histolysis of many larval tissues and the simultaneous differentiation of adult structures from imaginal discs [reviewed in 1] . In addition , multiple pulses of 20E that occur during the last larval instar ( L3 ) trigger different responses within the same target tissue , raising the interesting question of how a generalized developmental signal is manifested into distinct physiological responses that are separated by time . The larval/prepupal salivary gland is an ideal assay system in which to investigate the molecular mechanisms responsible for such temporally specific developmental specifications . In a 36-hour period , the gland responds to three distinct pulses of 20E in three fundamentally different ways . During most of larval life , the salivary gland is engaged in the synthesis of non-digestive enzymes that most likely aid in the lubrication of the food through the gut [2]–[4] . However , about midway through the L3 stage , the pattern of gene expression is altered dramatically by the synchronous activation of a small number of genes ( ∼8 ) that are abundantly expressed in the salivary gland [5] . These are known to encode components of the glue mix that cements animals to a solid surface during metamorphosis , and they were first identified because their induction is responsible for the “intermolt” puffs formed on the giant polytene chromosomes of the gland [6] , [7] . Approximately 18 hours later , in response to the pulse of 20E that occurs at the end of L3 , glue synthesis abruptly ceases [5] , [8] because the hormone represses transcription from these genes [9] , [10] . At this time , the salivary gland begins to express another set of genes , many of which were originally described because they formed “early” and “late” puffs on the polytene chromosomes [reviewed in 11] . The end result of this 20E-mediated response is that glue granules are secreted into the lumen of the gland [12] , [13] . Finally at the end of prepupal development 10–12 hours later , the salivary gland responds to yet another pulse of 20E to initiate the programmed cell death of the tissue via a pathway that involves components of both autophagy and caspase activation [14] , [15] . The details of how 20E initiates glue secretion and gland histolysis are well understood . The hormone is known to bind to a receptor consisting of a heterodimer of EcR ( FBgn0000546 ) and USP ( FBgn0003964 ) proteins [16]–[18] . Both receptor components are members of the nuclear-hormone receptor superfamily , both contain well conserved DNA- and ligand-binding domains , and both are needed for the physiological responses of target tissues to 20E at these times [reviewed in 19] . However , little is known concerning the mechanism of receptor mediation during the middle of L3 when glue genes are coordinately activated . Although it is generally assumed that these events are also mediated by a receptor consisting of EcR and USP , other explanations can be invoked including the use of a different 20E receptor . Here we examine the requirements of EcR and USP for the induction of the glue genes at mid L3 . By employing the GAL4/UAS binary expression system [20] with transgenic inducible dominant-negative and RNAi constructs , we are able to limit perturbations of 20E signaling specifically to the salivary gland at defined developmental stages . We show that 20E is responsible for inducing a tagged glue transgene as a secondary response to the hormone , and that the 20E-inducible primary-response genes of the Broad Complex ( BRC ) ( FBgn0000210 ) are sufficient to initiate this programmed developmental response . However , we clearly demonstrate that the mid-instar hormone response requires a receptor that has not yet been characterized . The receptor consists of EcR but not USP . These results challenge the traditional model that most developmental events triggered by 20E must signal through a heterodimer of EcR and USP , and they support an alternative explanation in which either EcR homodimers or other members of the nuclear-hormone receptor superfamily play an active role in the diversity of responses to 20E during Drosophila development .
It is generally assumed that the glue genes [Sgs1 ( FBgn0003372 ) , Sgs3 ( FBgn0003373 ) , Sgs4 ( FBgn0003374 ) , Sgs5 ( FBgn0003375 ) , Sgs6 ( FBgn0003376 ) , Sgs7 ( FBgn0003377 ) , Sgs8 ( FBgn0003378 ) , and I71-7 ( FBgn0004592 ) ] are induced by a pulse of 20E that occurs midway through the third instar . This inference is based on the dramatically coordinated developmental induction at mid L3 of most of these genes [5] , and on studies in which Sgs expression is examined in backgrounds mutant for genes thought to be involved in 20E production or transport [21] , [22] . The model further proposes that induction of the glue genes occurs as a secondary response to 20E because Sgs expression is significantly perturbed in mutants defective for BRC and E74 ( FBgn0000567 ) , which are known to be direct targets for the hormone/receptor complex [23]–[25] . However , an Sgs3-derived reporter transgene is induced when temperature-sensitive ecd1ts ( FBgn0000543 ) mutants—known to produce low circulating levels of 20E [26]—are shifted to the non-permissive temperature before the L3 stage , and the same GFP reporter is also induced in animals that are mutant for USP , EcR-B1 , and EcR-B2 receptor components [13] . Thus , the literature contains contradictory reports concerning the role of 20E in inducing the glue genes . Therefore , we began this analysis with an in-vitro culture of salivary glands dissected from mid L3 because we are not aware of any published reports that directly test if glue-gene transcription can be induced by 20E in glands cultured from wildtype animals . To simplify the analysis , we dissected salivary glands from a line of flies in which the coding information for Sgs3 had been tagged with GFP ( glueGRN ) ( FBst0005884 ) . This stock ( previously called SgsGFP ) contains adequate regulatory information for the proper temporal , spatial , and high-level expression of the Sgs3 gene . It has also been extensively characterized and shown to be an accurate reporter for the secretion and expectoration of endogenous SGS3 glue protein [13] . Larvae were synchronized at hatching and raised to the early-L3 stage approximately 4–5 hours prior to the normal transcriptional induction of the glue genes . Salivary glands were then dissected and exposed to media containing different concentrations of 20E ( ranging 10−9 to 10−6 M ) or in medium without hormone . Under these circumstances glueGRN accumulation was detected 4–6 hours later in cultures incubated with low 20E concentrations ( Figure 1 ) , but not in untreated cultures or those incubated with higher concentrations of the hormone . It should be noted that this response is not robust because only ∼30% of the dissected glands produce glueGRN when treated . Presumably the results are variable because the culture conditions have not been optimized and/or the animals are not staged in a precise enough manner . However , it is significant to note that the only cultures in which glueGRN was detected were those incubated in either 10−8 M ( 8 out of 20 ) or 10−9 M ( 4 out of 20 ) . Thus , the concentration needed for production of glueGRN is 2–3 orders of magnitude lower than the titer ( ∼10−6 M ) reported to trigger “early” polytene puff formation , imaginal disc eversion , and glue secretion [13] , [27] , [28]—all developmental events that occur near puparium formation in response to a much better characterized pulse of 20E . The result is consistent with the concentration of a small pulse of hormone that has been reported to occur in the hemolymph of developing larvae a few hours prior to the transcriptional activation of the glue genes [29] . The EcR gene encodes three different protein isoforms , EcR-A , EcR-B1 , and EcR-B2 . All three contain the same DNA- and ligand-binding domains , but they contain different amino terminal A/B sequences due to the use of alternative promoters and differential splicing [16] . Null mutations for EcR die early in development and cannot be assayed for glue synthesis [30] . However , mutations that remove EcR-B1 and EcR-B2 [31] do produce glue [13] . These observations raise the possibility that either EcR is not required for the induction of Sgs3 , or that any EcR isoform is sufficient for the process . To distinguish between these possibilities and to take advantage of more powerful genetic tools that allow for tissue-specific manipulations of gene products , we utilized the GAL4/UAS binary expression system [20] to analyze glue-gene induction in developing salivary glands . To perform this analysis in the most precise way , it was first necessary to identify a temporally and spatially restricted driver—a transgenic stock of flies in which the Gal4 transcription factor is under the control of specific Drosophila enhancers that limit its expression to larval salivary glands at least 10 hours preceding the normal induction of Sgs3 . In our search for the best reagent , we noticed that a number of the drivers classified as salivary-gland specific were produced from the P{GawB} enhancer-trap element ( FBtp0000352 ) . Our lab and others have observed that GawB-derived elements display constitutive expression of GAL4 in larval salivary glands [32] , perhaps because part of the GawB vector contains a cryptic larval salivary-gland-specific enhancer element . To test this hypothesis we used a hs-Gal4 stock that contains the Hsp70Bb ( FBgn0013278 ) controlling elements driving Gal4 in a GawB vector . In the absence of heat stress these animals produced GAL4 in the L1 ( first instar ) , L2 ( second instar ) , and L3 salivary glands as indicated when they were crossed to the GFP . nls responder . In this stock GFP is expressed under GAL4 control ( it contains UAS elements that are binding sites for the GAL4 transcription factor ) and it is targeted to nuclei ( Figure 2 ) . Thus , in subsequent experiments we used hsGal4 ( now referred to as sgGal4 ) to drive spatially restricted expression of UAS-transgenes only in larval salivary glands . With a spatially restricted driver on hand , we now crossed sgGal4 to both UAS-dominant-negative- ( EcR-DN ) and UAS-RNA-interference- ( EcRi ) constructs of EcR . The EcR-DN protein is defective in ligand-activated transactivation so that it competes with endogenous EcR isoforms to block normal hormone responses [33] . The EcRi construct contains an inverted repeat of a DNA region common to EcR-A , B1 , and B2 so that its expression silences all isoforms [34] . When either was crossed to a tester stock containing sgGal4 and glueGRN , no green fluorescence was detected in L3 larval glands . These results are consistent with a requirement that at least one EcR isoform must be present in the salivary gland for glueGRN synthesis ( data not shown ) . One potential caveat with the above experiments is that by perturbing EcR in the salivary gland , we were killing it or causing it to develop too slowly to induce the Sgs3 transgene . To address this possibility , we utilized another tester stock containing three transgenic elements: glueRED; GFP . nls; sgGal4 . The glueRED element is an endogenously tagged Sgs3 gene ( under its own promoter/enhancer elements ) . It contains the same DNA sequence as glueGRN except the coding information for GFP is replaced with that of DsRED [35] . As with glueGRN , the glueRED element produces a protein that is synthesized ( Figure 3A ) , secreted ( Figure 3B ) , and expectorated in exactly the same manner as endogenous SGS3 . Thus , when this tester stock was crossed to EcR-DN ( Figure 3C ) or EcRi ( Figure 3D ) no glueRED was produced , but GFP is still localized to nuclei that are similar in size to those of the control glands producing glueRED ( Figure 3A , B ) . These results indicate that neither EcR-DN nor EcRi expression is killing the cells or preventing their normal nuclear polytenization . Thus , EcR function in the salivary gland is required for glueGRN and glueRED production . To test the hypothesis that any isoform of EcR can be used to induce glue synthesis , we crossed each UAS-EcR isoform-specific transgene into a background in which EcR-DN was expressed in the salivary gland ( under sgGal4 control ) using the glueRED and GFP . nls transgenes to assay gland physiology . To confirm that extra copies of UAS-transgenes were not diluting the effects of EcR-DN in a non-specific manner , we included a UAS-control construct that contains a cassette of UAS/GAL4 binding sites . By itself , the expression of the UAS-control does not lead to a block in glueRED synthesis when driven by sgGal4 ( data not shown ) . Furthermore when crossed into an animal producing EcR-DN and GFP . nls , it does not overcome the block in glueRED synthesis ( Figure 4 ) . This control eliminates the concern that the expression of EcR-DN may be reduced by the introduction of an additional transgene containing UAS elements . In contrast to the UAS-control , introducing each of the known EcR-specific isoforms into the same genetic background completely rescues the block in the production of glueRED caused by EcR-DN . The rescue is fully penetrant and normal in the semi-quantitative scoring scheme that is presented in Figure 4 . It is even more interesting because an artificially constructed EcR isoform—EcR-C , which contains only the common-regions of EcR because it is missing the isoform-specific A/B domain—also rescues the block in glueRED synthesis in approximately 90% of the animals examined . These results confirm an earlier conclusion that any isoform of EcR expressed in the salivary gland is capable of transmitting the 20E signal to induce the transcription of Sgs3-derived genes . The BRC is a large transcription unit that produces several different isoforms of a transcription factor containing C2H2 zinc-fingers . Although multiple transcripts are derived from the locus [36] , only four general types of proteins are produced . Each isoform contains an identical NH2 terminus , but it has a different combination of DNA binding domains [37] . The four proteins , referred to as BRC-Z1 , BRC-Z2 , BRC-Z3 , and BRC-Z4 , have been shown to play an important role in the production of SGS3 and other glue proteins . This conclusion is based on the phenotypic analyses of null- or isoform-specific hypomorphic mutants that either do not produce SGS3 or display a prolonged developmental delay in the accumulation of transcripts from the locus [13] , [23] , [24] , [38] . Because it has been shown that BRC is regulated as a primary response to 20E ( the hormone/receptor complex directly binds to DNA elements within the gene and induction does not require de novo protein synthesis ) [39] , the above effects on Sgs3 activation have led to a model in which glue production occurs as a secondary response to the hormone . Thus , the BRC zinc-finger transcription factors are probably responsible for activating promoter/enhancer elements within the glue genes as suggested by DNA binding studies on Sgs4 [40] . To test this hypothesis in more detail , we utilized transgenic stocks in which each BRC-Z isoform was expressed under UAS control in larvae also containing glueRED; sgGal4; GFP . nls; and EcR-DN . As indicated in Figure 4 , each BRC-Z isoform is capable of partially rescuing the block in glue synthesis imposed by the production of EcR-DN . Rescue was scored using five categories that indicated the approximate percentage of cells within a gland that produced glueRED ( none , few , ∼25% , ∼50% , 100% ) . However , not all BRC isoforms are equal in their ability to suppress the synthesis defect imposed by EcR-DN . BRC-Z2 ( no glands were observed that were completely empty of glue , and 58% had full wildtype levels ) and BRC-Z4 rescue the best; whereas , BRC-Z1 and BRC-Z3 ( 60% of the animals have glands with no glueRED ) rescue poorly . The variability in rescuing the synthesis-blocked phenotype may reflect the partially redundant activities or regulatory dependencies that have been reported among the four types of BRC isoforms [37] , or it may reflect the differences in expression levels among the different transgenes . Two additional points are worth noting . First , expression of all forms of UAS-BRC altered the expression/localization of GFP . nls in some cells , but this failure to localize GFP did not correlate with a defect in glueRED production . In all cases , a few cells producing glueRED were observed with large prominent nuclei that did not contain GFP . Because we never observe this effect in the experiments performed with EcR isoforms or the UAS-control , it is unlikely that extra transgenes containing UAS elements are titrating a limiting amount of GAL4 transcription factor . Second , we sometimes observe the appearance of glueRED in L1 and L2 animals when BRC isoforms are ectopically expressed ( data not shown ) . This early expression of glueRED or glueGRN is never observed in control animals or in crosses where EcR-specific isoforms are ectopically expressed . This result may indicate that BRC proteins are sufficient for SGS3 production at any stage of larval salivary gland development , but that critical levels of BRC isoforms are normally restricted to mid-to-late L3 stages in wildtype animals [5] . Glue is a mixture of at least eight different glycoproteins [41] , [42] , which are coordinately induced midway through the third instar in a tissue-restricted fashion . To test whether perturbing EcR signaling disrupts the synthesis of most glue proteins , we assayed glue production in EcR-compromised glands in two different ways . First , we examined the glands directly . The cytoplasm of EcR-compromised cells is very small with no detectable secretory granules ( Figure 3C , D ) . If other abundant non-tagged glue proteins were being loaded into granules , this result would not be expected . Second , when we examined the expression pattern of Sgs3 , Sgs4 , Sgs5 , Sgs7 , and Sgs8 transcripts by Northern analysis , we found very little signal for any of the five glue genes tested in animals in which EcR was compromised in the salivary gland ( Figure 5 ) . Note the normal developmental expression pattern in the control lanes ( C-1; C-2 ) . Transcript levels for all glue genes should be high in wandering larvae ( L ) , and they should be low or undetectable at the time of puparium formation ( W ) . Because all known 20E signaling pathways that control in-vivo developmental events are thought to be mediated through an ecdysone receptor consisting of EcR and USP , we wanted to test the requirement for USP in the synthesis of glue . Thus , we utilized a transgenic RNAi construct that contains an inverted repeat of USP under UAS control ( USPi ) . We expressed this construct using the sgGal4 driver and the reporter genes ( glueRED; GFP . nls ) described above in order to selectively silence USP in larval salivary glands . Under these circumstances glands were indistinguishable from parental stocks ( compare Figure 3A with Figure 6A ) , and 100% of the glands produced wildtype levels of glueRED ( Table 1 ) . This result suggests that USP is not part of the receptor needed for glueRED expression . An alternative explanation is that the USPi construct is not effectively knocking down USP levels in the salivary gland , but three lines of evidence make this possibility very unlikely . First , we examined wildtype- and USPi-compromised salivary glands for USP protein using a well-characterized USP antibody . As shown in Figure 6 , no USP protein can be detected in the nuclei of salivary glands in which USPi is expressed . This is in contrast to the wildtype glands of similar L3 stages ( compare the tissues marked as SG in C-E with those outlined by a dashed line in C′-E′ ) , and in contrast to USPi animals where the fat body ( FB ) , central nervous system ( CNS ) , imaginal discs ( ID ) and midgut ( MG ) clearly display the expected nuclear staining . This result is consistent with sgGal4 driving USPi only in salivary glands and not in other tissues . In addition , no USP protein is detected in salivary-gland extracts when a Western-blot analysis is performed on glands expressing the USPi construct ( Figure 7 ) . Second , because glue secretion ( dumping of granules into the lumen of the gland ) at the end of L3 has been shown to be 20E dependent [12] and to require functional EcR and USP [13] , we expected that USPi glands would not be able to secrete the glueRED that was produced at an earlier stage . This prediction is always supported by data . Note that the photograph of the gland in Figure 6A was taken at the time of puparium formation and that no glueRED can be detected in the lumen ( L ) of the tissue . In wildtype parental glands , secretion of the tagged glue into the lumen ( Figure 3B ) always occurs by the white prepupal stage . Third , because it has been reported that USP is necessary to repress the glue genes at the time of puparium formation , we expect that transcript accumulation for each Sgs gene should not decrease at the white prepupal stage . The data presented in Figure 5 ( compare L with W in the USPi lanes ) support this hypothesis . Another possible caveat for the observation that RNAi against USP does not prevent glueRED expression is that a small amount of USP protein may be very stable in the salivary gland and thus not subject to efficient silencing by the RNAi mechanism . Following this logic , the protein turn over might take 4 days to reach a critical threshold level . Thus , there would be enough USP protein for glueRED synthesis in 3-day old larvae ( the age when glue genes are induced by 20E ) , but not enough in 4-day old larvae ( the age when 20E causes glue secretion ) . To test the ability of the USPi construct to silence USP effectively in a short time frame , we used the glueGal4 driver ( FBst0006870 ) to express transgenes in the salivary gland from mid-L3 until puparium formation . Under these circumstances glue secretion was blocked even though the USPi responder was only being expressed for 24 hours prior to the assay ( data not shown ) . Because it is known that USP can heterodimerize with EcR at the end of the larval period , we predicted that an overproduction of USP at mid-L3 might prevent a critical amount of EcR from forming the functional receptor needed for glue-gene induction . However , if even a small amount of a receptor consisting of EcR and USP is required to induce the glue genes , overproducing the USP component at an earlier time should not affect the response . Thus , we generated transgenic flies in which the coding information for wildtype USP was placed under UAS controlling elements . When this transgene ( USP+ ) was driven by sgGal4 , a large amount of USP protein was detected on Western blots of salivary glands ( Figure 7 ) , and the production of glueRED was reduced ( Figure 6B; Table 1 ) . We verified that this construct produces functional protein by crossing it to flies carrying both the glueGal4 driver and USPi responder . Under these conditions the USP+ construct was able to rescue the block in glue secretion caused by USPi . Although the overproduction of USP in the salivary gland perturbs glueRED expression ( 34% of the glands produce no product ) , the block was not complete because animals were able to express varying levels of glueRED in some salivary-gland cells ( Table 1 ) . To more precisely quantify the amount of glueRED produced under these conditions , we performed the Western blot presented in Figure 8 . As expected , no DsRED-tagged protein can be detected in the lanes in which EcR-DN or EcRi are expressed in the salivary glands ( Figure 8B ) . In addition , the levels of glueRED are not reduced when USPi is expressed in the salivary glands because both control lanes ( w1118 x sgGal4; glueRED ) and experimental lanes ( USPi x sgGal4; glueRED ) contain the same band intensities when quantified and adjusted for protein loading using α-Tubulin ( Figure 8A , B ) . However , the levels of glueRED are reduced 3 fold when USP is overexpressed ( USP+ x sgGal4; glueRED ) in the salivary gland compared to the control and USPi lanes ( Figure 8B ) . One explanation for the reduction , but not elimination of glueRED , is that the amount of USP produced under these conditions is at a threshold level needed to antagonize the 20E-signaling pathway mediated by EcR . To test this hypothesis , we crossed the USP+ line to sgGal4; GFP . nls; glueRED and raised the larvae derived from the cross at two different temperatures ( 25°C and 29°C ) . Because temperatures closer to 30°C are reported to produce higher GAL4 activities [43] ( probably because GAL4 is a yeast transcription factor ) , we predicted that larvae raised at 29°C would produce less glueRED ( due to the overproduction of more USP that should antagonize 20E receptor formation ) . As indicated in Table 1 , these differences were observed when animals were raised at the two different temperatures ( 53% of the glands failed to produce any glue when raised at 29°C compared to 34% that failed to produce any glue when raised at 25°C ) . In addition , we confirmed that raising control animals at 29°C did not perturb glueRED production , and raising experimental animals at the elevated temperature did not cause a non-specific induction of the heat shock promoter in other tissues because GFP . nls was only detected in the nuclei of salivary glands ( data not shown ) . Finally , to ascertain the role of USP in the production of other glue proteins , we compared the overall pattern of protein synthesis using Coomassie staining of SDS-PAGE . As shown in Figure 8C , the appearance of most of the glue proteins can be identified when whole salivary-gland-protein extracts are stained because the Sgs genes are abundantly expressed in this tissue . We were able to confirm the presence of the major glue bands by comparing extracts of secreted glue plugs [6] that were prepared as ethanol precipitates from the lumens of white prepupae ( data not shown ) . As expected , the accumulation of most glue proteins is reduced drastically in glands in which EcRi and EcR-DN are expressed . Also as expected , they are not reduced when USPi is expressed , but they are affected when USP is overproduced . Taken together these results are very compelling , and they indicate that USPi is very efficient at gene silencing in the salivary gland when driven by sgGal4 . Therefore , USP is not needed for the 20E-mediated induction of the glue genes through the BRC .
Previous reports using mutants that are defective in 20E production or signaling yielded contradictory results concerning the role of 20E in the induction of the glue genes in the salivary gland . Here we demonstrate that a glue-gene reporter derived from the Sgs3 gene can be induced by 20E in cultured glands dissected from wildtype animals at mid L3 . Furthermore , unlike the 20E mediated events that occur at the end of the larval period , the induction of Sgs3 and other glue genes is mediated by a lower titer of hormone ( 10−9 to 10−8 M ) . This result is consistent with a report of a small titer of 20E that has been detected in a population of synchronized animals two hours prior to the induction of the glue genes [29] . In addition , because the ecd1ts mutation probably reduces the concentration of 20E in the hemolymph , mutant animals shifted to the non-permissive temperature might still be exposed to enough 20E to induce the Sgs genes . We have also shown that the induction of the glue genes occurs as a secondary response to the hormone because the requirement for EcR can be bypassed if BRC isoforms are ectopically expressed . This finding is supported by published evidence that some 20E-regulated transcription factors ( BRC , E74B ) can be induced in cultured organs by a pulse of hormone that is much lower than that produced at the end of the third instar , ∼10−8 M versus ∼10−6 M [44] . The dogma for the action of 20E during Drosophila development is that EcR and USP are associated as a heterodimer and often bound to the EcREs of target genes . When not bound by ligand , the heterodimer associates with a repressor complex to prevent transcription from those genes . Hormone binding ( to the ligand-binding domain of EcR ) leads to a conformational change in the complex , the dissociation of the repressor complex , and the recruitment of co-activators for high-level transcriptional activation [reviewed in 19] . Although this model is well supported by evidence that both EcR and USP are required to initiate events during the late-larval and prepupal periods , our study presents compelling evidence for the existence of another bona fide receptor for 20E that consists of EcR but does not use USP as its heterodimeric partner . We have provided evidence that SGS3 production ( and probably glue synthesis in general ) is a 20E-mediated event . We have also demonstrated that EcR is required for the induction of the glue genes , and that any isoform of EcR can be involved in the activation of Sgs3 . This result is interesting because EcR-B1 is reported to be the predominant form that is normally expressed in the larval salivary gland [45] . Also , because expression of BRC is necessary and sufficient for the induction of Sgs3 , these experiments suggest that the A/B domain of EcR does not participate in the expression of BRC by the smaller pulse of 20E that occurs midway through the L3 stage . In contrast to the results for EcR , we have provided convincing evidence that USP is not the other half of the heterodimer needed for the 20E-mediated initiation of glue synthesis . In a previous report [13] we confirmed that USP mutants can be rescued from embryonic lethality by providing exogenous USP from a heat-shock driven transgene [46] . Furthermore , if these animals are not provided with a source of USP during the L2 and L3 stages ( by being deprived of subsequent heat pulses that would induce the transgenic cDNA ) , they will not pupariate , but they will grow , molt , and express an Sgs3 derived reporter [13] . In the current study we have used strong tissue-specific drivers that are exclusively expressed in the salivary gland at two different time points . We have demonstrated that the USPi stock is an effective reagent for silencing endogenous USP in the salivary gland ( Figures 6; 7 ) , even if it is only produced for 24 hours before the assay ( i . e . inducing it with glueGal4 blocks glue secretion ) . Thus , when it is driven during all larval stages ( 3 days before glue synthesis ) no USP protein can be detected by immunostaining , and this absence of USP protein has no effect on the production of glue . To further confirm that USP is not needed for glue synthesis , we demonstrated that when wildtype USP is overexpressed in the salivary gland during the larval stages , glue protein production is drastically reduced . Because USP is known to heterodimerize with EcR at a later developmental stage , the simplest explanation for this observation is that extra USP protein is preventing EcR from forming the functional 20E receptor needed for glue synthesis in mid L3 . Such a result is not expected if only a small amount of functional EcR/USP is needed to induce the glue genes . Interestingly , other researchers have observed similar effects . One report generated clones of usp-/usp- mutant tissue in the salivary gland , and although they do not discuss the effects of glue production in mutant tissue , the presence of glue granules is apparent in the clones from late-L3 glands [47] . This and other studies also describe the developmental differences of clones of usp- tissue in imaginal discs . For example , movement of the morphogenetic furrow—a 20E mediated event responsible for eye development [48]—is actually accelerated across a usp- patch of tissue [49] , [50] . In addition , others have noted that the 20E dependent differentiation of chemosensory neurons in the wing margin occurs precociously in the absence of USP function [51] . Furthermore , when target-gene expression is examined , transcripts from the BRC ( BRC-Z1 ) accumulate earlier in development in mutant clones within the eye and wing discs [47] , [51] . These observations led to the hypothesis that in the absence of ligand , the EcR/USP heterodimer can act as a repressor in some tissues by binding to the response elements of a select group of target genes . The function of the hormone is to de-repress the target genes by removing the EcR/USP complex from the promoter region allowing other bound transcription factors to activate transcription [34] . Thus in a usp- clone , genes controlled by this mechanism should be precociously activated . We do not think that the induction of the glue genes is controlled by a de-repression of BRC through EcR/USP for two reasons . First , the glue genes are not induced ( de-repressed ) if EcR is silenced with an EcRi construct . Second , we do not see precocious activation of glue genes when a USPi construct is expressed . Our model proposes that USP is acting as a repressor by heterodimerizing with EcR to prevent the association of EcR with another nuclear-hormone receptor ( NR-X ) . Our hypothesis may also explain some of the data generated with the use of usp- clones in imaginal discs . For example , if we assume that movement of the morphogenetic furrow is induced by an earlier and lower pulse of 20E ( as has been reported for Manduca ) [52] , we would speculate that furrow movement is controlled by EcR/NR-X regulating downstream genes including BRC-Z1 . The normal presence of USP in this tissue at that time might serve to control the amount of functional EcR/NR-X available for high-affinity hormone binding . Thus in a usp- clone , we would expect the morphogenetic furrow to move faster over the patch and the induction of BRC-Z1 to be premature . Such observations were reported [47] , [49] , [50] . The normal expression of USP in the salivary gland at mid L3 ( Figures 6; 7 ) may also be needed to ensure that the response of glue-gene induction is precisely regulated . In any case , the induction of a 20E regulated pathway that does not require USP as part of the receptor has no precedence in the Drosophila literature . Thus , a better characterization of this response at the molecular level is critical for our understanding of normal insect development . In this report we demonstrate that EcR is necessary for the expression of most of the glue genes at mid L3 , and that USP is not needed for this expression . In addition , we show that any isoform of BRC can be sufficient for Sgs3 transgene expression even if the EcR component of the receptor is compromised with EcR-DN , and that overexpression of some BRC isoforms in first- and second-instar larvae is enough to induce expression of the Sgs3 transgenes days before they would normally be transcriptionally active . However , it is interesting to note that although Sgs3 and Sgs4 appear to be coordinately expressed in mid-L3 salivary glands , different binding sites for regulatory proteins have been identified in their promoter/enhancer regions . These include response elements for EcR/USP , and binding sites for BRC [40] , GEBF-I ( FBgn0013970 ) [53] , Forkhead ( FBgn0000659 ) [54]–[56] , and SEBP3 ( FBgn0015293 ) [57] . The binding of different transcription factors to these sites may modulate the levels of expression of the two genes or they may contribute to their restricted expression patterns in the salivary gland or other tissues . For example , although we have shown that Sgs3 derived transgenes are exquisitely restricted to the salivary glands of third-instar larvae , others have reported the expression of different glue genes in tissues outside this cell type . These include Sgs4 expression in the proventriculus [58] and I71-7 expression in the midgut and hemocytes [59] . Such expression patterns raise the interesting possibility that these highly glycosylated mucin secretions may perform other functions stemming from their propensity to form a sticky substance in aqueous solution . These functions could include the formation of the peritropic membrane around the food or the formation of extracellular aggregates that might be involved in antimicrobial responses [59] . If we assume that members of the nuclear-hormone receptor superfamily form dimers to produce the active receptor needed for glue-gene expression , we can formulate two hypotheses concerning the composition of that functional receptor . First , the active receptor may be a homodimer of EcR proteins . Homodimers are known to function as receptors for steroid hormones in vertebrates using a different mechanism of ligand activation than that observed with RXR heterodimeric receptors ( USP is the insect homolog of RXR ) , but to our knowledge no biological activity has been ascribed to EcR homodimers during Drosophila development . Our analysis does not rule out the possibility that EcR homodimers are responsible for the induction of the glue genes . The second possibility is that another member of the superfamily may be able to complex with EcR to transmit the hormone signal . Many of these receptors have pre-existing mutations and many more have UAS-RNAi lines that are now available from the RNAi Stock Centers in Vienna ( http://www . vdrc . at ) and Japan ( http://www . shigen . nig . ac . jp/fly/nigfly/index . jsp ) . At this point we have assayed production of glueRED in mutants or RNAi lines that knock down DHR38 ( FBgn0014859 ) and DHR78 ( FBgn0015239 ) , but no effects on glueRED synthesis were observed ( A . Andres , unpublished observations ) . However , the existence of transgenic RNAi lines should simplify the analysis because it is expected that when a specific nuclear receptor is silenced in the salivary gland , it should display a phenotype that is defective in glue synthesis . It would then be very interesting to screen the controlling region of the BRC to establish the nature of the EcRE ( s ) that control the response at the molecular level , and to test if this type of receptor could control other developmental events ( perhaps molting of the instars or some aspect of early imaginal disc development ) that are regulated by 20E during earlier larval stages .
All flies were raised on standard cornmeal-molasses medium supplemented with live baker's yeast as recommended by the Bloomington Stock Center ( Bloomington , Indiana , United States ) ( http://flystocks . bio . indiana . edu/Fly_Work/media-recipes/bloomfood . htm ) . w1118 ( FBst0307124 ) , GFP . nls [{UAS-GFP . nls}14 ( FBst0004775 ) ] , EcRi [{UAS-EcR-RNAi}104 ( FBst0009327 ) ] , EcR-DN [{UAS-EcR . B1-ΔC655 . F645A}TP1 ( FBst0006869 ) ] , and the EcR isoform stocks [EcR-A {UAS-EcR . A}3a ( FBst0006470 ) , EcR-B1 {UAS-EcR . B1}3b ( FBst0006469 ) , EcR-B2 {UAS-EcR . B2}3a ( FBst0006468 ) , and EcR-C {UAS-EcR . C}Tp1-4 ( FBst0006868 ) ] were obtained from the Bloomington Stock Center . The following stocks were provided as generous gifts: UAS-hid [60] from Eric Baehrecke , the hsGal4 driver on the third chromosome [20] from Robert Holmgren , and the stocks containing specific isoforms of the BRC ( UAS-BRC-Z1 , UAS-BRC-Z2 , UAS-BRC-Z3 , and UAS-BRC-Z4 ) [61] from Xiaofeng Zhou . Transgenic flies containing glueRED were prepared by digesting pDsRed2-C1 ( Clonetech , Palo Alto , California , United States ) with AgeI and KpnI restriction enzymes to isolate a DNA fragment containing the open reading frame for DsRED . This fragment was cloned into pBS-SgsΔ3GFP [13] that was digested with the same enzymes to remove the eGFP tag and generate a vector with compatible ends . The resulting intermediate construct was digested with AgeI and the 3′ recessed ends were filled in and religated to restore the open reading frame between Sgs3 and DsRED . The Sgs3-DsRED sequence was removed from the Bluescript vector ( Stratagene , La Jolla , California , United States ) as a NotI/KpnI fragment and inserted into the NotI and KpnI sites of the pCaSpeR-4 fly transformation vector ( FBmc0000178 ) . DNA was sent to the vonKalm laboratory at the University of Central Florida for the generation of transgenic flies using standard techniques [62] . To produce the UAS-USPi stock , a PCR fragment was amplified from a USP cDNA plasmid [63] using the primers AAGAATTCGGTACCAGTATCCGCCTAACCATCC and TTAGATCTCGCTTCATCTTTACACTCAG . The resulting amplification product ( corresponding to a 924 bp fragment between positions 467 and 1390 relative to the USP mRNA sequence ) was cloned in the pUAST vector ( FBmc0000383 ) using two steps . First a reverse fragment was placed between the vector BglII and KpnI sites . A second forward-orientated fragment was cloned between EcoRI and BglII sites . Recombinant UAS-USPi constructs were transformed at 30°C in Sure-competent bacteria ( Stratagene ) to minimize DNA recombination and screened using appropriate restriction enzyme digestions . Transgenic lines were generated as previously described using a w1118 strain as a recipient stock . UAS-USP+ stocks were prepared as follows: The vector pUAST-USP+ was constructed by PCR amplification of the USP open reading frame with the forward primer TTTTGCGGCCGCACC ATG GAC AAC TGC GAC CAG GAC and the reverse primer TTTTTCTAGA CTA CTC CAG TTT CAT CGC CAG using pZ7-1 cDNA as a template [63] . The NotI and XbaI restriction sites flanking the PCR product were used for subsequent ligation into the corresponding sites in the pUAST vector . The pUAST-USP+ vector was transformed into flies at the Duke University Medical Center . The UAS-Control line ( LA1216 ) contains an insert of the construct P{Mae-UAS . 6 . 11} ( FBtp0001327 ) . This vector was designed for gene-mis-expression screens because it contains a copy of the UAS/GAL4 binding sequences oriented to express flanking genes when inserted into the genome [64] . We tested four Gal4-drivers obtained from the Bloomington Stock Center [AB1-Gal4 ( FBst0001824 ) , C147-Gal4 ( FBti0024396 ) , T155-Gal4 ( FBti0002598 ) , and 34B-Gal4 ( FBst0001967 ) ] with expression patterns reported to be restricted to the larval salivary gland . To ascertain which of these was best for tissue-specific expression studies , we crossed them to a stock in which the hid/Wrinkled cell-death gene ( FBgn0003997 ) was expressed under UAS control . Because the major function of the salivary gland in the larval stages is the reported synthesis of mucin-like proteins that help lubricate the food as it moves through the gut [2] , [3] , we reasoned that animals could survive without a salivary gland only if they were provided a diet of freshly produced moist yeast paste . Thus , by using UAS-hid we could ablate the salivary gland and test if such animals were viable when raised on soft food . The initial analysis using the above listed drivers indicated that no larvae were able to survive , probably due to expression of the hid gene in other vital tissues . But because these are derived from the GawB vector , we used a heat shock 70-Gal4 driver that is also GawB derived . These animals were able to survive to puparium formation when crossed to UAS-hid and raised on a diet of freshly prepared yeast paste . Crossing hsGal4 to a stock containing both UAS-hid and UAS-GFP . nls confirmed that larval salivary glands could not be detected and were ablated . Great care was exercised to raise the animals crossed to sgGal4 at temperatures below 30°C to prevent exposing them to a stress that might induce Gal4 in all cells . Because the GFP . nls transgene was used in most of the experiments , a non-specific response could easily be detected by the presence of green nuclei in other tissues . 20E ( Sigma , St . Louis , Missouri , United States ) was prepared as a stock solution of 10−2 M in 100% ethanol and stored at −20°C . The stock solution was diluted to the proper working concentration in Schneider's medium ( Sigma ) . Flies of the appropriate genotype were crossed and reared in a small population cage containing approximately 500 females and 500 males . The cage was presented with hard-agar plates ( 10% molasses , 3 . 5% agar ) containing a dab of fresh yeast paste ( prepared as a 1∶1 mixture of dry baker's yeast with water ) 2–3 times per day to collect fertilized eggs . Collection plates were aged at 25°C and first-instar larvae were collected in 1-hour intervals as they hatched . The first-instar larvae were added in groups of 100 to vials containing standard cornmeal-molasses-yeast medium and aged at 25°C for approximately 68 hours ( a developmental stage that precedes glue induction by approximately 4 hours ) before being washed from the food with Schneider's medium . Animals were torn in half lengthwise using small dissecting forceps ( Fine Scientific Tools , Foster City , California , United States ) . Larvae prepared in this manner were transferred to clean microscope slides containing 25 µl of Schneider's medium with or without 20E . A range of 20E dilutions ( 10−6 , 10−7 , 10−8 , 10−9 M ) was prepared for each experiment . Small strips of Number 1 Whatman filter paper ( Millipore , Billerica , Massachusetts , United States ) were placed around the culture as spacers before adding a 22 mm2 coverslip . The culture was placed on a platform shaker in a box into which O2 was continuously infused during the culture period . Cultures were incubated at 25°C for 4–6 hours before being assayed for glue production as detected by the presence of green fluorescent protein from the glueGRN transgene . Whole larvae were selected from the food , washed 3× in water , blotted on filter paper , placed in a depression slide , and killed with a few drops of ether . After the ether evaporated , animals were mounted in glycerol between two slides using glass coverslips as spacers . Larvae were photographed within 30 minutes of preparation . For isolated tissues , animals were dissected in Drosophila PBS ( DPBS ) [65] or Schneider's medium . Low-resolution images of whole animals or dissected tissues were obtained on a Leika fluorescent stereo microscope containing filter cubes for GFP and/or DsRED . Images were captured with the Spot Insight QE Model #4 . 2 digital camera ( McBain Instruments , Chatsworth , California , United States ) and prepared with Canvas ( ACD Systems , Miami , Florida , United States ) graphics software . High-resolution images of dissected salivary glands were imaged on a LSM 510 Axioplan confocal microscope ( Carl Zeiss SMT , Peabody , Massachusetts , United States ) equipped with LSM 510 image-analysis software . Northern blots were prepared as previously described [5] . Briefly , RNA was isolated from larvae by grinding animals in SDS lysis buffer , digesting the homogenate with 250 μM Proteinase K ( NEB , Ipswich , Massachusetts , United States ) , extracting the sample with phenol/chloroform , and precipitating the aqueous phase with ethanol . Ten micrograms of total RNA were fractionated on 1% formaldehyde/MOPS/agarose gels and blotted onto Duralon-UV membranes ( Stratagene ) . Probes for each glue gene and the rp49 control were prepared as gel-isolated fragments from digested clones and hybridized with labeled random oligonucleotides using a Prime-it kit ( Strategene ) and 32P dCTP ( GE Healthcare , Piscataway , New Jersey , United States ) as previously described [5] . After washing , signals were detected using the Typhoon 8600 Variable Mode Phosphorimager equipped with Image Quant scanning software ( GE Healthcare ) . Dissected tissues were prepared for antibody staining as previously described [66] . Tissues were stained using the AB11 USP mouse monoclonal antibody [67] ( gift from Carl Thummel ) at a dilution of 1∶50 . Protein levels were visualized using a goat-anti-mouse secondary antibody conjugated to FITC ( Jackson Immuno Research , West Grove , Pennsylvania , United States ) . To prepare protein extracts for Coomassie staining or Western-blot analysis , salivary glands were dissected in DPBS as described above . Typically 10–20 pairs of glands were collected in DPBS , pelleted in a microfuge , and resuspended in lysis buffer containing a cocktail of protease inhibitors [68] . Glands were homogenized and boiled for 5 minutes before being stored at −20°C for less than one week . Samples were divided in two and resolved on separate 12% SDS polyacrylamide gels that were run in the same electrophoresis rig . One was stained with Coomassie brilliant blue ( J . T . Baker , Phillipsburg , New Jersey , United States ) and the other was transferred to Immobilon P membranes ( Millipore ) as previously described [66] . Blots were incubated with the following antibodies: mouse anti-α-Tubulin primary ( Sigma ) diluted 1∶15 , 000; rabbit anti-DsRED primary ( Clontech ) diluted 1∶15 , 000; mouse anti-USP primary diluted 1∶100; goat anti-mouse-HRP secondary ( Jackson Immuno Research ) diluted 1∶40 , 000; and goat anti-rabbit-HRP secondary ( Jackson Immuno Research ) diluted 1∶25 , 000 . Protein levels were visualized and quantified using Chemi-luminescence ECL ( + ) Western-blotting detection system ( GE Healthcare ) and a Typhoon 8600 Variable Mode Phosphorimager ( GE Healthcare ) . The FlyBase ( http://flybase . bio . indiana . edu/search/ ) identification numbers are used in this work to describe genes , gene products , vectors , and Drosophila stocks .
|
During animal development the physiological response of individual tissues is often “reprogrammed” in response to signaling molecules . One important example is the activity of nuclear-hormone receptors that are controlled by small lipid compounds such as steroids and retinoids . Thus , understanding how tissue-specific developmental and physiological responses are regulated by these systemic ligands is a fundamental question of cell biology . Drosophila is an important model system in which to investigate this question because of its 100-year history of analyzing mutants that affect complex biological processes , and because researchers possess a powerful “toolkit” that allows for precise tissue- and temporal-specific expression and silencing of almost any gene in the genome . Furthermore , during the metamorphosis of Drosophila , the body plan is completely reorganized from that of a larva ( specialized for growth and feeding ) to that of an imago ( specialized for reproduction and dispersal ) by a single steroid hormone . Here we examine the molecular events that control different physiological responses within a single target tissue to different pulses of 20E . We show for the first time that these temporally specific events within the same tissue are controlled in part by different 20E receptors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/transcription",
"initiation",
"and",
"activation",
"genetics",
"and",
"genomics/gene",
"expression",
"cell",
"biology/cell",
"signaling",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"genetics",
"and",
"genomics/gene",
"function",
"developmental",
"biology/developmental",
"molecular",
"mechanisms"
] |
2008
|
A Novel Ecdysone Receptor Mediates Steroid-Regulated Developmental Events during the Mid-Third Instar of Drosophila
|
Dengue is the most prevalent arboviral disease in humans and a major public health problem worldwide . Systemic plasma leakage , leading to hypovolemic shock and potentially fatal complications , is a critical determinant of dengue severity . Recently , we and others described a novel pathogenic effect of secreted dengue virus ( DENV ) non-structural protein 1 ( NS1 ) in triggering hyperpermeability of human endothelial cells in vitro and systemic vascular leakage in vivo . NS1 was shown to activate toll-like receptor 4 signaling in primary human myeloid cells , leading to secretion of pro-inflammatory cytokines and vascular leakage . However , distinct endothelial cell-intrinsic mechanisms of NS1-induced hyperpermeability remained to be defined . The endothelial glycocalyx layer ( EGL ) is a network of membrane-bound proteoglycans and glycoproteins lining the vascular endothelium that plays a key role in regulating endothelial barrier function . Here , we demonstrate that DENV NS1 disrupts the EGL on human pulmonary microvascular endothelial cells , inducing degradation of sialic acid and shedding of heparan sulfate proteoglycans . This effect is mediated by NS1-induced expression of sialidases and heparanase , respectively . NS1 also activates cathepsin L , a lysosomal cysteine proteinase , in endothelial cells , which activates heparanase via enzymatic cleavage . Specific inhibitors of sialidases , heparanase , and cathepsin L prevent DENV NS1-induced EGL disruption and endothelial hyperpermeability . All of these effects are specific to NS1 from DENV1-4 and are not induced by NS1 from West Nile virus , a related flavivirus . Together , our data suggest an important role for EGL disruption in DENV NS1-mediated endothelial dysfunction during severe dengue disease .
The four dengue virus serotypes ( DENV1-4 ) are mosquito-borne flaviviruses that are responsible for ~390 million infections per year worldwide [1] . Of these , up to 96 million manifest in clinical disease . The majority of these cases are dengue fever ( DF ) , the uncomplicated form of disease . However , a subset develop severe dengue disease , including dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) , characterized by increased vascular leak , leading to shock and potentially death [2] . Pleural effusion resulting in respiratory distress is one of the most common signs of plasma leakage in DHF/DSS cases [3] . Vascular hyperpermeability arises as a result of endothelial barrier dysfunction , leading to increased passage of fluids and macromolecules across the endothelium . Traditionally , tight and adherens junctions have been considered to be the primary determinants of endothelial barrier function [4] . Over the past few years , however , the endothelial glycocalyx layer ( EGL ) has been recognized as a key regulator of vascular permeability [5] . The EGL is a network of glycoproteins bearing acidic oligosaccharides and terminal sialic acid ( N-acetyl-neuraminic acid , Sia ) , as well as membrane-bound proteoglycans associated with glycosaminoglycan ( GAG ) side chains including heparan sulfate ( HS ) , hyaluronic acid , and chondroitin sulfate [6] . The EGL extends along the endothelial layer coating the luminal surface of blood vessels . Secondary DENV infection with a serotype distinct from the first DENV infection is a known risk factor for severe dengue disease . Several hypotheses have been proposed to explain severe dengue disease , including poorly neutralizing , cross-reactive antibodies and exacerbated T cell responses that together lead to production of vasoactive cytokines , causing vascular leakage that can result in shock [7] . Another potential component is DENV nonstructural protein 1 ( NS1 ) , a glycosylated 48 kDa protein that is the only viral protein secreted from infected cells , with high concentrations circulating in the blood of patients with severe dengue disease . NS1 plays a role in viral replication , immune evasion , and pathogenesis via activation of complement pathways [8] . More recently , we and others demonstrated that DENV NS1 alone can trigger endothelial hyperpermeability , resulting in vascular leakage [9 , 10] . Modhiran et al . [10] showed that NS1 acts as a pathogen-associated molecular pattern ( PAMP ) , activating mouse macrophages and human peripheral blood mononuclear cells ( PBMCs ) via toll-like receptor 4 ( TLR4 ) to secrete pro-inflammatory cytokines such as tumor necrosis factor–α ( TNF-α ) , interleukin-6 ( IL-6 ) , interferon-β ( IFN-β ) , IL-1β , and IL-12 . This effect was inhibited by a TLR4 antagonist ( LPS-RS ) and an anti-TLR4 antibody [10] . Further , we found that inoculation of mice with NS1 alone causes increased vascular leakage and induction of pro-inflammatory cytokines ( TNF-α , IL-6 ) , while NS1 combined with a sub-lethal DENV inoculum results in a lethal vascular leak syndrome [9] . Our in vitro experiments showed that NS1 also increases the permeability of human endothelial cells [9] . The increased permeability in vitro , as well as mortality in mice , was prevented by administration of NS1-immune polyclonal mouse sera or anti-NS1 monoclonal antibodies [9] . Likewise , immunization with recombinant NS1 from each of the four DENV serotypes protected against lethal challenge in the vascular leak model [9] . However , the mechanism by which DENV NS1 stimulates endothelial cells to induce vascular leak is poorly understood . NS1 has been proposed to bind to heparan sulfate on the surface of endothelial cells [11] , but how this interaction leads to an increase in endothelial permeability has not been described . Therefore , we evaluated whether NS1 triggers disruption of the EGL and defined the mechanism through which this occurs .
Soluble DENV2 NS1 attaches to the surface of human endothelial cells , especially pulmonary microvascular endothelial cells [11] . In severe dengue disease , major accumulation of fluids occurs in the pleura ( pleural effusion ) , a thin membrane that lines the surface of the lungs [12] . This suggests that the lung represents an important site of endothelial barrier dysfunction characteristic of severe dengue . In this study , we used an in vitro model of endothelial permeability to initially examine the ability of soluble NS1 from DENV serotype 2 and West Nile virus ( WNV NS1 ) to interact with cultured human pulmonary microvascular endothelial cells ( HPMEC ) . In the first experiment , we found that DENV2 NS1 showed dose-dependent binding ( 1 . 25–10 μg/ml ) to HPMEC monolayers at one hour post-treatment ( hpt ) ( Fig 1A and 1C ) . In contrast , WNV NS1 ( 2 . 5–10 μg/ml ) , from a closely-related member of the Flavivirus genus , displayed significantly less binding ( Fig 1B and 1C ) . A time course for DENV2 NS1 ( 5 μg/ml ) attachment to the surface of HPMEC showed a maximum peak for NS1 staining between 3 and 12 hpt; no NS1 could be detected on the surface of HPMEC after 24 hpt ( S1A and S1B Fig ) . This NS1 binding pattern reflected decreased trans-endothelial electrical resistance ( TEER ) observed in HPMEC and other endothelial cell lines , including primary human umbilical vein endothelial cell ( HUVEC ) [9] and human dermal microvascular endothelial cell ( HMEC-1 ) monolayers exposed to DENV2 NS1 ( Figs 1D and 1E and S2A ) . Increased endothelial permeability is induced by NS1 from DENV1-4 [9] after 3 hpt in a dose-dependent manner , and the effect persists for more than 12 hours ( Fig 1E ) . Although we previously reported [9] that all NS1 proteins tested negative for bacterial endotoxin using the Endpoint Chromogenic Limulus Amebocyte Lysate ( LAL ) QCL-1000TM kit ( Lonza ) ( <0 . 1 EU/ml per 25 mg of protein ) , we included an additional test using DENV2 NS1 pre-treated with the LPS-binding antibiotic polymyxin B ( 25 μg/ml ) . Polymyxin B did not inhibit DENV2 NS1-induced endothelial hyperpermeability in HPMEC , further supporting that this effect is specific to DENV2 NS1 and is not due to residual LPS contamination ( S2B Fig ) . The EGL on the surface of the endothelium plays an important role in several cellular functions , including cell-to-cell communication , cell-matrix interaction , and vascular homeostasis [5] , and a mature EGL has been shown to exist on cultured HPMEC in vitro [13] . To examine the effect of flavivirus NS1 proteins on the integrity of the EGL , HPMEC monolayers were exposed to DENV2 or WNV NS1 ( 5 μg/ml ) , in the range of NS1 concentrations seen in severe dengue in humans [14 , 15] . The expression of Sia , a major component of the EGL [16] , was visualized using the lectin wheat germ agglutinin ( WGA ) conjugated to Alexa 647 [17 , 18] . WGA has been described to not only bind to Sia but also to N-acetylglucosamine ( Molin et al . , 1986 ) , another monosaccharide expressed on the surface of endothelial cells . In this study , abundant binding of WGA at 30 minutes ( min ) and 1 hpt reflects normal distribution of Sia residues on the surface of HPMEC ( Fig 2A ) . In contrast , HPMEC monolayers treated with exogenous neuraminidase ( 0 . 5 UI , Clostridium perfringens , Sigma ) , which specifically cleaves Sia , almost completely eliminated WGA staining , indicating that WGA binds most abundantly to Sia on the surface of HPMEC ( S3A and S3B Fig ) . The homogenous distribution of Sia observed in untreated HPMEC was significantly disrupted in a dose-dependent manner 3–12 h after addition of DENV2 NS1 but not WNV NS1 ( Fig 2A , 2B and 2C ) . This same effect was observed in both HUVEC and HMEC-1 exposed to DENV2 NS1 after 3 and 6 h ( S3C and S3D Fig ) . Normal distribution of Sia was re-established by 24 hpt ( Fig 2A and 2B ) . Binding of DENV2 NS1 to HPMEC appeared to co-localize with the WGA staining of Sia residues in the EGL , suggesting that DENV2 NS1 may use Sia-linked glycans as adhesion molecules to mediate NS1-endothelial cell surface interaction ( Figs 2C and S1C ) . Next , to examine whether Sia was degraded or released from the surface of HPMEC exposed to DENV2 NS1 , we assessed the presence of free Sia in cultured HPMEC supernatant using a specific Sia immunoassay . Supernatant collected from endothelial monolayers treated with DENV2 NS1 showed a significant time-dependent decrease in Sia levels compared with supernatant from untreated cells and WNV NS1-treated monolayers ( Fig 2D ) , indicating Sia is not being released into the medium of DENV2 NS1-treated HPMEC . Interestingly , expression of Neu1 , Neu2 , and Neu3 , three mammalian sialidases found in endothelial cells , was strongly increased in HPMEC monolayers treated with DENV2 NS1 but not WNV NS1 at 3 hpt , potentially contributing to Sia degradation ( Fig 2E ) . To determine the functional significance of DENV2 NS1-triggered disruption of Sia in the EGL , sialidase activity was inhibited using Zanamivir , an influenza neuraminidase inhibitor that has been shown to significantly inhibit Neu2 and Neu3 [19] , and 2-deoxy-2 , 3-didehydro-N-acetyl-neuraminic acid ( DANA ) , a transition state analog inhibitor of influenza virus neuraminidase found to be active against mammalian Neu3 [20] . Both Zanamivir ( 50 , 100 μM ) and DANA ( 25 μg/ml ) partially protected HPMEC monolayers from DENV2 NS1-mediated endothelial hyperpermeability as measured by TEER ( Fig 2F ) , indicating that alteration of Sia distribution on the surface of HPMEC induced by DENV2 NS1 contributes to increased permeability . In addition to Sia , the EGL contains a large variety of heparan sulfate proteoglycans ( HSPGs ) [21 , 22] , including syndecans , which consist of a core protein modified by HS chains [23] . Syndecan-1 is considered the primary syndecan of endothelial cells , including the vascular endothelium [24]; thus , alteration of its expression or distribution can affect the integrity of the EGL as well as endothelial barrier function [25] . The expression and distribution of syndecan-1 was evaluated on HPMEC stimulated with DENV2 NS1 or WNV NS1 . WNV NS1 did not modify the distribution of syndecan-1 on HPMEC monolayers , but treatment with DENV2 NS1 resulted in increased staining of syndecan-1 starting 30 min post-treatment and persisting for more than 12 h ( Figs 3A and S4A ) . Similar results were observed for the extracellular matrix ( ECM ) HSPG perlecan ( S4B Fig ) . However , syndecan-1 levels were similar in HPMEC treated with DENV2 NS1 or WNV NS1 compared to untreated controls , as detected by Western blot ( Fig 3B and 3C ) . At 24 hpt , a small increase of syndecan-1 protein levels was detected by confocal microscopy and Western blot in DENV2 NS1-treated cells compared to control and WNV NS1-treated cells . Using an immunoassay for detection of soluble syndecan-1 ectodomain , increased levels of syndecan-1 ectodomain were found in conditioned media from DENV2 NS1-stimulated HPMEC at 1–24 hpt compared to untreated and WNV NS1-treated HPMEC ( Fig 3D ) . Notably , recombinant syndecan-1 alone was able to increase the permeability of HPMEC monolayers in a dose-dependent manner ( Fig 3E ) , suggesting that syndecan-1 shed from HPMEC after DENV2 NS1 stimulation may be involved in modulating endothelial barrier function . As a result of the dynamic equilibrium between biosynthesis and shedding of various HSPG components , perturbation of the EGL upon shearing stress or increased enzymatic activity ( i . e . , metalloproteinases or heparanase ) results in the alteration of distinct EGL functions , including vascular permeability [6 , 17] . Heparanase , an endo-β-D-glucuronidase that cleaves GAGs such as HS , is involved in structural remodeling of the ECM and EGL [26 , 27] . Analyses of the expression/activation of human heparanase in HPMEC demonstrated that DENV2 NS1 increases the expression of heparanase starting 30 min post-treatment , with a maximum peak expression detected at 6 hpt ( Fig 4A and 4B ) . Heparanase levels induced by DENV2 NS1 were significantly greater than expression levels in untreated control and WNV NS1-treated monolayers . Human heparanase is produced as an inactive precursor ( 65 kDa ) whose activation involves excision of an internal linker segment , yielding the active heterodimer composed of 8 and 50 kDa subunits [28] . Along with the augmented expression of heparanase , increased proteolytic processing of pro-heparanase into an active form ( ~50 kDa ) was detected in HPMEC stimulated with DENV2 NS1 to a much greater degree than WNV NS1-treated and untreated controls ( Fig 4C and 4D ) . Increased enzymatic activity of heparanase has been shown to enhance remodeling of the EGL and ECM , particularly by increasing levels of soluble syndecan-1 on endothelial cells [29 , 30] . Immunolocalization of heparanase and syndecan-1 in DENV2 NS1-treated HPMEC showed a temporal pattern of expression and co-localization on the surface of endothelial monolayers ( Fig 4E ) , suggesting that heparanase may induce increased shedding of syndecan-1 in DENV2 NS1-exposed endothelial cells . Activation of heparanase occurs after proteolytic processing by cathepsin L , a ubiquitously expressed endosomal/lysosomal cysteine endopeptidase that is involved in degradation of the ECM [31 , 32] . Assessment of cathepsin L activity levels demonstrated that DENV2 NS1 increases the proteolytic activity of intracellular cathepsin L in a time-dependent manner ( 30 min-12 hpt ) in cultured HPMEC to a significantly greater degree than WNV NS1 ( Fig 5A , rows 2 and 3 and Fig 5B ) . This same effect was observed in both HUVEC and HMEC-1 exposed to DENV2 NS1 after 3 and 6 h ( S5A and S5B Fig ) . The activation of cathepsin L by DENV2 NS1 was blocked by a cathepsin L inhibitor but not a cathepsin B inhibitor ( Fig 5A , rows 4 and 5 and Fig 5B ) . To confirm the role of cathepsin L in DENV2 NS1-mediated disruption of the EGL , a cathepsin L inhibitor ( 10 μM ) , alongside a cathepsin B inhibitor as a control for specificity , was tested in HPMEC monolayers . Alterations of the HPMEC EGL induced by DENV2 NS1 , including degradation of sialic acid , shedding of syndecan-1 , and increased expression of heparanase , were prevented in the presence of cathepsin L but not cathepsin B inhibitors ( Fig 6A ) . Next , the effect of blocking cathepsin L and/or heparanase , using cathepsin L inhibitor and the heparanase inhibitor OGT 2115 ( 1 . 0 μM ) [33] , respectively , on syndecan-1 and Sia shedding in supernatants of NS1-treated HPMEC was examined by ELISA . The decrease in Sia as well as the increase in syndecan-1 observed in HPMEC supernatants in response to NS1 treatment were both reversed by cathepsin L and/or heparanase inhibitors ( Fig 6B and 6C ) . Both OGT 2115 and cathepsin L inhibitor were used at concentrations that do not affect endothelial cell viability as determined by CellTox Green Cytotoxicity Assay ( Promega ) . Finally , to characterize the role of endothelial heparanase and cathepsin L in DENV2 NS1-mediated endothelial permeability , cathepsin L inhibitor and OGT 2115 were tested in HPMEC monolayers treated with DENV2 NS1 . OGT 2115 induced substantial protection against DENV2 NS1-induced hyperpermeability in HPMEC at 3–7 hpt ( Fig 6D ) , as measured by TEER . HPMEC monolayers exposed to cathepsin L inhibitor were also protected in a dose-dependent manner from DENV2 NS1-induced endothelial hyperpermeability ( Fig 6E ) . In contrast , DENV2 NS1 still increased permeability of HPMEC monolayers in the presence of a cathepsin B-specific inhibitor ( Fig 6E ) . Further , the use of an inhibitor cocktail containing DANA ( 50 μg/ml ) , OGT 2115 ( 1 . 0 μM ) , and cathepsin L inhibitor ( 10 μM ) completely prevented DENV2 NS1-induced endothelial hyperpermeability in HPMEC ( S6 Fig ) . Together , these data demonstrate the functional significance of the cathepsin L-heparanase pathway , in that the inhibition of either enzyme prevented both the disruption of the EGL and the hyperpermeability of HPMEC triggered by DENV2 but not WNV NS1 . Because TLR4 has been implicated as a component of DENV2 NS1-induced vascular leak , we investigated the impact of LPS-RS , a TLR4 antagonist , on NS1-induced effects in HPMEC monolayers . We first evaluated Sia expression and found that treatment with DENV2 NS1 in the presence of LPS-RS ( 50 μg/ml ) significantly increased the staining of Sia on the surface of HPMEC by 68–98% compared to DENV2 NS1 alone , suggesting that TLR4 is somehow involved in the disruption of Sia in the EGL ( S7A and S7C Fig ) . LPS-RS also increased the surface staining of syndecan-1 by 14–36% ( S7B and S7D Fig ) but slightly decreased the activity of cathepsin L by 8–19% in HPMEC ( S8A and S8C Fig ) ; the expression of heparanase was unaffected ( S8B and S8D Fig ) . However , monolayers treated with LPS-RS and DENV2 NS1 still showed significant differences in syndecan-1 surface staining ( 5-fold ) and cathepsin L activity ( 10-fold ) compared to untreated controls . Overall , these data suggest that TLR4 may play a role in Sia disruption in the EGL of HPMEC but only minimally affects the cathepsin L-heparanase pathway following binding of DENV2 NS1 to HPMEC , as this pathway is still strongly activated even with inhibition of TLR4 . To determine whether the effects observed in endothelial cells were specific to DENV2 NS1 , NS1 from DENV1 , 3 , and 4 was evaluated using the same experimental setup as previously described . As we have shown previously [9] , endothelial permeability of HPMEC was significantly increased following addition of DENV1-4 NS1 , as measured by TEER ( Fig 7A ) . Staining for Sia on the surface of HPMEC was significantly decreased 1–12 hpt after treatment with DENV1 and 2 NS1 and 2–12 hpt after treatment with DENV3 and 4 NS1 ( Figs 7B and S9 ) . Increased expression of Neu1 was observed in HPMEC monolayers 1–12 hpt following treatment with DENV1-4 NS1 when compared to untreated controls and WNV NS1-treated cells ( Figs 7C and S10 ) . Expression of Neu2 was significantly increased 1–12 hpt following treatment with NS1 from DENV1 , 2 , and 4 and 3–12 hpt following treatment with DENV3 NS1 when compared to untreated and WNV-treated HPMEC ( Figs 7D and S11 ) . Neu3 expression was similarly increased 1–12 hpt following treatment with DENV1 , 2 , and 3 NS1 and 6–12 hpt following treatment with DENV4 NS1 when compared to untreated and WNV NS1-treated controls ( Figs 7E and S12 ) . Significantly increased staining of syndecan-1 on the surface of HPMEC monolayers was observed following DENV1-4 NS1 treatment , although the kinetics varied depending on serotype ( DENV1 , 2–1–12 hpt; DENV3–3–12 hpt; DENV4–3–6 hpt ) ( Figs 7F and S13 ) . Expression of heparanase was also significantly increased following treatment with NS1 from all four DENV serotypes , though the effect was slightly delayed in DENV3-4 ( 3–12 hpt ) compared to DENV1-2 ( 1–12 hpt ) ( Figs 7G and S14 ) . Further , cathepsin L activity was significantly increased from 1–12 hpt following treatment with DENV1-4 NS1 ( Figs 7H and S15 ) . Taken together , these data demonstrate that NS1 from all four DENV serotypes induces hyperpermeability in endothelial cells using similar molecular mechanisms .
Secondary DENV infection with a serotype different from primary infection is considered an epidemiological risk factor for severe disease . Immune responses after primary DENV infection lead to protective immunity against homologous re-infection but may either protect against or cause increased disease severity in a subsequent DENV infection with a different serotype [34] . The latter is thought to be mediated by serotype cross-reactive T cells or antibody-dependent enhancement that triggers an exaggerated and skewed immune response to a previously infecting serotype , resulting in a “cytokine storm” , including TNF-α and IL-6 , that leads to endothelial permeability and vascular leak [7] . New evidence has demonstrated the ability of DENV NS1 to directly induce release of vasoactive cytokines via TLR4 stimulation of PBMCs , leading to the disruption of endothelial barrier function in vitro and increased vascular leakage in vivo [9 , 10] . However , NS1-mediated mechanisms specific to the endothelial barrier itself have yet to be defined . Here , we show that binding of DENV NS1 to endothelial cells triggers endothelial barrier dysfunction through alterations to the EGL . DENV NS1 induces the degradation of Sia , a major constituent of the EGL , an effect that is mediated by cellular sialidases . Further , DENV NS1 increases the activity of cathepsin L , which subsequently increases expression and activation of heparanase in endothelial cells , leading to shedding of heparan sulfate proteoglycans from the EGL , thus altering its integrity . Inhibition of sialidases or the cathepsin L-heparanase pathway prevents DENV NS1-mediated disruption of the EGL as well as endothelial hyperpermeability . These results were observed during treatment with amounts of DENV NS1 similar to levels reported in DHF/DSS patients [14 , 15] and suggest a novel mechanism whereby soluble NS1 directly interacts with endothelial cells , inducing the activation of endothelial cell-intrinsic pathways that lead to hyperpermeability . A model summarizing these findings is shown in Fig 8 . Endothelial cells are the most important cellular component of the vasculature , separating blood from underlying tissue [35] . In severe dengue disease , plasma leakage occurs in multiple organs around the time of defervescence; however , profuse accumulation of fluids usually takes place in organs such as the lung , where pleural effusion can lead to respiratory distress and shock [3] . Secreted hexameric DENV NS1 has been shown to bind to the surface of cultured human microvascular endothelial cells [11] , and aortic and umbilical vein endothelial cells in vitro and lung and liver tissues in vivo can act as targets for NS1 binding [11] . In vitro , we had shown that NS1 from all four DENV serotypes triggers increased permeability of HPMEC monolayers [9] . Our results here demonstrate that NS1 from DENV2 but not from WNV , a closely related flavivirus , binds in a dose-dependent fashion to the surface of HPMEC , and this binding pattern is reflected in the dose-dependent decrease of TEER following the addition of NS1 from DENV2 . Similar results were obtained when endothelial cells from different tissues such as HUVEC ( umbilical cord ) and HMEC-1 ( dermis ) were exposed to DENV and WNV NS1 proteins [9] . These results support previous observations where inoculation of DENV NS1 alone increased vascular leakage in vivo [9] and also suggest that DENV NS1 can modulate endothelial barrier function in different microvascular beds and organs , thereby contributing to the systemic vascular leakage observed in patients experiencing severe dengue disease . Following treatment with DENV NS1 , we observe a time-dependent but transient increase in endothelial permeability , and endothelial monolayers recover normal barrier function by 24 hpt , potentially due to loss of NS1 from culture medium as a result of passage to the basolateral compartment , internalization , or degradation . Our in vitro model utilizes a single administration of NS1 , whereas an acute DENV infection in humans results in continuous production of NS1 from infected cells until the virus is cleared . Following viral clearance , NS1 levels decrease , vascular leakage subsides , and patients recover , reflecting our observations that endothelial hyperpermeability is reversed as NS1 stimulus is lost . Over the last several decades , the EGL has emerged as a potential regulator of vascular permeability [6] . The negative charge provided by glycoproteins bearing terminal monosaccharides , such as Sia residues , and proteoglycans bearing GAGs , such as HS , chondroitin sulfate , and hyaluronic acid [5 , 36 , 37] , contributes to the barrier function of the EGL . To examine the integrity of the EGL , we initially evaluated the distribution of Sia by staining with the lectin WGA and found that NS1 from all four DENV serotypes significantly reduces Sia staining on the surface of HPMEC monolayers . This effect does not occur in the presence of WNV NS1 . Due to its prominent position as the outermost monosaccharide unit on the glycan chains of glycolipids and glycoproteins in the EGL as well as its negative charge , Sia is involved in a variety of functions , including regulation of vascular permeability [5 , 16 , 36–39] . Therefore , removal of Sia from the EGL may result in reduction of the net negative charge and hydrophilicity of the endothelial surface [5 , 36] . Accordingly , disruption of Sia on the EGL may play a key role in DENV NS1-induced endothelial barrier dysfunction observed in HPMEC . DENV NS1 has been reported to bind to uninfected cells primarily via interactions with HS and chondroitin sulfate E [11] . In this study , soluble NS1 showed a similar binding pattern to that of the lectin WGA on HPMEC . WGA has been shown to bind to Sia and to N-acetylglucosamine [40] . However , the striking reduction of WGA binding to HPMEC after neuraminidase treatment suggests that Sia is a major constituent of the glycan moieties present on HPMEC , consistent with previously reports for other microvascular beds [17] , and is thus a major interaction partner for DENV NS1 . In eukaryotic systems , Sia can be metabolized via enzymatic release or degradation by sialidases/neuraminidases or Sia-specific pyruvate lyases [16] . As such , reduced Sia expression in HPMEC exposed to DENV NS1 may be a consequence of enzymatic trimming by endothelial sialidases . Our data demonstrate that free Sia levels in conditioned media were significantly reduced in DENV NS1-treated HPMEC compared to untreated or WNV NS1-treated monolayers , indicating that DENV NS1 may trigger degradation rather than release of Sia from the cell surface . Furthermore , no endothelial sialidase activity was found in supernatant collected from DENV NS1-treated HPMEC , indicating that Sia on endothelial cells is removed by the action of specific membrane-associated sialidases and/or metabolized by intracellular lyases . Analyses by confocal microscopy identified that Neu1 , Neu2 , and Neu3 sialidases were selectively upregulated in HPMEC in the presence of all DENV NS1 proteins but not WNV NS1 . Neu1 is mainly localized in lysosomes but is also capable of translocation to the cell surface [41] . Neu2 , also known as the soluble sialidase , is a cytosolic enzyme that cleaves a variety of substrates , including oligosaccharides , glycoproteins , and gangliosides ( Sia-containing glycolipids ) [42] . Neu3 is found on the cell membrane , acting specifically on the sialic acids of gangliosides [39] . Thus , increased expression of endothelial sialidases triggered by DENV NS1 may lead to trimming of Sia on the surface of HPMEC , resulting in initial degradation of the EGL and increased endothelial permeability . Additionally , we found that treatment of HPMEC monolayers with Zanamivir or DANA , influenza neuraminidase inhibitors that have also been shown to significantly inhibit human sialidases [19] , substantially protects endothelial monolayers from DENV NS1-induced hyperpermeability . These data suggest that removal of Sia from the EGL by human sialidases contributes to increased permeability of human endothelial cell monolayers following binding of DENV NS1 . In vertebrates , mammalian sialidases and their target substrates have been implicated in crucial biological processes , including the regulation of cell proliferation/differentiation , clearance of plasma proteins , control of cell adhesion , metabolism of gangliosides and glycoproteins , immunocyte function , and modification of receptors [39] . More recently , a novel role for Neu1 in controlling the activation of TLR4 signaling pathways was described [43 , 44] . Briefly , Neu1 activity has been shown to influence receptor desialylation and disruption of TLR4:Siglec-E interaction , which subsequently activates TLR4 signaling , leading to the production of nitric oxide and pro-inflammatory cytokines in dendritic and macrophage cells [43–47] . Further , TLR4 signaling has been shown to be required for translocation of Neu1 to the cell membrane [45] . Thus , DENV NS1 stimulation of Neu1 may lead to TLR4 signaling , in turn contributing to the translocation of Neu1 to the cell membrane and subsequent disruption of Sia in the EGL of HPMEC . Interestingly , we found that when HPMEC monolayers are treated with LPS-RS , a TLR4 antagonist that binds MD-2 in the TLR4 complex , DENV NS1-induced disruption of Sia is significantly decreased . This suggests that treatment with LPS-RS may prevent TLR4 signaling and ensuing translocation of Neu1 to the cell membrane , thereby partially preventing the disruption of Sia that occurs after treatment with DENV NS1 alone . In addition to Sia residues , cell surface proteoglycans and their associated GAG side chains help to preserve the stability and function of the EGL . Transmembrane syndecans , membrane-bound glypicans , and basement matrix-associated perlecans are the three major protein core families of HSPGs found on endothelial cells [6 , 23] . Structurally , syndecans are composed of an N-terminal signal peptide , an extracellular domain containing several consensus sequences for GAG attachment , a single transmembrane domain , and a short C-terminal cytoplasmic domain [48] . Syndecan ectodomains can be shed intact by proteolytic cleavage of their core proteins [49 , 50] . Due to its HS chains , syndecan-1 can function as a co-receptor on the cell surface and also as a soluble HSPG that binds to a wide variety of extracellular ligands , including matrix proteins , cytokines , and chemokines . In this study , a specific immunoassay to detect soluble syndecan-1 from conditioned HPMEC media demonstrated that DENV NS1 induces enhanced shedding of the syndecan-1 ectodomain from the EGL . Since the in vitro HPMEC monolayer system is static , this shedding may lead to increased deposition and accumulation of syndecan-1 on the surface of HPMEC , thereby explaining the increased signal for syndecan-1 detected by confocal microscopy . The shedding of syndecan-1 can then result in increased stimulation of inflammatory signaling pathways in the endothelium . Elevated levels of syndecan-1 ectodomains have been implicated in adhesion , migration , cytoskeleton organization , cell differentiation , and vascular permeability [48] . Here , we showed that recombinant syndecan-1 increases permeability when added to HPMEC , suggesting that altered expression , distribution , and release of HSPGs ( e . g . , syndecan-1 ) from the surface of HPMEC after stimulation with DENV NS1 may result in the activation of inflammatory processes that contribute to endothelial barrier dysfunction . Accelerated shedding of syndecan-1 has been shown to result from direct proteolytic cleavage by matrix metalloproteinases ( MMP ) [49 , 50] . However , syndecan shedding has also been found to be enhanced by enzymatic degradation of HS chains , indicating that non-MMP mechanisms are also involved in this process [29 , 51] . Heparanase is a β-D-endoglucuronidase that cleaves HS , facilitating degradation of the EGL and the ECM and resulting in release of proteoglycans bearing HS , such as syndecan-1 [27 , 29 , 30] . Remodeling of the EGL and ECM by heparanase is important for various physiological and pathological processes , including inflammation , wound healing , tumor angiogenesis , and metastasis [52] . Human pro-heparanase is produced as an inactive precursor protein ( ~543 amino acids ) whose activation involves excision of an internal linker segment ( Ser110–Gln157 ) , yielding the active heterodimer composed of 8 and 50 kDa subunits [27] . Processing and activation of pro-heparanase requires cathepsin L , a papain-like lysosomal cysteine proteinase that is ubiquitously expressed in human tissues and is involved in normal cellular protein degradation and turnover [32] . Here , analyses of HPMEC monolayers by confocal microscopy demonstrated an increase of heparanase staining and cathepsin L protease activity , detected as early as 30 min after endothelial cell stimulation with DENV but not WNV NS1 . Increased expression of the active form of heparanase ( ~50 kDa ) was also shown , indicating that the DENV NS1-induced endothelial hyperpermeability may result from enhanced processing and activation of heparanase by intracellularly expressed cathepsin L . Cathepsin L and heparanase may thus play a critical role in NS1-induced disruption of HS and HSPG components of the EGL , such as syndecan-1 . Though MMPs are primarily responsible for the homeostasis of the ECM , cysteine proteases can significantly contribute to its destruction under disease conditions [32] . Increased cathepsin L activity has been found to promote disease pathogenesis by creating an inflammatory environment associated with degradation of the ECM in cardiovascular disease , cancer , and rheumatoid arthritis [32] . Further , heparanase is upregulated in numerous human diseases such as cancer , diabetes , renal disease , and Alzheimer disease [52 , 53] . Therefore , overexpression of endothelial heparanase and its increased processing by lysosomal cathepsin L may constitute a key component of the intrinsic endothelial mechanisms initially triggered by DENV NS1 , leading to the disruption of EGL integrity that contributes endothelial barrier dysfunction in endothelial cell monolayers . The mechanism by which DENV NS1 induces increased activity of cathepsin L is still unclear . Cathepsins are lysosomal cysteine proteases mainly responsible for the remodeling of the extracellular matrix ( ECM ) [32] . They are optimally active at a slightly acidic pH; however , the mechanism of their activation is not fully understood . We have obtained preliminary results that indicate that DENV NS1 is not only able to interact with the surface of the endothelium but also may be internalized and subsequently transported through endothelial monolayers via unidentified endocytic pathways , leading to its accumulation in basolateral compartments . It is possible that this NS1 internalization process leads to the activation of cathepsin L in endosomes of HPMEC , thus contributing to subsequent degradation of the ECM and NS1-induced endothelial hyperpermeability . Alternatively , it is possible that cathepsin L is activated via a sequence of molecular signals following DENV NS1 binding to the surface of endothelial cells . Our data suggest that DENV NS1 induces endothelial hyperpermeability through significant disruption of the EGL , a phenomenon that may be primarily regulated by the activation the cathepsin L-heparanase pathway . This conclusion was further tested through the use of specific inhibitors of both heparanase ( OGT 2115 ) and cathepsin L ( cathepsin L inhibitor ) . Endothelial hyperpermeability induced by DENV NS1 in HPMEC monolayers was significantly reversed in the presence of OGT 2115 and cathepsin L inhibitor and was completely reversed in the presence of an inhibitor cocktail containing DANA , OGT 2115 , and cathepsin L inhibitor . Further , disruption of Sia , increased surface staining of syndecan-1 , and increased activation of heparanase were prevented after inhibition of cathepsin L activity . Notably , when an inhibitor for cathepsin B , a related cysteine protease [32] , was used , neither increased endothelial permeability nor EGL disruption was inhibited in DENV NS1-treated HPMEC monolayers . These data support our conclusion that activation of EGL remodeling pathways play a significant role in the endothelial barrier dysfunction induced by DENV NS1 . This work provides insight into endothelial cell-intrinsic mechanisms that contribute to endothelial hyperpermeability triggered by DENV NS1 protein . We have identified multiple pathways that were previously not known to play a role in severe DENV disease , including disruption of the EGL through endothelial sialidases and the cathepsin L-heparanase pathway . The full story is still incomplete , as the precise timing and signaling cascades remain to be defined , and future work will need to further elucidate these kinetics . More comprehensive studies are underway to understand the relative contribution of these endothelial-intrinsic mechanisms in the context of dengue disease , as other factors , including vasoactive cytokines triggered by NS1 [9 , 10] and immunopathogenic mechanisms [54] , are known to play an important role in DHF/DSS . Overall , these findings add to the novel functions of DENV NS1 and the discovery of new potential pathways contributing to endothelial dysfunction and vascular leak during severe dengue disease , and they may contribute to future advancements in dengue treatment and diagnostics .
The human pulmonary microvascular endothelial cell line HPMEC-ST1 . 6R was kindly donated by Dr . J . C . Kirkpatrick ( Institute of Pathology , Johannes Gutenberg University , Germany ) and propagated ( passages 5–8 ) and maintained at 37°C in humidified air with 5% CO2 in endothelial cell basal medium-2 supplemented with growth factors , antibiotics , and fetal bovine serum as per the manufacturer’s specifications ( Clonetics , Lonza ) . The human dermal microvascular endothelial cell line HMEC-1 was kindly donated by Dr . M . Welch ( University of California , Berkeley ) and propagated ( passages 20–25 ) and maintained at 37°C in humidified air with 5% CO2 in MCDB 131 medium ( Sigma ) supplemented with 0 . 2% Epidermal Growth Factor and 0 . 4% hydrocortisone . Human Umbilical Vein microvascular endothelial cells ( HUVEC ) were grown as previously described [9] . For staining of EGL components , the following monoclonal antibodies ( mAbs ) and lectins were used: Wheat germ agglutinin ( WGA ) lectin conjugated to Alexa 647 ( WGA-A647 , Molecular Probes ) to stain N-acetyl neuraminic acid ( Sia ) ; anti-human heparanase 1 ( HPA1 , Santa Cruz Biotech ) ; anti-human cathepsin L ( eBioscience ) ; anti-heparan sulfate proteoglycan 2 for perlecan ( Abcam ) , anti-human CD138 for syndecan-1 ( eBioscience ) ; Neu1 antibody ( H-300 ) : sc-32936 ( Santa Cruz Biotech ) ; Neu2 antibody PA5-35114 ( Thermo Scientific ) ; Ganglioside sialidase antibody ( N-18 ) : sc-55826 for Neu3 ( Santa Cruz Biotech ) . Recombinant NS1 proteins from DENV1 ( strain Nauru/Western Pacific/1974 ) , DENV2 ( strain Thailand/16681/84 ) , DENV3 ( strain Sri Lanka D3/H/IMTSSA-SRI/2000/1266 ) , DENV4 ( strain Dominica/814669/1981 ) and WNV ( New York NY99 strain ) used in all experiments were produced by Native Antigen ( Oxfordshire , United Kingdom ) in HEK 293 cells and were shown to be >95% pure and oligomeric , as demonstrated by native PAGE and Western blot analyses [9] . In addition , the NS1 proteins were tested and shown to be free of endotoxin contaminants , as determined using the Endpoint Chromogenic Limulus Amebocyte Lysate ( LAL ) QCL-1000TM kit ( Lonza ) ( <0 . 1 EU/ml ) and as certified by the manufacturer . Recombinant syndecan-1/CD138 used in TEER assays was >95% pure and <1 . 0 EU/μg endotoxin by LAL assay according to the manufacturer’s indications ( R&D Systems ) . Recombinant neuraminidase from Clostridium perfringens ( C . welchii ) was obtained from Sigma . Selective inhibitors of human heparanase ( OGT 2115 , Tocris ) , cathepsin L ( Cathepsin L inhibitor I , Calbiochem ) , cathepsin B ( CA-074 , Tocris Bioscience ) , neuraminidase ( Zanamivir and N-Acetyl-2 , 3-dehydro-2-deoxyneuraminic acid ( Sigma ) ) were used in TEER assays at concentrations that do not affect cell viability . Cell viability was determined by the Promega CellTox Green Cytotoxicity Assay following manufacturer’s instructions . Confluent HPMEC monolayers grown on gelatin-coated coverslips ( 0 . 2% , Sigma ) were exposed to different concentrations of DENV2 NS1 ( 1 . 25–10 μg/ml ) and WNV NS1 ( 5–10 μg/ml ) and incubated for one hour at 37°C . NS1 protein bound to the cell surface was then detected using the anti-NS1 mAb 9NS1 conjugated to Alexa 488 ( cross-reactive to WNV and DENV2 NS1; gift from Dr . M . S . Diamond , Washington University in St . Louis ) [55] and the anti-NS1 mAb 7E11 conjugated to Alexa 568 ( gift from Dr . R . Putnik , Walter Reed Army Institute of Research ) . For the time course of DENV2 NS1 binding , 5 μg/ml of NS1 was used , and cell monolayers were incubated as described above and fixed ( PFA 2% ) at 1 , 3 , 6 , 12 and 24 hpt . Images were acquired using a Zeiss LSM 710 AxioObserver-34-channel spectral detector confocal microscope and processed using ImageJ software [56] . A quantification of NS1 protein bound to the cell surface was expressed as mean fluorescence intensity ( MFI ) compared to untreated cells used as a negative control . The effect of recombinant NS1 proteins on endothelial permeability was evaluated by measuring TEER [Ohms ( Ω ) ] in HPMEC monolayers grown on a 24-well Transwell polycarbonate membrane system ( Transwell permeable support , 0 . 4 μM , 6 . 5 mm insert; Corning Inc . ) as previously described [9] . Untreated HPMEC grown on Transwell inserts were used as negative untreated controls , and inserts with medium alone were used for blank resistance measurements . Relative TEER represents a ratio of resistance values ( Ω ) obtained at sequential 2-h time points following the addition of test proteins as follows: ( Ω experimental condition—Ω medium alone ) / ( Ω non-treated endothelial cells– Ω medium alone ) . After 24 h of treatment , 50% of upper and lower chamber media was replaced by fresh endothelial cell medium . An Epithelial Volt Ohm Meter ( EVOM ) with “chopstick” electrodes ( World Precision Instruments ) was used to measure TEER values . For imaging experiments , HPMEC were grown on coverslips and imaged on a Zeiss LSM 710 Axio Observer inverted fluorescence microscope equipped with a 34-channel spectral detector . Images acquired using the Zen 2010 software ( Zeiss ) were processed and analyzed with ImageJ software [56] . Cells were counted and MFI values were obtained by using ImageJ cell counter analyses with a viewing area of ~103 μm2 ( 10 . 28x10 . 28 μm ) , which contains roughly 200 cells . For representative pictures , an area of ~1 . 8 μm2 ( 1 . 25x1 . 40 μm ) containing ~28–30 cells was used . All RGB images were converted to grayscale , then mean grayscale values and integrated density from selected areas were taken along with adjacent background readings and plotted as mean fluorescence intensity ( MFI ) . To assess the effect of flavivirus NS1 on integrity of the endothelial architecture , the distribution of EGL components was examined on confluent HPMEC monolayers treated with DENV or WNV NS1 proteins ( 5 μg/ml ) and fixed with 2% paraformaldehyde ( PFA ) and ethanol-methanol ( 1:1 ) at different time points ( 0 , 30 min , 1 , 3 , 6 , 12 and 24 hpt ) . Primary antibodies were incubated overnight at 4°C , and detection was performed using secondary species-specific anti-IgG antibodies conjugated to Alexa fluorophores ( 488 , 568 and 647 ) . For protein expression , confluent HPMEC monolayers ( ~1x106 cells/well , 6-well tissue culture-treated plates ) were treated with DENV and WNV NS1 proteins ( 5 μg/ml ) , and at different time points ( 0 , 30 min , 1 , 3 , 6 , 12 and 24 hpt ) , cell monolayers were scraped on ice using RIPA lysis buffer ( 50 mM Tris [pH 7 . 4] , 150 mM NaCl , 1% [v/v] Nonidet-P40 , 2 mM EDTA , 0 . 1% [w/v] SDS , 0 . 5% Na-deoxycholate and 50 mM NaF ) supplemented with complete protease inhibitor cocktail ( Roche ) . After total protein quantification using a bicinchoninic acid ( BCA ) -based colorimetric assay ( Pierce BCA Protein Assay Kit , Thermo Scientific ) , 10 μg of total protein per sample was boiled and placed in reducing Laemmeli buffer and separated by 4–20% gradient SDS-PAGE . After immunoblotting using specific primary antibodies for syndecan-1 , human heparanase , human cathepsin L , and GAPDH ( used as housekeeping protein control ) and secondary species-specific anti-IgG antibody conjugated to Alexa 680 or Alexa 750 , protein detection and quantification was carried out using the Odyssey CLx Infrared Imaging System ( LI-COR ) . Relative densitometry represents a ratio of the values obtained from each experimental protein band over the values obtained from loading controls ( GAPDH ) after subtracting background from both using Image Studio Lite V 5 . 2 ( LI-COR Biosciences ) . ELISAs for human syndecan-1 ( CD138 ) , Sia ( NANA ) , and human cathepsin L were performed following the manufacturer’s instructions ( Abcam ) . Cathepsin L activity in living cells was monitored using the Magic Red Cathepsin L detection kit ( Immunochemistry Technologies , Inc . ) . Briefly , confluent HPMEC monolayers grown on coverslips were exposed to DENV and WNV NS1 proteins ( 5 μg/ml ) , and at different time points , a cell membrane-permeant fluorogenic substrate MR- ( Phe-Arg ) 2 , which contains the cresyl violet ( CV ) fluorophore branded as Magic Red ( MR ) , was added . Cultured cell monolayers expressing active cathepsin L catalyze the hydrolysis of the two Phe-Arg target sequences , generating a red fluorescent species that can be detected by immunofluorescence microscopy . Magic Red excites at 540–590 nm ( 590 nm optimal ) and emits at >610nm ( 630 nm optimal ) . For neuraminidase detection , culture supernatants from NS1-exposed HPMEC monolayers were collected at different time points and processed for neuraminidase activity using the Amplex Red reagent-based assay and fluorescence detection following recommended procedures ( Molecular Probes ) . Statistical analysis was performed using GraphPad Prism 6 software , and all graphs were generated using Prism 6 . Comparison between MFI , ELISA , and densitometry data was conducted using multiple t-tests with a False Discovery Rate of 1% . For TEER experiments , statistical significance was determined using a two-way analysis of variance ( ANOVA ) .
|
Dengue is the most prevalent mosquito-borne disease in humans and represents a major public health problem worldwide . Leakage of fluids and molecules from the bloodstream into tissues can lead to shock and potentially death and is a critical determinant of dengue disease severity . Recently , we showed that a secreted protein from dengue virus ( DENV ) -infected cells , non-structural protein 1 ( NS1 ) , can trigger increased leakage both in human cell culture and mouse models . It has been shown that NS1 can activate toll-like receptor 4 on peripheral blood mononuclear cells , leading to secretion of pro-inflammatory cytokines that can result in vascular leak . However , the mechanism by which NS1 triggers hyperpermeability directly in human endothelial cells remained undefined . The endothelial glycocalyx layer ( EGL ) is a network of membrane-bound molecules that lines endothelial cells on the inside of blood vessels , helping to regulate proper vascular function . Here , we show that DENV NS1 can disrupt the integrity of the EGL , inducing breakdown and shedding of key components . This is mediated by NS1 induction of cellular enzymes ( e . g . , sialidases , heparanase , and cathepsin L ) that contribute to EGL alterations . Inhibitors that block these enzymes prevent both EGL disruption and endothelial permeability . These effects were all demonstrated to be specific to NS1 from DENV serotypes 1–4 , as NS1 from the related West Nile Virus did not produce EGL alterations or increased leakage . Our study suggests a novel role for DENV NS1 in inducing EGL disruption to increase fluid leakage during severe dengue disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"cell",
"binding",
"cell",
"physiology",
"medicine",
"and",
"health",
"sciences",
"nuclear",
"staining",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"endothelial",
"cells",
"permeability",
"microbiology",
"light",
"microscopy",
"epithelial",
"cells",
"viruses",
"physiological",
"processes",
"rna",
"viruses",
"microscopy",
"materials",
"science",
"confocal",
"microscopy",
"research",
"and",
"analysis",
"methods",
"specimen",
"preparation",
"and",
"treatment",
"staining",
"molting",
"animal",
"cells",
"medical",
"microbiology",
"microbial",
"pathogens",
"vascular",
"permeability",
"biological",
"tissue",
"west",
"nile",
"virus",
"cell",
"biology",
"flaviviruses",
"anatomy",
"viral",
"pathogens",
"physiology",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"material",
"properties",
"vascular",
"medicine",
"organisms"
] |
2016
|
Dengue Virus NS1 Disrupts the Endothelial Glycocalyx, Leading to Hyperpermeability
|
Viral attachment to target cells is the first step in infection and also serves as a determinant of tropism . Like many viruses , mammalian reoviruses bind with low affinity to cell-surface carbohydrate receptors to initiate the infectious process . Reoviruses disseminate with serotype-specific tropism in the host , which may be explained by differential glycan utilization . Although α2 , 3-linked sialylated oligosaccharides serve as carbohydrate receptors for type 3 reoviruses , neither a specific glycan bound by any reovirus serotype nor the function of glycan binding in type 1 reovirus infection was known . We have identified the oligosaccharide portion of ganglioside GM2 ( the GM2 glycan ) as a receptor for the attachment protein σ1 of reovirus strain type 1 Lang ( T1L ) using glycan array screening . The interaction of T1L σ1 with GM2 in solution was confirmed using NMR spectroscopy . We established that GM2 glycan engagement is required for optimal infection of mouse embryonic fibroblasts ( MEFs ) by T1L . Preincubation with GM2 specifically inhibited type 1 but not type 3 reovirus infection of MEFs . To provide a structural basis for these observations , we defined the mode of receptor recognition by determining the crystal structure of T1L σ1 in complex with the GM2 glycan . GM2 binds in a shallow groove in the globular head domain of T1L σ1 . Both terminal sugar moieties of the GM2 glycan , N-acetylneuraminic acid and N-acetylgalactosamine , form contacts with the protein , providing an explanation for the observed specificity for GM2 . Viruses with mutations in the glycan-binding domain display diminished hemagglutination capacity , a property dependent on glycan binding , and reduced capacity to infect MEFs . Our results define a novel mode of virus-glycan engagement and provide a mechanistic explanation for the serotype-dependent differences in glycan utilization by reovirus .
Virus infections are initiated by attachment of the virus to target cells of susceptible hosts . Receptors facilitate attachment , determine host range , and govern susceptibility of particular cells to infection . While viral attachment can be a monophasic event , this process frequently involves multiple receptors , and adhesion strengthening is a common mechanism that facilitates virus entry [1] . Thus , a virus may interact with an attachment factor , commonly a carbohydrate , to adhere via low-affinity interaction to the cell-surface , where it then binds to an additional receptor with high affinity that leads to viral entry . The identities of the low-affinity attachment factors are not known for many viruses . Mammalian orthoreoviruses ( reoviruses ) serve as highly tractable models to study virus-receptor interactions . These viruses replicate to high titer , facilitating biochemical and biophysical studies , and both the virus and host can be manipulated genetically . Reoviruses contain ten segments of double-stranded RNA ( dsRNA ) encapsidated within two protein shells . Reoviruses can infect the gastrointestinal and respiratory tracts of a variety of mammals but rarely cause systemic disease outside of the immediate newborn period [2] . Most children are seropositive for reovirus by the age of 5 years [3] . Reoviruses preferentially infect tumor cells and are being tested in clinical trials for the treatment of a variety of cancers [4]–[6] . It is not yet clear why reoviruses infect tumor cells more efficiently than untransformed cells , but it is likely that distribution , accessibility , and density of cellular receptors contribute to this process . The three known reovirus serotypes are represented by the prototype strains type 1 Lang ( T1L ) , type 2 Jones ( T2J ) , and type 3 Dearing ( T3D ) . These three strains differ markedly in cell tropism and viral spread , and these properties have been studied extensively using newborn mice [7] . T1L spreads hematogenously and infects ependymal cells , leading to non-lethal hydrocephalus [8] , [9] . In contrast , T3D disseminates hematogenously and neurally and infects neurons , causing lethal encephalitis [7]–[12] . These serotype-dependent differences are linked to sequence variations in the σ1 outer-capsid protein [7] , [9] . The σ1 protein mediates the attachment of the virus to target cells [9] , [13] . It is a 150 kDa homotrimeric protein that assembles into a long fiber that protrudes from the virion surface [14] . The σ1 protein can be partitioned into three functionally and structurally distinct domains: the tail , body , and head . The N-terminal tail spans about 170 residues and is predicted to form an α-helical coiled coil [15]–[17] . The body domain comprises approximately 100 residues and primarily consists of β-spiral repeats [18] , [19] . The C-terminal 150 residues fold into the compact head domain composed of eight antiparallel β-strands that assemble into a jelly-roll [18] . The head binds with high affinity to junctional adhesion molecule-A ( JAM-A ) [20] , which serves as a receptor for all known reovirus serotypes [21] . JAM-A is a homodimeric member of the immunoglobulin superfamily [22] located in tight junctions [23] . The structure and receptor-binding properties of reovirus T3D σ1 have been studied most extensively [18] , [19] , [24] , [25] . Interactions of T3D σ1 and JAM-A exclusively involve the σ1 head , which binds the N-terminal D1 domain of JAM-A [25] , [26] . JAM-A binds with higher affinity to σ1 than to itself; thus , the engagement of σ1 to JAM-A disrupts the JAM-A homodimer . The JAM-A-binding site is highly conserved among the three reovirus serotypes; thus , it is predicted that the T1L , T2J , and T3D reovirus σ1 proteins engage JAM-A in a similar manner and with similar affinities . Although binding to JAM-A is required for hematogenous dissemination , differences in target cell selection within the CNS displayed by T1L and T3D are retained in JAM-A deficient mice inoculated with the viruses intracranially [11] . Therefore , interactions with JAM-A are unlikely to dictate the serotype-specific differences in cell tropism in the nervous system . Instead , these differences in tropism are likely a consequence of virus binding to serotype-specific receptors . In addition to JAM-A , reoviruses bind to cell-surface glycans . However , the limited knowledge of glycan coreceptors for reovirus is an obstacle to a precise understanding of the contribution of individual receptors to viral tropism and disease . While there is considerable information about carbohydrate-mediated interactions of T3D with host cells , the role of glycan binding in other reovirus serotypes is not known . T3D σ1 interacts with α-linked 5-N-acetyl neuraminic acid ( Neu5Ac ) [19] , [27] , and crystal structures of T3D σ1 in complex with sialyllactose-based compounds terminating in α2 , 3- , α2 , 6- , and α2 , 8-linked Neu5Ac have identified the glycan-binding site [19] . The N-terminal portion of the T3D σ1 body , which lies close to the mid-point of the molecule , engages Neu5Ac via a complex network of interactions that are identical for the three linkages tested . Contacts include a bidentate salt bridge , which connects arginine 202 with the Neu5Ac carboxylate , and a number of augmenting hydrogen bonds and non-polar interactions . The additional sugar rings of the lactose backbone make minimal contacts with T3D σ1 , suggesting that T3D σ1 recognizes a different carbohydrate sequence on the cell-surface [19] . Much less is known about the interaction of type 1 reovirus with cell-surface glycans . Hemagglutination is dependent on glycan-engagement , and serotypes 1 and 3 display differences in hemagglutination profiles , suggesting that they differentially engage cell-surface glycans [28] . Type 1 reoviruses agglutinate human and not bovine red blood cells , whereas type 3 reoviruses agglutinate bovine erythrocytes well and human erythrocytes less efficiently than type 1 strains [29] . Hemagglutination studies using chimeric and truncated σ1 proteins expressed in insect cells using baculovirus vectors suggest that the carbohydrate-binding site of T1L σ1 resides just beneath the head domain [27] . Additionally , neuraminidase treatment diminishes infection of intestinal M cells by T1L , suggesting that type 1 reoviruses can engage sialic acid at least in some contexts [30] . T1L reovirus does not bind to sialylated glycophorin , whereas T3D reovirus does [27] , [31] . Therefore , the glycan recognized by type 1 reoviruses differs from that recognized by type 3 strains . In this study , we employed glycan microarray analyses to identify ganglioside GM2 as a glycan receptor for reovirus T1L , and we used structural and infectivity data to define the glycan-protein interaction and the biological relevance of glycan binding to infection of host cells . Taken together , our structure-function data provide insight into how the GM2 glycan is specifically recognized by type 1 reovirus and explain the serotype-specific nature of reovirus glycan utilization .
To investigate glycan engagement by T1L , we established a cell-culture system in which glycan binding could be evaluated . Binding to sialic acid is dispensable for infection of murine L929 ( L ) fibroblast cells by either type 1 or type 3 reovirus [27] , [32] , [33] . However , sialic acid engagement is required for optimal infection of MEFs [11] , [33] and HeLa cells by type 3 reoviruses [25] , [27] , [33] . To determine whether sialylated glycan engagement is required for efficient infection by T1L , we pretreated L cells ( Figure 1A ) and MEFs ( Figure 1B ) with Arthrobacter ureafaciens neuraminidase to remove cell-surface sialic acid . Neuraminidase treatment did not impair the capacity of T1L to infect L cells , as previously shown [32] . In contrast , neuraminidase treatment reduced T1L infectivity of MEFs ( Figure 1B ) and also HeLa cells ( data not shown ) , suggesting that sialic acid engagement by T1L is required for optimal infection of some cell types . Of note , GM2 is expressed on MEFs [34] , which display glycan-dependent infection , and L cells [35] , which do not require glycan-binding for infection . While both L cells and MEFs are of murine origin , differences in sialic acid requirements are likely accounted for by differences in the expression on these cells of the known proteinaceous reovirus receptor , JAM-A . L cells , which do not require sialic acid for efficient entry , express higher levels of cell-surface JAM-A than do MEFs ( Figure 1C ) . Thus , T1L may infect MEFs using an adhesion-strengthening mechanism in which binding to glycans must precede engagement of the relatively low abundance JAM-A receptor . To assess the carbohydrate-binding specificity of T1L reovirus , we expressed and purified recombinant hexahistidine-tagged T1L σ1 protein for binding analyses in neoglycolipid-based glycan microarrays . Based on sequence alignment with T3D σ1 , for which several crystal structures exist [18] , [24] , [25] , two constructs were designed . The first construct , σ1long , comprised amino acids 261–470 , which were predicted to fold into three β-spiral repeats and the C-terminal head domain . The second construct , σ1short , comprised amino acids 300–470 , which were predicted to form only the most C-terminal β-spiral and the head domain . Both σ1 constructs included the predicted carbohydrate-binding site , which was reported to lie in close proximity to the head domain [27] . Glycan microarray analyses were carried out initially with σ1long using an array composed of 124 lipid-linked oligosaccharide probes . Among these are 119 sialylated probes with differing sialic acid linkages , backbone sequences , chain lengths , and branching patterns; five non-sialylated probes were included as negative controls ( Table S1 ) . The results from the glycan array screening showed a signal for the ganglioside GM2 that , despite its low intensity , was significantly stronger than the other signals ( Figure S1 ) . The GM2 glycan sequence contains two terminal sugars , Neu5Ac and N-acetylgalactosamine ( GalNAc ) , that are both linked to a central galactose ( Gal ) via α2 , 3 and β1 , 4 linkages , respectively . The Gal is connected , via a β1 , 4 linkage , to a glucose ( Glc ) , which is attached to a ceramide anchor . Additional analyses were carried out with the σ1short construct , which was predicted to have less steric hindrance imposed by the long body domain and , therefore , to perhaps yield clearer results . Since the initial screen with σ1long revealed GM2 as a likely carbohydrate receptor , the second array was comprised of 21 ganglioside-related saccharide probes that included GM2 ( Table S2 ) . The results from this screen confirmed binding of the protein to GM2 and yielded a higher signal-to-noise ratio than the initial screen ( Figure 2A ) . GM2 clearly exhibited the highest signal among the probes investigated , whereas several other structurally closely related probes ( Figure 2B ) , e . g . , the “a series” gangliosides GM3 , GM1 , and GD1a ( sequences in Table S2 ) , elicited marginally detectable low signals . The overall binding intensity of the σ1 protein , even with the short construct , is lower than that of other proteins tested in the same arrays , e . g . , the VP1 proteins of polyoma viruses JCV and SV40 , and the fiber knobs of adenovirus Ad37 ( data not shown ) . To verify that T1L σ1 binds specifically to the GM2 glycan , we performed STD NMR spectroscopy experiments with σ1 and the glycan . This method is especially well suited to detect low-affinity binding between a large molecule , such as σ1 , and a small oligosaccharide [36]–[38] . In an STD NMR experiment , the protein is selectively excited , and magnetization transfer to the ligand is observed if complex formation and rapid release of the ligand take place . If these conditions are fulfilled , the STD spectrum contains ligand resonances belonging to the binding epitope . A control experiment without protein serves to exclude direct excitation of the ligand . Using STD NMR , we found that T1L σ1 binds to the GM2 oligosaccharide in solution . Moreover , the STD analysis identified the protons of the carbohydrate that lie in close proximity ( about 5 Å ) to σ1 in the complex ( Figure 2C , Figure S2A ) . All of the GM2 protons in the σ1-GM2 complex are part of the terminal Neu5Ac or the GalNAc moieties . The most prominent peak in the STD NMR spectrum belongs to the Neu5Ac methyl group , which receives considerably more saturation than the GalNAc methyl group . Protons H5 , H6 , H7 , and one of the two H9 protons of Neu5Ac also are readily identified in the STD NMR spectrum , while the axial and equatorial H3 protons of this moiety receive little , if any , magnetization from the protein . Saturation transfer to the Neu5Ac protons H4 and H8 cannot be evaluated unambiguously because the resonances of both overlap with each other and with the GalNAc H6 resonance . Protons H1 through H4 of the GalNAc ring also are seen in the difference spectrum , although they are generally less prominent than the Neu5Ac protons . No noteworthy transfer was observed for the GM2 galactose and glucose rings . Thus , the STD NMR spectroscopy data show that the T1L σ1-GM2 glycan interaction is based on contacts with ring atoms and the glycerol side chain of Neu5Ac , with additional contacts contributed by GalNAc ring atoms . The STD NMR experiment was repeated with the linear GM3 glycan ( Figure S2B ) , which lacks the terminal GalNAc present on GM2 . The difference spectrum demonstrates that the GM3 trisaccharide interacts with T1L σ1 and that saturation transfer is observed to Neu5Ac protons only . The STD NMR experiments allow no direct estimate of relative affinities for GM2 and GM3 , but it is likely that T1L σ1 binds with greater affinity to the GM2 glycan because of the additional contacts with the terminal GalNAc of this compound . This assumption is consistent with our observation that the GM2 binding signal on the microglycan array is much higher compared with the GM3 signal ( Figure 2A ) . To investigate whether GM2 serves as a functional receptor for T1L reovirus , we tested the soluble GM2 glycan for the capacity to inhibit T1L infection of MEFs . Preincubation of the GM2 glycan with T1L resulted in a dose-dependent decrease in T1L infectivity ( Figure 3A ) . However , preincubation of T1L with the GM3 glycan diminished infectivity to a lesser extent and was not dose-dependent ( Figure 3B ) . As a specificity control , incubation of reovirus T3D with the GM2 glycan did not diminish the capacity of T3D to infect MEFs ( Figure 3C ) . These findings demonstrate that the GM2 glycan is specifically recognized by T1L and serves as a physiologically relevant coreceptor . To visualize interactions between T1L σ1 and its coreceptor , we determined the crystal structure of the σ1long construct in complex with the GM2 glycan . The overall structure of the monomer and the organization of the trimer are similar to the T3D σ1 structure [18] . The crystallized T1L σ1 protein folds into three β-spiral repeats and a globular C-terminal head domain ( Figure 4A–C ) . The head domain , comprising amino acids 327–470 , is constructed from two Greek-key motifs , each consisting of four β-strands ( β-strands A–D and E-H ) . β-spiral repeats 1 ( amino acids 310–326 ) and 3 ( residues 268 to 287 ) form proline-type β-turns , with both prolines being in the cis-configuration , again similar to T3D σ1 . β-spiral repeat 2 ( amino acids 288–305 ) is initiated by a serine residue ( S291 ) . In T3D σ1 , threonine 278 occupies an analogous position . Both residues are non-standard , as normally only glycines or prolines are tolerated at this position [18] , [39] . Although the structure has only intermediate resolution , it has good refinement statistics ( Table 1 ) . The unbiased electron density map shown in Figure 4 was determined prior to inclusion of the glycan in the refinement and therefore does not contain any information about GM2 . The map has interpretable electron density for all four sugar moieties of GM2 , including the unique features of Neu5Ac , in all three T1L σ1 monomers . The three copies of the glycan are crystallographically independent but nevertheless make nearly identical contacts with their respective binding pockets , providing additional support for the validity of the observed interactions . The GM2 glycan binds to the upper region of the T1L σ1 head and thus not near the β-spiral region as predicted earlier [18] . A schematic representation of the σ1 domain organization is shown in Figure 4D , including the localization of the respective binding sites for carbohydrate and JAM-A in T1L and T3D σ1 . The Neu5Ac residue contributes the majority of the contacts between GM2 and T1L σ1 and is wedged into a shallow groove bordered on each side by β-strands B and C . Additional contacts involve the GalNAc moiety . The lactose component , which forms the backbone of the branched glycan and would be linked to the ceramide anchor in the GM2 ganglioside , points away from the protein . The mobilities of the sugar moieties are reflected in their thermal factors ( B-factors ) . The average B-factors of Neu5Ac and GalNAc are in the same range as those of the neighboring protein residues , indicating nearly complete occupancy of the glycan-binding pockets ( Table 1 ) . The remaining two sugars , and especially the glucose moiety , have elevated B-factors , in agreement with their lack of contacts to protein residues and resultant higher mobility ( Table 1 ) . The Neu5Ac residue can be unambiguously placed in the electron density map due to unique identifying features of this sugar compound ( Figure 5A , B ) . The N-acetyl and glycerol chains of Neu5Ac insert between β-strands B and C , where they form hydrogen bonds with backbone atoms of both β-strands ( Figure 5A , B ) . Additionally , the methyl group of the Neu5Ac N-acetyl chain inserts into a hydrophobic pocket flanked by V354 , F369 , and M372 , consistent with the dominance of this group in the STD NMR spectrum . The side chain of Q371 likely forms a hydrogen bond with the Neu5Ac carboxylate . However , at 3 . 6 Å resolution , the conformations of protein side chains cannot be unambiguously determined . There are two possible orientations for the GalNAc group as a result of the electron density . For our crystallographic model , we selected the sugar conformation that is favored according to the corresponding Carbohydrate Ramachandran plot ( CaRp ) ( Figure S3 , Table S3 ) [40] . This orientation of GalNAc also is preferred by GM2 in solution as assessed by NMR spectroscopy [41] . The GalNAc moiety does not form any hydrogen bonds with T1L σ1 , but it clearly interacts with the protein through van der Waals contacts ( Figure 5A ) . Similar contacts are made for each of the two possible orientations of the GalNAc ring . The GM3 glycan differs from the GM2 oligosaccharide in lacking the GalNAc moiety ( Figure 2B ) . Although GM3 exhibited only very weak binding to T1L σ1 in the glycan arrays ( Figure 2A ) , the structure of T1L σ1 in complex with the GM2 glycan indicated that GM3 contains most of the essential features for complex formation and could potentially engage T1L σ1 , albeit with lower affinity compared to GM2 . We therefore determined a crystal structure of T1L σ1 in complex with the GM3 glycan at 3 . 5 Å resolution ( Table 2 ) . The structure shows that T1L σ1 binds to the GM3 glycan at the same site as the GM2 glycan , using identical contacts for the Neu5Ac group ( Figure 6 ) . The Neu5Ac residues of the T1L σ1-GM3 and T1L σ1-GM2 complex structures superimpose with an r . m . s . d . value of 0 . 76 Å ( Figure S4 ) . As is the case for the T1L σ1-GM2 complex , the lactose moiety of the GM3 glycan points away from the protein . To identify residues in T1L σ1 required for glycan binding , we generated T1L reoviruses carrying point mutations in the GM2-binding site using plasmid-based reverse genetics [42] . Residues V354 , S370 , Q371 , and M372 were chosen for mutational analysis , as inspection of the T1L σ1-GM2 complex structure showed that each of these residues is in close proximity to the bound glycan ( Figure 5B ) . For point mutants V354F , V354L , and M372L , the amino acids present in T1L σ1 were replaced with residues predicted to partially block the putative Neu5Ac-binding pocket . Residue Q371 was replaced with an acidic residue to introduce a negative charge that was expected to repel the Neu5Ac moiety and interfere with binding to the GM2 glycan ( Figure 5B ) . Point mutants S370P , Q371A , and M372F were generated to replace a T1L σ1 residue with the corresponding residue in T3D σ1 , which does not bind a carbohydrate receptor via its head domain [19] ( Figure 5C ) . The S1 genes of all mutant viruses were sequenced to confirm the fidelity of mutagenesis . We thought it possible that mutations within the putative carbohydrate-binding site might result in diminished infectivity in MEFs due to impaired glycan engagement or some other impairment in viral fitness . To eliminate the latter possibility and normalize infectious units for the virus strains tested , we used L cells , which do not require sialylated glycan engagement to support infection , likely due to an abundance of JAM-A on the cell surface . Unlike our findings with MEFs , neither neuraminidase treatment of cells ( Figure 1 ) nor pretreatment of virus with GM2 ( data not shown ) altered T1L infectivity in L cells . To determine whether the mutant σ1 proteins are properly folded , we tested the conformation-sensitive monoclonal antibody ( mAb ) 5C6 for the capacity to inhibit mutant virus infection of L cells . Neutralization-resistant T1L mutants selected by mAb 5C6 have alterations at Q417 and G447 in T1L σ1 [43] . These residues are located at the upper part of the T1L σ1 head domain , close to the intersubunit interface ( Figure 7A ) . An antibody that recognizes these residues likely binds a trimeric conformer of the T1L σ1 head and thus indicates the presence of properly folded and assembled σ1 trimers . Preincubation with mAb 5C6 significantly diminished the capacity of wildtype and mutant T1L viruses to infect L cells ( Figure 7B ) , suggesting that the σ1 head domain of the mutants is recognized by mAb 5C6 and not grossly misfolded . To test whether the σ1 point mutants have impaired glycan binding , we quantified the capacity of wildtype and mutant viruses to agglutinate human erythrocytes ( Figure 8 ) , a property linked to carbohydrate binding [28] . All of the mutants had a significant defect in hemagglutination , with alterations of V354 , S370 , and Q371 showing the greatest impairment . To determine whether the point mutants have an altered capacity to infect cells in a carbohydrate-dependent fashion , we quantified infectivity in MEFs , which require carbohydrate binding for optimal infection ( Figure 1 ) . MEFs were inoculated with wildtype and mutant viruses at an MOI of 1 FFU/cell for each virus as equilibrated in assays using L cells . The V354F , S370P , Q371A , and Q371E mutants displayed a significant defect in infectivity in MEFs ( Figure 9 ) . Taken together , these data suggest that residues V354 , S370 , and Q371 , which flank the carbohydrate-binding site of T1L σ1 , are required for functional engagement of the GM2 glycan .
Although all known reovirus serotypes utilize JAM-A as a receptor , they display striking differences in viral tropism and spread . These differences segregate with the S1 gene , which encodes the σ1 attachment protein [7] . The σ1 residues that interact with JAM-A are conserved among the serotypes [25] , and serotype-dependent tropism in the CNS is observed in JAM-A-null mice [11] . These observations suggest that serotype-dependent differences in host disease are attributable to σ1 engagement of cell-surface receptors other than JAM-A . T3D σ1 binds to sialic acid using residues in its body domain , interacting with α2 , 3 , α2 , 6 , and α2 , 8-linked sialic acid in a similar manner [19] , [27] . Although hemagglutination data [28] and lectin-based studies [30] demonstrate that T1L interacts with α2 , 3-linked sialic acid , neither the identity of the specific glycan nor the molecular basis of T1L-glycan interactions was known . In this study , we found that T1L uses the GM2 glycan as a functional receptor , which is the first identification of a specific glycan recognized by any reovirus serotype . Hemagglutination assays have been used in many previous studies of reovirus-glycan interactions [27]–[29] . Reovirus displays serotype-dependent hemagglutination profiles . Type 1 reoviruses agglutinate human but not bovine erythrocytes , whereas type 3 reoviruses preferentially agglutinate bovine erythrocytes and agglutinate human erythrocytes less efficiently [29] . These observations suggest that the glycan-binding sites of type 1 and type 3 reovirus are distinct , a hypothesis that is now confirmed by this study and that of Reiter , et al [19] . Analysis of the respective crystal structures sheds light on the potential species differences in hemagglutination behavior . Whereas human erythrocytes express the Neu5Ac form of sialic acid [44] , bovine cells express mostly Neu5Gc and less Neu5Ac [45] . The additional hydroxyl group of Neu5Gc would face a hydrophobic pocket in the type 1 σ1 glycan-binding site , making a favorable interaction unlikely . In contrast , the type 3 σ1 binding site likely could accommodate either Neu5Ac or Neu5Gc ( D . M . Reiter and T . Stehle , unpublished data ) . The GM2 glycan binds to the head domain of T1L σ1 and not , as predicted earlier , to the body region of the protein [27] . It is possible that cell-surface structures in addition to glycans contribute to hemagglutination by type 1 reovirus and this may explain why the chimeric σ1 proteins used in the earlier study had diminished , but not abolished , hemagglutination capacity . Alternatively , disruption of the neck domain of σ1 in the chimeric proteins used in the previous study [27] might have altered the conformation of the glycan-binding domain in the head . Inspection of the carbohydrate-binding site reveals that the two terminal sugar moieties of the branched GM2 glycan , Neu5Ac and GalNAc , contact the protein , explaining the observed specificity of T1L σ1 for this receptor . Most of the contacts are contributed by Neu5Ac , which is wedged into a cleft between β-strands B and C at the side of the σ1 head , while the GalNAc docks onto a shallow protein surface using van der Waals interactions . Although the GM3 oligosaccharide is also able to bind T1L σ1 in solution , infectivity studies indicate that GM2 is the preferred glycan receptor for T1L reovirus . While preincubation with either GM2 or GM3 oligosaccharides resulted in diminished infectivity of MEFs , the GM2 glycan blocked infectivity more efficiently and in a dose-dependent fashion . The “extra” GalNAc moiety of GM2 is likely responsible for the selectivity of T1L σ1 for this glycan . At only 41 Å2 , the surface area in T1L σ1 buried by interactions with GalNAc is very small compared to the 284 Å2 surface buried by contacts with Neu5Ac in the same complex ( Table S4 ) , but the small additional interactions are nevertheless expected to mediate higher-affinity binding of the GM2 glycan compared with GM3 , which lacks GalNAc . In addition , due to its branched structure , the GM2 glycan has less conformational freedom in solution than the linear GM3 molecule [41] , which may also facilitate interactions with the virus . Entropy furthermore favors binding of the branched GM2 glycan over the linear GM3 molecule . In support of this idea , limited conformational freedom of the branched glycan GM1 is essential for its selective engagement by cholera toxin over related compounds [46] . Therefore , the branched sequence of the GM2 glycan sequence is preferred over the linear sequence of GM3 . Interactions between T1L σ1 and GM2 are primarily comprised of hydrogen bonds between the sugar molecule and backbone atoms of the protein . Nevertheless , we were able to identify residues required for functional glycan engagement by introducing mutations into the glycan-binding site . All mutants displayed impaired hemagglutination capacity , with mutations altering V354 , S370 , and Q371 having the greatest effect ( Figure 8 ) . Mutations affecting these same residues resulted in the greatest defect in infectivity of MEFs ( Figure 9 ) . Residue V354 flanks a hydrophobic pocket into which the methyl group of the N-acetyl chain of Neu5Ac inserts . Mutation of V354 to phenylalanine impairs infectivity of MEFs , while mutating the residue to leucine had a less dramatic effect . Changing S370 to proline introduces a protruding and rigid ring structure , which is expected to create steric hindrance within the glycan-binding pocket ( Figure 5 ) . Q371 likely forms a hydrogen bond with the carboxyl group of Neu5Ac . In the point mutants Q371E and Q371A , this hydrogen bond would be lost , which would lead to reduced ligand binding and , in the case of Q371E , electrostatic repulsion . Interestingly , for the mutants S370P , and Q371A , the residue in T1L σ1 was changed to the corresponding residue in T3D σ1 . Structural data suggest that the T1L glycan-binding pocket does not exist in T3D ( Figure 10 ) , which likely explains the serotype-dependent inhibition of infection by GM2 ( Figure 3 ) . Collectively , these data suggest that residues V354 , S370 , and Q371 , which flank the carbohydrate-binding site of T1L σ1 , are important for recognition and engagement of the GM2 glycan despite the predominant role of main-chain interactions in the crystallographic model . The GM2-binding site in T1L σ1 is distinct from the site of JAM-A binding , and we think that T1L σ1 can bind both receptors , perhaps in a sequential manner ( Figure 11A , B ) . The N-terminal D1 domain of human JAM-A is not glycosylated [47] . Therefore , the glycan receptor must be an independent entity . Reovirus engagement of host cells is likely a multistep process in which interactions with glycans function in adhesion strengthening [33] . We anticipate that the virus first encounters cell-surface GM2 and binds with relatively low affinity ( in line with the NMR data ) and then binds JAM-A with high affinity [20] , [48] , followed by integrin-mediated uptake [49] . This model is supported by the finding that glycan binding is required for T1L infection of MEFs , which express modest levels of JAM-A , and dispensable in L cells , which display significantly higher levels of JAM-A expression . Glycan binding also can function independently of JAM-A engagement , as the relatively modest infectivity of JAM-A-null MEFs can be further reduced by neuraminidase treatment ( data not shown ) . Furthermore , it is possible that the glycan functions with unknown receptors in the host or serves as the sole cell-surface molecule used by T1L in some tissues . The function of adhesion-strengthening and the interactions or lack thereof between GM2 and other reovirus receptors is an important topic for future research . The precise tissue distribution of GM2 is not completely understood , but the glycan is a component of the mammalian nervous system [50]–[52] . In mice , T1L reovirus infects ependymal cells and causes hydrocephalus [8] , [53] . The presence of GM2 in the brain provides an attractive explanation for the use of this coreceptor by T1L . Because ganglioside expression may differ in cell types that serve as targets for reovirus infection in vivo , there may be cells in which one glycan or another predominates as a T1L coreceptor . Type 3 reoviruses differing only in the capacity to engage cell-surface glycans display marked differences in tropism [54] , [55] . We anticipate that glycan binding also functions in the pathogenesis of type 1 reovirus infections , which is an area of current investigation in our laboratories . Reovirus is being tested in clinical trials as an oncolytic adjunct to conventional cancer therapy . Some tumor cells have altered ganglioside expression compared with untransformed cells , and some overexpress GM2 [56]–[58] . Humanized antibodies directed against GM2 prevent the formation of organ metastases in mice with small-cell lung cancer [59] . It is possible that ganglioside overexpression in tumor cells alters the susceptibility of certain cancers to reovirus infection . Understanding the molecular basis of reovirus-glycan interactions might improve the design of effective oncolytics . Although T1L and T3D reoviruses bind sialylated glycans as receptors using their σ1 proteins , the locations of the respective carbohydrate-binding sites differ substantially ( Figure 11A , B ) . The T1L σ1 glycan-binding site resides in the head domain . In contrast , the T3D σ1 glycan-binding site is in the N-terminal part of the body domain , close to the midpoint of the σ1 molecule . Structure and sequence comparisons show that the head of T3D σ1 would not be capable of engaging Neu5Ac-based receptors because the carbohydrate-binding site of the T1L σ1 head is blocked in T3D σ1 ( Figures 5C , 10 ) . It also is unlikely that the region of T1L σ1 corresponding to the T3D σ1 glycan-binding site would interact with sialic acid . T3D σ1 residue Arg202 forms critical interactions with Neu5Ac and , in T1L σ1 , there is an aspartate instead of an arginine at the equivalent position . The negatively charged aspartate side chain would probably repel Neu5Ac and , thus , carbohydrate engagement at this site is impeded ( Figure 11C ) . The different locations of the carbohydrate-binding sites contrast with the conserved interactions of both σ1 proteins with JAM-A . The JAM-A-binding sites of both T1L and T3D σ1 proteins are located at the base of the head domain , and interactions between σ1 and JAM-A are similar in both serotypes [25] , [26] . Assuming that both protein- and carbohydrate-binding sites are accessible for both serotype 1 and serotype 3 reoviruses , it is possible that the mechanisms of attachment are not conserved between the reovirus serotypes , which may contribute to the observed differences in viral tropism and spread .
Construct σ1long comprises the three most C-terminal predicted β-spirals of T1L σ1 and the head domain ( amino acids 261–470 ) . Construct σ1short comprises the most C-terminal predicted β-spiral of T1L σ1 and the head domain ( amino acids 300–470 ) . Expression and purification of T1L σ1long and T1L σ1short were facilitated by attaching a trimeric version of the GCN4 leucine zipper [60] , [61] to the N-terminus of the σ1 sequence , similar to the strategy we used to express T3D σ1 [19] . The σ1 construct was cloned into the pQE-80L expression vector ( Qiagen ) , which includes a non-cleavable N-terminal His6-tag . The protein was expressed in E . coli Rosetta 2 ( DE3 ) ( Novagen ) by autoinduction at 20°C for 48 to 72 h . Bacteria were lysed using an EmulsiFlex ( Avestin ) homogenizer and purified via Ni-affinity chromatography ( His-Trap FF column , GE Healthcare ) . The fusion protein was eluted from the column , and the protein solution was desalted using a PD10 desalting column ( GE Healthcare ) . The GCN4 domain and the His6-tag were removed from the fusion protein using 1 µg trypsin per mg protein at 20°C for 4 h . The resultant products were subjected to size-exclusion chromatography ( Superdex 200 ) to remove the tags , trypsin , and other minor impurities . Undigested versions of both constructs were used for glycan array screening . STD NMR experiments were performed using σ1long . Both constructs were used for structural analysis . Uncleaved σ1short yielded crystals diffracting to 2 . 6 Å resolution . This higher resolution structure was used as a reference model for refinement of the lower-resolution structures of cleaved σ1long in complex with the GM2 or GM3 glycan . Microarrays were composed of lipid-linked oligosaccharide probes , neoglycolipids ( NGLs ) and glycolipids , robotically printed on nitrocellulose-coated glass slides at 2 and 7 fmol per spot using a non-contact instrument , and analyses were performed as described [62] , [63] . For analysis of T1L σ1long , the results of 124 oligosaccharide probes ( 5 non-sialylated and 119 sialylated , Glycosciences Array Set 40–41 ) , at 5 fmol per spot are shown in Figure S1 and Table S1 . For the analysis of T1L σ1short , a different version of the microarray ( in house designation Ganglioside Dose Response Array set 1 ) was used; results of the 21 ganglioside-related probes ( Table S2 ) each arrayed at four levels: 0 . 3 , 0 . 8 , 1 . 7 and 5 . 0 fmol/spot , are shown in Figure 2A . For the initial analysis of His-tagged T1L σ1long , the protein was incubated with mouse monoclonal anti-poly-histidine ( Ab1 ) and biotinylated anti-mouse IgG antibodies ( Ab2 ) ( both antibodies from Sigma ) at a ratio of 4∶2∶1 ( by weight ) . The σ1long-antibody complexes were prepared by preincubating Ab1 with Ab2 at ambient temperature for 15 min , followed by addition of His-tagged T1L σ1long and incubation on ice for 15 min . The σ1long-antibody complexes were diluted in 5 mM HEPES ( pH 7 . 4 ) , 150 mM NaCl , 0 . 3% ( v/v ) Blocker Casein ( Pierce ) , 0 . 3% ( w/v ) bovine serum albumin ( Sigma ) , 5 mM CaCl2 and 40 mM imidazole ( referred to as HBS-Casein/BSA-imidazole ) , to provide a final σ1long concentration of 150 µg/ml , and overlaid onto the arrays at 20 °C for 2 h . Binding was detected using Alexa Fluor 647-labeled streptavidin ( Molecular Probes ) at 1 µg/ml . Microarray data analyses and presentation were facilitated using dedicated software [64] . For the analyses of His-tagged T1L σ1short , different assay conditions were evaluated with and without complexation ( not shown ) . The condition selected as optimal was without precomplexation . His-tagged σ1short was diluted in HBS-Casein/BSA-imidazole , overlaid at 300 µg/ml , followed by incubation with Ab1 and Ab2 ( each at 10 mg/ml , precomplexed at ambient temperature for 15 min ) . Binding was detected using Alexa Fluor 647-labeled streptavidin . Crystals of uncleaved σ1short formed in 0 . 1 M MES/imidazole ( pH 6 . 5 ) , 10% PEG 4000 , 20% glycerol , 0 . 02 M sodium formate , 0 . 02 M ammonium acetate , 0 . 02 M trisodium citrate , 0 . 02 M sodium potassium L-tartrate , 0 . 02 M sodium oxamate at 4°C using the sitting-drop-vapor-diffusion method . No additional cryoprotection was necessary . Crystals of σ1long formed in 0 . 1 M Na cacodylate ( pH 6 . 0–6 . 6 ) , 1 . 2–1 . 5 M ( NH4 ) 2SO4 at 4°C using the sitting-drop-vapor-diffusion method . For preparation of complexes , these crystals were transferred to 20 mM GM2 or GM3 oligosaccharide ( Elicityl ) in the crystallization solution for 5–10 min . Prior to flash-freezing , the crystals were transferred to a solution containing 0 . 1 M Na cacodylate , 1 . 34 M ( NH4 ) 2SO4 , 25% glycerol , and 20 mM GM2 or GM3 glycan . The crystals belonged to space group P3221 and contained one trimer in the asymmetric unit . A complete data set was collected at the Swiss Light Source , beamline X06SA . XDS was used to index and scale the reflection data [65] . The structure was determined by molecular replacement with Phaser ( CCP4 ) [66] , [67] using the coordinates of T1L σ1 derived from the previously determined T1L σ1-JAM-A complex structure as a search model [26] . Manual model building was carried out using coot [68] . Structural refinement was performed using Refmac5 ( CCP4 ) [69] , Phenix [70] , and autoBUSTER [71] , [72] . Inspection of the 2Fo-Fc maps for the structures of the T1L σ1-glycan complexes revealed clear , unambiguous electron density for most of the GM2 and GM3 oligosaccharides at a 1 . 5 σ contour level . The glycans also were visible in difference electron density maps . The unbiased electron density maps in Figures 4 , 6 , and S3 show the initial Fo-Fc maps of the T1L σ1-GM2 and T1L σ1-GM3 glycan complexes obtained after molecular replacement using the previously solved structure of unliganded T1L σ1 . The carbohydrates were included in the model at this point . Refinement of the ligands was performed using the CCP4 library and user-defined constraints . Structure images were created using PyMOL [73] . Coordinates and structure factors of both complexes have been deposited in the Protein Data Bank with accession codes 4GU3 ( T1L-σ1-GM2 glycan complex ) and 4GU4 ( T1L σ1-GM3 glycan complex ) . Sequence alignments were performed using T-Coffee [74] and analyzed using Jalview [75] , [76] . Structure alignments were calculated by secondary-structure matching ( SSM ) superposition in coot [77] . The Ramachandran plot was generated with Rampage ( CCP4 ) [78] . Buried surface areas were calculated using AreaImol ( CCP4 ) [79] , [80] . NMR spectra were recorded using 3 mm tubes and a Bruker AVIII-600 spectrometer equipped with a room temperature probe head at 283 K and processed with TOPSPIN 3 . 0 ( Bruker ) . Samples containing 1 mM GM2 or GM3 glycan ( Elicityl ) , 20 mM potassium phosphate ( pH 7 . 4 ) , and 150 mM NaCl with and without 20 µM T1L σ1 were used for the STD NMR measurements and the frequency control , respectively . Samples were prepared in D2O , and no additional water suppression was used to preserve the anomeric proton signals . The sample without protein also was used for spectral assignment . The off- and on-resonance irradiation frequencies were set to −30 ppm and 7 . 3 ppm , respectively . The irradiation power of the selective pulses was 57 Hz , the saturation time was 2 s , and the total relaxation delay was 3 s . A 50 ms continuous-wave spin-lock pulse with a strength of 3 . 2 kHz was employed to suppress residual protein signals . A total number of 512 scans were recorded . A total of 10 , 000 points were collected , and spectra were multiplied with a Gaussian window function prior to Fourier transformation . Spectra were referenced using HDO as an internal standard [81] . Spinner adapted murine L cells were grown in suspension culture in Joklik's minimum essential medium ( Lonza ) supplemented to contain 5% fetal bovine serum ( FBS ) ( Gibco ) , 2 mM L-glutamine , 100 U/ml penicillin , 100 µg/ml streptomycin ( Invitrogen ) , and 25 ng/ml amphotericin B ( Sigma-Aldrich ) . MEFs were generated from C57/BL6 mice at embryonic day 13 . 5 as described [82] . MEFs were maintained in Dulbecco's modified Eagle's minimum essential medium ( DMEM ) ( Gibco ) supplemented to contain 10% FBS , 2 mM L-glutamine , 100 U/ml penicillin , 100 µg/ml streptomycin , 1X MEM nonessential amino acids ( Sigma-Aldrich ) , 20 mM HEPES , and 0 . 1 mM 2-mercaptoethanol ( Sigma-Aldrich ) . Cells at passages 3–6 were used in this study . Viruses were generated using plasmid-based reverse genetics [42] , [83] . BHK-T7 cells ( 5×105 ) were seeded in 60 mm tissue-culture dishes ( Corning ) and allowed to incubate at 37°C overnight . OptiMEM ( Invitrogen ) ( 0 . 75 ml ) was mixed with 53 . 25 µl TransIT-LT1 transfection reagent ( Mirus ) and incubated at RT for 20 min . Plasmid constructs representing cloned gene segments from the T1L genome , pT7S1 T1L , pT7S2 T1L , pT7L3S3 T1L , pT7S4 T1L , pT7M1 T1L , pT7L1M2 T1L , and pT7L2M3 T1L were mixed into the OptiMEM/TransIT-LT solution . Equal amounts of each plasmid were added for a total of 17 . 75 µg DNA . The plasmid-transfection solution was added to BHK-T7 cells and incubated for 3–5 days . Following two freeze-thaw cycles , recombinant viruses were isolated by plaque purification using L-cell monolayers [84] . Purified virions were generated using second-passage L cell-lysate stocks . Viral particles were Freon-extracted from infected cell lysates and layered onto 1 . 2 to 1 . 4 g/cm3 CsCl gradients and centrifuged at 62 , 000×g for 18 h . Bands were collected and dialyzed exhaustively in virion-storage buffer as described [12] , [85] . To generate mutant viruses , resides V354 , S370 , Q371 , and M372 in the S1 gene plasmid were altered by QuickChange ( Stratagene ) site-directed mutagenesis . S1 gene sequences were confirmed using the OneStep RTPCR kit ( Qiagen ) , gene-specific primers , and viral dsRNA extracted from infected L cells ( RNAeasy , Qiagen ) . Primer sequences for mutagenesis and sequencing are available from the corresponding authors by request . Sanger sequencing was performed using purified PCR products ( Gene Hunter and Vanderbilt Sequencing Core ) . Genotypes were confirmed by electrophoresis of viral particles in 4-to-20% gradient sodium dodecyl sulfate polyacrylamide gels stained with ethidium bromide and visualized by UV illumination [86] . Particle concentrations were determined using the conversion 1 AU260 = 2 . 1×1012 particles [85] . Viral titers were quantified by plaque assay [84] or fluorescent focus assay [33] . Reovirus polyclonal immunoglobulin G ( IgG ) raised against T1L and T3D was used to stain for reovirus antigen [87] . Alexa-488 conjugated goat anti-rabbit antibody ( Invitrogen ) was used as a secondary antibody . Monoclonal rat anti-mouse JAM-A ( Abcam , clone H202-106 ) was used to stain for JAM-A expression followed by goat anti-rat secondary antibody conjugated to Alexa-488 ( Invitrogen ) . Conformation-sensitive neutralizing mAb 5C6 specific for T1L [43] , [88] was used in neutralization assays as described [89] . L cells ( 105 ) or MEFs ( 5×104 ) were incubated in 24-well plates ( Costar ) at 37°C overnight . To evaluate the importance of sialic acid engagement in T1L infection , cell monolayers were treated with 100 mU/ml of A . ureafaciens neuraminidase diluted in PBS ( MP Biomedicals , LLC ) or PBS alone ( mock ) at RT for 1 h prior to virus adsorption at an MOI of 1 PFU/cell in L cells or 100 PFU/cell ( as titered in L cells ) in MEFs . Following incubation at RT for 1 h , the inoculum was removed , and cells were washed twice with PBS and incubated at 37°C for 20 h . Cells were fixed in methanol and visualized by indirect immunofluorescence [33] with the addition of a DAPI stain to quantify cell nuclei . Cells were blocked in PBS supplemented to contain 5% bovine serum albumin ( BSA ) ( Sigma ) . Infected cells were detected by staining with reovirus polyclonal antiserum diluted 1∶1000 and secondary Alexa-488 goat anti-rabbit Ig 1∶1000 ( Invitrogen ) . Nuclei were quantified using DAPI ( 1∶1000 ) . All antibodies were diluted in PBS supplemented to contain 0 . 5% Triton X-100 . Infectivity studies were performed in triplicate wells . Three fields of view per well were quantified using the Axiovert 200 fluorescence microscope ( Carl Zeiss ) . To determine the effect of soluble glycans on viral infectivity , virus was incubated with various concentrations of GM2 or GM3 glycan ( Elicityl ) at room temperature for 1 h . The virus-glycan mixture was adsorbed to MEFs ( MOI of 100 PFU/cell as titered on L cells ) at room temperature for 1 h . The cells were washed twice , and infectivity was determined by immunofluorescence assay . To determine the relative amount of JAM-A on L cells and MEFs , 5×105 cells were stained with rat anti-mouse JAM-A at a dilution of 1∶200 followed by staining with Alexa-488 labeled goat anti-rat Ig at 1∶1000 . All staining was done in PBS supplemented to contain 2% FBS . Fluorescence was measured using an LSRII ( BD , Vanderbilt University Flow Cytometry Shared Resource ) . Mean fluorescence intensity of a forward and side scatter gated population was determined using FlowJo software . Purified reovirus virions ( 1011 particles ) were distributed into 96-well U-bottom microtiter plates ( Costar ) and serially diluted twofold in 0 . 05 ml of PBS . Human type O erythrocytes ( Vanderbilt Blood Bank ) were washed twice with PBS and resuspended at a concentration of 1% ( vol/vol ) . Erythrocytes ( 0 . 05 ml ) were added to wells containing virus particles and incubated at 4°C for 3 h . A partial or complete shield of erythrocytes on the well bottom was interpreted as a positive HA result; a smooth , round button of erythrocytes was interpreted as a negative result . HA titer is expressed as 1011 particles divided by the number of particles/HA unit . One HA unit equals the number of particles sufficient to produce HA . Statistical analysis was performed using Prism ( Graphpad ) . Two-tailed Student's t tests were used for all infectivity studies . The hemagglutination assays were analyzed using a one-way Anova followed by a Bonferroni's correction . P values of less than 0 . 05 were considered to be statistically significant .
|
Receptor utilization plays an important role in viral disease . Viruses must recognize a receptor or sometimes multiple receptors to infect a cell . Mammalian orthoreoviruses ( reoviruses ) serve as useful models for studies of viral receptor binding and pathogenesis . The reovirus experimental system allows manipulation of both the virus and the host to define mechanisms of viral attachment and disease . Like many viruses , reoviruses engage carbohydrate molecules on the cell-surface , but the oligosaccharide sequences bound and the function of glycan binding in infection were not known prior to this study . We used glycan array screening to determine that serotype 1 reoviruses bind ganglioside GM2 and found that this interaction is required for efficient infection of some types of cells . To better understand how reovirus engages GM2 , we determined the structure of the reovirus attachment protein σ1 in complex with the GM2 glycan and defined residues that are required for functional receptor binding . Reoviruses are being tested in clinical trials for efficacy in the treatment of cancer . Cancer cells commonly have altered glycan profiles . Therefore , understanding how reoviruses engage cell-surface glycans might lead to improvements in oncolytic therapy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biomacromolecule-ligand",
"interactions",
"biochemistry",
"proteins",
"virology",
"protein",
"structure",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"biophysics"
] |
2012
|
The GM2 Glycan Serves as a Functional Coreceptor for Serotype 1 Reovirus
|
Chromodomains are found in many regulators of chromatin structure , and most of them recognize methylated lysines on histones . Here , we investigate the role of the Drosophila melanogaster protein Corto's chromodomain . The Enhancer of Trithorax and Polycomb Corto is involved in both silencing and activation of gene expression . Over-expression of the Corto chromodomain ( CortoCD ) in transgenic flies shows that it is a chromatin-targeting module , critical for Corto function . Unexpectedly , mass spectrometry analysis reveals that polypeptides pulled down by CortoCD from nuclear extracts correspond to ribosomal proteins . Furthermore , real-time interaction analyses demonstrate that CortoCD binds with high affinity RPL12 tri-methylated on lysine 3 . Corto and RPL12 co-localize with active epigenetic marks on polytene chromosomes , suggesting that both are involved in fine-tuning transcription of genes in open chromatin . RNA–seq based transcriptomes of wing imaginal discs over-expressing either CortoCD or RPL12 reveal that both factors deregulate large sets of common genes , which are enriched in heat-response and ribosomal protein genes , suggesting that they could be implicated in dynamic coordination of ribosome biogenesis . Chromatin immunoprecipitation experiments show that Corto and RPL12 bind hsp70 and are similarly recruited on gene body after heat shock . Hence , Corto and RPL12 could be involved together in regulation of gene transcription . We discuss whether pseudo-ribosomal complexes composed of various ribosomal proteins might participate in regulation of gene expression in connection with chromatin regulators .
Chromatin structure strongly impacts on regulation of gene expression . Indeed , post-translational histone modifications ( methylations , acetylations , phosphorylations etc… ) called epigenetic marks , are recognized by protein complexes that shape chromatin ( reviewed in [1] ) . A number of protein domains specifically interact with these modifications , thus inducing recruitment of chromatin remodeling or transcriptional complexes . Bromodomains recognize acetylated histones ( reviewed in [2] ) whereas 14-3-3 domains recognize phosphorylated histones ( reviewed in [3] ) . Methylated histones are recognized by chromodomains ( chromatin organization modifier ) [4] , which therefore belong to the Royal family of domains , known for their methylated lysine or arginine binding activity ( reviewed in [5] ) . Chromodomains share a common structure encompassing a folded three-stranded anti-parallel ß-sheet supported by an α-helix that runs across the sheet . This structure contains two to four well-conserved aromatic residues that form a cage around the methylated ligand [5] , [6] . Chromodomains were first identified in Polycomb ( PC ) and Heterochromatin Protein 1 ( HP1 ) [4] . They are found in many other chromatin-associated proteins that belong to three classes according to their global structure: ( 1 ) PC/CBX family proteins harbor a single N-terminal chromodomain , ( 2 ) HP1 family proteins have an N-terminal chromodomain followed by a region termed a chromoshadow domain , and ( 3 ) CHD ( Chromodomain/Helicase/DNA-binding domain ) family proteins present two tandem chromodomains ( reviewed in [5] ) . Most chromodomains specifically recognize particular methylated residues on histones . For instance , the chromodomain of PC , which is a subunit of the PRC1 complex ( Polycomb Responsive Complex 1 ) , binds specifically H3K27me3 [7] , [8] . Once recruited , PRC1 prevents RNA Polymerase II recruitment or transcriptional elongation and therefore mediates gene silencing ( reviewed in [9] ) . The chromodomain of HP1 binds H3K9me2 and H3K9me3 , which are epigenetic marks characteristic of heterochromatin , and thus participates in heterochromatin shaping [10] , [11] . Very few cases of non-histone chromodomain substrates are known [12] . For example , the HP1 chromodomain also recognizes an autocatalytically methylated residue of the G9a histone H3 methyl-transferase [13] . The D . melanogaster corto gene encodes an Enhancer of Trithorax and Polycomb ( ETP ) , i . e . a Polycomb ( PcG ) and Trithorax ( TrxG ) complex co-factor , involved in both silencing and activation of gene expression [14] , [15] . Indeed , Corto participates in transcriptional regulation of several homeotic genes together with these complexes and other ETPs [16] , [17] . Corto binds chromatin and contains in its N-terminal part a single structured domain identified by hydrophobic cluster analysis and structural comparison as a chromodomain [18] . Hence , Corto would be closer to CBX proteins of the PcG class [5] . However , its chromodomain is rather divergent , since only two aromatic residues are conserved among the four that make a cage around the methylated residue . How Corto anchors to chromatin and more specifically , whether the chromodomain addresses Corto to chromatin , is not known . Here , we address this question by expressing a tagged Corto chromodomain in flies or in S2 cells . We show that the Corto chromodomain is a functional chromatin-targeting module . Surprisingly , peptide pull-down , mass spectrometry and Biacore show that the Corto chromodomain interacts with nuclear ribosomal proteins , and notably binds with high affinity RPL12 tri-methylated on lysine 3 ( RPL12K3me3 ) . Co-localization of Corto and RPL12 with active transcriptional epigenetic marks on polytene chromosomes suggests that both proteins are involved in fine-tuning transcription of genes located in open chromatin . Investigation of Corto and RPL12 transcriptional targets by RNA-seq reveals that many are shared by both factors . Analysis of hsp70 occupancy by chromatin immunoprecipitation suggests that Corto and RPL12 cooperate in transcriptional regulation . Interestingly , the potential common targets of Corto and RPL12 are enriched in genes involved in heat response and ribosomal biogenesis .
To address the role of the Corto chromodomain in vivo , we used germline transformation and the binary UAS/Gal4 system to produce transgenic flies . These lines expressed either FLAG and HA double-tagged cortoCD fused to a nuclear localization signal coding sequence to force its entry into nuclei ( FH-cortoCD ) , FLAG and HA double-tagged corto deleted of the chromodomain sequence ( FH-cortoΔCD ) , or corto full-length ( both FH-CortoΔCD and Corto full-length spontaneously enter the nucleus although no nuclear localization signal was detected , data not shown ) . Whereas transgenic flies ubiquitously over-expressing cortoΔCD [using either Actin5C ( Act::Gal4>UAS::FH-cortoΔCD ) or daughterless ( da::Gal4>UAS::FH-cortoΔCD ) drivers] were perfectly viable and had no visible phenotype , over-expression of corto using the same drivers was 100% lethal . Over-expression of cortoCD using again these drivers also induced high lethality at all developmental stages ( from 63% to 100% depending on the transgenic line and the driver , Table S1 ) . Escaper flies displayed rotated genitalia and duplicated macrochaetae as well as very penetrant homeotic phenotypes ( Figure 1 ) . Many flies presented a partial transformation of arista into leg , a homeotic phenotype called Aristapedia that could reflect down-regulation of the spineless-aristapedia gene [19] . Similar phenotypes were observed when over-expressing full-length corto using the weaker ubiquitous driver armadillo ( arm::Gal4 ) ( Table S1 ) . Males over-expressing cortoCD also displayed smaller sex combs , a phenotype opposed to that of corto mutant males who have ectopic sex combs [15] , [20] , and which could reflect reduced expression of the homeotic gene Sex combs reduced ( Scr ) [21] . Taken together , these results suggest that the chromodomain is critical for Corto function . Corto binds polytene chromosomes of third instar larva salivary glands at many sites [18] . To test the role of Corto chromodomain in chromatin binding , we immunostained polytene chromosomes of larvae over-expressing cortoCD in salivary glands [escargot Gal4 driver , ( esg::Gal4>UAS::FH-cortoCD ) ] with anti-FLAG antibodies . FH-CortoCD bound polytene chromosomes at many discrete sites ( Figure 2A ) . Like endogenous Corto , FH-CortoCD preferentially bound DAPI interbands and puffs , i . e . regions corresponding to open or actively transcribed chromatin . Comparison of endogenous Corto binding in wild-type larvae and FH-CortoCD binding in esg::Gal4>UAS::FH-cortoCD larvae at the tip of chromosome 3L showed that these proteins shared most of their binding sites ( Figure 2B ) . These results indicate that FH-CortoCD mimics Corto binding on polytene chromosomes and that the Corto chromodomain is a genuine chromatin-addressing module . These results prompted us to identify the anchor ( s ) of Corto chromodomain on chromatin . We incubated GST-CortoCD covalently bound on agarose beads with nuclear or cytoplasmic extracts from embryos and resolved retained polypeptides by SDS-PAGE . Four bands between 30 and 15 kDa ( P30 , P21 , P20 and P15 ) were consistently retained by GST-CortoCD and were enriched in peptide pull-down experiments performed with nuclear extracts versus cytoplasmic extracts ( Figure 3A ) . The contents of the bands were identified by mass spectrometry . Surprisingly , all four bands contained ribosomal proteins ( RPs ) : RPL7 for P30 , RPS11 for P21 , RPS10 , RPL12 and RPL27 for P20 , and RPS14 for P15 ( Table S2 ) . Although RPs are usually considered as contaminants , their consistent enrichment after incubation with nuclear extracts as well as the previously shown association of RPS11 , RPL12 and RPS14 with polytene chromosomes [22] prompted us to consider their binding to CortoCD . These proteins might then interact with Corto directly on chromatin . To verify the interaction between RPs and CortoCD , we generated vectors to produce FLAG-tagged CortoCD supplied with a nuclear localization signal and Myc-tagged RPs in Drosophila S2 cells . Co-immunoprecipitations were performed on cell extracts from transfected cells , using either anti-FLAG or anti-Myc antibodies . No co-immunoprecipitation was observed between CortoCD and RPL7 , RPS10 or RPS14 ( Figure S1 ) . However , anti-FLAG co-immunoprecipitated Myc-RPL12 with FLAG-CortoCD whereas anti-Myc co-immunoprecipitated FLAG-CortoCD with Myc-RPL12 ( Figure 3B ) . In a similar experiment using FLAG-tagged full-length Corto , co-immunoprecipitation was again observed in both directions ( Figure 3C ) . However , no co-immunoprecipitation was observed between FLAG-tagged CortoΔCD and Myc-tagged RPL12 ( Figure 3D ) . These experiments demonstrate that RPL12 and Corto interact and that the Corto chromodomain is necessary and sufficient for this interaction . The identification of other RPs among the pulled-down polypeptides suggests that CortoCD interacts with a complex of RPs via a direct interaction with RPL12 . Since chromodomains typically recognize methylated lysines , we asked whether Corto chromodomain could bind a methylated form of RPL12 . D . melanogaster RPL12 was aligned with RPL12 from several other species to identify conserved residues described to be methylated in some of them [23]–[25] ( Figure 4A ) . Lysines 3 , 10 , 39 and 83 , as well as arginine 67 fulfilled these criteria . Using site-directed mutagenesis , we replaced their codons with alanine codons in the Drosophila RPL12 cDNA , thus generating a series of mutants ( RPL12K3A , RPL12K10A , RPL12K39A , RPL12R67A and RPL12K83A ) . These mutant cDNAs were introduced into a plasmid allowing their expression as mRFP-tagged proteins in Drosophila S2 cells . Similarly , the cortoCD cDNA , supplied with a nuclear localization signal , was introduced into a plasmid allowing its expression as an EGFP-tagged protein in S2 cells . When expressed in these cells , EGFP-CortoCD artificially entered the nucleus where it exhibited a punctuated pattern that recalled Polycomb bodies ( Figure 4B ) [26] . A similar nuclear pattern was observed after immunostaining untransfected S2 cells with anti-Corto antibodies . However , these “Corto bodies” did not overlap with Polyhomeotic ( PH ) , a component of the PRC1 complex , but with RNA Polymerase II suggesting that they were transcriptional factories rather than Polycomb bodies ( Figure S2 ) . RPL12-mRFP expressed alone was present in the cytoplasm and the nucleus , where it appeared slightly punctuated ( Figure 4B ) . Interestingly , when co-expressed with EGFP-CortoCD , all RPL12-mRFP localized in the nucleus ( Figure 4C ) . Both proteins perfectly colocalized in a punctuated nuclear pattern , corroborating the interaction between CortoCD and RPL12 and suggesting that Corto could drive RPL12 in the nucleus . Similar experiments were carried out using the RPL12 mutant forms . Whereas RPL12K10A , RPL12K39A , RPL12R67A and RPL12K83A co-localized with CortoCD , RPL12K3A did not , strongly suggesting that RPL12 lysine 3 is required for Corto chromodomain-RPL12 interaction ( Figure 4C ) . To test whether Corto directly interacted with RPL12 lysine 3 , we measured real-time binding between CortoCD and several RPL12 peptides using Biacore . GST-CortoCD and GST were immobilized on a CM5 sensor chip . Then , several RPL12 peptides [unmodified ( RPL12um ) , methylated on lysine 3 ( RPL12K3me2 , RPL12K3me3 ) , methylated on lysine 10 ( RPL12K10me3 ) or lysine 3 mutated ( RPL12K3A ) ] were assayed for their binding to GST-CortoCD or GST ( Figure 5 , Figure S3 ) . None of these peptides bound GST . Furthermore , unmodified RPL12 , RPL12K3me2 , RPL12K10me3 and RPL12K3A peptides did not interact with CortoCD ( no binding or unspecific binding i . e . KD>200 µM; Figure 5C ) . Only RPL12K3me3 interacted with high specificity with CortoCD ( KD = 8 µM ) . To investigate whether RPL12K3me3 could bind to other chromodomains , we repeated these experiments using that of HP1 ( HP1CD ) . GST-HP1CD was immobilized on the sensor chip and binding of either RPL12 , RPL12K3me3 , RPL12K10me3 or RPL12K3A was tested . None of these peptides specifically interacted with HP1CD ( KD>200 µM ) ( Figure 5C , Figure S3 ) . Although no histones were revealed among peptides pulled down by CortoCD , we monitored binding of several histone H3 peptides to CortoCD . No binding of unmodified H3 , H3K27me3 , H3K9me3 or H3K4me3 peptides was observed ( Figure 5D ) while , as expected , the H3K9me3 peptide bound HP1CD with high affinity ( KD = 0 . 4 µM ) . Surprisingly , the H3K27me3 peptide bound HP1CD with a similar affinity ( KD = 0 . 7 µM ) , probably because sequences adjacent to the chromodomain ( i . e . the hinge region ) are required for selective targeting [27] . Altogether these data demonstrate that the Corto chromodomain specifically recognizes RPL12 trimethylated on lysine 3 ( RPL12K3me3 ) . RPL12 , along with 19 other ribosomal proteins , is known to bind polytene chromosomes of Drosophila larval salivary glands where it specifically associates with sites of transcription [22] . To investigate the potential role of the Corto-RPL12 interaction in gene expression regulation , we first analyzed the binding of these proteins on polytene chromosomes . For this , we generated Myc-tagged RPL12 transgenic fly lines ( UAS::RpL12-Myc ) . Unlike corto or cortoCD , RpL12-Myc over-expression using ubiquitous Gal4 drivers ( da::Gal4>UAS::RpL12-Myc or Act::Gal4>UAS::RpL12-Myc ) induced no lethality and adult flies presented no visible phenotype except a shortened development ( data not shown ) . RpL12-Myc was then expressed in salivary glands with the esg driver ( esg::Gal4>UAS::RpL12-Myc ) to test its binding to polytene chromosomes . RPL12-Myc bound polytene chromosomes at numerous sites , preferentially at DAPI interbands and puffs , suggesting that it mimics the binding of endogenous RPL12 [22] ( Figure 6A ) . Co-immunostaining of RPL12 and the endogenous Corto protein showed that about 40% of the Corto sites were bound by RPL12 ( Figure 6A , 6C ) . Simultaneous over-expression of FH-CortoCD and RPL12-Myc ( esg::Gal4>UAS::FH-cortoCD , UAS::RpL12-Myc ) established that CortoCD co-localized with RPL12 on a similar number of sites ( Figure 6B ) . Chromatin environment of Corto and RPL12 was further analyzed using antibodies against epigenetic marks ( H3K27me3 , H3K4me3 ) and RNA Polymerase II ( paused , i . e . phosphorylated on serine 5: RNAPolIIS5p; elongating i . e . phosphorylated on serine 2: RNAPolIIS2p ) ( Figure 7 and Figure 8 ) . In agreement with our Biacore analyses , Corto did not bind centromeric heterochromatin – marked by H3K9me3 – and did not overlap with H3K27me3 ( except at the tip of chromosome X ) ( Figure 7A ) . Similarly , very few co-localizations with H3K27me3 were observed for RPL12-Myc ( Figure 8A ) . Corto , as well as RPL12-Myc , partially co-localized with H3K4me3 ( Figure 7B , Figure 8B ) . However , whereas Corto showed preferential co-localization with RNAPolIIS5p versus RNAPolIIS2p ( Figure 7 ) , RPL12 shared but few sites with RNAPolIIS5p and strongly co-localized with RNAPolIIS2p ( Figure 8 ) , as previously described [22] . Taken together , these data suggest that Corto and RPL12 mostly bind open , transcriptionally permissive chromatin . To address the role of Corto and RPL12 in transcriptional regulation , we deep-sequenced transcripts from wing imaginal discs of third instar larvae over-expressing either FH-cortoCD or RpL12-Myc under control of the wing-specific scalloped::Gal4 driver ( sd::Gal4>UAS::FH-cortoCD or sd::Gal4>UAS::RpL12-Myc ) ( hereafter called assays ) . Total RNA from the assays , the sd::Gal4/+ control or a w1118 reference line were isolated from pools of wing imaginal discs and subjected to RNA-seq on an Illumina high throughput sequencer . Sequence reads were aligned with the D . melanogaster genome to generate global gene expression profiles . Sequence reads of the assays were compared to sequence reads of the sd::Gal4/+ control . Differential analyses were performed to obtain adjusted P-values associated to expression changes for the assays compared to the sd::Gal4/+ control . In addition , sequence reads from the w1118 reference line were compared to sequence reads of the sd::Gal4/+ control . This reference was used to fix the threshold of the adjusted P-value to get only 1% of transcripts as differentially expressed in this control experiment ( false discovery rate ) . By doing so , we obtained an adjusted P-value cutoff of 4 . 10−18 . Using this threshold , we retrieved the highest expression variations from the two assays [with absolute log2 ( assay/control ) >1] . 463 genes were upregulated when over-expressing cortoCD ( Table S3 ) . Among them , 314 were also upregulated when over-expressing RpL12 , representing 75% of all genes upregulated by RpL12 over-expression ( Table S4 ) . Furthermore , 211 genes were down-regulated when over-expressing cortoCD ( Table S5 ) . Among them , 197 were also down-regulated when over-expressing RpL12 , representing 67% of all genes down-regulated by RpL12 over-expression ( Table S6 ) . These results are summarized on Figure 9 and Table S7 . They suggest that Corto and RPL12 share many transcriptional targets . Strikingly , analysis of Gene Ontology ( GO ) revealed that common upregulated genes were enriched in the “translation” ( 54 . 4% for Corto and 38 . 3% for RPL12 ) and “response to heat” ( 11 . 9% for Corto and 9 . 8% for RPL12 ) categories ( Figure 10 and Tables S8 , S9 , S10 , S11 ) . The high correlation between genes deregulated when over-expressing either cortoCD or RpL12 ( R2 = 0 . 634 ) ( Figure 9 ) as well as the numerous co-localizations of CortoCD and RPL12 on polytene chromosomes suggest that some deregulated genes were direct targets of Corto and RpL12 . To test this hypothesis and to get insight in the functional interaction between Corto and RPL12 , we focused on hsp70 that was one of the shared upregulated genes ( Figure S4 ) . We analyzed binding of CortoCD and RPL12 by chromatin immunoprecipitation before and after heat shock in wing imaginal discs ( Figure 11 ) . qPCR analyses were performed using a set of primers that cover the promoter and gene body of hsp70 [28] . At 25°C , in control w1118 discs , higher RNAPolII occupancy of the promoter as compared to the gene body suggests that hsp70 was paused , corroborating previous results [28] . In wing imaginal discs overexpressing either cortoCD or RpL12 ( sd::gal4>UAS-FH-CortoCD or sd::gal4>UAS-RpL12-Myc ) , CortoCD and RPL12 bound hsp70 indicating that this gene was a direct target of both proteins . Simultaneously , RNAPolII binding was increased but kept the same profile suggesting that the gene was still paused but more loaded with RNAPolII . This could explain why more transcripts were generated . Thus , these data suggest that Corto , as well as RPL12 , favors recruitment of RNAPolII on hsp70 in absence of heat shock . After a short heat shock ( 5 minutes ) , CortoCD , as well as RPL12 , were massively recruited on hsp70 . Interestingly , CortoCD and RPL12 displayed the same binding profile i . e . increased binding from 5′ to 3′ of the gene body . CortoCD and RPL12 recruitment followed hsp70 transcription as revealed by enhancement of RNAPolII on gene body . Strikingly , recruitment of RNAPolII was higher in wing discs expressing cortoCD or RpL12 than in control wing discs , suggesting that Corto and RPL12 control transcriptional activation of hsp70 .
The ETP Corto is a partner of Polycomb and Trithorax complexes and participates in epigenetic maintenance of gene expression , notably of homeotic genes [15] , [17] . Multiple Corto binding sites on polytene chromosomes as well as pleiotropic phenotypes of corto mutants show that Corto transcriptional targets are numerous and involved in many developmental pathways . The interaction reported here between Corto and RPL12 raises the interesting possibility of a connection between RPs and epigenetic regulation of gene expression . Our previous investigations into Corto partners have highlighted its interaction with several PcG proteins , leading to the conclusion that Corto might regulate PRC1 and PRC2 functions [18] . Strikingly , RPs also co-purify with PRC1 [29] . Moreover , the ETP DSP1 , that binds Corto , directly interacts with RPS11 [30] . Another ETP , ASXL1 , belongs to the repressor complex H1 . 2 that also contains RPs [31] . Presence of RPs in the direct environment of chromatin binding factors , notably ETP , seems then to be a widespread situation . However , the role of RPs in these cases could be related to structure preservation and not to transcriptional regulation per se . Apart from protein synthesis , RPs are involved in many cellular functions referred to as “extra-ribosomal” ( reviewed in [32] ) . The first report on an RP's role in transcriptional regulation came from E . coli where RPS10 is involved in anti-termination of transcription [33] . Many eukaryotic RPs , notably RPL12 , regulate their own transcription , basically by regulating their own splicing ( reviewed in [34] ) . For more than 40 years , many genetic screens to isolate new Polycomb ( PcG ) and trithorax ( trxG ) genes in flies have identified Minute mutants as PcG and trxG modifiers [14] . Indeed , Minute mutations suppress the ectopic sex comb phenotype of Polycomb or polyhomeotic mutants [35] , [36] . D . melanogaster Minute loci are disseminated throughout the genome and many correspond to RP genes ( [37] and references therein ) . Minute mutations might indirectly suppress phenotypes of PcG mutants by lengthening development , thus globally counteracting homeosis . However , Minute mutants can exhibit PcG mutant phenotypes , which is at variance with this assumption . For example , mutants in stubarista that encodes RP40 exhibit transformation of arista into legs [38] . Quasi-systematic presence of RPs at sites of transcription on Drosophila polytene chromosomes [22] as well as direct interaction between several RPs and histone H1 in transcriptional repression [39] , suggest that RPs could actively participate in transcription modulation . Massive recruitment of Corto and RPL12 on hsp70 upon transcriptional activation as well as similarity between their occupancy profiles and the one of RNA polymerase II suggest that these two proteins could travel along the gene body together with the transcriptional machinery . Interestingly , BRM , the catalytic subunit of the SWI/SNF TrxG complex , associates with components of the spliceosome [40] that contains several RPs including RPL12 [41] . Overall , these findings lead us to favor the hypothesis of an active involvement of RPs in regulation of gene expression . Whether individual RPs regulate transcription independently of other RPs or in the context of a ribosome-like complex is an interesting and much debated question ( reviewed in [42] ) . Many data point to a collaborative role of RPs in transcription . In D . melanogaster , at least 20 RPs as well as rRNAs are present at transcription sites on polytene chromosomes , suggesting that they could be components of ribosome-like subunits [22] . Genome-wide ChIP-on-chip analyses of RPL7 , L11 and L25 in S . pombe reveal a striking similarity of their binding sites , suggesting that they might bind chromatin as complexes [43] . Along the same line , mass spectrometry of Corto partners identified not only RPL12 but also RPL7 , L27 , S10 , S11 and S14 , indicating that Corto might interact via RPL12 with several RPs that could form a complex . Interestingly , RPL12 and L7 form a flexible protruding stalk in ribosomes that acts as a recruitment platform for translation factors [44] . Our results might point to the existence of pseudo-ribosomes composed of several RPs on chromatin . The role of RPs in nuclear translation has been very much debated and whether these pseudo-ribosomes are involved in translation is still unknown [45] . However , this possibility seems unlikely in view of the numerous data showing lack of translation factors in nuclei as well as association on chromatin between RPs and both nascent coding and non-coding RNAs [46] . Overall , these data suggest that pseudo-ribosomal complexes composed of various RPs are associated on chromatin and could thus participate in transcriptional regulation . Like histones , RPs are subjected to a plethora of post-translational modifications including ubiquitinylations , phosphorylations , acetylations and methylations ( [47] and references therein ) . We show here that the Corto chromodomain binds RPL12K3me3 . Strikingly , the chromodomain protein CBX1 , a human homolog of Drosophila HP1β , also interacts with RPL12 [48] , suggesting that chromodomain binding to methylated RPL12 might be conserved . It is tempting to speculate about a role for RPL12 methylation in chromodomain protein recruitment to chromatin . This mechanism might be analogous to the one by which histone methylation marks , such as H3K27me3 , recruit the PRC1 complex , i . e . by binding of the Polycomb chromodomain to methyl groups . Under this hypothesis , RPL12K3me3 might recruit Corto to chromatin . In yeast and A . thaliana , RPL12 can be trimethylated on lysine 3 by methyl-transferase SET11/Rkm2 [25] , [47] , [49] . Rkm2 is conserved in Drosophila and abundantly transcribed in S2 cells as well as all along development [50] . It would be interesting to determine whether this enzyme is an RPL12K3 methyl-tranferase in Drosophila . Based on the existence of a panel of ribosomes composed of diverse RPs bearing various post-translational modifications , it was proposed that selective mRNA translation might depend on a ribosome code similar to the histone code [51] . Our results lead us to suggest that such a ribosome code might also concern regulation of gene transcription . Surprisingly , GO analysis of RPL12 and Corto upregulated genes reveals that the “translation” and “structural component of ribosomes” categories are over-represented . Interestingly , the expression of RP genes decreases in RPL12A mutants in yeast [51] . Our finding that over-expression of Drosophila RpL12 increased RP gene expression reinforces the idea that RPL12 can activate RPs at the transcriptional level . Moreover , up-regulation of ribosome related genes is also observed in mutants of ash2 that encodes a TrxG protein , and that genetically interacts with corto [15] , [52] . Hence RPL12 , Corto and chromatin regulators of the TrxG family might all participate in dynamic coordination of ribosome biogenesis thus controlling cell growth . Intriguingly , we have recently shown that Corto interacts with an atypical cyclin , namely Cyclin G that also binds chromatin . This cyclin is suspected to control transcription of many genes , and controls cell growth [17] , [53] , [54] . These combined findings provide new avenues of research concerning transcriptional regulation of tissue growth homeostasis . Global regulation of genes involved in ribosome biogenesis could be a way to maintain this homeostasis . Co-regulation of genes involved in a given function has already been documented in eukaryotes . In Drosophila , housekeeping genes are co-regulated by the NSL complex and , in yeast , RPL12 coordinates transcription of genes involved in phosphate assimilation as well as RP genes [51] , [55] , [56] . As regulation of ribosome biogenesis is essential for cellular health and growth homeostasis [57] , such a transcriptional co-regulation of RP genes might have evolved to insure that the cell's protein synthesis capacity can be rapidly adjusted to changing environmental conditions .
Clones and site-directed mutagenesis are described in Text S1 . Primers are described in Table S12 . D . melanogaster stocks and crosses were kept on standard medium at 25°C . UAS::FH-cortoCD , UAS::FH-cortoΔCD and UAS::RpL12-Myc transgenic lines were established by standard P-element mediated transformation . Over-expression was carried out using Gal4 drivers either ubiquitous [daughterless ( da::Gal4 ) ; Actin5C ( Act::Gal4 ) ] , expressed in salivary glands [escargot ( esg::Gal4 ) ] , or wing-specific [scalloped ( sd::Gal4 ) ] . Five females bearing the Gal4 driver were crossed with three males bearing the UAS transgene or w1118 as a control . Crosses were transferred to new vials every third day . The sd::Gal4 , UAS::FH-cortoCD and UAS::RpL12-Myc lines were isogenized for six rounds with the isogenic w1118 line , prior to deep-sequencing , as described [58] . Lethality was calculated as described [59] . Cytoplasmic and nuclear extracts were prepared from 0–16 h embryos as described in [60] . GST or GST-CortoCD were covalently linked on agarose beads using the GST orientation kit ( Pierce ) following the manufacturer's instructions . 1 mg of protein extract was incubated with 200 µg of purified GST or GST-CortoCD in binding buffer [0 . 5 mM DTT , 0 . 1 mM EDTA , 4 mM MgCl2 , 0 . 05% Igepal , 20 mM Hepes , 300 mM KCl , 10% glycerol , protease inhibitor cocktail ( Roche ) ] for 1 h at 25°C . After 5 washes in binding buffer , bound polypeptides were resolved on a large 15% SDS–polyacrylamide gel and stained either with EZblue ( Sigma ) or with SilverQuest staining kit ( Invitrogen ) . Bands were excised from the gel and were analyzed by LC-MS/MS mass spectrometry . S2 cells were cultured at 25°C in Schneider's Drosophila medium ( Lonza ) supplemented with 10% heat inactivated fetal bovine serum and 100 units . mL−1 of penicillin and streptomycin . Cells were transfected using Effecten ( Qiagen ) as described [61] . Co-immunoprecipitations were performed as described [61] using anti-FLAG ( Sigma F-3165 ) or anti-Myc ( Santa Cruz , sc-40 ) . S2 cells were harvested 24 h after transfection and treated as described [62] . For each transfection , 30 to 60 nuclei were analyzed with an SP5 confocal microscope ( Leica microsystems ) using LAS ( Image Analysis Software ) . GST or GST fusion proteins were dialyzed using a Slide-A-Lyser cassette ( Thermo Scientific ) in running buffer ( 10 mM Hepes pH7 . 4 , 150 mM NaCl , 3 mM EDTA , 0 . 005% P20 surfactant ) ( GE Healthcare ) . Real-time protein interaction assays were performed using a Biacore 3000 . Kinetics and binding tests were first performed on empty surfaces . Data presented here result from substraction of empty surface RU ( 1 to 5 depending on the experiment ) from active surface RU . GST was covalently coupled to a CM5 sensor chip ( GE Healthcare ) via its N-terminal amino acid . The carboxymethylated dextran surface was activated by injecting a mixture of 0 . 2 M 1-ethyl-3- ( 3-dimethylaminopropyl ) and 0 . 05 M N-hydroxysuccinimide . GST was immobilized on the chip by injecting a 30 µg . mL−1 solution in NaAc pH5 buffer . GST-CortoCD and GST-HP1CD were immobilized by injecting a 100 µg . mL−1 solution in the same buffer . Binding tests were performed by injecting peptides at 1 or 10 µM in running buffer at a flow rate of 5 µL . min−1 during 5 min . Kinetic assays were performed only when the binding test was positive . Real-time monitoring was displayed in a sensorgram as the optical response ( RU ) versus time ( s ) . To calculate association constants , peptides were diluted in series from 1 to 10 µM in running buffer and dilutions were injected sequentially at a flow rate of 5 µL . mn−1 during 5 min . Dissociation kinetics were then run during 10 min to calculate dissociation constants . Between assays , the chip was regenerated with 10 mM glycine pH2 . 0 . Kinetic constants were calculated with BIAevaluation Software ( Biacore ) using the Fit kinetic simultaneous ka/kd ( 1∶1 binding; Langmuir algorithm ) . RPL12 peptides were synthesized at the IFR83 Peptide synthesis facility ( Table S13 ) . Histone peptides were provided by Diagenode ( H3K27me3: sp-069-050; H3K4me3: sp-003-050; H3K9me3: sp-056-050; H3K4/K9um: sp-999-050; H3K27um: sp-998-050 ) . Polytene chromosome immunostainings were performed as described [63] for all antigens except RNA Pol II , for which we used experimental conditions described in [64] . Mouse anti-FLAG ( 1∶20 ) ( Sigma , F-3165 ) , mouse anti-Myc ( 1∶20 ) ( Santa Cruz , sc-40 ) , rabbit anti-H3K4me3 ( 1∶40 ) ( Diagenode , pAB-003 ) , rabbit anti-H3K27me3 ( 1∶70 ) ( Diagenode , pAB-069 ) , rabbit anti-RNA Pol II Ser2p ( 1∶200 ) ( Abcam , an5095 ) , rabbit anti-RNA Pol II Ser5p ( 1∶40 ) ( Covance , MMS-134R ) and rabbit anti-Corto ( 1∶30 ) [18] were used as primary antibodies . Secondary antibodies [Alexa Fluor 488 goat anti-rabbit IgG ( Molecular Probes , A-11008 ) , Alexa Fluor 594 goat anti-mouse IgG ( Molecular probes , A-11005 ) and Alexa Fluor 488 goat anti-mouse IgG , IgA and IgM ( Molecular Probes , A-10667 ) ] were used at a 1∶1000 dilution . Wing imaginal discs of third instar female larvae ( one disc per larva ) were dissected by batches of 50 in ice-cold PBS and frozen in liquid nitrogen . 300 discs ( 6 batches ) were pooled and homogenized in lysis buffer using a FastPrep-24 during 20 s at 4 m . s−1 ( MP Biomedicals , Lysing Matrix D ) . Total RNA were extracted using RNeasy kit ( Qiagen ) . Library preparation and Illumina sequencing ( multiplexed 50 bp paired-end sequencing on HiSeq 2000 ) were performed at the BC Cancer Agency Genome Sciences Center ( Canada ) . Messenger ( polyA+ ) RNAs were purified from 4 µg of total RNA with oligo ( dT ) . Libraries were prepared using the bi-directional RNA-Seq library preparation kit ( Illumina ) . A mean of 46±11 million reads was obtained for each of the 4 samples ( w1118 , sd::Gal4/+; sd::Gal4>UAS::cortoCD; sd::Gal4>UAS::RpL12 ) . Detailed informations on Paired-End read counts at each step of the analysis workflow are available in Table S14 . Before mapping , poly N read tails were trimmed , reads ≤11 bases were removed , and reads with quality mean ≤12 were discarded . Reads were then aligned against the D . melanogaster genome ( dm3 genome assembly , BDGP Release 5 . 38 ) using Bowtie mapper ( version 0 . 12 . 7 ) [65] . Alignments from reads matching more than once on the reference genome were removed using Java version of samtools . To compute gene expression , D . melanogaster GFF3 genome annotation from FlyBase ( version 5 . 38 ) was used . All overlapping regions between alignments and referenced exons were counted . Technical replicates coming from paired-end reads were first summed . Then , all samples were normalized together . Data were normalized according to the scaling normalization proposed by Robinson and Oshlack and implemented in the edgeR package version 1 . 6 . 10 [66] . A Fisher's Exact Test was then performed using the sage . test function of the statmod package version 1 . 4 . 6 . Finally , a Benjamini and Hochberg ( BH ) P-value adjustment was made . The RNA-Seq gene expression data and raw fastq files are available at the GEO repository ( www . ncbi . nlm . nih . gov/geo/ ) under accession number: GSE38435 . Wing imaginal discs of third instar larvae were dissected by batches of 100 in serum-free Schneider medium at room temperature . They were fixed in 500 µL of paraformaldehyde 1% in PBS for 10 minutes at room temperature under gentle agitation . Cross-link reaction was stopped by adding 50 µL of glycine 1 . 25 M . Fixed wing discs were washed 3 times with PBS , dried , flash-freezed in liquid nitrogen and stored at −80°C . Cell lysis was performed by adding 100 µL of lysis buffer ( 140 mM NaCl , 10 mM Tris-HCl pH8 . 0 , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate , Roche complete EDTA-free protease inhibitor cocktail ) complemented with 1% SDS , and sonicated in a Bioruptor sonifier ( Diagenode ) . Conditions were established to obtain chromatin fragments from 200 to 1000 bp in length ( 30″ ON 30″ OFF , high power , 15 cycles ) . Pooled chromatin was centrifuged for 10 min at 13000 g at 4°C . The supernatant ( soluble chromatin ) was recovered and 5 µL were kept as input sample . For each IP , 10 µl of 50% ( v/v ) protein A or G coated paramagnetic beads ( Diagenode ) were washed once in lysis buffer , 1 µg of antibody was added , and beads were incubated for 2 h at 4°C on a rotating wheel . After washing , antibody coated beads were resuspended in 450 µL of lysis buffer and 50 µl of chromatin were added . After incubation on a rotating wheel overnight at 4°C , beads were washed at 4°C five times for 10 min each in lysis buffer , once in LiCl buffer ( Tris-HCl 10 mM pH8 , LiCl 0 . 25 M , 0 . 5% NP-40 , 0 . 5% sodium deoxycholate , 1 mM EDTA ) and twice in TE ( 10 mM Tris-HCl , pH 8 . 0 , 1 mM EDTA ) . Immunoprecipitated as well as input DNAs were purified with the IPure kit following the manufacturer's instructions ( Diagenode ) . Elution was performed twice with 35 µl of water . 2 µl of DNA were used per PCR . Real-time PCR data were normalized against Input sample and depicted as percentage of Input ( see Table S12 for primers ) . Antibodies used for chromatin immunoprecipitation were anti-RNA Polymerase II S2p ( Abcam , ab5095 ) , anti-HA tag ( Abcam , ab9110 ) and anti-Myc tag ( Abcam , ab9132 ) . Mouse IgGs were used as a negative control ( Mock , Diagenode ) . Heat shock treatments were performed as previously described [28] . Briefly , wing discs were subjected to instantaneous heat shock by addition of an equal volume of 48°C pre-heated Schneider medium . After keeping tubes at 37°C for 5 minutes , discs were immediately cooled down by addition of 1/3 total volume of 4°C medium .
|
Chromatin , the combination of DNA and histones , strongly impacts transcriptional regulation of genes . This is achieved thanks to various protein complexes that bind chromatin and remodel its structure . These complexes bind specific motifs , also called epigenetic marks , through specific protein domains . Among these domains , chromodomains are well known to bind methylated histones . Investigating the chromodomain of the Drosophila melanogaster chromatin factor Corto , we found that it interacts with methylated ribosomal protein L12 rather than with methylated histones . This is the first time that such an interaction is shown . Moreover , Corto and RPL12 co-localize with active epigenetic marks on polytene chromosomes , suggesting that both are involved in fine-tuning transcription of genes . Our results represent a major breakthrough in the understanding of mechanisms by which ribosomal proteins achieve extra-ribosomal functions such as transcriptional regulation . Genome-wide analysis of larval tissue transcripts reveals that Corto and RPL12 deregulate large sets of common genes , which are enriched in ribosomal protein genes , suggesting that both proteins are implicated in dynamic coordination of ribosome biogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"molecular",
"development",
"genetics",
"gene",
"expression",
"epigenetics",
"biology",
"molecular",
"genetics",
"gene",
"regulation",
"chromatin",
"genetics",
"and",
"genomics",
"dna",
"transcription",
"gene",
"function"
] |
2012
|
New Partners in Regulation of Gene Expression: The Enhancer of Trithorax and Polycomb Corto Interacts with Methylated Ribosomal Protein L12 Via Its Chromodomain
|
ES cells are defined as self-renewing , pluripotent cell lines derived from early embryos . Cultures of ES cells are also characterized by the expression of certain markers thought to represent the pluripotent state . However , despite the widespread expression of key markers such as Oct4 and the appearance of a characteristic undifferentiated morphology , functional ES cells may represent only a small fraction of the cultures grown under self-renewing conditions . Thus phenotypically “undifferentiated” cells may consist of a heterogeneous population of functionally distinct cell types . Here we use a transgenic allele designed to detect low level transcription in the primitive endoderm lineage as a tool to identify an immediate early endoderm-like ES cell state . This reporter employs a tandem array of internal ribosomal entry sites to drive translation of an enhanced Yellow Fluorescent Protein ( Venus ) from the transcript that normally encodes for the early endodermal marker Hex . Expression of this Venus transgene reports on single cells with low Hex transcript levels and reveals the existence of distinct populations of Oct4 positive undifferentiated ES cells . One of these cells types , characterized by both the expression of the Venus transgene and the ES cells marker SSEA-1 ( V+S+ ) , appears to represent an early step in primitive endoderm specification . We show that the fraction of cells present within this state is influenced by factors that both promote and suppress primitive endoderm differentiation , but conditions that support ES cell self-renewal prevent their progression into differentiation and support an equilibrium between this state and at least one other that resembles the Nanog positive inner cell mass of the mammalian blastocysts . Interestingly , while these subpopulations are equivalently and clonally interconvertible under self-renewing conditions , when induced to differentiate both in vivo and in vitro they exhibit different behaviours . Most strikingly when introduced back into morulae or blastocysts , the V+S+ population is not effective at contributing to the epiblast and can contribute to the extra-embryonic visceral and parietal endoderm , while the V−S+ population generates high contribution chimeras . Taken together our data support a model in which ES cell culture has trapped a set of interconvertible cell states reminiscent of the early stages in blastocyst differentiation that may exist only transiently in the early embryo .
ES cells are an in vitro cell line derived from the inner cell mass ( ICM ) of the early mammalian blastocyst [1] , [2] . In mouse they are defined functionally as a karyotypically normal immortal cell line that can give rise to all the future lineages of the conceptus [3] . Thus they can self-renew indefinitely and continually generate progeny with equivalent pluripotent properties . The pluripotent properties of ES cells can be demonstrated by in vitro differentiation or by reintroduction of these cells back into chimeric embryos by blastocyst injection or morula aggregation . ES cells can be described based on a characteristic morphology , the presence of cell surface markers such as SSEA-1 and Pecam1 , or the expression of the key transcription factors such as Oct4 , Sox2 , Nanog , and a number of ES cell-specific transcripts ( ECATs ) [4]–[6] . However , while these markers are useful tools , ES cells can only be defined based on retrospective function . A culture can be said to contain ES cells , if a chimera generated from the injection of these cells contains “ES cell derived , ” somatic , and in particular , germ line tissue . Interestingly , attempts to define the number of founder ES cells in chimera experiments suggest that most somatic tissues are formed from one or two of the 10–15 cells injected into a typical blastocyst [7] . Thus despite indistinguishable morphology and apparent homogenous expression of pluripotent markers such as Oct4 , functional ES cells may represent only a small component of any ES cell culture . Recent observations suggest that there may be lineage-specific markers expressed in sub-populations of ES cell cultures . In particular , the expression of the ICM markers Nanog , Rex1 , and Stella has been shown to be heterogeneous [8]–[12] . Does this heterogeneity define a functional subpopulation of cells in ES cell cultures ? While levels of Nanog can affect the propensity to differentiate , Nanog−/− ES cells are able to contribute to all lineages of the conceptus with the exception of the germ cells [8] . Moreover , all of these studies compare the pluripotent potential of the marked ICM-like population to mixed fractions that are considered a single further differentiated intermediate cell type . Interestingly , while not linked to Nanog , the somite segmentation clock gene Hes1 also displays heterogeneous expression that is related to periodic oscillations and differential rates of differentiation [13] . ES cells are derived from a stage of development in which key early lineage specification events are occurring . ICM cells are formed from the inner cells of the morula as the outer cells form the first extra-embryonic or trophoblast lineage . A day later , at implantation ( 4 . 5 dpc . ) , the ICM then gives rise to two lineages , primitive ectoderm ( PrEc or epiblast ) and primitive endoderm ( PrEn ) . The epiblast is the source of all embryonic tissue and the PrEn the source of both extra-embryonic endoderm lineages , visceral and parietal . Although the visceral endoderm ( VE ) itself does not contribute to the embryo proper , an important early embryonic signalling centre is formed in VE at the embryo's distal tip and these cells will then migrate anteriorly to form the anterior visceral endoderm ( AVE ) [14]–[16] . When injected into host blastocysts , cells derived directly from the ICM of an expanded blastocyst stage can contribute to the PrEn as well as the fetus [17] , [18] . However , cells derived from the early epiblast are only able to contribute to embryonic lineages and not those derived from the PrEn [18]–[20] , while PrEn cells can only contribute to their own lineage by colonizing the visceral and mostly parietal endoderm in chimera experiments [20]–[22] . While ES cells are derived from the ICM , they predominantly contribute to embryonic lineages . This notion , that ES cells can contribute only to the somatic lineages , has been exploited for the study of embryonic versus extra-embryonic phenotypes [14] and is the reason they are defined as pluripotent , rather than totipotent . However , despite this consensus view there is some evidence from blastocyst injection that ES cells can colonize the yolk sac descendants of the PrEn [23] . In vitro , ES cells can generate PrEn-like cells either in response to LIF withdrawal [24] or through forced expression of the transcription factors Gata4 or Gata6 [25] , [26] . ES cell cultures also express low levels of Gata4 and Gata6 , suggesting the presence of either background levels of PrEn gene expression or basal levels of PrEn differentiation [25] , [27] . One of the earliest markers of anterior asymmetry in the AVE is the homeobox transcription factor Hex . While Hex is discretely expressed in the VE on the anterior side of the embryo , it is initially expressed throughout the early PrEn [28] and like the GATA factors , Hex transcripts are also detectable in some ES cell cultures [29] . However , the levels of this transcript are presumably extremely low as they were not detected in fluorescent Hex reporter ES cell lines [30] . Here we explore the significance of this low transcript level and ask what it represents in ES cell culture . We use an ES cell line in which low levels of Hex transcript are visualized based on the expression of the enhanced YFP , Venus coupled to a unique translational amplifier . Using this cell line we show that apparently undifferentiated ES cell cultures consist of at least three cell types defined by this lineage-specific low-level transcription and the expression of the ES cell markers Oct4 and Nanog . Venus positive cells experiencing low-level transcription at the Hex locus , but still expressing the ES cell markers SSEA-1 and Oct4 , show elevated levels of PrEn gene expression and reduced levels of early ICM markers such as Nanog . This early PrEn state does not appear to represent differentiation but rather exists in equilibrium with the Venus negative cell states . Manipulation of either FGF signalling or Nanog expression levels can alter the ratio of cell types present in this state and single Venus positive or negative cells can regenerate this equilibrium with apparently identical kinetics under self-renewing conditions . However , when ES cells are purified based on expression of this Venus allele and the ES cell marker SSEA-1 , and then followed in differentiation either in vivo or in vitro , the two populations of ES cells have very different properties . The Venus negative population contributes efficiently to the epiblast in chimeras and remains in the centre of differentiating embryoid bodies ( EBs ) . The Venus positive population does not efficiently contribute to somatic lineages , appears at the outside of EBs , and has the capacity to colonize the visceral and parietal endoderm in chimeras . Taken together , our data suggest that ES cell culture may represent trapped steady-state equilibrium between immediate early states of differentiation normally present in the early mammalian embryo . This state of equilibrium may exist in vivo for a limited period of time but in vitro is established by the active maintenance of blocks to differentiation in all available lineages and selective cell growth .
To generate a reporter cell line that gives real time read outs of low-level early endodermal gene expression , we introduced a synthetic internal ribosomal entry site ( IRES ) designed to amplify translation upstream of a fluorescent reporter [31] into the first exon of the Hex genomic locus ( Figure 1A ) . This IRES consisted of 10 tandem reiterations of nine base pair elements from the Gtx locus , previously shown to generate synergistic translation of a bicistronic message [32] , driving expression of the enhanced fluorescent protein Venus . The reporter and a LoxP flanked selection cassette was inserted downstream of a tagged Hex cDNA to generate the Hex-IRES-Venus ( HV ) ( Fig . 1A ) targeting vector . The tagged Hex cDNA ensured wild-type levels of Hex expression and contains a sequence for in vivo biotinylation by the BirA ligase . ES cells were targeted and hygromycin resistant clones screened by Southern blot . Three clones were expanded for removal of the selection cassette by transfection with a plasmid expressing the Cre recombinase ( Figure 1B , 1C ) . We confirmed that all three clones had a normal karyotype and contained the modification based on direct sequencing of the region containing the insertion ( Figure S1 and unpublished data ) . To confirm that the expression of the Venus allele reflects endogenous Hex expression [28] , [33] , [34] , we used two HV clones to generate chimeras and examined the sites of high-level Venus expression during embryonic development . As expected , Venus expression was detected in the pharyngeal pouch endoderm , endocardium , inter-somitic vessels , and dorsal aorta ( Figure 1D ) . We also tested the expression of the Venus allele during differentiation of the HV cells towards ES cell derived ADE that normally expresses high levels of Hex . This protocol was established with another Hex reporter line , Hex RedStar ( HexRS ) , and requires 5 d of continuous exposure to the Nodal related TGF-β , activin [30] . Thus we differentiated these cell lines alongside HexRS reporter cells and examined the activin dependence of Venus expression ( Figure S2 ) . We also confirmed that this high level of Venus expression reflected quantitative induction of both endogenous Hex and another anterior endoderm marker Cerberus ( Figure S2 ) . Interestingly , while high levels of fluorescence and the expression of Hex and Cerberus mRNA required activin , low levels of Venus fluorescence were detected in the absence of activin . The detection of this level of Venus expression in the presence of low levels of Hex mRNA suggests that this reporter is indeed extremely sensitive to the low levels of Hex transcript produced in the absence of activin , earlier in differentiation , and in undifferentiated ES cells . The low levels of Hex transcript observed in undifferentiated ES cells ( Figure S2C ) were sufficient to generate a significant Venus positive ( V+ ) sub-population in undifferentiated ES cell cultures grown under standard feeder free conditions . Intriguingly , this population also expresses the ES cell marker , SSEA-1 ( Figure 2A ) . Figure 2A shows that in the presence of the cytokine LIF , the majority of Venus-positive cells ( 70% ) were also SSEA-1 positive ( V+S+ ) , while LIF withdrawal both increased the percentage of the population expressing high levels of the Venus transgene ( mean level of fluorescence increases approximately 2-fold , Figure 2A ) and led to a substantial increase in a second Venus positive population that is SSEA-1 negative ( V+S− ) . Morphologically the majority of V+ cells grown in the presence of LIF appear indistinguishable from their V− counterparts and the level of fluorescence in these morphologically normal V+ cells is substantially lower than that observed in cells that either appear differentiated or have been differentiated in response to LIF withdrawal ( Figure 2B ) . Thus while the majority of the V+ population existing in ES cell cultures are indistinguishable from undifferentiated ES cells , we also observe differentiated cells expressing high levels of the Venus transgene ( arrows in Figure 2B ) that resemble the high-level Venus expressors generated in response to differentiation and that probably represent spontaneous PrEn differentiation . As we were initially surprised by these observations , we asked whether the expression level of Venus RNA was equivalent to that generated by endogenous Hex . Using quantitative PCR , we compared the levels of Hex transcript from the wild-type and transgenic alleles to those from the transgenic allele only . We found that the endogenous Hex was expressed at extremely low levels with the transgene representing between 50% and 75% of this value ( Figure 2C ) . Thus , Hex reporter gene expression appears to faithfully reflect the very low level of endogenous Hex transcript . Since V+ cells were found abundantly in the SSEA-1 positive population , we asked whether this population expressed other markers of the undifferentiated state . Antibody staining for Nanog and Oct4 , imaged alongside YFP/Venus fluorescence , indicated that while the Venus positive cells were also Oct4 positive , they expressed low levels of Nanog ( Figure 3A ) . To further address what the co-expression of these markers represented , we purified populations of cells from ES cell culture based on the expression of the Venus transgene and SSEA-1 by flow cytometry . Quantitative real time PCR based on RNA extracted from both SSEA-1 positive fractions revealed that while Oct4 levels remained constant , the Venus positive fractions from two different clones expressed higher levels of the PrEn markers Gata4 , 6 , Dab2 , Sox7 , and Hnf4α and lower levels of ICM markers such as Nanog , Klf4 , Stella , and Rex1 ( Figure 3B ) . Interestingly , we observed no enrichment of epiblast , neural , or mesodermal markers in the V+S+ fraction ( Figure 3B , bottom panel ) indicating that this fraction likely contained only progenitor cells specific to PrEn differentiation . During pre-implantation development Gata6 expression precedes Pdgfrα in putative PrEn precursors[35] and our V+S+ and V−S+ fractions expressed the same low to non-existent level of this transcript supporting the notion that V+S+ fractions contains early PrEn progenitors . Interestingly we observed approximately a 2-fold change in Nanog transcript levels between the two populations , and thus while the V+S+ cells appear Nanog negative based on antibody staining , they still express some Nanog transcript . To test the notion that this low level of transcription at the Hex locus producing the V+S+ fraction in ES cell culture represented an immediate early state in PrEn differentiation , we examined global differences in gene expression . RNA was isolated from all four fractions ( V−S+; V+S+; V−S−; V+S− ) in two independent clones of HV ES cells and hybridised to NIA Mouse 44K Microarray chips v2 . 3 ( GEO Accession GSE13472 ) [36] . Hierarchical clustering of differentially expressed genes identified in a pair-wise analysis of all four fractions in both clones is shown in Figure 4A . Significant changes in the expression of 2 , 169 genes ( FDR <0 . 05 ) resulted in the identification of three to four expression groups , depending on whether clonal variation is taken into account ( Table S1 ) . The greatest changes in gene expression were seen when the V−S− and V+S− fractions were compared ( Figure 4B ) with over a thousand genes changing in each direction . However , the differences between the two SSEA-1 positive fractions were relatively small , with only 139 non-redundant genes overexpressed and 123 underexpressed ( FDR <0 . 05 , 1 . 5-fold ) . While this group of genes is not large , what became apparent from inspection of the heat map in Figure 4A is that the majority of genes upregulated in the V+S− cells are also marginally upregulated when the V−S+ to V+S+ fractions are compared . The size of this gene set varies somewhat depending on the particular clone , but this trend is particularly obvious when one considers sets of PrEn markers ( Figure 4C and Figure S3 ) . Thus for every PrEn marker examined we found subtle increases in gene expression were detected when the V−S+ and V+S+ fractions were compared and that these then translated into more robust increases in the V+S− fraction . We analyzed overrepresentation of Gene Ontology ( GO ) terms in the non-redundant genes that were overexpressed in the V+S− and V+S+ fractions based on 1 . 5-fold change with a 0 . 05 FDR ( Tables S2 and S3 ) . We found that the V+S+ population expressed sets of genes that fell into major functional categories that were associated with “Cell adhesion” and “Cell migration . ” The V+S− fraction also featured these categories in addition to “Proliferation , ” “Apoptosis , ” and “Cytoskeleton . ” An equally consistent pattern of gene expression is observed in the set of ICM markers ( contained within Group 2 in Figure 4A , Figure 4C , and Figure S3 ) . Most of these genes were significantly down-regulated in both V+ fractions and remain high in the V−S− fraction , indicating that this fraction contained a significant proportion of undifferentiated ES cells . This is consistent with the small number of gene expression changes ( 40 genes ) , with no significant pattern or common G0 annotation , that fluctuate with SSEA-1 when these two populations are compared to each other ( Figure S4 ) . While the majority of pluripotency genes were down-regulated in both V+ populations , there were some exceptions , including Oct4 and a class of differentiation inhibitors normally regulated by BMP4 including Id1 , Id2 , and Id3 [37] . Oct4 was expressed through the V−S+ , V+S+ , V−S− fractions and down-regulated in V+S− , while the Id transcripts appeared to follow the PrEn genes , suggesting that they function to block neural differentiation in an early endoderm sub-population . To confirm that early differentiation pattern exhibited in the V+S+ fraction was indeed an early state in PrEn differentiation , rather than a metastable pro-differentiation state similar to that described for the Oct4 positive populations that do not express Nanog , Rex1 , or Stella [8]–[11] , we examined the behaviour of gene sets representing other lineages in our data set ( Figure 3C and Figure S3 ) . Neither neuroectoderm nor mesodermal genes were upregulated in V+S+ fraction . As Nanog is rarely expressed in the Venus positive cells , we asked whether enforced Nanog expression would suppress baseline transcription at the Hex locus and thereby reduce expression of the Venus reporter . Nanog was misexpressed in HV ES cells under control of the CAG promoter driving an IRES puro cassette [38] . Western blotting showed increased levels of Nanog in 2 clones compared to parental and control cells ( Figure 5A ) . As overexpression of Nanog in ES cells supports LIF independent growth [6] , [38] , we confirmed Nanog overexpression in the HV line by observing the persistence of ES cells following 10 d culture in the absence of LIF ( Figure 5B ) . Nanog overexpressing HV cells were grown in the presence of LIF and the fraction of these cultures that expressed the amplified Venus transgene quantitated by flow cytometry . In two independent clones we observed a dramatic reduction in V+S+ population ( 3–6-fold , Figure 5C ) , suggesting that Nanog can regulate low transcription at the Hex locus . The ability of Nanog to suppress early Hex positive endoderm states is consistent with both the mutually exclusive nature of Nanog and Gata6 expression in vivo [17] and the ability of Nanog to suppress Gata6 positive PrEn differentiation , in vitro [39] . The shift between a Nanog positive ICM-like state and Gata6 positive PrEn is also regulated through FGF signalling via the Grb2/Mek pathway [17] , [40] . As the V+S+ population appeared to be an immediate early state of PrEn differentiation in which extremely low levels of PrEn determinants ( e . g . Hex ) are expressed , we wanted to ask whether FGF signalling promoted this state or acted to push cells already in this state further into differentiation . Thus we examined whether FGF signalling could alter the dynamics between the V+ and V− states within the S+ population by culturing HV cells in the presence of the FGFR inhibitor PD173074 [41] for 48 h . As expected , treatment of HV cultures with PD173034 suppresses background levels of PrEn differentiation at the level of Gata6 and Nanog transcription ( Figure 6A ) . However , the inclusion of PD173034 in these cultures also reduced the size of V+S+ fraction ( Figure 6B ) . In addition to feeder free serum and LIF containing media , ES cells can be cultured in minimal serum free media ( referred to as 2i ) containing the MEK inhibitor PD0325901 that targets the phospho ERK branch of the FGF pathway and the GSK3-β antagonist CHIR99021 [42] . When maintained in 2i culture , cells are grown under constant blockade to phospho-Erk signalling . As expected the culture of HV cells under these conditions resulted in a significant reduction in the V+S+ population ( Figure 6B ) . Thus induction of a robust V+S+ state of low-level PrEn transcription requires FGF signalling . However , while the expression of the Venus transgene is greatly reduced in 2i , it is still present ( Figure 6B , 6D ) . Moreover , while antibody staining and microscopy of ES cell colonies grown in 2i showed uniform morphology , no detectable Gata6 expression and reduced Nanog heterogeneity , Venus positive cells were visible within these colonies and this Venus positive expression was rarely found within cells expressing high levels of Nanog ( Figure 6D ) . While expression of the Nanog protein in the V+S+ fraction appears largely reduced or absent , we have been unable to detect differences between 2i generated V+S+ and V−S+ cells by RT-PCR ( unpublished data ) . This is not surprising as the amplified transgene was already detecting very low transcript levels in serum and the levels of Venus expression in 2i were 2–3-fold lower . We confirmed the ability of Fgf signalling to regulate the V+S+ population by treating suspension cultures with the phosphatase inhibitor sodium vanadate to stimulate the FGF/Grb2/Mek pathway . Treatment of cell aggregates with sodium vanadate in the presence of LIF has been shown to repress Nanog and stimulate PrEn differentiation [40] . Thus when HV cells were cultured under these conditions , the addition of sodium vanadate suppressed Nanog expression , lead to a significant increase in Gata6 ( Figure 6B ) , and produced a 25% increase in the percentage of the culture that was V+S+ ( Figure 6B ) . These observations appear specific for early PrEn , as treatment of Sox1-GFP cells with either PD173034 or sodium vanadate had little effect on GFP expression ( unpublished data ) . Taken together these data support the notion that low-level transcription at PrEn promoters such as Hex is dependent on signalling via the FGF/Grb2/Mek pathway . Interestingly when ES cells were fractionated based on the Venus transgene , the V+S+ cells contained almost all detectable phospho-Erk activity ( Figure 6C ) . Heterogeneous ES cell states have been observed with respect to Nanog , and while the Nanog expression state appears reversible , there are significant differences in the ability of Nanog positive and negative cells to clonally reconstitute each other in vitro [8] . Thus we asked whether the V+S+ population and V−S+ could efficiently interconvert . To test this we plated cells sorted by flow cytometry clonally and assessed the extent to which colonies could re-establish steady-state equilibrium . While the plating efficiency of the V+S+ fraction was reduced and produced 4-fold less colonies than the V−S+ fraction , both fractions gave rise to identical colonies that contain equivalent populations of V+ and V− cells ( Figure 7A , Table 1 ) . Thus , while there appears a difference in the colony forming potential of the two fractions , once colony formation is initiated , the two cell types are identical in their ability to give rise to each other . To determine the length of time required for the two states to interconvert we purified populations V+S+ and V−S+ cells and examined the extent to which the original distribution was re-established and observed significant changes in both populations within 24 h of plating ( Figure 7B ) . To further test the notion that V+ and V− cells were both equally capable of clonally regenerating the equilibrium normally present in ES cell cultures , we deposited single cells in 96 well plates following sorting by flow cytometry . Consistent with our previous observations , single V+S+ and V−S+ cells were equivalent in their ability to regenerate normal Venus distribution upon expansion in 30 independent clonal cultures ( Figure 7C ) . In this instance we did not detect a plating difference in the populations and approximately 16% of the deposited cells survived to give rise to day 10 cultures ( unpublished data ) . Taken together these data support the notion that the V+S+ fraction represents an early state of PrEn differentiation that exists in equilibrium with other cell states present in ES cell cultures . The ability of these populations to interconvert in vitro combined with their subtle differences in gene expression lead us to ask if there was any functional significance to this low level of PrEn gene expression . As ES cells are defined based on their ability to contribute to all tissues of the future conceptus in chimeras , we asked whether the embryo contribution activity of ES cells was contained in either V+S+ or V−S+ fraction or both . Initially we injected purified fractions of HV ES cells into Rosa26 blastocysts that constitutively express β-galactosidase ( β-gal ) and examined embryos at 9 . 5 dpc for ES cell ( β-gal negative ) contribution ( Table S4 and Figure S5 ) . In these experiments the Venus positive fraction never gave rise to high-contribution chimeras and less than half of the injected embryos showed any contribution whatsoever . This contrasted starkly with the Venus negative fraction , which contained cells that were effective at generating high-contribution chimeras . Thus the modest changes in gene expression that accompany basal level PrEn expression interfere with the capacity of these cells to actively contribute to blastocysts . The loss in ability to contribute to blastocysts generated in this transient PrEn-like state was interesting , but we wanted to establish if these cells had gained new properties . To ascertain this we generated cell lines that both contained the HV cassette and constitutively expressed β-gal as a lineage label . We used this cell line for morula aggregation and obtained the chimeric embryos shown in Figure 8A . These results validate our observations obtained with blastocyst injection and indicate that the V−S+ fraction is particularly effective at contributing to the epiblast ( Table 2 ) . Interestingly , while the V+S+ cells did not effectively contribute to the epiblast , V+S+ ES cells were found in both the visceral and parietal endoderm ( Figure 8A , Table 2 ) , suggesting that their reduced ability to contribute to the epiblast may reflect a change in potency . To confirm this observation by another method we asked about the potency of these fractions to differentiate in EB aggregates . However , while V−S+ cells generated normal EBs , the V+S+ cells formed small irregular aggregates ( Figure 8B ) , suggesting that the adhesive properties of the cells within these fractions were different . This would not be surprising as early PrEn delaminates from the ICM during the transition between ICM and epiblast and this cell sorting behaviour is reproduced in EB culture where the VE is always found on the outside . Thus when Xen ( extra-embryonic endoderm ) cells are mixed with ES cells , the Xen cells segregate to end up on the outside layer [43] of the EB . In a similar way , we used HV lacZ ES cells to ask whether the V+S+ fraction would preferentially segregate to the outside of chimeric EBs . Figure 8C shows that this is indeed the case . Labelled fractions of V+S+ cells ended up on the outside of chimeric EBs , while the reciprocal fraction of V−S+ populated the centre of the aggregate . We then stained these EBs with three antibodies to the endoderm markers Gata6 , FoxA2 , and Sox17 to confirm that these outside cells were endoderm and indeed all three markers were expressed throughout the outside layer ( Figure 8D ) . Taken together our data support the notion that the reversible and immediate early PrEn state marked by low-level transcription at the Hex locus is biased towards the formation of extra-embryonic endoderm .
In this paper we have used translational amplification to detect an immediate early and reversible state in PrEn differentiation that appears an inherent component of standard ES cell culture . The existence of ES cell precursors to this lineage is supported by the observed heterogeneous expression of other PrEn genes , Lefty1 , Cerl , and Gata6 in the ICM of blastocyst stage embryos , the stage from which ES cells are derived [17] , [44]–[46] . Cells in this ES cell state express low levels of PrEn markers such as Hex and maintain expression of some standard ES cell markers such as Oct4 and SSEA-1 . These cells can be isolated based on the expression of an amplified Hex Venus transgene and SSEA-1 ( V+S+ ) and exist under ES cell conditions in a steady-state equilibrium with at least one other more ICM-like cell state , V−S+ . When purified V+S+ or V−S+ cells are placed back into self-renewing conditions , individual cells from purified fractions of either cell type regenerate their counterparts . However , when these fractions are placed into differentiation either in vivo or in vitro , the V+S+ population tends to colonize the PrEn lineages , while V−S+ cells tend towards epiblast . A number of recent studies have suggested that ES cell cultures are heterogeneous and can be split into two developmental states , one that resembles the ICM and the other early epiblast or PrEc . Thus it has been suggested that ES cell cultures can be split based on Rex1 and Oct4 [9] , into Rex1 , Oct4 positive ICM , and Rex1 negative Oct4 positive PrEc . Similar observations have also been made with an ICM-specific , Stella-GFP reporter [10] that can be used to split ES cell cultures into Stella positive ICM-like and Stella-negative epiblast-like . In both instances , the ICM state appears to express higher levels of Nanog and this observation is consistent with the heterogeneous expression of Nanog reporter ES cells [8] , [12] . Elevated levels of Nanog are also associated with a reduced probability of differentiation leading to the suggestion that ES cells exist in equilibrium between a stable self-renewing , ICM-like state referred to as the “ground state” and a transient metastable intermediate that is both able to revert to the self-renewing state or proceed into differentiation [8] , [11] , [47] . The transition between the ground state and this metastable pro-differentiation intermediate is thought to be regulated by FGF/Erk signalling [47] , [48] . While our data do not provide insight into the dynamics of the entire Nanog low population , it suggests that a sub-fraction of low Nanog cells represents PrEn precursors , in addition to the already characterized PrEc precursor population . Moreover , in PrEn precursors , the Nanog low population can itself be split based on the expression of Oct4 or SSEA-1 into a state expressing reasonably high level of PrEn genes ( V+S− ) , and a less differentiated cell type exhibiting a PrEn bias , but with similar regenerative capacities to the Nanog high population ( V+S+ ) . We believe that a similar early precursor may exist to the PrEc lineage ( Figure 9 ) , and while we have no direct evidence for this , we did observe Oct4 positive cells that neither expressed Nanog nor the Venus transgene and there also appears a slight enrichment of early neural markers in the V−S+ population ( Figure 4 ) . However , we were not able to discern this state based on SSEA-1 expression , as a number of both ICM and PrEc markers are expressed at equivalent levels in the V−S+ and V−S− fractions . Thus while SSEA-1 may be an effective marker for undifferentiated cells when used in combination with a PrEn marker , its utility may be limited to this lineage . In addition to expressing slightly increased PrEn gene expression , V+S+ cells also contain almost all the phospho-ERK activity in our ES cell cultures ( Figure 6 ) . As this population does not express elevated levels of transcripts specific to other lineages , it suggests that FGF signalling does not promote the formation of a general metastable pro-differentiation state but rather supports the formation of the V+S+ reversible PrEn intermediate . How then do we explain the requirement for FGF/Erk signalling in ES cell differentiation towards other lineages [48] , [49] ? One possibility is that V+S+ cells produce additional factors required for these lineages . The notion that a Nanog positive , ICM-like population of high probability self-renewing cells is a developmental ground state is supported by the expansion of this state in the presence of a blockade on the major signalling pathways known to promote ES cell differentiation , the MAP kinase/ERK cascade and GSK3β [42] , [50] . Thus when extrinsic inputs are reduced , ES cells revert homogenously to this Nanog positive ground state . Interestingly , while these 2i conditions reduced the extent of the Venus positive population in steady-state culture , it remains a significant component of ES cell culture and exclusive of high Nanog expression . We also observed that single cells from either the V+S+ or V−S+ fractions were both equally effective at generating clonal cultures with the normal range of Venus expression and in no cases did V+S+ cells give rise to differentiated colonies . As a result we conclude that both fractions are equivalent with respect to their capacity for ES cell self-renewal and V+S+ cells do not constitute a metastable early state in differentiation but rather an integral uncommitted component of ES cell culture . In the model shown in Figure 9 , we suggest that a similar uncommitted and self-renewing state may exist in the direction of ectodermal differentiation and we imagine the ground state could consist of at least three distinct populations in equilibrium . These cell states would all appear as morphologically undifferentiated and express equivalent levels of Oct4 . Based on the equivalent regenerative capacity of V+S+ and V−S+ cells , the small number of significant gene expression changes , and the identical morphology , we assume that these two cell states have not drifted significantly apart . Rather these states may represent distinct reversible transcriptional signatures affecting key lineage regulators . Comparison of the differences in gene expression between the V+S+ and V−S+ fractions supports this idea . Every PrEn marker present in our data set increased in the differentiated V+S− cells and importantly showed small but consistent increases when the V+S+ fraction was compared to V−S+ cells . As a result we believe that ES cells in culture consist of a mixture of early self-renewing precursors that can alternatively express low-level transcription of different lineage-specific promoters related to the states surrounding the early blastocyst ( Figure 9 ) . Whether the ICM-like state is central to this equilibrium remains to be seen . The model in Figure 9 represents a stable dynamic system in which the transcriptional state of individual cells shifts , but only within the boundaries defined in red . This suggests that the behaviour of transcriptional networks downstream of Nanog , FGF signalling , and other key ES cell regulators produce an attractor or attractor states occupied by these cell types . The existence of multiple sub-states within a single ES cell basin of attraction or multiple interrelated attractors representing distinct lineages could account for pluripotency . Similar dynamic models have been extensively discussed as a means to explain stem or progenitor cell potency ( reviewed in [51] , [52] ) . In these models , the capacity of a progenitor cell to differentiate into multiple lineages is determined by a form of “multi-lineage priming” [53] , in which cells fluctuate through the early states of multiple lineage programs but remain within a stable basin of attraction . When the culture is removed from the constraints of self-renewal , lineage primed states drive commitment to a direction of differentiation based on the location of a cell in a specific state or attractor . In ES cells , early V+S+ PrEn would become extra-embryonic endoderm and early PrEc would become epiblast . However , when maintained in ES cell culture , cells transit between these states . One possible mechanism for the movement of cells from one state to another would be the combination of stochastic changes in low-level gene expression or noise , combined with positive feedback loops . Indeed this sort of model has been used to explain the existence of a stable attractor and associated lineage primed states in EML cells , a haematopoietic progenitor cell line [54] , and as the basis for heterogeneity in Nanog expression in ES cells [11] . However , both these cases consider the ability of stochastic variation to drive the formation of a single stable attractor . While the small changes in lineage transcription observed in our data set would be consistent with a stochastic model , the ES cell model described in Figure 9 would require both cross-repression and additional positive feedback loops to drive these random changes in gene expression down multiple distinct routes . An alternative mechanism that might explain the ability of cells to transit between multiple states is oscillating gene expression . It was recently suggested that Hes1 expression can cycle in ES cell culture [13] , although the link between this oscillation , low-level gene expression , and developmental bias is not clear . Regardless of whether the gene expression changes are deterministic or random , feedback between cell types may help to stabilize this heterogeneous culture system . The existence of a paracrine inter-dependent equilibrium would suggest that the culture conditions have selected for the stable coexistence of mutually dependent and metastable cell types that only transiently exist in vivo . Our observation that the V+S+ fraction preferentially contributes to the VE when mixed with more ICM-like cells indicates that low-level lineage-specific changes in gene expression have functional consequences . That we have observed a direct contribution of ES cells to both visceral and parietal endoderm also has implications for canonical definitions of pluripotency . Pluripotency is defined based on the ability of ES cells to contribute to the embryonic but not extra embryonic lineages and our observations suggest this definition may need to be somewhat modified . Alternatively it might be more appropriate to consider ES cells as closer to totipotent , but that the pluripotent ICM fraction of ES cell cultures has a competitive advantage when tested in chimera generation . In support of this idea , Beddington and Robertson originally observed ES cell contribution to all the extra-embryonic lineages , but in particular to parietal endoderm [23] . However , these observations have been seen as the exception rather than the norm because of the low-level contribution observed . As the principle significant gene expression changes observed in the V+S+ fraction are related to adhesion and migration ( Table S2 ) , this might explain the decreased capacity of these cells to incorporate into a host ICM and instead colonize the extra-embryonic endoderm . The lower level of endodermal contribution we observe in chimeras suggests that even in the PrEn , V+S+ ES cells may be at a proliferative disadvantage . The observation that some ES cells retain the capacity to contribute to the extra-embryonic lineages begins to resolve a number of conflicting observations . Why should ES cells be able to generate PrEn in vitro but not in vivo ? Moreover , as it has recently been shown that VE can contribute to the embryonic gut [55] , the distinction between visceral and definitive endoderm begins to blur and the inability of ES cells to contribute to the VE becomes more puzzling . Chazaud et al . observed that heterogeneous expression of Nanog and Gata6 in early blastocysts was dependent on Grb2-MAPK signalling and suggested that the reason that ES cells are unable to colonize the PrEn meant they had lost the capacity to respond to this signal [56] . Our observations reconcile these apparent discrepancies . ES cells exhibit the same heterogeneity as the early blastocyst and respond to the same signalling pathways . They have the capacity to contribute to both epiblast and PrEn lineages in vivo and in vitro , but when mixed populations of ES cells are combined with embryonic ICM in a situation where a limited number of cells can be accommodated , a competition ensues that is regulated by a combination of differential adhesion and proliferation . That we observe cell sorting in EB culture also provides direct evidence , albeit in vitro , for the differential adhesion model proposed for the resolution of early PrEn and PrEc in the mammalian blastocyst in this same paper [56] . That this occurs once cells enter differentiation , is consistent with a requirement for sustained FGF signalling for commitment and segregation of the PrEn lineage in cultured blastocysts [57] . The capacity of V+S+ cells to colonize the exterior of EBs and extra-embryonic endoderm in chimeras is similar to the properties of extra-embryonic endoderm ( Xen ) cells derived from the mammalian blastocyst [43] . Xen cells are more parietal than visceral in character , whereas our cells expressed more anterior visceral or early PrEn markers . However , we have not attempted to culture the more endodermal V+S− cells and it will be interesting to see if these cells can be expanded in vitro . Whether they can retain their visceral or primitive qualities in absence of a more epiblast-like population remains to be seen . Interestingly when parietal endoderm is grafted next to epiblast , it becomes visceral and when VE is removed from epiblast it becomes parietal [58] . We recently performed a genome wide screen looking for Hex targets in ES cells and found a number of genes with ICM expression patterns [59] , consistent with the notion that as Hex levels build up it would repress ICM identity and promote commitment to the PrEn lineage . As these targets appeared conserved in evolution , it would seem likely that they are not specific to ES cells and that the same low-level expression states might exist for a limited window of time in vivo . Recent time lapse studies of pre-implantation development suggest that cells that are initially Pdgfα PrEn can revert to ICM [35] , indicating that at least some reversible sampling of these low-level transcription states might occur in vivo . Although Pdgfrα appears downstream of the fluorescent signal observed here , the dynamic nature of cell fate specification appears similar . In ES cells these events would have been amplified , as potential developmental intermediates have been trapped and are maintained in a stable dynamic equilibrium . In this way embryo-derived stem cell lines and ES cell differentiation may be providing access to potential “transition states , ” required for lineage specification in vivo .
ES cells were cultured on 0 . 1% gelatin-coated flasks or plates ( IWAKI ) in Glasgow modified Eagle's medium ( Gibco ) containing non-essential amino-acids , glutamine and sodium pyruvate , 0 . 1 mM mercaptoethanol , and 10% Fetal Calf Serum ( FCS ) together with LIF [30] , [60]–[63] . ES cells were differentiated toward ADE in aggregation culture according to [30] . Differentiation towards PrEn in the presence of sodium vanadate is as described in [40] . LIF withdrawal in monolayer culture was done according to [25] . The 5′ and 3′ arms used for homologous recombination were described by Martinez Barbera et al . [33] with AscI and PacI sites inserted downstream of the Hex ATG ( a gift from Shankar Shrinivas ) . A Hex cDNA with a recognition sequence for bacterial BirA ligase was linked via an artificial IRES consisting of a tandem array of repeated Gtx sequences to the gene encoding Venus followed by a cytomegalovirus driven hygromycin-thymidine kinase dual selection cassette flanked by loxP sites . This entire cassette was fused in frame with the ATG of Hex in the targeting vector . Following electroporation into R26 BirA cells , a cell line that expresses bacterial BirA ligase from the ROSA26 locus , hygromycin resistant clones ( 200 µg/ml ) were expanded for Southern analysis to identify correct targeting events . The selection cassette was then excised from two clones , HV 5 and HV 16 , from which Gancyclovir resistant clones were selected for further analysis . HV cells overexpressing Nanog were generated by electroporation with a vector containing the Nanog cDNA under the control of a CAG promoter and upstream of IRES Puro cassette followed by selection in puromycin ( 2 µg/ml ) for 2 wk . HV cells constitutively expressing the LacZ gene were generated by electroporation with a vector containing a CAG driven β-Geo cDNA followed by selection in G418 ( 150 µg/ml ) for 2 wk . Cells grown in 12 well plates were washed 2× in PBS before fixation in 4% paraformaldehyde . Cells were then permeabilised in PBST ( 1× PBS , 0 . 1% Triton X ( Sigma ) ) . Blocking was performed by adding 1% Bovine serum albumin ( Sigma ) in PBST solution to the fixed cells for 30 min at room temperature ( rt ) . Primary antibodies were added at a dilution of 1∶1000 , and incubation continued overnight ( o/n ) at 4°C . Following 3×10 min washes in PBST , Alexa568 conjugated secondary antibodies diluted ( 1∶1000 ) in blocking solution were added to the cells and incubation took place at rt for 1 h . Also included at this step was DAPI solution ( 1∶1000 ) . Finally , cells were washed 3 times , then stored in PBS . Primary antibodies used were mouse anti-Oct3/4 ( Santa Cruz ) and rabbit anti-Nanog peptide specific antibodies ( a gift from Ian Chambers ) [8] . Secondary conjugated antibodies ( Alexa568 ) against mouse and rabbit were obtained from Invitrogen . ES cells or EBs were collected into Cell Dissociation Buffer ( Gibco ) and incubated at 37°C for 10 min . Single cells suspension was achieved by gentle repeated pipetting . Following washes in PBS , cells were resuspended in 500 µl FACs buffer ( 1× PBS , 10% FCS ) and 7AAD solution ( BD Pharmingen , 5 µl/1×106 cells ) to exclude dead cells . Analysis of fluorescence took place in a FACSCalibur flow cytometer ( BD Biosciences ) . Dotplots were generated using CellQuest software ( BD Biosciences ) . In the case of additional labelling of specific cell surface proteins , primary antibodies were added at a dilution of 1∶1000 to cells resuspended in FACs buffer . Incubation took place for 10 min on ice . Following three washes in FACs buffer , cells were resuspended in fresh FACs buffer containing appropriate conjugated antibody at a dilution of 1∶1000 and incubated as before . After three washes in FACs buffer , cells were finally resuspended in 500 µl FACs buffer and analysed as above . For collection of populations , cells were prepared as above and subjected to flow cytometry using the MoFlo MLS high speed sorting apparatus ( DakoCytomation ) . Cells were collected in FACs buffer and stored on ice for further analysis . Chimera mouse generation was performed by morula aggregation with or injection of ES cells into host blastocysts . Injected or aggregated blastocysts were then transferred into pseudopregnant recipient mothers . Embryos were dissected at the stages indicated in the figures and imaged by fluorescent and conventional microscopy . X-gal staining of embryos and EBs was performed as follows . Embryos and EBs were washed in PBS solution ( 80 mM sodium phosphate , 15 mM potassium phosphate , 27 mM KCl , and 1 . 37 M NaCl ) , then fixed with X-gal fix solution ( 1× PBS , 2 mM MgCl2 , 5 mM EGTA , 1% paraformaldehyde , 0 . 2% Glutaradehyde , 0 . 02% NP-40 ) at 4°C for 20 min . Following 3×20 min washes in PBS they were then stained with X-gal staining solution ( 5 mM potassium ferricyanide , 5 mM potassium ferrocyanide , 2 mM MgCI , 0 . 01% sodium deoxycholate , 0 . 02% Nonidet P-40 ( NP-40 ) in PBS ) o/n in the dark at rt . Following 3×5 min washes in PBS , stained embryos or EBs were then fixed in 4% paraformaldehyde . X-gal stained , paraformaldehyde fixed embryos were embedded in paraffin wax and sectioned transversely in a microtome at 7 micron intervals . X-gal stained or unstained EB or embryos were also cryosectioned . Samples were sunk in 30% sucrose in PBS , frozen in Tissue Teck , and sections were cut on a Cryostat ( Leica ) . Sections were collected on poly lysine microscope slides ( VWR International ) , air-dried for 30 min to 1 h , and stored at −20°C until used . Immunocytochemistry was performed essentially as described above for cells . RNA was extracted from different cell populations using Trizol™ ( Invitrogen ) and precipitated with isopropanol . Biological and technical replicates for each population were hybridised to NIA Mouse 44K Microarray v2 . 3 ( whole genome 60 mer oligonucleotide probe; manufactured by Agilent Technologies , #014951 ) [36] . Fluorescently labelled microarray targets were prepared from 2 . 5¼ µg aliquots of total RNA samples using a Low RNA Input Fluorescent Linear Amplification Kit ( Agilent ) . A reference target ( Cy5-CTP-labeled ) was produced from Stratagene Universal Mouse Reference RNA ( UMR ) , and all other targets were labelled with Cy3-CTP . Targets were purified using an RNeasy Mini Kit ( Qiagen ) according to the manufacturer's protocol and quantified on a NanoDrop scanning spectrophotometer ( NanoDrop Technologies ) . All hybridizations were carried out by combining a Cy3-CTP-labeled experimental target and a Cy5-CTP-labeled UMR target . Microarrays were hybridized and washed according to Agilent protocol ( G4140-90030; Agilent 60 mer oligonucleotide microarray processing protocol—SSC Wash , v1 . 0 ) . Slides were scanned on an Agilent DNA Microarray Scanner , using standard settings , including automatic PMT adjustment . Pairwise comparisons were performed using standard statistical conditions ( FDR <0 . 05 , >1 . 5-fold expression levels ) to unveil genes up-regulated or down-regulated between the populations . Log intensity plots for each gene were created to find pattern matches between those of similar tissue origin .
|
Embryonic stem ( ES ) cells are karyotypically normal , embryo-derived cell lines that are pluripotent , i . e . capable of generating all the cell types of the future organism , but not the extra-embryonic lineages . What gives ES cells this unique capacity ? Here , we use a fluorescent reporter cell line that employs translational amplification to visualize single ES cells expressing low levels of lineage-specific genes . With this reporter we split ES cell cultures into two fractions that both express certain stem cell markers but only one of which expresses low levels of an endodermal marker gene . Following purification , single cells from either fraction are equally competent to re-establish a heterogeneous culture . However , when challenged to differentiate immediately after purification , each exhibits strong lineage bias , with the endoderm marker-expressing fraction unexpectedly able to contribute to the extra-embryonic endoderm in chimeric embryos . These data suggest that ES cells expand under steady-state conditions as a heterogeneous mix of lineage-biased—but not lineage-committed—cell types . We propose that these observed uncommitted substates exist temporarily in vivo , but are perpetuated in vitro under the selectively self-renewing conditions of ES cell culture . Our findings suggest that pluripotency is determined by the capacity of a mixed population of lineage-biased intermediates to commit to different cell fates in specific contexts .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"developmental",
"biology/stem",
"cells",
"cell",
"biology/cell",
"signaling",
"genetics",
"and",
"genomics/gene",
"expression",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"developmental",
"biology/cell",
"differentiation",
"cell",
"biology/gene",
"expression"
] |
2010
|
Functional Heterogeneity of Embryonic Stem Cells Revealed through Translational Amplification of an Early Endodermal Transcript
|
Superoxide dismutase-1 ( SOD1 ) maturation comprises a string of posttranslational modifications which transform the nascent peptide into a stable and active enzyme . The successive folding , metal ion binding , and disulphide acquisition steps in this pathway can be catalysed through a direct interaction with the copper chaperone for SOD1 ( CCS ) . This process confers enzymatic activity and reduces access to noncanonical , aggregation-prone states . Here , we present the functional mechanisms of human copper chaperone for SOD1 ( hCCS ) –catalysed SOD1 activation based on crystal structures of reaction precursors , intermediates , and products . Molecular recognition of immature SOD1 by hCCS is driven by several interface interactions , which provide an extended surface upon which SOD1 folds . Induced-fit complexation is reliant on the structural plasticity of the immature SOD1 disulphide sub-loop , a characteristic which contributes to misfolding and aggregation in neurodegenerative disease . Complexation specifically stabilises the SOD1 disulphide sub-loop , priming it and the active site for copper transfer , while delaying disulphide formation and complex dissociation . Critically , a single destabilising amino acid substitution within the hCCS interface reduces hCCS homodimer affinity , creating a pool of hCCS available to interact with immature SOD1 . hCCS substrate specificity , segregation between solvent and biological membranes , and interaction transience are direct results of this substitution . In this way , hCCS-catalysed SOD1 maturation is finessed to minimise copper wastage and reduce production of potentially toxic SOD1 species .
Copper binding and disulphide bond formation are strongly discouraged in the eukaryotic cytoplasm despite widespread use within extracellular spaces and organelles . In the latter case , the preponderance of reduced glutathione and the thioredoxin system almost completely prevent their existence . In the former case , copper concentration is minimised to prevent adventitious binding or toxic chemistry [1] . However , production of superoxide dismutase-1 ( SOD1 ) requires sequential copper and intra-subunit disulphide bond acquisition by a zinc-loaded precursor [2 , 3] . SOD1 folding , zinc and copper acquisition , and disulphide bond formation can be catalysed by the copper chaperone for SOD1 ( CCS ) , which forms a transient heterodimeric complex with SOD1 to facilitate its maturation [4–8] . CCS is thought to retrieve copper primarily from membrane-bound sources , including direct transfer from copper transporter-1 ( Ctr1 ) or indirectly through Atox1 and glutathione [9–12] . It then interacts with a pool of pre-existing SOD1 and selects the zinc-metalated , disulphide-reduced SOD1 substrate from at least 16 possible other states [5 , 13] . The molecular recognition event that dictates CCS specificity is a fulcrum point for the efficient management of intracellular copper , maintenance of an adequate antioxidant response , and redox signalling but also helps to avoid accumulation of aggregation-prone immature human SOD1 ( hSOD1 ) [14 , 15] ( S1A Fig ) . Indeed , the human copper chaperone for SOD1 ( hCCS ) activates at least 80% of hSOD1 molecules [16] . The harmful effects of incomplete hSOD1 maturation are clearly seen in the motor system diseases amyotrophic lateral sclerosis ( ALS ) and possibly Parkinson disease [17 , 18] . Typically of neurodegenerative disease , reduced stability of hSOD1 potentiates formation of toxic oligomers [19] , aggregation [20] , and irretrievable sequestration into distinct cytoplasmic compartments [21 , 22] . Once posttranslation modification ( PTM ) transfer processes are complete , hCCS must disengage from hSOD1 . Transience of the interaction is paramount , as hSOD1 must homodimerise for full activity and stability [23] , but it is not clear how that ephemerality has been engineered into the system . Here , we describe several structures of the hSOD1 activating complex crystallised by inhibiting complex dissociation and aggregation through discerning mutagenesis of cysteines involved in normal and aberrant disulphide bond formation . Combinations of mutants yielded crystallographic structures of full-length hCCS in two different conformers complexed with hSOD1; an hCCS domain II truncation complexed with hSOD1; an hCCS domain II homodimer structure at higher resolution than previously available ( 1 . 55 Å ) ; and the hSOD1 disulphide knock-out mutant used to promote complexation ( Fig 1 , S1B Fig and S1 Table ) . These snapshots of reaction precursors , intermediates , and products are important landmarks on a journey through a transient interaction that has critical importance in the maintenance of normal metabolic processes , including regulation of respiration . We find the functionality of the complex is driven by an evolutionarily fine-tuned affinity gradient . The initial molecular recognition and complexation event imposes a structure on hSOD1 and facilitates sequential copper and disulphide PTMs . At the core of this system , a single methyl group resulting from the conserved substitution of alanine for glycine within the hCCS dimer interface orchestrates sequential steps in the folding and PTM pathway to produce stable and active hSOD1 .
Molecular recognition and affinity are dictated by binding free energy . When homodimeric hSOD1 and hCCS domain II are physiologically zinc metalated , many of the terms that comprise binding free energy are equal due to their sequence and structural similarity . Indeed , orientation of monomers and the presence of four inter-subunit hydrogen bonds found in homodimeric hSOD1 are maintained by hCCS ( S1B Fig , S2A and S2B Fig , S2 and S3 Table ) . While fully mature hSOD1 has low nanomolar affinity homodimer affinity [24] , zinc-metalated , disulphide-reduced hSOD1 has a dimer dissociation constant of 51 μM [25] . Thus , within cells , the majority of hCCS’s substrate will be in a monomeric state . It is axiomatic that hCCS has a higher affinity for this species than for itself , but given mature hCCS forms SOD1-like homodimers , the question remains , why ? The presence of a stabilising Coulombic interaction between opposing hCCS Arg104 and Asp136 residues ( 3 . 55 Å ) ( Fig 2A ) , which is not present in hSOD1 , appears counterintuitive in this regard . However , the hCCS dimer interface is replete with positive charge and therefore not dominated by the hydrophobic effect seen in hSOD1 and many other protein complexes ( Fig 2B ) . Most importantly , however , the presence of hCCS Ala231 significantly weakens half of the homodimer interface hydrogen bonds conserved from SOD1 . Specifically , Ala231 side-chain methyl pushes Arg232 and Gly135 apart through steric repulsion , lengthening the carbonyl-amine hydrogen bond between them to 3 . 08 Å from 2 . 75 Å , in the case of the homologous SOD1 residues Ile151 and Gly51 ( Fig 2C ) . Without this rearrangement , hCCS Ala231 Cβ would be an energetically unfavourable 2 . 3 Å from the Gly135 carbonyl oxygen . Introduction of the hCCS Ala231Gly amino acid substitution to mimic the hSOD1 dimer interface increases hCCS dimer affinity ( S2D Fig ) , slows complexation with hSOD1 more than 300-fold ( Fig 2D ) , impedes hSOD1 activation ( S2E Fig ) , and hSOD1 disulphide formation ( S2F Fig ) . Glycine and alanine are near ubiquitous at these positions in eukaryotic copper/zinc superoxide dismutases ( Cu/ZnSODs ) and their cognate chaperones , respectively ( Fig 2E and 2F ) . Thus , relatively weak CCS homodimer affinity has been evolutionarily maintained to provide a pool of monomeric CCS available to interact with and activate nascent monomeric SOD1 within a physiologically relevant timescale . An interesting exception is the nematode CuZnSODs , which have a deforming alanine in place of hSOD1 Gly150 but are exclusively activated by a CCS-independent means [26] . The SOD1-like , hCCS domain II found within each complex structure has an intact intra-subunit disulphide bond in contrast to the yeast orthologue [27] ( S3A Fig ) . This disulphide does not dictate complexation ( S3B Fig ) but does thermally stabilise the hCCS homodimer and the complex with hSOD1 ( S3C and S3D Fig ) . In contrast , hSOD1 must be in the disulphide-reduced state to complex with hCCS . We recently predicted hSOD1 disulphide sub-loop ( amino acids His48–His63 ) movement on complexation with hCCS based on small-angle x-ray scattering ( SAXS ) data [28 , 29] . This is proven true by our new structures , where it adopts a conformation that could not be accommodated if the hSOD1 disulphide were present ( Fig 3A ) . This conformation is not found in mutant or wild-type hSOD1 disulphide intact dimer [30] , Cys57/146Ala disulphide knock-out dimer , or mutation-dependent obligate monomer structures [31 , 32] despite the extensive conformational sampling present ( S4A–S4I Fig ) . In illustration of this point , a recent nuclear magnetic resonance ( NMR ) characterisation indicated SOD1 amino acids 49–54 are disordered in the zinc-metalated , disulphide-reduced state [25] . Conversely , human and yeast SOD1 adopt an identical sub-loop conformation when complexed with yeast CCS [12 , 33] . Without the Cys57-Cys146 covalent tether , the hSOD1 disulphide sub-loop can adapt to the presence of hCCS Ala231 , mitigating repulsive effects . As a result , several strong hydrogen bonds are formed across the heterodimer interface that are not found in the SOD1 homodimer ( S4 Table ) . Particularly important is the hydrogen bond/salt bridge network between hCCS Arg104/Arg232 and opposing hSOD1 Asp52 ( Fig 3B ) . In this way , hCCS provides a surface of repulsive and attractive noncovalent interactions that mould the plastic hSOD1 disulphide sub-loop into a stable but novel conformation . This provides a mechanism for the molecular chaperone activity of hCCS [7] . The SOD1 disulphide sub-loop is also stabilised by several internal hydrogen bonds not found in the mature enzyme ( S4J Fig and S5 Table ) . An accumulation of these effects separates Cβ carbons of residues 57 and 146 by 8 . 0 Å ( Fig 3A ) . Rotation of the alanine residue , here replacing Cys57 , orientates this functional side chain like a flagpole marking the entrance to the hSOD1 active site . Thus , hCCS-SOD1 molecular recognition and complexation proceed by an induced fit mechanism reliant on the conformational adaptability of the hSOD1 disulphide sub-loop and hCCS domain II only . We suggest this primes hSOD1 for reception of copper and the disulphide , before any interaction with hCCS domain I or III takes place . The SOD1 disulphide sub-loop Gly51-Asp52-Asn53-Thr54 ( GDNT ) tetrad motif forms many of the interactions across the hSOD1-hCCS heterodimer interface . It is evolutionarily conserved in eukaryotic Cu/ZnSOD enzymes , with the β-barrel–facing Asn53 substituted by a variety of amino acids , including threonine , leucine and serine; Gly51-Asp52-Xxx-Thr54 ( GDXT ) ( S5A Fig ) . Disruption of the hCCS Arg104-GDNT interaction by the human hSOD1 substitution Thr54Arg inhibits hCCS-catalysed disulphide formation and is causative for a subset of ALS [12] . A GDXT/S tetrad is also present in hCCS and its orthologues ( S5B Fig ) . Negation of the Arg104-GDNT interaction with an hCCS R104A mutation does not inhibit complex formation or hSOD1 thiol oxidation and activation but destabilises the hCCS homodimer and the hCCS-SOD1 complex , introducing a low-temperature melting transition ( S2E , S2F and S5C Figs ) . hCCS Arg104 is near ubiquitously conserved among CCS orthologues ( S5B Fig ) with the only exception being the cetacean conservative mutation to histidine ( S5D Fig ) , which also induces a low-temperature melting transition for both hCCS homodimer and hCCS-SOD1 heterodimer ( S5C and S5E Fig ) . Together , this indicates that the length and charge of the Arg104 side chain is important for structural stability in homodimeric or heterodimeric states as a consequence of the distance between hCCS β-strand 2 and the opposing disulphide sub-loop motif . If the hSOD1 disulphide is formed before copper is passed from hCCS , the complex will dissociate , leaving the hSOD1 product inactive . Thus , PTM transfer events must be correctly sequenced . The hSOD1 active site is formed in part by the Arg143 side chain , which directs superoxide to the copper centre and hydrogen bonds with the substrate during catalysis [34] . The conformation of the Arg143 side chain is sensitive to the position of residue 57 and therefore the propensity of hSOD1 to form a homodimer [31 , 34] ( S4A and S4B Fig ) . When hSOD1 complexes with hCCS its copper site is exposed to solvent by a shift in the Arg143 side chain ( Fig 3C ) . This is due to inability of the Arg143 guanidinium group to hydrogen bond with Gly61 and Cys57 carbonyls found within the disulphide sub-loop , due to increased distance ( 3 . 0 to 3 . 5 Å ) . As a result , it interposes between residues 57 and 146 in the space normally occupied by the hSOD1 disulphide . In this conformation , the Arg143 guanidinium hydrogen bonds with the hSOD1 disulphide sub-loop GDNT tetrad Asn53 . We propose that this effect is an integral part of the activation mechanism; communication from hCCS via Arg104 through the hSOD1 GDNT motif to the Arg143 side chain ensures the amenability of the active site to receive copper in response to complexation with hCCS . Simultaneously , Arg143 forms a physical barrier between disulphide bonding residues and occludes the electropositive cavity found when hSOD1 is complexed with mutant yeast CCS [12] . Arg143 side-chain movement has been observed in the yeast CCS-SOD1 complex , where it is found hydrogen bonded to the alanine amide of the yeast CCS ( yCCS ) Cys-Xxx-Cys ( CXC ) C-terminal motif [33] . Copper transfer or recruitment of the hCCS CXC motif therefore switches the Arg143 side chain removing a potential block to disulphide formation . This mechanism would prioritise copper transfer over disulphide formation so that interactions that yield inactive or unstable SOD1 product are minimised . From a conformation that facilitates copper acquisition from Ctr1/Ctr2 or Atox1 , hCCS domain I must move to a position that enables transfer to hSOD1 . The very high positional dynamism necessary of the hCCS copper-binding domain is evidenced by intra-lattice conformational flexibility and a 47 . 4-Å domain movement between conformers ( S6A and S6B Fig ) . Conformational plasticity is therefore not restricted to the hCCS homodimer state [35] but is an intrinsic property of the activating complex . The act of copper transfer between hCCS domain I and hSOD1 is driven by the higher affinity of the hSOD1 tetrahistidine site compared with the domain I bis-cysteine site and facilitated by intermediate chelating side-chain interactions from the hCCS CXC motif and hSOD1 Cys57 within the disulphide sub-loop [12 , 36] . Comparison of different hCCS conformers indicate that the C-terminal tail is also conformationally dynamic , as has been observed for the yeast orthologue [12 , 33] and predicted from SAXS data for hCCS [35] . When hCCS domain I is free to move within the lattice or inhabits the extended conformer , the position of domain III is partially or entirely unrestricted . In the latter case , it forms the interface of a supramolecule comprising four hCCS monomers and four hSOD1 monomers in crystallo ( S6C Fig ) . By contrast , when domain I is positioned close to the substrate hSOD1 molecule in the compact conformer , it stabilises domain III by restricting space and forming a series of conserved interdomain hydrogen bonds ( S6D and S6E Fig ) . As a result , domain III arches over the hSOD1 disulphide loop and forms a side-chain hydrogen bond between hCCS Asn239 and hSOD1 Thr58 carbonyl , which can only exist when the hSOD1 disulphide sub-loop is in the induced fit conformation ( Fig 3D and S6F Fig ) . Both the conformation and hydrogen bonding are again conserved and provide the impetus to bring the functionally important hCCS CXC motif into position next to Cys57 . The noncovalently bonded C-terminal conformation existing in the compact structure presented here is therefore a precursor of the yeast mixed disulphide–bonded structure [33] . Thus , induced-fit SOD1 disulphide sub-loop conformation change upon complexation ultimately recruits the hCCS functional motifs necessary to ensure timely PTM transfer . For the Cys57 and 146 sulphydryls to form the disulphide bond , the whole sub-loop pivots on Gly51 , and a Cys57 orientation change is accommodated by Gly56 ( Fig 4A ) . Here , the disulphide sub-loop operates as a class I lever forcing the Gly51 carbonyl too close to hCCS Ala231 and deforming the hSOD1 Gly51-hCCS Arg232 interface hydrogen bond ( Fig 4B and 4C ) . hSOD1 then dimerises due to of mitigation of repulsive effects by substitution of Gly150 for hCCS Ala231 , formation of the four strong interface hydrogen bonds , breaking of both Arg104- and Arg232-GDNT heterodimer noncovalent bonding interactions ( S6 Table ) , loss of electrostatic repulsion as Arg232 is replaced by hSOD1 Ile151 , and maximising the stable , hydrophobic interface surface . Thus , interactions between the GDNT disulphide sub-loop tetrad across the heterodimer interface dictate the specificity of hCCS for disulphide-reduced hSOD1 , the timing of copper and disulphide transfer , and complex dissociation . The affinities that regulate these events are finely balanced as a necessity of the similarity of the proteins involved and the small disulphide loop conformation change that directs the interaction . Only 3% of the amino acids present dictate complex recognition , while the hCCS Ala231 methyl , which orchestrates complexation and dissociation , constitutes less than 0 . 04% of the total mass of the complex . In addition , while hSOD1 disulphide flexibility is viewed negatively as an aspect of the pathogenesis of hSOD1-related ALS and now possibly Parkinson disease [17 , 18] , here we find that Gly51-pivoted sub-loop conformational switching is an absolute necessity for hCCS-catalysed hSOD1 activation . While hCCS is 27% identical to yCCS , there are important differences in sequence , structure , and behaviour . Homodimeric hCCS and yCCS associate with negatively charged lipid bilayers representative of the inner surface of the plasma membrane . This is thought to minimise the spatial sampling necessary to locate membrane-bound copper sources [9] . Primary and secondary structure elements that facilitate membrane association are , however , not conserved from yeast to human CCS ( Fig 5A and 5B and S7A Fig ) . Consequently , an hCCS domain II truncation does not strongly associate with lipids ( Fig 5C ) . Despite similarities between hCCS domain I and Atox1 ( S7B Fig ) , a monomeric hCCS domain I truncation also does not segregate significantly with liposomes ( Fig 5C ) . When hCCS domain I and II are both present , they engender a stronger membrane association . This truncated protein exists in a monomer-dimer equilibrium at low micromolar concentration , with the majority as monomer ( S7C Fig ) . An hCCS domain II–III construct has increased dimer affinity and exists as a dimer at micromolar concentrations ( S7D Fig ) , but a positively charged patch in the conformationally plastic domain III ( S7E and S7F Fig ) does not aid association of hCCS to membranes . Increasing hCCS dimer affinity through the hCCS Ala231Gly mutation increases membrane interaction . Conversely , removing two positively charged Arg30 and Lys31 residues , which are sited close to the domain I copper site , decreases membrane association ( Fig 5C ) . Thus , hCCS membrane association is mediated by the combination of globular domains I and II together with the increased interaction surface area provided by domain III–mediated dimerisation . While metal-free , disulphide-reduced wild-type hSOD1 associates with and even penetrates lipid membrane [37 , 38] , on zinc binding , this association is greatly reduced ( Fig 5C ) . Thus , the substrate for hCCS provides little additional membrane attraction , and half of the interfacial interacting surface provided by hCCS homodimerisation is lost on heterodimerisation . The hCCS-hSOD1 complex has little affinity for the lipid bilayer as a result ( Fig 5C ) . Copper acquisition by hCCS is therefore likely to occur in the homodimeric state while membrane bound , and prior to complexation with hSOD1 . Subsequent activation of hSOD1 is more likely to occur in solvent , off the bilayer . In summary , the synthesis of sequence analysis , biophysical assays , and long-awaited crystallographic structures of hCCS in multiple states have provided us with insight on the intricate mechanisms that catalyse assembly of stable and active SOD1 . The weakened dimer affinity of hCCS resulting from Ala231 steric effects , its intracellular concentration , and the strength of lipid association appear finely tuned to establish a dynamic equilibrium that retrieves copper from membrane-bound sources and delivers it to membrane-free hSOD1 ( Fig 6 ) . A combination of repulsive and attractive interactions across the hCCS-SOD1 dimer interface assists SOD1 folding , prepares it for PTM acquisition , and dictates the timing of those modifications . Ultimately , complex dissociation is affected by a molecular lever operating with SOD1 Gly51 as its fulcrum , forcing an unfavourable steric clash and weakened hydrogen bonding across the dimer interface . Thus , SOD1 homodimerisation becomes energetically favourable , and it attains a stable , active state ( Fig 6 ) . SOD1 and CCS appear to have diverged from their common ancestor very early in eukaryotic evolution , given the presence of both orthologues in almost all species . The near ubiquity of alanine in a destabilising position within the CCS dimer interface indicates that the finely balanced affinity gradient that drives human CCS-catalysed SOD1 maturation applies to all eukaryotic Cu/ZnSOD-chaperone pairs . It is not clear whether the CCS and SOD1 common ancestor harboured the stabilising glycine or destabilising alanine variation given the presence of the destabilizing variant in nematode CuZnSODs , however , the nonsynonymous mutation leading to this substitution happened very soon after the gene duplication event that separated SOD1 and CCS coding sequences . Evolution appears to have very quickly traded CCS stability for SOD1 stability . In great apes , including humans , this effect is particularly pronounced , with SOD1 having undergone strong positive selection to limit instability and thereby extend life span [39] . In contrast , a weakened CCS dimer interface does not appear to swamp cellular proteostasis machinery in the same way that SOD1 dimer interface destabilising mutations do [20 , 40] , possibly due to reduced relative expression [41] or more efficient degradation . This adaptation , and the mechanism we propose , may be a critical milestone in the development of the large , highly compartmentalised forms into which eukaryotic cells have developed .
Protein expression , purification , and complex formation was performed as previously described [29 , 35 , 42] , with the exception of SOD1 C57/146A in the pET3A vector , which was transformed into Escherichia coli BL21 ( DE3 ) , and expression was induced with 0 . 4 mM of IPTG with the addition of 0 . 2 μM ZnCl2 and incubated at 37°C for 6 hours . All crystals were grown using the hanging-drop vapour diffusion method at 20°C from proteins in 20 mM tris ( hydroxymethyl ) aminomethane-HCl ( Tris-HCl ) , pH 7 . 4 , 150 mM NaCl , 1 mM dithiothreitol ( DTT ) . hSOD1 C57/146A was crystallised from 1 . 0 μL of protein at 15 mg/mL , mixed in equal proportions with 0 . 2 M lithium sulphate; 0 . 1 M Tris-HCl , pH 8 . 0; and 24% w/v PEG 4000 . Crystals appeared after 10 days of incubation . hSOD1 C57/146A− hCCS domain II at 15 mg/mL was crystallised in 25% ( w/v ) PEG 1500; 0 . 1 M PCTP buffer , pH 7 . 0 ( sodium propionate , sodium cacodylate , and bis-tris propane in the molar ratios 2:1:2 , respectively ) . Crystals grew after 15 days in space group H32 , with four heterodimers in the asymmetric unit ( ASU ) . Heterocomplexes , hSOD1 ( C57/146A ) —full-length hCCS ( C22/25S ) ( elongated conformer ) ; hSOD1 ( C57/146A ) —full-length hCCS ( C12/22/25/244/246A ) ( compact conformer ) ; and hCCS D2 were crystallised from nondiffracting seeds prepared from hSOD1 ( C57/146A ) —full-length hCCS ( C22/25S ) crystals grown in 0 . 2 M sodium malonate , 20% ( w/v ) PEG 3350 , frozen in the same solution in liquid nitrogen and stored at −80°C . For seed preparation , crystals were crushed and diluted serially: 1:5 , 1:25 , 1:125 , and 1:625 in buffer dependent on the crystallisation condition with 20% ( w/v ) PEG 3350 . Drop volume ratio consisted of 3 parts protein ( 1 . 2 μL ) :2 parts reservoir solution ( 0 . 8 μL ) :1 part stock ( 0 . 4 μL ) . For hSOD1 ( C57/146A ) —hCCS ( C22/25S ) ( elongated conformer ) , the complex was crystallised at 20 mg/mL in 0 . 1 M MES , pH 6 . 0 , 0 . 2 M magnesium chloride , 20% ( w/v ) PEG 6000 . Crystals appeared within 45 days . hSOD1 ( C57/146A ) —full-length hCCS ( C12/22/25/244/247A ) ( compact conformer ) at 8 mg/mL was crystallised in 0 . 1 M PCTP buffer , pH 9 . 0 , 20% ( w/v ) PEG 3350 . Crystals appeared within 25 days . Crystals for hCCS domain II homodimer grew from 1 . 2 μL of 15 mg/mL full-length hCCS with the addition of 0 . 2 M sodium chloride , 20% ( w/v ) PEG 6000; 0 . 1 M HEPES , pH 7 . 0 , reservoir solution after 8 months . All crystals were transferred into cryoprotective solution consisting of the respective reservoir solution and 20% glycerol and then flash frozen in liquid nitrogen . Data for all structures except C57/146A hSOD1 were collected at Soleil on beamline Proxima 1 with 0 . 97857 Å wavelength . C57/146A hSOD1 data were collected on Diamond beamline IO3 using 0 . 97626 Å wavelength . In all cases , a PILATUS 6 M detector was used . Images were integrated with iMosflm [43] or XDS [44] and scaled with SCALA [45] or AIMLESS [46] . All structures were solved by molecular replacement using PHASER [47] or MOLREP [48] . hCCS domain II homodimer and SOD1 C57/146A–hCCS domain II used hCCS and SOD1 structures 1DO5 and 2CV9 , respectively , as the search model . Full-length forms were solved using a hSOD1 C57/146A–hCCS domain II heterodimer structure . The structures presented were constructed with successive rounds of manual model building in COOT [49] and refinement with a combination of Phenix [50] and Refmac [51] . Structures were validated with PDB validation tool and deposited in the Protein Data Bank with accession codes 6FOI , 6FN8 , 6FOL , 6FON , and 6FP6 . Thermal stability was assayed by differential scanning fluorometry with a protein concentration of 10 μM , 20× Sypro Orange , in 20 mM Tris-HCl , 150 mM NaCl , pH 7 . 4 , and with the addition of 4 mM DDT when necessary . Unfolding was monitored over a temperature gradient from 25 to 95°C with 1°C min−1 ramp rate . Data were normalised and melting transitions assigned as the peak maximum of the first differential of the unfolding curve . One hundred micromolar wild-type and mutant hCCS in 20 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 0 . 5 mM DTT was incubated with stoichiometric amounts of tetrakis ( acetonitrile ) copper ( I ) hexafuorophosphate on ice for 30 minutes . This results in 87% copper occupancy [28] . Zinc-metalated , wild-type SOD1 was reduced with 40 mM DTT overnight at 4°C and desalted into oxygen-free 50 mM potassium phosphate buffer , pH 7 . 4 , with a Minitrap G25 column under anaerobic conditions . hCCS and SOD1 were mixed stoichometrically , diluted to 12 . 5 μM with oxygenated 50 mM potassium phosphate , pH 7 . 4 , and incubated at room temperature , with samples taken at 30 and 60 minutes for activity or thiol assays . SOD1 activity was measured according to McCord and Fridovich [52]; absorbance at 550 nm was measured from a solution of 50 mM potassium phosphate , pH 7 . 4 , 0 . 2 mM EDTA , 20 μM equine heart cytochrome c′ , 50 μM xanthene , 0 . 007 units of xanthene oxidase , and 50 picomoles of SOD1-hCCS complex . Activity was calculated from ΔA550 nm/Δtime between time 0 and 20 seconds , with hCCS mutant activity stated as a percentage of wild-type SOD1 activity by interaction with wild-type hCCS . hCCS-SOD1 complex-free thiols were blocked with a 20-fold excess 4-acetamido-4′-maleimidylstilbene-2 , 2′-disulfonic acid for 2 hours at 37°C , and then proteins were separated by reducing , denaturing 15% SDS-PAGE . Surface charge was calculated using the Coulombic Surface Coloring tool within Chimera [53] using dialectic constant 4 . 0 , d 1 . 4 Å . Dihedral angles were calculated using Biopython [54] . Dihedral angle changes on SOD1 disulphide formation were calculated by averaging across two high resolution hSOD1 structures ( PDB: 2C9V and 2V0A ) and subtraction of the average hSOD1 angles from the compact conformer structure presented here . Complexation between dimer interface mutants was observed as previously described [28] , but over a time course from 8 minutes to 4 days , using an Agilent BioSec Advance 300 Å 4 . 6 × 300 mm SEC column . Complexation was then plotted as a function of the heterodimer peak height on elution from size exclusion chromatogram and rendered on a log scale to aid curve fitting . The complexation reaction does not go to completion for Ala231Gly hCCS , with some homodimeric SOD1 and hCCS remaining in solution after 4 days . Analysis of oligomeric states was performed as above or using a Superdex 75 10 × 300 mm SEC column . Lipid membranes were prepared from cholesterol ( CHOL ) , 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine ( POPS ) , and 1 , 2-dipalmitoyl-sn-glycero-3-phosphocholine ( PC ) at 1:7:2 , respectively . CHOL , POPS , and PC powder were dissolved in chloroform and dried under nitrogen gas . To generate the liposome , 500 nmol of lipids were hydrated with 50 μL of 20 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , and incubated with agitation at room temperature for 45 minutes . The lipid was sonicated in a water bath . Liposomes were incubated with 5 μg of protein at 37°C for 60 minutes . Reactions were centrifuged at 16 , 000g for 30 minutes at 22°C and the supernatant removed . The lipid pellet was resuspended in 200 μL buffer , pelleted , and the supernatant discarded . The liposome pellet was resuspended in 24 μL of buffer and 6 μμL of 4× SDS-PAGE sample buffer . Cytochrome bc1 complex was used as positive control in 25 mM potassium phosphate , pH 7 . 5 , 100 mM NaCl , 3 mM sodium azide , 0 . 015% DDM . The supernatant and pellet fractions were analysed by reducing , denaturing SDS-PAGE , and densitometry was performed with ImageJ . Human SOD1 and hCCS sequences were compared with the BLAST Model Organisms Database and the OMA database . Prokaryotic sequences were removed before alignment with Clustal Omega and visualisation with WebLogo 3 . 0 . A few eukaryotic sequences with atypical insertions in the regions of interest were also removed to aid visualisation .
|
Cellular complexity necessitates an equally complex network of courier proteins to internalise , sort , and deliver biologically useful metals like copper . These relay systems negotiate a landscape of metal-binding sites through handshake–handoff interactions , but the mechanisms that impart a necessary transience are often not clear . Superoxide dismutase-1 ( SOD1 ) is one of the most abundant human proteins and is an important part of our antioxidant , redox signalling and respiratory control mechanisms . If newly synthesised SOD1 is not correctly processed by the addition of copper , zinc , and an unusual disulphide bond , it will remain inactive or can misfold , as is the case in some neurodegenerative diseases . Here , we discover the mechanisms that govern SOD1 maturation and stabilisation through interaction with the chaperone protein hCCS . Conservation of our proposed mechanism across eukaryotes indicates it developed very soon after the gene duplication event that separated SOD1 and CCS coding sequences . SOD1 stability appears to have been quickly traded at the expense of CCS following the dawn of eukaryotic life , in order to efficiently produce this important enzyme .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Materials",
"and",
"methods"
] |
[
"chemical",
"bonding",
"chemical",
"compounds",
"oxides",
"enzymes",
"enzymology",
"dismutases",
"organisms",
"fungi",
"crystals",
"materials",
"science",
"sequence",
"motif",
"analysis",
"oligomers",
"hydrogen",
"bonding",
"physical",
"chemistry",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"polymer",
"chemistry",
"bioinformatics",
"proteins",
"zinc",
"chemistry",
"yeast",
"biochemistry",
"superoxide",
"dismutase",
"eukaryota",
"database",
"and",
"informatics",
"methods",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"dimers",
"materials",
"superoxides",
"chemical",
"elements"
] |
2019
|
Molecular recognition and maturation of SOD1 by its evolutionarily destabilised cognate chaperone hCCS
|
Bam32 , a 32 kDa adaptor molecule , plays important role in B cell receptor signalling , T cell receptor signalling and antibody affinity maturation in germinal centres . Since antibodies against trypanosome variant surface glycoproteins ( VSG ) are critically important for control of parasitemia , we hypothesized that Bam32 deficient ( Bam32-/- ) mice would be susceptible to T . congolense infection . We found that T . congolense-infected Bam32-/- mice successfully control the first wave of parasitemia but then fail to control subsequent waves and ultimately succumb to their infection unlike wild type ( WT ) C57BL6 mice which are relatively resistant . Although infected Bam32-/- mice had significantly higher hepatomegaly and splenomegaly , their serum AST and ALT levels were not different , suggesting that increased liver pathology may not be responsible for the increased susceptibility of Bam32-/- mice to T . congolense . Using direct ex vivo flow cytometry and ELISA , we show that CD4+ T cells from infected Bam32-/- mice produced significantly increased amounts of disease-exacerbating proinflammatory cytokines ( including IFN-γ , TNF-α and IL-6 ) . However , the percentages of regulatory T cells and IL-10-producing CD4+ cells were similar in infected WT and Bam32-/- mice . While serum levels of parasite-specific IgM antibodies were normal , the levels of parasite-specific IgG , ( particularly IgG1 and IgG2a ) were significantly lower in Bam32-/- mice throughout infection . This was associated with impaired germinal centre response in Bam32-/- mice despite increased numbers of T follicular helper ( Tfh ) cells . Adoptive transfer studies indicate that intrinsic B cell defect was responsible for the enhanced susceptibility of Bam32-/- mice to T . congolense infection . Collectively , our data show that Bam32 is important for optimal anti-trypanosome IgG antibody response and suppression of disease-promoting proinflammatory cytokines and its deficiency leads to inability to control T . congolense infection in mice .
African trypanosomiasis , also called sleeping sickness in man , is a deadly disease of humans and livestock caused by blood parasites belonging to the genus Trypanosoma . In human , the disease is caused by Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense; whereas the animal form of the disease is primarily caused by Trypanosoma congolense , Trypanosoma vivax and Trypanosoma brucei brucei with T . congolense being the most important [1] . According to the World Health Organization ( WHO ) report , an estimated 60 million people are at risk of getting the infection with 300 , 000 cases of the disease occurring annually [2] . However , this is a gross under estimation because only about 10% of the cases are appropriately diagnosed and treated [2] . The animal form of the disease poses a huge agricultural and economic problem in the affected region due to reduced animal yield [3] . It is estimated that elimination of the disease would spare Africa an estimated $4 . 5 billion yearly as a result of improved animal production [4] . The control of parasitemia and resistance to African trypanosomes in mice have been linked to early interferon gamma ( IFN-γ ) production , which is important for activating macrophages to produce nitric oxide that has both trypanostatic and trypanotoxic effects [5–9] . In addition , IFN-γ is also important for production of optimal amounts and isotypes of parasite-specific IgG antibodies that are important for resistance via enhanced phagocytosis and complement-mediated lysis [10–12] . However , uncontrolled production of IFN-γ and other proinflammatory cytokines ( including tumor necrosis factor-α [TNF-α] , IL-6 , IL-1β and IL-12 ) has been incriminated as the major cause of death in the highly susceptible mice [13–17] . On the other hand , IL-10 plays a regulatory role in dampening the excessive proinflammatory cytokines produced during infection [13] . Bam32 is a 32 kDa B lymphocyte adaptor protein that plays an important role in B cell receptor ( BCR ) cross-linking-mediated downstream events [18] and has been shown to be expressed in B cells , T cells , dendritic cells and macrophages [19 , 20] . Bam32 has also been shown to be important in BCR internalization [21] , BCR-induced signalling , B cell survival [22] and antigen presentation [23] . Upon B cell antigen receptor cross-linking , Bam32 is tyrosine-phosphorylated and has been shown to be associated with phospholipase Cγ2 in human B cell lines [18] . However , deficiency of Bam32 does not affect the development of B and T cells , but significantly impairs B cell proliferation following BCR cross linking [24] . Importantly , Bam32-/- mice also show reduced T-independent type two ( TI-II ) B cell responses [25] and high susceptibility to Streptococcus pneumoniae infection due to defective antibody response [24] . Bam32 is also implicated in T cell receptor signalling and regulation of CD4+ T cell cytokine production [26 , 27] . A strong antibody response ( especially IgG and its subclasses ) resulting from germinal centre reaction is required for the clearance of many pathogens including African trypanosomes [28] . The germinal centre is an extensive area of B cell proliferation , somatic hypermutation , selection , and class switch recombination leading to production of various antibody isotypes with high antigen binding affinity [29] . Upon encountering their cognate antigen , B cells become activated and require help from other immune cells ( especially CD4+ follicular T helper cells and dendritic cells ) to initiate germinal centre formation [30] . The follicular CD4+ T helper cells ( Tfh ) provide signals to antigen-specific B cells that guide their survival , expansion or differentiation into high affinity antibody-producing cells . Thus , Tfh are indispensable for germinal centre response [29] and hence for optimal antibody-mediated immunity . Since antibodies and T cell cytokine production are both required for T . congolense clearance in infected animals , we investigated the role of Bam32 in experimental African trypanosomiasis using Bam32-/- mice . Our report shows than Bam32 deficiency leads to inability to control late waves of undulating parasitemia , increased production of disease exacerbating proinflammatory cytokines by immune cells , impaired germinal centre B cell response and significantly lower serum levels of trypanosome specific IgG antibodies leading to early death in the otherwise relatively resistant strain of mice .
All experiments involving mice were approved by the University of Manitoba Animal Care Committee in accordance with the regulation of the Canadian Council on Animal Care ( Protocol Number 12–072 ) . Six to eight week-old female C57BL/6 mice and CD1 ( outbreed Swiss white mouse ) were purchased from the Central Animal Care Services ( CACS ) , University of Manitoba , Winnipeg , Canada . The origin and phenotype of Bam32-/- mice have been previously described [18] . B cell deficient ( μMT ) mice on the C57BL/6 background were obtained from The Jackson Laboratory ( Bar Harbor , ME , USA ) . Bam32-/- mice on C57BL/6 background ( > 12 generations ) were bred by the CACS and supplied as needed . The origin and phenotype of p110δ knock-in mice ( p110δ KI ) has been previously described [31] . The housing , handling and feeding of experimental animals were in accordance to the recommendations of the Canadian Council of Animal Care . Throughout the experiment , Trypanosoma congolense ( Trans Mara strain ) , variant antigenic type TC13 was used and the origin of this strain has been previously described [32] . CD1 mice were immunosuppressed intraperitoneally ( i . p . ) with cyclophosphamide ( Cytoxan; 200 mg/kg ) and after two days , infected i . p . with TC13 stabilates [32] . Three days after infection , mice were deeply anaesthetized by isoflourane and blood was collected by cardiac puncture and parasites were purified by anion-exchange chromatography by passing through DEAE-cellulose chamber [33] . Eluted parasites were washed in Tris-saline glucose ( TSG ) , counted , resuspended in TSG containing 10% heat-inactivated FBS and diluted to the desired concentration ( 104/ml ) . In all the experiments , mice were infected intraperitoneally ( 100 μl ) with 103 parasites . To estimate parasitemia , a drop of blood was taken from the tail vein of each T . congolense-infected mouse unto a microscopic slide , covered with cover slip and parasitemia was determined by counting the number of parasites presents in at least 10 fields at 400x magnification of light microscope . During periods of heavy parasite load , estimation was done as described previously [34] . Following sacrifice , the spleens were made into single-cell suspensions and contaminating red blood cells were lysed with ACK lysis buffer . Cells were washed 2 times with PBS , resuspended at final concentration of 4 x 106/ml in complete tissue culture medium ( DMEM supplemented with 10% heat-inactivated fetal bovine serum , 2 mmol L-glutamine , 100 U/mL Penicillin and 100 μg/ml streptomycin ) , and plated at 1 mL/well in 24-well tissue culture plates ( Falcon; VWR Edmonton , AB , Canada ) in the presence or absence of whole trypanosome lysate ( 106/ml parasite equivalent ) . After 3 days , the cell culture supernatant fluids were collected and stored at -20°C until analyzed for cytokines by ELISA . Some cells were directly stained ex-vivo for CD4 and CD25 expression and intracellularly for Foxp3 using the Tregs staining kit ( eBioscience , San Diego , CA ) in accordance with the manufacturer’s recommendations . Germinal centre B cell response was assessed by staining splenic cells with fluorochrome-conjugated antibodies against B220 , GL7 and Fas ( all purchased from eBiosciensce ) . Antibodies against CD4 , PD1 and ICOS ( eBiosciensce ) were used to assess T follicular helper cells response while antibodies against CD19 , Fas , GL7 , CD80 and PD-L2 were used to assess memory B cells . In some experiments , cells were stimulated with phorbol myristic acetate ( PMA; 50 ng/mL ) , ionomycin ( 500 ng/mL ) , and brefeldin A ( BFA; 10 μg/mL ) for 4 hrs and stained for CD4 surface expression and for intracellular cytokine ( TNF-α , IFN-γ and IL-10 ) expression . After staining , all samples were washed routinely in FACS buffer , acquired using BD FACS Canto II cytometer ( BD Bioscience , San Diego CA ) , and analysed using FlowJo software ( BD Bioscence ) . The levels of IL-6 , TNF-α , IL-10 , and IFN-γ in the culture supernatant fluids were determined by sandwich ELISAs using antibody pairs purchased from BD Biosciences according to the manufacturer's suggested protocols . The sensitivities of the ELISAs were 15 , 31 , 15 and 7 . 5 pg/ml for IL-6 , TNF-α , IL-10 , and IFN-γ , respectively . Serum levels of alanine aminotransferase ( ALT ) and aspartate aminotransferase ( AST ) were measured using Stanbio kits ( Stanbio Laboratory , Boerner , TX ) according to the manufacturer’s suggested protocols . The spleens from infected mice were collected on indicated days and embedded in OCT ( Tissue-Tek , Torrence , CA ) before being snap frozen in liquid nitrogen . The frozen sections were cut to 8–10 μm size and fixed with paraformaldehyde for 15 min , washed with PBS and air-dried . Sections were blocked for 30 min with mouse Fc Block , washed with PBS containing Tween 20 ( 0 . 05% ) and stained for 1 hr at room temperature with antibody cocktail containing FITC-labelled anti-PNA ( Vector laboratories , Burlington , ON ) , PE-labelled anti-CD4 and APC-labelled IgD ( BD Biosciences ) . The slides were mounted in Prolong Gold anti-fade reagent ( Molecular Probes ) after washing with PBS and viewed with Zeiss AxioObserver Spinning Disk Confocal Microscope . Mature B ( CD19+ ) cells were isolated from the spleens of naïve WT or Bam32-/- mice by negative selection using a mouse B cell enrichment kit ( StemCell Technology , Vancouver , BC ) according to the manufacturer’s suggested protocol . The purity of the enriched B cells was greater than 95% as assessed by flow cytometry . Enriched B cells were washed in complete medium , resuspended in PBS and approximately 30 million cells were injected into each mouse through the tail vein . Recipient mice were infected with T . congolense ( 103 ) 48 hr after cell transfer . Data are represented as means and Standard Error of Mean ( SEM ) . Two-tailed Student'st-test or ANOVA were used to compare means and SEM between two groups using GraphPad Prism software . Differences were considered significant at p < 0 . 05 .
C57BL/6 mice are considered relatively resistant to T . congolense infection because they control several waves of undulating parasitemia and survive for more than 100 days post-infection before eventually dying [35] . Because Bam32-/- mice exhibit increased susceptibility to infection with Streptococcus pneumoniae due to failure to generate opsonising IgG antibodies [24] , we hypothesized that they will be susceptible to T . congolense infection . Therefore , we infected WT and Bam32-/- mice intraperitoneally with 103 T . congolense ( TC13 ) and monitored parasitemia as well as survival period after infection . There were no differences in the prepatent period and level of parasitemia at the early phase of the infection between WT and Bam32-/- mice ( Fig 1A ) . However , Bam32-/- mice developed fulminating parasitemia towards the late phase of the infection and succumbed to the infection ( mean survival time 58 ± 9 days , Fig 1B ) . In contrast , all the WT mice survived with low parasitemia until day 80 when the experiment was terminated . These findings suggest that Bam32 molecule is important for effective control of parasitemia and survival in T . congolense-infected mice . Trypanosoma congolense infection in mice is associated with hepato-splenomegaly and an accompanying increase serum levels of liver enzymes including alanine aminotransferase ( ALT ) and aspartate aminotransferase ( AST ) [36] . This marked hepato-splenomegaly has been variously linked with increased pathology , activation and expansion of the reticuloendothelial system [37 , 38] and a compensatory extramedullary haematopoiesis [39 , 40] . Therefore , we determined whether there was a correlation between increased susceptibility of Bam32-/- mice and liver pathology . Prior to day 28 post-infection , the liver and spleen sizes of WT and Bam32-/- mice were indistinguishable ( Fig 1C and 1D ) . However , from day 48 post-infection , the liver and spleens of infected Bam32-/- mice were significantly larger than those of their WT counterpart mice . The increase in liver and spleen sizes coincided with the onset of increased and uncontrolled parasitemia in the late phase of the infection in Bam32-/- mice ( see Fig 1A ) . Interestingly , despite the significantly larger spleen and liver sizes in infected Bam32-/- mice , there was no difference in serum levels of ALT ( Fig 1E ) and AST ( Fig 1F ) between infected WT and Bam32-/- mice , suggesting that the hepatomegaly may not be associated with significant liver pathology and may not be directly responsible for the early death of infected Bam32-/- mice . Previous studies have shown that susceptibility to T . congolense infection in mice is associated with the production of high levels of proinflammatory cytokines ( including TNF-α , IL-6 , IL-12 , and IFN-γ ) by spleen cells from infected mice leading to increased serum levels of the cytokines , systemic inflammatory response syndrome ( SIRS ) and death [14–17] . Because infected Bam32-/- mice had higher and uncontrolled late phase parasitemia and succumbed to the infection significantly earlier than their infected WT counterpart mice ( Fig 1A ) , we hypothesized that the levels of these cytokines would be significantly higher than those of WT mice . Throughout the infection , the levels of IFN-γ ( Fig 2A ) , TNF-α ( B ) and IL-6 ( C ) in culture supernatant fluids of splenocytes from infected Bam32-/- mice were significantly ( p < 0 . 05–0 . 01 ) higher than those from WT mice , with the difference being more pronounced towards the time infected Bam32-/- mice were unable to control parasitemia . Interestingly , there was no significant change in the secretion of IL-10 ( Fig 2D ) , an anti-inflammatory cytokine that plays a critical role in dampening systemic inflammatory response syndrome , leading to survival in T . congolense-infected mice [13] . This pattern of cytokine response was also observed by intracellular cytokine staining where we found that the percentages and absolute numbers of IFN-γ ( Fig 2E , 2H and 2I ) and TNF-α ( Fig 2F , 2J and 2K ) -producing CD4+ cells from spleens of Bam32-/- infected mice were significantly higher than those from infected WT mice . Interestingly and consistent with the ELISA data , the percentages of IL-10-producing CD4+ cells were not different between WT and Bam32-/- mice , although the absolute numbers were higher than those from WT mice ( Fig 2G , 2L and 2M ) , which could be due to higher splenomegaly during the late stage of infection ( see Fig 1D ) . In addition and as shown in S1 Fig , CD3+ T cells were the major producers of IFN-γ , TNF-α and IL-10 in the spleens throughout the infection . Together , these results show that the absence of Bam32 molecule in mice infected with T . congolense leads to enhanced production of disease-exacerbating proinflammatory cytokines , which could account for the early death observed in infected Bam32-/- mice . Naturally occurring regulatory T cells ( Tregs ) have been shown to play a pathogenic ( disease-promoting ) role in experimental African trypanosomiasis [41–43] . Because we found that Bam32-/- mice were more susceptible than their WT counterpart mice , we investigated whether their spleens contained higher numbers of CD4+CD25+Foxp3+ T cells ( Tregs ) . As shown in Fig 3 , there was no difference in the pattern of expansion ( Fig 3A ) and percentages ( Fig 3B ) of Tregs in the spleen of WT and Bam32-/- mice on the indicated days . However , the absolute numbers of Tregs ( Fig 3C ) were significantly higher in Bam32-/- mice towards the later time points during the infection than those from WT mice , which may be a consequence of increased splenomegaly in these mice . A strong IgG antibody response is important for survival of T . congolense infection in mice [28] . Bam32 is required for optimal affinity maturation in germinal centres leading to production of high affinity IgG1 and IgG2a antibodies [44] and for production of IgG3 antibodies in response to TI-II antigens [24] . Therefore , we investigated whether susceptibility of Bam32-/- to T . congolense was related to defective IgG antibody response against the parasites . WT and Bam32-/- mice infected with T . congolense were assessed for their serum levels of parasite-specific IgM and IgG antibodies at different time points . There was no difference in the kinetics and magnitude of IgM antibody responses in infected WT and Bam32-/- mice ( Fig 4A ) . In contrast , infected Bam32-/- mice showed significantly reduced serum levels of trypanosome-specific IgG ( Fig 4B ) , IgG1 ( Fig 4C ) and IgG2a ( Fig 4D ) starting from day 28 ( IgG and IgG1 ) or 48 ( IgG2a ) post-infection , which corresponds to the onset of uncontrolled parasitemia in these mice . Collectively , these results indicate that deficiency of Bam32 leads to impaired production of trypanosome-specific IgG , IgG1 and IgG2a responses . The reduced production of parasite-specific IgG1 and IgG2a antibodies in infected Bam32-/- mice suggested a defect in germinal centre responses . Flow cytometric analysis show that the onset and magnitude of germinal centre B cell response ( day 7 post-infection ) were similar in infected WT and Bam32-/- mice ( Fig 5A ) . However , from day 28 post-infection , infected Bam32-/- mice have significantly ( p < 0 . 05–0 . 001 ) lower percentages ( Fig 5A and 5B ) and absolute numbers ( Fig 5C ) of germinal centre B cells compared to their WT counterpart mice . P110δ knock-in mice that lack the ability to form germinal centres [45 , 46] were included in the experiment to serve as additional control . We further performed immunofluorescence staining in order to validate the flow cytometry results . The data presented as Fig 5F showed that the onset of germinal centre formation in Bam32-/- mice was comparable to that of WT mice . However , as infection progressed , the germinal centre structure in infected Bam32-/- mice began to disintegrate , such that by day 48 post-infection , very few follicular B cells were evident ( Fig 5F ) . Interestingly , there were no differences in the percentages of T follicular helper cells in infected WT and Bam32-/- mice at all times after infection ( Fig 5D ) despite the significant differences in germinal centre B cells , suggesting that the effects of Bam32 deficiency may be intrinsically restricted to B cells in this model of infection . Next , we assessed whether the impaired germinal centre response in Bam32-/- mice was also associated with impaired memory B cell response . At different times after infection , we gated on CD19+GL7-Fas- cells and assessed their co-expression of PD-L2 and CD80 molecules ( S2 Fig ) , markers previously used to delineate memory B cells [47] . As shown in ( Fig 5G and 5H ) , although the frequency of PD-L2+CD80+ ( memory B ) cells increased in the spleens of infected WT and Bam32-/- mice as the infection progressed , there was no difference between the two mouse strains . Collectively , these results suggest that the defective parasite-specific IgG responses observed in T . congolense-infected Bam32-/- mice may be related to the impaired germinal centre B cell response . Although the preceding observations strongly suggest that impaired B cells responses may be primarily responsible for the enhanced susceptibility of Bam32-/- mice to infection , it is plausible that other cells may also be important . This is because the Bam32-/- mice used in our studies are globally deficient in Bam32 signalling in other immune cells including T cells . Therefore , we wished to determine whether the enhanced susceptibility of Bam32-/- mice is related to primary defects in their B cells . We transferred WT and Bam32-/- B cells into μMT mice ( which have intact T cells ) and infected them with T . congolense . Results presented in ( Fig 6 ) show that μMT mice that received Bam32-/- B cells failed to control first wave of parasitemia ( Fig 6A ) and succumbed to the infection within 15 days ( Fig 6B ) akin to μMT mice given only PBS . In contrast , μMT mice that received WT B cells controlled their first wave of parasitemia and survived until the termination of the experiment ( Fig 6A and 6B ) . Interestingly , CD4+ T cells from μMT mice that received Bam32-/- B cells produced significantly ( p < 0 . 01–0 . 001 ) more IFN-γ ( Fig 6C and 6D ) and TNF-α ( Fig 6E and 6F ) compared to the group that received WT B cells or PBS . Taken together , these results indicate that intrinsic B cell defect is primarily responsible for the enhanced susceptibility of Bam32-/- mice to T . congolense infection .
The primary objective of this study was to investigate the role of Bam32 , a B cell adaptor molecule critical for BCR signalling and antibody responses , in experimental African Trypanosomiasis in mice . We found that deficiency of Bam32 results in failure to control parasitemia during the chronic phase of the disease , leading to significantly decreased survival in an otherwise relatively resistant strain of mice . This was associated with increased production of disease-exacerbating proinflammatory cytokines ( IFN-γ , IL-16 and TNF-α ) , impaired production of parasite-specific IgG , IgG1 and IgG2a antibodies and failure to sustain strong germinal centre responses during the chronic phase of infection . Since effective clearance of parasitemia is mediated by IgG antibodies against the variant surface glycoprotein and common antigens [28] and death of infected mice is usually associated with overproduction of proinflammatory cytokines , it is conceivable that impaired production of IgG antibodies and high production of proinflammatory cytokines contribute to the susceptibility of Bam32-/- mice to T . congolense infection . To the best of our knowledge , this is the first report showing the contribution of Bam32 in resistance to a protozoan parasite . The susceptibility to T . congolense infection in mice has been associated with several factors , including immunosuppression [48–51] , systemic inflammatory response syndrome resulting from cytokine storm [14–17] , impaired antibody response [28 , 52 , 53] , induction of regulatory T cells [41 , 43] , and hepatotoxicity particularly during the chronic phase of the disease [36] . We found that infected Bam32-/- mice were unable to control chronic ( late stage ) parasitemia and show shorter survival time than their wild type counterpart mice and this was associated with significantly increased splenomegaly and hepatomegaly . However , serum levels of ALT and AST were not different , suggesting that hepatotoxicity may not account for the increased susceptibility of Bam32-/- mice to T . congolense infection . Interestingly , cells from infected Bam32-/- mice produced significantly higher amounts of disease exacerbating proinflammatory cytokines ( including IFN-γ , TNF-α and IL-6 ) , suggesting that the uncontrolled production of these cytokines may be related to death of infected Bam32-/- mice . Studies on the role of Bam32 in proinflammatory cytokine production are limited . A study by Sommers et al showed that Bam32 deficiency does not affect CD4+ T cell proliferation and their production of IL-17 and TNF-α . However and consistent with our results , there was a trend of increased IFN-γ following polyclonal stimulation with anti-CD3 and anti-CD28 mAbs [27] . We found that spleen cells from infected Bam32-/- mice produced significantly higher amount of proinflammatory cytokines ( IFN-γ , TNF-α and IL-6 ) following in vitro restimulation , suggesting that Bam32 might act as a negative regulator of these cytokines in the context of T . congolense infection . The differences in the effects of Bam32 deficiency on cytokine production might be related to differences in the experimental models . The studies of Sommers et al focused primarily on T cells from uninfected mice following polyclonal stimulation with anti-CD3 and anti-CD28 mAbs whereas our studies were carried out under parasitic infection condition using unfractionated spleen cells . Splenic and hepatic macrophages are producers of proinflammatory cytokines ( including IL-6 and TNF-α ) [13 , 54] , and plastic-adherent T cells that exhibit macrophage-like properties are the major producers of IFN-γ in T . congolense-infected mice [16] . It is conceivable that signalling via Bam32 suppresses cytokine production in immune cells . In line with this , we found that the production of IFN-γ and TNF-α by CD4+ T cells from μMT mice that received Bam32-/- B cells was significantly higher than those that received WT B cells or PBS ( Fig 6C–6F ) . This suggests that signalling via Bam32 could endow B cells with the ability to downregulate cytokine production in CD4+ T cells . Previous reports have linked Tregs with susceptibility to experimental T . congolense infection in mice , the mechanism possibly being through the production of IL-10 to dampen immune response [43 , 54] . We found no difference in the frequency of Tregs in spleens of infected WT and Bam32-/- ( Fig 3A and 3B ) mice , suggesting that Bam32 does not influence the expansion and/or survival of Tregs in mice . However , spleens of infected Bam32-/- mice contained significantly higher numbers of Tregs than those of WT counterpart mice , due primarily to increased splenomegaly . The increase in absolute numbers of Tregs in infected Bam32-/- mice suggests that Tregs might contribute to the enhanced susceptibility of these mice to T . congolense . B cells are critical for clearance of parasitemia and survival in murine experimental African trypanosomiasis . T . congolense infection has been shown to cause depletion of several B cell subsets [55] and B cell deficient mice are highly susceptible to various strains of African trypanosomes [56] . The susceptibility of B cell deficient mice to T . congolense infection is reversed by passive transfer of VSG-specific antibody or primed B cells [56] . In line with this , we found that μMT mice were unable to control their first wave of parasitemia and succumbed within 15 days post-infection . While adoptive transfer of B cells from WT mice resulted in effective parasite control and survival , the transfer of B cells from Bam32-/- mice did not result in parasite control in μMT mice . In a previous study where we mixed and adoptively transferred equal numbers of WT and Bam32-/- B cells into μMT mice , we showed that Bam32 KO B cells do not have an engraftment disadvantage , but rather have some proliferative advantage over WT B cells in the recipient μMT mice [44] . This suggests that the differences in parasitemia and survival observed here were not related to poor engraftment of Bam32-/- B cells in μMT mice . Collectively , our results suggest that intrinsic B cell defects may be primarily responsible for the enhanced susceptibility of Bam32-/- mice to T . congolense infection . VSG-specific antibodies mediate complement-mediated lysis in vitro [57 , 58] , agglutination [59] , immobilization [60] and increased uptake of trypanosomes by macrophages [61 , 62] . Although both IgM and IgG antibody subclasses have been shown to mediate anti-trypanosome clearance [63] , it is generally accepted that the different IgG antibody classes are more important than IgM in mediating parasite control and survival of trypanosome-infected mice [28 , 64] . Thus , although serum levels of trypanosome-specific IgM antibodies were comparable in infected WT and Bam32-/- mice , the impaired production of parasite-specific IgG , IgG1 and IgG2a antibody classes in infected Bam32-/- mice could be responsible for their enhanced susceptibility to infection . Interestingly , IgG levels were most significantly decreased at later time points ( from day 28 post-infection ) , suggesting a failure to sustain protective antibody responses over time . The crosslinking of B cell receptors by their cognate antigens leads to the generation of intracellular signalling events that ultimately result in B cell activation . The activated B cells migrate to the lymphoid follicles where they undergo extensive proliferation and differentiation into antibody-producing plasma cells . These follicular areas of extensive B proliferation and differentiation ( also called germinal centres ) are critically important for somatic hypermutation , class-switching and affinity maturation events that are dependent on cognate interaction with follicular CD4+ T helper cells . We found that Bam32-/- mice have impaired germinal centre B cell response starting from day 28 post-infection , a time that correlated with onset of uncontrolled parasitemia , hepatomegaly , splenomegaly and lower serum levels of IgG , IgG1 and IgG2a in infected Bam32-/- mice . Interestingly , there was no significant difference in germinal centre B cells numbers between infected WT and Bam32-/- mice early in the infection , suggesting that premature germinal centre crash occurs in infected Bam32-/- mice . In support of this , both flow cytometry and immunofluorescence staining clearly revealed germinal centre deterioration by day 48 post-infection in infected Bam32-/- mice . This is consistent with a previous study that showed germinal centre collapse in Bam32-/- mice after immunization with ova/alum [44] . Surprisingly , despite the significant differences in germinal centre B cells , we found no differences in the percentages or absolute numbers of T follicular helper cells in the spleens of infected WT and Bam32-/- mice throughout the course of infection . In fact , Bam32-/- mice had higher T follicular cells at the later phase of infection , suggesting that the suboptimal GC response is not related to intrinsic defects in T follicular helper cells numbers and/or function . Collectively , these observations suggest that the impaired IgG response in infected Bam32-/- mice may be due to B cell intrinsic defects as described previously [21 , 23 , 44] . When memory B cell subsets in these mice were assessed , we found as expected that the frequency of memory B cell population ( i . e . CD19+GL7-Fas-CD80+PD-L2+ ) increased in the spleens of infected WT and Bam32-/- mice as the infection progressed ( see S2 Fig for gating strategy ) . However , there was no difference in the frequency of memory B cells in the spleens of infected WT and Bam32-/- mice at different times after infection , suggesting that differences in generation of memory B cells could not account for the enhanced susceptibility of infected Bam32-/- mice . Collectively , the new set of data support our conclusion that the susceptibility of Bam32-/- mice to T . congolense infection is due in part to their inability to mount a strong and sustainable germinal centre response , which ultimately results in impaired parasite-specific antibody production . In conclusion , we have demonstrated that Bam32 is an important molecule that contributes to optimum resistance to experimental T . congolense infection in mice . Deficiency of this adaptor molecule negatively impacts on parasite-specific germinal centre formation and IgG responses in vivo . In addition , it dramatically enhances proinflammatory cytokine production , suggesting that Bam32 may act as a negative regulator of proinflammatory cytokine gene expression . Collectively , these findings identify Bam32 as an indispensable molecule for optimal anti-trypanosome IgG antibody response and suppression of disease-promoting proinflammatory cytokines and its deficiency leads to inability to control T . congolense infection in mice .
|
African trypanosomiasis continues to be a major threat to human health and economic development in sub-Saharan Africa . Despite intense studies , the immunopathogenesis of the disease remains poorly understood . Understanding the factors that regulate disease pathogenesis would be important in designing effective immunotherapeutic strategies . Here , we demonstrate that the B cell adaptor molecule , Bam32 , contributes to optimum resistance to experimental T . congolense infection in mice because its deficiency negatively impacts optimal B cell responses including germinal centre formation and parasite-specific IgG responses in vivo . In addition , Bam32 deficiency significantly enhances proinflammatory cytokine production by splenic cells , suggesting that Bam32 may act as a negative regulator of cytokine gene expression following T . congolense infection . Collectively , these findings identify Bam32 as an indispensable molecule for optimal germinal centre formation , anti-trypanosome IgG antibody response and suppression of disease-promoting proinflammatory cytokines and its deficiency leads to inability to control T . congolense infection in mice .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
The B Cell Adaptor Molecule Bam32 Is Critically Important for Optimal Antibody Response and Resistance to Trypanosoma congolense Infection in Mice
|
The localization of specific mRNAs can establish local protein gradients that generate and control the development of cellular asymmetries . While all evidence underscores the importance of the cytoskeleton in the transport and localization of RNAs , we have limited knowledge of how these events are regulated . Using a visual screen for motile proteins in a collection of GFP protein trap lines , we identified the Drosophila IGF-II mRNA-binding protein ( Imp ) , an ortholog of Xenopus Vg1 RNA binding protein and chicken zipcode-binding protein . In Drosophila , Imp is part of a large , RNase-sensitive complex that is enriched in two polarized cell types , the developing oocyte and the neuron . Using time-lapse confocal microscopy , we establish that both dynein and kinesin contribute to the transport of GFP-Imp particles , and that regulation of transport in egg chambers appears to differ from that in neurons . In Drosophila , loss-of-function Imp mutations are zygotic lethal , and mutants die late as pharate adults . Imp has a function in Drosophila oogenesis that is not essential , as well as functions that are essential during embryogenesis and later development . Germline clones of Imp mutations do not block maternal mRNA localization or oocyte development , but overexpression of a specific Imp isoform disrupts dorsal/ventral polarity . We report here that loss-of-function Imp mutations , as well as Imp overexpression , can alter synaptic terminal growth . Our data show that Imp is transported to the neuromuscular junction , where it may modulate the translation of mRNA targets . In oocytes , where Imp function is not essential , we implicate a specific Imp domain in the establishment of dorsoventral polarity .
The subcellular localization of mRNAs is a conserved means of localizing protein concentration gradients that underlie the establishment of cellular asymmetries and specialized cell functions [1 , 2] . For example , the localization of β-actin mRNA to the leading edge of neurite growth cones and embryonic fibroblasts is important for cell motility and growth [3] . Oligodendrocytes move the mRNA encoding myelin basic protein to distal processes , where it is required for myelination [4 , 5] , and the accumulation of mRNAs in dendrites in response to synaptic activity has fostered the hypothesis that mRNA localization might mediate synaptic plasticity [6–8] . In Drosophila and Xenopus oocytes , the localization of maternal mRNA is required to establish axial polarity of the embryo [9] . The localization of mRNA requires cis-acting localization elements within the RNA , as well as associated trans-acting factors that bind the RNA and/or one another to form a ribonucleoprotein ( RNP ) complex [10] . Localization elements , or “zipcodes , ” have been defined for a number of localized RNAs and generally reside in the UTR . The trans-acting proteins are required for multiple functions that include regulating mRNA translation and degradation , physically linking RNPs to the transport machinery , and tethering mRNAs to cortical anchors at specific locations [9 , 11] . Not surprisingly , the assemblage of trans-acting factors that carry out these diverse functions is complex and dynamic . Drosophila oogenesis provides an excellent system in which to study the localization and transport of RNPs [12] . Germline cysts are composed of 16 cells interconnected by cytoplasmic bridges; one cell in each cyst becomes the oocyte , while the others become nurse cells that support oocyte growth . RNAs transcribed in the nurse cells are assembled into RNPs and transported to the oocyte at early stages of development , and later are correctly positioned within the oocyte . Both transport to the oocyte and localization within the oocyte are microtubule-dependent processes , suggesting directed transport of RNP complexes by microtubule motors . In early egg chambers , a polarized array of microtubules extends through the cytoplasmic bridges that connect the nurse cells with the oocyte , while later in oogenesis , microtubule reorganization facilitates proper positioning of axial determinants within the oocyte [13] . The microtubule motors , dynein and kinesin , as well as the dynactin regulatory complex , have been implicated in transport events during oogenesis , but the regulation of their interactions with specific RNP cargoes is only beginning to be addressed . Genetic screens in Drosophila have identified protein components of RNPs that are required for the proper localization of known mRNA axial determinants [14] . Recently , the use of live imaging techniques has allowed the direct visualization of RNP transport during Drosophila oogenesis [15–17] as well as apical localization of transcripts in the blastoderm embryo [18–20] . While genetic and biochemical approaches have revealed the diversity and complexity of RNPs , our understanding of their assembly , transport , localization and translational control is limited . To identify additional factors involved in these functions , we undertook a visual screen of GFP-tagged gene products that incorporate into motile particles in Drosophila egg chambers . Here , we report our analysis of one line with a GFP insert in the Drosophila ortholog of mammalian IGF-II mRNA binding protein ( Imp ) . Imp belongs to a conserved family of proteins that regulate mRNA localization , translation and stability ( reviewed in [21] . The chicken ZBP-1 and Xenopus Vg1-RBP were the founding members of this family , identified by their ability to bind to cis-acting localization elements in the 3' UTR of specific mRNAs . ZBP-1 targets the localization of β-actin mRNA to the leading edge of chicken fibroblasts and promotes cell migration [22] . Recent work has identified ZBP1 as a potential suppressor of the invasive behavior of mammary carcinoma cells [23] . In Xenopus , Vg1-RBP tethers Vg1 RNA at the vegetal pole to help establish embryonic polarity [24–26] , and also regulates the asymmetric translation of β-actin mRNA involved in axon guidance [27–29] . In mice and humans , the related CRD-binding protein binds to and regulates the stability of mRNA , including c-Myc and CD44 [30–32] . Elevated levels of Imp-related proteins in cancer cells , and their role in cell migration , have raised the level of interest in their function . Nonetheless , our knowledge of the complement of mRNAs targeted by the Imp proteins and the cytoskeletal mechanisms involved in Imp transport and localization is uncertain . Two recent studies have provided initial characterizations of the RNA-binding functions of Imp in Drosophila oogenesis , with some conflicting results [33 , 34] . Munro et al . proposed that Imp associates with oskar mRNA through putative Imp-binding elements ( IBE ) that are required for proper localization of Imp , though not for the initial localization of oskar mRNA . Geng and Macdonald [34] provided evidence that Imp binds to oskar mRNA with low affinity , and suggested instead that gurken mRNA is the major target to which Imp binds [34] . They show by immunoprecipitation experiments that Imp associates with Squid and Hrp48 , two RNP components with known roles in regulating gurken and oskar expression , respectively . Our work complements these analyses by examining Imp transport characteristics . We have conducted a mutational analysis of Imp to investigate its functions in Drosophila , and in addition to its redundant function in oogenesis , have identified requirements for Imp in embryogenesis and in synaptic terminal growth .
The collection of protein-trap lines is described in Morin et al . 2001 [35] . The dynein heavy chain mutations Dhc6–6 and Dhc6–12 are described in Gepner et al . 1996 [36] , and the khc27 FRT stock in Brendza et al . 2000 [37] . The UASp-ΔGl transgenic stock , expressing a truncation of the p150/Glued subunit of dynactin , is described in Mische et al . 2007 [17] . Inducible Imp transgenes UASp-RE and UASp-SD were constructed using EST clones RE72930 and SD07045 , respectively , which represent the two different Imp polypeptides identified in FlyBase . The UASp-KH transgene is a truncation derived from UASp-RE . Transformant fly lines were generated by standard methods [38 , 39] . Fly stocks for RNAi were obtained from the National Institute of Genetics , Japan , as follows: 5433R-1 ( kinesin light chain ) and 7765R-2 ( kinesin heavy chain ) . All other stocks were obtained from the Bloomington Drosophila Stock Center , and are listed in FlyBase ( http://www . flybase . org ) . We used mata4-GAL4-VP16 ( maternal a-tubulin ) and nanos-GAL4-VP16 to drive expression in the ovary; elav-GAL4 to drive expression in the neuron; and actin5C-GAL4 for ubiquitous expression . The molecular characterization of the small bristle ( sbr ) locus is described in Korey et al . 2001 [40] . The extents of duplications and deficiencies of the cytogenetic region 9E-F of the X chromosome , and the stress sensitive B ( sesB ) locus are described in Zhang et al . 1999 [41] . Df ( 1 ) HC133 has been shown to have a breakpoint in the 5′UTR of the Imp gene [40] . We verified the regions included in the Df ( 1 ) HC133 ( breakpoints 9B9–10 , 9E-F ) and the Y-linked duplication , Dp ( 1:Y ) v+ y+ ( breakpoints 9F3 , 10C2; h1-h25B ) by complementation . Inverse PCR was performed as described by the Berkeley Drosophila Genome Project . Briefly , 5μg of genomic DNA was digested with restriction enzymes BfaI or HhaI , in separate reactions . The DNA was ligated and PCR-amplified for 30 cycles using primers at the 5′ end of the P-element: 5′-CTTCGGTAAGCTTCGGC-3′ ( Primer 1 ) and 5′-CAGTGCACGTTTGCTGTTG-3′ ( Primer 2 ) ; followed by reamplification of 2μl of the primary PCR product using Primer 1 and a nested primer , 5′-GCACCTGCAAAAGGTCAG-3′ ( Primer 3 ) . The PCR product was ligated into the TA cloning vector pGEM-T Easy ( Promega , Madison , WI ) , and mini-prep DNA from the resulting plasmid was sequenced using vector primers . The protein-trap transposon was excised by crossing the protein-trap flies to flies expressing Δ2–3 transposase , and selecting flies lacking the mini-white eye color marker carried by the P-element . Imprecise excisions were identified by testing white-eyed flies for X-linked lethality , and were maintained as balanced stocks . We examined 158 lines and identified three lethal excision events . The extents of the deletions generated by imprecise excision of the P-element were tested by complementation analyses , using deficiencies and duplications of region 9A and mutations in the flanking genes , small bristles ( sbr ) and stress sensitive B ( sesB ) . One line was not rescued by a small Y-linked duplication of the Imp region , and probably represents a large deletion or rearrangement ( data not shown ) . The remaining two lines , H44 and H149 , were rescued by the Y-linked duplication , and failed to complement the deficiency . Deletions were characterized molecularly by blot analysis and by sequencing targeted regions of genomic DNA . To assess the requirement for Imp during oogenesis , lethal excision lines were recombined onto chromosomes containing FRT inserts at 14A-B ( genotype: y w v P[mini-w+ , FRT]101 ) . Recombinant chromosomes were identified using the marker yellow and the mini-white marker associated with the FRT insertion . Presence of the excision mutations was determined by testing the recombinant chromosomes for X-linked lethality . Germline clones were produced in the presence of the dominant female sterile mutation , ovoD1 , by crossing balanced excision-FRT females to males of the genotype w , ovoD1 , v P[mini-w+ , FRT]101; P[hsFLP]38 . Eggs were collected for 3–4 days and then larvae were heat-shocked for 1 . 5 hours in a 37°C water bath to induce expression of the FLP recombinase enzyme . Females of the genotype excision-FRT/ovoD1-FRT; hsFLP were crossed to sibling males and then examined for the presence of eggs and larvae , or ovaries were dissected and fixed for antibody staining . The lethal phase analyses were conducted by standard protocols and similar to those previously described [42] . Sucrose density gradient analysis was based on Wilhelm et al . 2000 [43] . Extracts were prepared from hand-dissected ovaries in DXB ( 25mM Hepes , pH 6 . 8 , 50mM KCl , 1mM MgCl2 , 1 mM DTT , 250 mM sucrose ) plus protease inhibitors ( 10 mg/ml aprotinin , 1 mg/ml leupeptin and pepstatin , 0 . 1 mg/ml each of soybean trypsin inhibitor , n-tosyl L-arginine methylester , and benzamidine ) . Samples were treated with RNasin ribonuclease inhibitor ( Promega ) , or , in parallel , RNase A followed by RNasin . Extracts were clarified at 100 , 000 × g prior to loading 700 μg total protein on 5 ml 10–40% sucrose gradients prepared in DXB , and centrifuged for 5 hrs at 44 , 000 rpm in a Beckman SW 50 . 1 rotor . Gradients were collected into 250μl fractions and frozen at −80°C . 15 μl of each fraction were analyzed by immunoblot . To examine protein expression levels , hand-dissected ovaries were homogenized and spun for 15 minutes at 4°C in a microfuge . Equal amounts of total protein were separated by SDS-PAGE and transferred to PVDF membrane by standard methods . Blots were processed using the Tropix chemiluminescence system ( Applied Biosystems ) . Anti-GFP monoclonal JL-8 ( BD Biosciences ) was diluted 1:1000 . The anti-Imp antibody was a gift from Paul Macdonald [34] . Ovaries were dissected from 2–4 day old females and fixed in formaldehyde/heptane as described in [46] . Fixed ovaries were examined for GFP expression or stained with antibodies diluted as follows: dynein heavy chain ( P1H4 ) 1:500 [46] , kinesin heavy chain 1:1500 ( Cytoskeleton ) , Gurken 1:10 ( T . Schupbach , Princeton University ) , Staufen 1:3 , 000 ( D . St Johnston , University of Cambridge ) , BicD 1:10 ( R . Steward , Rutgers University; clones 4C2 and IB11 ) , Oskar 1:100 ( A . Ephrussi , EMBL Heidelberg ) , and Orb 1:25 ( Developmental Studies Hybridoma Bank at University of Iowa ) . Egg chambers were examined by confocal microscopy , on either a Nikon TE200 or a Zeiss Axiovert 200M microscope . To examine synaptic morphology , third instar larvae were dissected in PBS , fixed in 4% formaldehyde for 20 minutes , then washed and processed as for ovaries . The anti-cysteine string protein ( CSP ) antibody 6D6 ( Developmental Studies Hybridoma Bank at University of Iowa ) was diluted1:200 , and anti-dFMR1 antibody 5A11 ( Developmental Studies Hybridoma Bank at University of Iowa ) was diluted 1:1000 . Rabbit anti-phosphorylated Mad ( PS1 ) was diluted 1:500 [47] . Alexa-488 or −567-conjugated secondary antibodies ( Molecular Probes ) were used at final concentration 1:200 . For colcemid treatment , young females were starved for 3 hours prior to treatment . Flies were fed a paste of baker's yeast made with either 200μg/ml demecolcine ( colcemid ) ( Sigma D7385 ) or , as a control , 200μg/ml lumicolchicine ( Sigma L0760 ) , and were dissected after 24 hours of feeding . Ovaries were fixed as above . For cytochalasin D treatment , ovaries were dissected from 2–4 day old females into 1X Robb's Minimal Medium with or without 20μg/ml cytochalasin D . Individual stage 8 and 9 egg chambers were teased apart and incubated 10–20 minutes at room temperature before imaging . GFP particle velocity and run-length were manually tracked using the “track points” function of Metamorph ( Molecular Devices ) image analysis software . Particles displaying linear movement for four consecutive frames were selected for analysis . The cursor was placed at the leading edge of each particle , and the X and Y positions were recorded . As the particle moved in each subsequent frame , the cursor was moved to the new position of the leading edge , and the new X and Y positions were recorded . This procedure continued until the particle ceased moving , or moved out of the plane of focus; consequently , the measurements of run lengths are underestimates . Mean velocity and run-length , and standard deviation , were calculated for each particle . To analyze synaptic terminal size , the NMJ at muscles 6 and 7 of abdominal segments 2 and 3 were examined to determine the number of synaptic boutons and muscle size . For each genotype , the synaptic terminal size was calculated as the total number of synaptic boutons in each hemi-segment divided by the surface area of the respective muscle . This relative synaptic terminal size was then averaged for each genotype . Statistical significance of the difference between mutant and wild type was determined using Student's t-test . Significance was established when p < 0 . 05 .
To identify molecules that are actively transported in oogenesis , we screened a previously isolated collection of Drosophila GFP-protein trap lines [35] . These transgenic lines were generated using a protein trap transposon ( PTT ) designed to tag proteins with GFP by random insertion within introns , generating an artificial GFP-exon that is spliced into mRNAs . The recovered lines were originally screened for GFP expression during embryonic and larval development [35] . We rescreened the collection to identify motile GFP particles in the nurse cells and oocytes of developing egg chambers . Ovaries were dissected live into halocarbon oil and individual egg chambers were examined for the pattern of GFP expression . Lines that showed GFP enrichment in the oocyte relative to the nurse cell cytoplasm were subsequently examined by time-lapse confocal microscopy for GFP particle movement , and were characterized molecularly to determine the identity of the trapped genes . Using this strategy we screened more than 300 trap lines and identified several genes , including the known RNP components , Ypsilon schachtel ( Yps ) and Squid ( Videos S1 and S2 ) . Yps has been found in an RNP complex with Exuperantia , and is reported to antagonize Orb function in the localization of oskar mRNA [43] . Squid also assembles into RNP complexes and is required for the proper localization of the dorsoventral determinant , gurken mRNA [48] . Our live imaging shows the GFP-tagged Yps and Squid gene products each incorporate into morphologically distinct particles , which move with significantly different velocities ( Yps: 0 . 59 μm/s ± 0 . 16 s . d . , compared to Squid: 0 . 50 μm/s ± 0 . 10; p < 0 . 05 ) and different average run lengths ( Yps: 4 . 28 μm ± 1 . 74 , compared to Squid: 3 . 89 μm ± 1 . 64; p < 0 . 05 ) . The differences in motility parameters for each of the GFP-tagged proteins suggest they incorporate into distinct particles subject to distinct regulatory mechanisms . We also identified a single PTT insertion within a large intron of Imp , an X-linked gene encoding the Drosophila ortholog of human IGF-II mRNA binding protein . The GFP-Imp particle exhibits robust motility in the nurse cells ( Video S3 ) and shows distinct accumulation in the oocyte , as well as in the embryonic and larval central nervous system ( Figure 1A–1C ) . Imp is a member of a conserved family of zipcode-binding proteins which regulate the localization , translation and stability of multiple mRNAs [21] . Imp is about 47% identical in sequence to its vertebrate orthologs across the region of four KH-type putative RNA-binding domains [49] . Two RNA recognition motif ( RRM ) domains are absent from Drosophila Imp , which is shorter at the N-terminus and longer at the C-terminus than the vertebrate proteins [49] ( see [48] , and Figure 1D ) . The level of Imp protein expression is apparently unaffected by the GFP insertion ( Figure 1E ) , and flies are homozygous viable and fertile , suggesting that the tag does not impair Imp function . To address whether the GFP-Imp particle is a RNP , we examined its stability following treatment with RNase . Extracts from GFP-Imp ovaries were fractionated over 10–40% sucrose gradients in the presence of RNase inhibitor or after treatment with RNase A ( Figure 1F ) . Intact complexes migrate near the bottom of the gradient , with sedimentation coefficients greater than 20S . Treatment of cell extracts with RNase A disrupts the complex , shifting the GFP-Imp peak toward the top of the gradient . These results indicate that GFP-Imp is part of a large RNA-containing complex in Drosophila , and are in agreement with recent reports describing the interaction of Imp with maternal and neuronal RNAs [33 , 34 , 50] . During oogenesis , GFP-Imp RNP particles are rapidly transported through the nurse cell compartment , become enriched in the early oocyte , and then later are concentrated at the posterior pole of the oocyte ( Figures 1 and 2 ) . We used time-lapse confocal imaging to document the unidirectional , linear movements of GFP-Imp particles in nurse cells ( Video S3 ) . Similar to other maternal RNP particles , the transit of GFP-Imp from nurse cells to the oocyte requires microtubules , but not actin filaments [17 , 51–53] . Movement of GFP-Imp to the oocyte is completely blocked in egg chambers collected from females treated with the microtubule-depolymerizing drug colcemid ( Figure S1 ) . In contrast , the actin inhibitor , cytochalasin D , does not block GFP-Imp motility in nurse cells ( Table 1 ) , but does disrupt its retention at the posterior pole of the oocyte ( Figure S1 ) . We analyzed particle movements in genetic backgrounds with reduced dynein and kinesin function , to identify the motor responsible for Imp transport to the oocyte ( Table 1; Figure 3 ) . In transheterozygous combination , the hypomorphic dynein heavy chain alleles Dhc6–6 and Dhc6–12 produce female sterile adults [36] . Egg chambers in these females arrest in late oogenesis , and normal dynein localization in the oocyte is disrupted [46] . Although particle movement is not completely abolished in the dynein hypomorphic mutant egg chambers , the number of GFP-Imp particles in motion is substantially reduced compared to wild type . In addition , the average particle velocity decreases from 0 . 65 μm/s ± 0 . 22 in wild type to 0 . 31 μm/s ± 0 . 12 in the dynein mutant . As another way to test dynein's contribution to GFP-Imp particle motility , we disrupted the function of dynactin , the dynein activating complex . We used nanos-GAL4 to drive germline expression of a dominant negative dynactin construct , UASp-ΔGl [17] and observed a reduction in transport velocities within the nurse cells ( 0 . 45 μm/s ± 0 . 14 ) , and the early mislocalization of GFP-Imp to the anterior of the oocyte ( Figure 2B ) . As in the dynein mutant , the subsequent accumulation of GFP-Imp at the posterior pole of the oocyte still occurs ( Figure 2B' ) . We examined the role of kinesin in GFP-Imp transport using germline clones homozygous for a null mutation in the kinesin heavy chain , khc27 [37] . In contrast to the dynein mutant background , particle velocity increases when kinesin function is lost ( 0 . 76 μm/s ± 0 . 29 ) . In the kinesin mutant background , the posterior localization of GFP-Imp is blocked ( Figure 2C and 2C' ) , similar to the behavior of the posterior determinants oskar mRNA and Staufen protein [17 , 54] . GFP-Imp localizes to the developing nervous system during embryogenesis and also during late larval development ( Figure 1B and 1C ) , where it exhibits rapid transport in the segmental axons ( Video S4 ) . Intriguingly , GFP-Imp particle movement along axonal microtubules is saltatory and bidirectional , exhibiting abrupt , short and frequent reversals in the direction of transport along a microtubule , regardless of whether transport is anterograde or retrograde . The reversals in GFP-Imp motility along axonal microtubules are not observed in the motility of GFP-Imp particles in the egg chamber , suggesting that transport is differentially regulated in the different cell types . The velocities of GFP-Imp particles ranged from 0 . 41 μm/s to 2 . 13 μm/s; we did not separately establish the minus- and plus-end velocities due to the mixed polarity of microtubules . As in ovaries , motility of GFP-Imp in axons requires microtubule motors . To reduce dynein motor activity , we expressed UASp-ΔGl with elav-GAL4 and observed a significant decrease in particle velocities ( p < 0 . 0001; Table 2 ) . To disrupt kinesin I function , we used dsRNAi to knock down the kinesin heavy and light chain polypeptides , again using the elav-GAL4 driver . Unlike ovaries , loss of kinesin function in the axon has a similar effect on Imp motility as loss of dynein , resulting in a significant decrease in velocity ( p < 0 . 0001 ) . The antagonistic relationship apparent between the dynein and kinesin motors in ovaries is not present in axons ( Table 2 ) . We generated mutations in the Imp gene by imprecise excision of the PTT , and identified two excision lines , H44 and H149 , which are homozygous lethal . These lines fail to complement a deficiency , Df ( 1 ) HC133 , that has been shown to remove the entire Imp coding sequence [40] , and their lethality is rescued by a small Y-linked duplication of the Imp region ( Table 3 ) . Additionally , both lines complement mutations in the two flanking genes , small bristles ( sbr ) and stress-sensitive B ( sesB ) . These results provide genetic evidence that the mutations affect only Imp , and demonstrate that Imp function is essential for Drosophila development . Molecular analyses of genomic DNA from each mutant line confirm that both alleles disrupt Imp , but not the adjacent genes . H149 contains an internal ∼5 . 1 kb deletion , which removes the intron containing the PTT element , as well as several adjacent exons . In the case of the H44 allele , an inversion and rearrangement of the PTT disrupts Imp exons . Consistent with these results , we do not detect Imp protein in extracts derived from mutant ovaries ( Figure S2 ) . To examine whether Imp is involved in the establishment of oocyte polarity , we generated germline clones homozygous for the lethal Imp mutations . We find that females with germline clones for either the H149 or H44 mutation produce phenotypically normal eggs . The expression and localization of known determinants of the anterior/posterior axis ( Staufen , Bicaudal-D , Orb , and Oskar ) and dorsal/ventral axis ( Gurken ) appear normal in the clonal egg chambers ( data not shown ) . Although no visible defects were observed in germline clones for either Imp mutation , the hatching rates of the resulting embryos were reduced . For H44 , 84 . 5% of the embryos failed to hatch ( n = 328 ) , compared to 8 . 2% for wild type ( n = 348 ) . Cuticle preparations from H44 dead eggs displayed two phenotypes . The first category of embryos looked normally developed , even though they still failed to hatch . The second category of embryos arrested earlier in development and appeared to have defects in the anterior of the embryo , perhaps in either germband retraction or head involution . All the surviving adult progeny that arose from germline clones were female , suggesting zygotic rescue by the paternal contribution of a wild type X chromosome . By comparison , Munro et al . ( 2006 ) previously reported 100% of the embryos derived from Imp mutant clones died in late embryogenesis [33] . As a more sensitive assay of Imp function in germline development , we examined the adult progeny produced from the homozygous mutant egg chambers to test for a “grandchildless” phenotype . These female progeny were fertile , supporting the conclusion that maternal Imp function is not required for the production of a normal female germline . To further probe the role of Imp in oogenesis , we overexpressed three engineered transgenic constructs . The Imp gene is predicted to have multiple transcripts that encode two different polypeptides varying in their N-termini , but sharing identical C-terminal KH-domains . Our Imp cDNA transgenes , UAS-RE and UAS-SD , encode these two distinct polypeptides . A third transgene , UAS-KH , is a truncation of Imp that encodes only the four KH domains and lacks the N-terminal sequences ( Figure S2A ) . Germline expression of all three constructs was driven with maternal alpha-tubulin-GAL4 ( a-tubGAL4 ) . Flies expressing the UAS-SD transgene ( a-tubGAL4/+; UAS-SD/+ ) exhibit a dorsal appendage phenotype ( Figure S2C ) and failed to hatch . Multiple independent transgenic lines were tested and all lines produced a similar dorsal appendage defect suggesting the phenotype is specific to the expression of the UAS-SD transgene . The penetrance of the phenotype varied between lines from 29% to 47% ( for each line , n > 270 ) , consistent with variation in the levels of expression of the transgene in the different transgenic lines . A similar defect in dorsoventral polarity was reported by Geng et al . ( 2006 ) [34] . However , flies overexpressing either the UAS-RE ( a-tubGAL4/+; UAS-RE ) or the UAS-KH ( a-tubGAL4/+; UAS-KH/+ ) transgene , show no deffect in oocyte development or female fertility ( Table 4 ) . These results identify an N-terminal region preceding the KH domains that is important for directing Imp function in oogenesis . The loss-of-function Imp alleles , H44 and H149 , exhibit increased lethality late in pupal development , at the pharate adult stage . Pharate adult lethality is commonly observed for mutants defective in synaptic transmission , and is consistent with a role for Imp in mRNA localization and translation during synaptogenesis [55–58] . We examined the neuromuscular junction ( NMJ ) in third instar larvae from homozygous and transheterozygous ( H44/H149 ) Imp mutant backgrounds , and counted the numbers of synaptic boutons ( axon terminal structures ) . Since the size of the synaptic terminal grows dramatically during the third larval instar stage , we took care to select larvae that were of comparable size for our analysis . Additionally , to correct for any size differences , we calculated and compared the relative terminal size for all lines ( see Materials and Methods ) . From this analysis , we observed significantly smaller synaptic junctions in the loss-of-function mutant backgrounds ( e . g . H44/H149 larvae , p < 0 . 01; Figure 4 ) . The reduction in size of the synaptic termini did not result in any obvious muscle twitching in mutant larvae , and although we did note that mutant larvae appear somewhat sluggish , it is difficult to quantitate ( compare Videos S5 and S6 ) . To further assess a neuromuscular defect , we examined the behavior of the adult mutant flies that escaped the pharate adult lethal phase and eclosed . These “escaper” flies have a largely normal morphological appearance , with the exception that their wings are raised and held back . The mutant flies are not able to climb the walls of the vials , but remain on the food and exhibit little movement . Closer inspection shows that the movement of the mutant flies is severely compromised , with highly uncoordinated twitching and grooming leg movements . The mutant flies frequently fall over as they try to walk and cannot readily upright themselves ( Videos S7 and S8 ) . These phenotypes are consistent with a neuromuscular defect , but in this transheterozygous mutant background the loss of Imp function extends beyond the nervous system . The apparent neuromuscular phenotype may result from the indirect consequences of defects in other tissues . We tested this possibility by examining animals ( elav-GAL4/+; UAS-RE ) that overexpress the UAS-RE Imp transgene presynaptically in neurons using elav-GAL4 . Similar to H44/H149 mutant larvae , the motility of larvae expressing the UAS-RE Imp transgene in neurons appeared sluggish ( Videos S9 and S10 ) . Only 4% of the expected test class eclosed , and the surviving adults have locomotion defects that are very similar to the transheterzygous ( H44/H149 ) Imp mutant flies . The flies exhibit sporadic tremors in the legs , an unstable walk , and frequently fall over and do not easily get back up ( Videos S11 and S12 ) . Surprisingly , UAS-RE overexpression generated a significantly larger synaptic terminal ( p < 0 . 0001 ) . Thus elevated levels of Imp expression and larger synaptic termini also appear to disrupt proper neuromuscular function . In parallel experiments , the overexpression of either UAS-SD or UAS-KH , using elav-GAL4 had no effect on survival , adult eclosure or synaptic terminal size ( p > 0 . 05 ) . In contrast to its apparently redundant function during oogenesis , Imp is required presynaptically for NMJ growth and may regulate neuromuscular activity . Genetic analysis has shown that the Bone Morphogenetic Protein type II receptor , Wishful thinking ( Wit ) , is also required for proper growth of the Drosophila NMJ , and similar to Imp exhibits a lethal phase during late pupal development [56 , 59] . The Wit receptor is required presynaptically , where it is thought to transduce a retrograde signal from the synapse to the cell body to coordinate synaptic growth with muscle growth . Wit signaling results in the phosphorylation and nuclear accumulation of the transcription factor , Mothers against dpp ( Mad ) . We hypothesized that Imp might regulate the translation of synaptic RNAs that encode components of this retrograde signaling pathway . Consistent with this possibility , GFP-Imp is detected in the synaptic boutons ( Figure 4E ) . If Imp mediates Wit signaling , then the loss of Imp function , which reduces terminal size , would be predicted to diminish Wit signaling . To test this hypothesis we asked if the nuclear accumulation of phospho-Mad ( pMad ) is blocked in the Imp mutant backgrounds ( Figure 5 ) . Unlike the requirement for Wit function , our results show that Imp function is not necessary for the accumulation of endogenous pMad in the larval ventral ganglion . We conclude that the requirement for Imp is downstream of Wit and the nuclear action of pMad . Alternatively , Imp function may lie in a separate , parallel pathway that regulates synaptic terminal growth .
In a visual screen for gene products that are actively transported in oogenesis , we recovered Imp , the Drosophila ortholog of the chicken zipcode binding protein , ZBP-1 . We confirm recent reports that while Imp is essential , it is not required for oocyte development or the proper localization of maternal determinants . However , results of our overexpression experiments are consistent with a redundant role for Imp in dorsoventral patterning , as previously reported [34] . From our study of Imp function in neurons , we show that Imp is required for the proper growth and/or maintenance of the synapse at the NMJ . Our analysis of the GFP-Imp trap line extends previous studies to characterize transport of Imp in both ovaries and axons . We show that Imp motility requires microtubule motor function . Similar to Exuperantia and Staufen RNPs [17] , the number and velocity of GFP-Imp particles in nurse cells is significantly reduced in a hypomorphic dynein mutant background . Loss of dynein function lowers the velocities of all Imp particles , suggesting that dynein is the motor that actively transports Imp in the nurse cell compartment . Moreover , we find that particle velocity is elevated in the kinesin null background , as we previously found for Stau-GFP and Exu-GFP RNP transport . This antagonistic interaction of dynein and kinesin suggests that both motors reside on the Imp RNP complex , but that in nurse cells only dynein actively promotes RNP translocation along microtubules [17] . Consistent with this interpretation , we do not observe saltatory , bidirectional movement of the Imp particles along microtubules in nurse cells . In contrast , Imp transport along microtubules within larval axons is saltatory , with frequent , short reversals in direction of transport . In axons , disruption of either dynein or kinesin reduces the velocity of dImp transport . It will be important to identify signaling pathways which regulate plus- and minus-end motor activities and consequently determine the directional bias of RNP transport in different tissues . The transport and localization of ZBP-1 particles at the leading edge of chicken fibroblasts is predominantly an actin-based process; localization of β-actin mRNA and ZBP-1 are disrupted by treatment with cytochalasin D , but not colchicine [60] . Moreover , chemical inhibition of myosin ATPase activity blocks ZBP-1 accumulation at the leading edge of fibroblasts [61] . We have no evidence that actin mediates active transport of Drosophila Imp , but the retention of Imp at the posterior pole of the oocyte is cytochalasin-sensitive and may require actin . Although Drosophila Imp lacks the RNA-binding “RRM” domains present in vertebrate orthologs , it contains the four KH domains which are reportedly sufficient for ZBP-1 association with actin filaments , as well as for RNP granule formation [22] . Nielsen et al . ( 2002 ) showed the KH domains of human Imp1 were sufficient for its correct localization and association with microtubules [62] . How the KH domain can mediate association with both actin and microtubules is not clear . The transport and localization of GFP-Imp during Drosophila oogenesis suggested a role for Imp in the localization of maternal RNA determinants that specify the embryonic axes . However , in germline clones of Imp loss-of-function mutations , we and others find no evidence of mispositioned mRNA determinants for either the anterior/posterior or dorsal/ventral axes [33 , 34] . We also looked at expression of Oskar protein in Imp germline clones and found no evidence that Imp is required for translational repression of oskar mRNA . This could reflect the existence of a functionally redundant factor that compensates for the loss of Imp during oogenesis [33] . Geng and MacDonald ( 2006 ) report that reducing Imp function partially suppresses a gurken mRNA misexpression phenotype [34] . In addition , Imp overexpression disrupts gurken mRNA localization and alters dorsoventral polarity , as reflected by defects in dorsal appendage formation . We observe a similarly penetrant dorsalization of the eggshell upon overexpression of the Imp splice variant encoded by our transgene , UAS-SD . This isoform varies from the alternate Imp polypeptide only in a small N-terminal region preceding the four KH domains . According to a recent analysis , the polypeptide represented by UAS-SD is normally expressed within the germline [63] . The isoform encoded by UAS-RE is apparently not abundantly expressed in the germline , and we find that its ectopic expression there does not generate the dorsalized phenotype . We propose that a domain preceding the KH domains and unique to the isoform represented by UAS-SD mediates an interaction that is critical for the proper regulation of Imp function in the germline . Our observations also address the zygotic function of Imp and show that Imp is essential for embryonic development . In contrast to the lack of germline phenotypes in oogenesis , a significant proportion of embryos derived from homozygous Imp mutant eggs fail to hatch . This phenotype appears unrelated to the function of maternal determinants , and as suggested by Munro and colleagues ( 2006 ) may result from the misregulation of other IBE-containing transcripts in the absence of Imp [33] . RNA targets are known to regulate cell migrations that contribute to tissue morphogenesis in other organisms [27 , 64] . Similarly , the loss of Drosophila Imp function might disrupt cell migration in early embryonic development . We noted anterior defects in cuticle preparations that could result from such defects . Imp is also required late in development; strong loss-of-function mutants die as pharate adults . The late lethal phase is consistent with neuronal function and the observed enrichment of Imp in the central nervous system ( Figure 1B ) [65] . Strong mutations in Drosophila dFmr1 also die as pharate adults [66] . dFMR1 is another KH-type RNA binding protein and overexpression or loss-of-function dFmr1 mutations are reported to generate neuronal and behavioral phenotypes [67 , 68] . Consistent with the similar phenotype , dFMR1 associates with Imp in neuronal RNP granules [50] . We report here that dFMR1 also associates with Imp in Drosophila ovaries , suggesting that the function of a dFMR1/Imp complex in RNA localization and translational control is not specific to neurons ( Figure S3 ) . The distribution of Imp and dFMR1 in neurons overlaps but is not identical , consistent with the interpretation that the two RNP components are not obligate partners . An earlier study has suggested that a fraction of the dFMR1 pool associates with PAR and lethal giant larvae proteins in a RNP complex [69] . How broadly Imp distributes among different RNPs is not known . Previous studies have reported that the overexpression of Drosophila Imp in the larval nervous system generates defects in axonal pathfinding and neural development [70 , 71] . We extend the analysis of Imp function in neurons and provide evidence that Imp acts to modulate synaptic terminal growth . We observe a decrease in the size of the synaptic terminal at the NMJ in loss-of function mutant backgrounds , and detect an expansion of the terminal when the UAS-RE transgene is overexpressed in neurons . The alterations in the size of synaptic termini correlate with aberrant neuromuscular activity . The defects in larval motility are mild , while eclosed adult flies exhibit severe neuromuscular defects , most notably very limited and uncoordinated sporadic leg movements . The defects observed are similar in both the loss-of-function Imp mutant background and in flies in which the RE isoform of Imp is specifically overexpressed in neurons . These results suggest that aberrant levels of Imp in neurons , either high or low , and the resultant increased or decreased size of synaptic termini can disrupt neuromuscular activity . Our results suggest that Imp function in neurons is not restricted to the guidance of growth cones during embryogenesis , and we speculate that Imp is also involved in the active delivery and translation control of transcripts at synaptic termini . The localization of Imp in synaptic boutons is not homogenously distributed , but appears to be enriched along the membrane . Thus , similar to the Drosophila oocyte , RNPs and the associated functional mRNA determinants become tethered to the cortical domains where local translation can quickly respond to changes in synaptic activities . Intriguingly , the delivery and local translation of mRNAs may regulate synaptic plasticity and homeostasis . However , we have not directly characterized the electrophysiological activity of synaptic terminals and can only speculate that the observed defects in neuromuscular activity result from the aberrant sizes and transmission of synapses in the Imp mutants . Moreover , it is worth noting that the electrophysiological activity and synaptic terminal size at the NMJ are not always tightly coupled . For example , highwire mutants have synaptic terminals that are double the size of controls , but the quantal content is reduced [72] . Similarly , spin mutants have a small or normal quantal content but a substantially increased synaptic terminal [57 , 73] . The opposite situation is also possible; presynaptic rescue of gbb mutants results in normal quantal content with a small synaptic terminal size [74] . All mutants in the BMP signaling pathway in Drosophila have a similar phenotype of decreased synaptic terminal size and quantal content [56 , 59 , 74–76] . In wit mutants the synaptic terminal at the NMJ is 60% of normal size , while quantal content is about 20% of controls [56 , 75] . Despite these significant defects at the morphological and electrophysiological level , wit larva do not show any obvious locomotion defects . A critical set of questions concerns how the transport and anchoring of Imp RNP complexes are influenced by signaling pathways . TGF-ß Bone Morphogenetic Protein signaling is required for synaptic development and plasticity in Drosophila . Mutations in either the ligand , glass bottom boat , or the corresponding type II receptor , Wit , impair synaptic growth and have a similar late lethal phase . The downstream consequence of Wit signaling is the nuclear accumulation of pMad . Our results establish that nuclear accumulation of pMad is not disrupted by the loss of Imp function and suggest that the mRNA targets of Imp do not function upstream of Wit . However , our data do not exclude the possibility that Imp acts downstream to communicate an anterograde cellular response to Wit signaling that modifies synaptic growth and plasticity . Synaptic development , homeostasis , and modulation of synaptic activities are important elements of functional neural circuits that depend on bi-directional signaling and communication between the synapse and cell body . While our observations underline the importance of Imp in synaptogenesis , we do not identify the target transcripts regulated by Imp . The composition of neuronal Imp RNP particles is an important area to pursue in future experiments . Indeed , there are many cytoplasmic RNA structures present within neurons and our knowledge of their functions is rudimentary [50 , 77] . Visual screens based on active cytoplasmic transport of RNP complexes should continue to provide an important tool for identifying additional gene products that regulate the transport and localization of mRNAs .
The National Center for Biotechnology Information ( NCBI ) database ( http://www . ncbi . nlm . nih . gov/gquery/gquery . fcgi ? itool=toolbar ) accession numbers for IGF-II mRNA-binding protein homologs are D . melanogaster isoforms NP_001036268 , NP_727457 , NP_727456 , NP_727455 , NP_727454 , NP_727453 , NP_727452 , NP_727451 , NP_511111; X . laevis , O57526 and O73932; Gallus gallus , O42254; Danio rerio , Q9PW80; Homo sapiens , Q9NZI8 and O00425; Mus musculus , O88477 and Q9CPN8; Rattus norvegicus , Q8CGX0; Caenorhabditis elegans , CAJ58502; and Saccharomyces cerevisiae , NP_009670 .
|
The localization of messenger RNA is a major mechanism to generate local asymmetries in protein activities and is utilized in a diverse array of biological functions . mRNA localization and the resultant protein gradients are critical for the establishment of embryonic axes , the polarized motility of cells and neurons , and the modulation of synaptic signaling . Presently , our knowledge of the many transacting factors required for the assembly , transport , and localization of mRNAs is rudimentary . In this study , we capitalize on an in vivo motility assay to screen for components of actively transported RNP complexes in live Drosophila egg chambers . One of the components identified , Drosophila IGF-II mRNA binding protein or Imp , is the homolog of chicken zipcode binding protein or human IGF-II mRNA binding protein . The human IGF-II mRNA binding protein is linked to the metastatic behavior of carcinoma cells in mammary tumors , but the mechanism is unclear . We demonstrate that the Drosophila Imp RNP complex , is actively transported in oogenesis , as well as in neurons by the microtubule motors , dynein and kinesin . We show that the regulation of transport is distinct in oocytes and neurons and report for the first time , that Drosophila Imp impacts growth of the neuromuscular synaptic terminal .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion",
"Supporting",
"Information"
] |
[
"drosophila",
"cell",
"biology"
] |
2008
|
Motility Screen Identifies Drosophila IGF-II mRNA-Binding Protein—Zipcode-Binding Protein Acting in Oogenesis and Synaptogenesis
|
The recently developed ‘two-step’ behavioural task promises to differentiate model-based from model-free reinforcement learning , while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables . These desirable features have prompted its widespread adoption . Here , we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance . The task involves a trade-off between the need for stochasticity , to allow strategies to be discriminated , and a need for determinism , so that it is worth subjects’ investment of effort to exploit the contingencies optimally . We show through simulation that under certain conditions model-free strategies can masquerade as being model-based . We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions . We confirm the power of a suggested correction to the analysis that can alleviate this problem . We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value . These generate behaviour that appears model-based under these , and also more sophisticated , analyses . Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues .
Humans and other animals are thought to use a mixture of different strategies to learn to choose actions that lead to positive outcomes and prevent negative outcomes [1 , 2] . Much interest is currently focused on the distinction between control systems which employ model-based and ( value-based ) model-free reinforcement learning ( RL ) [3–14] . Model-based RL works by learning a predictive model of the specific consequences of actions , and planning by using this model to evaluate the different options prospectively . By contrast , model-free RL directly learns the value of actions through prediction errors , which quantify the difference in worth between actual and expected outcomes . These different strategies offer distinct advantages and disadvantages . Model-based RL is computationally costly and time consuming , because of the demands of planning many steps into the future before action . However , it can , in principle , use information efficiently , particularly in the face of a changing environment . This is because the implications that a change has for control in other parts of the environment can be evaluated immediately using the model without having to be directly experienced . Model-free RL incurs little computational cost and supports rapid action selection . However , it is statistically inefficient as it discards information about the specific outcomes of actions , and learns by propagating initially incorrect predictions from states to their sequential predecessors . Dissociating the contributions of model-based and model-free RL to behaviour is challenging because under many circumstances , including most laboratory based reward guided decision making tasks , they are expected to produce similar behaviour . Outcome devaluation ( or , more generally , revaluation ) has traditionally been used as a gold-standard test to demonstrate the use of a simple forward model predicting the specific outcomes of actions [1 , 15 , 16] . In an outcome devaluation experiment , the subject is trained to perform two different actions , each of which obtains a different reward , e . g . pressing left or right levers for pellets of two different flavours . One reward is then devalued , for example by pairing it with illness in another context . The impact of this devaluation on the subjects’ propensity to press the levers is then tested in extinction , i . e . , without any longer providing the outcomes . Model-based lever-pressing depends on a representation of the outcome to which the pressing leads , implying that subjects would prefer the lever associated with the non-devalued outcome . However , model-free lever-pressing is based on past experience of its utility , implying that subjects would not differentiate between the two levers . Use of two levers controls for general motivational effects of devaluation and extinction . In psychological terms , model-based behaviour is considered goal-directed , and model-free , habitual [1 , 17] . Research using outcome devaluation paradigms has established that learnt actions are initially specified by model-based RL , but can transition to being devaluation insensitive given extensive training under appropriate conditions [17 , 18] . This has been interpreted as a shift to model-free RL [4] . Distinct sets of brain regions have been identified as necessary for devaluation sensitive and devaluation insensitive behaviour [19–30] , a finding that has been taken as implying that model-based and model-free RL are implemented by partially separate neural circuits . Recent approaches to behavioural neuroscience derive substantial explanatory value from parametric variation of decision variables in the context of large decision datasets . It is therefore desirable to develop tasks which achieve these ends , but also exhibit the critical feature of outcome devaluation–namely the wherewithal to discriminate model-based and model-free RL . The two-step task [7] represents one recently popular approach to creating such a task , attracting a substantial number of human studies [8 , 11 , 12 , 31–43] . The task is so named because each trial consists of two distinct steps ( see task diagram , Fig 1A ) . At the first step the subject chooses between two actions , termed action A and action B . After making this choice the subject reaches one of two second-step states termed state a and state b . Action A normally leads to state a , and action B normally leads to state b; however , on a randomly selected 30% of trials , a rare transition occurs , such that action A leads to state b and action B to state a . In each second-step state two further actions are available . The subject chooses one of these actions and either receives or not a reward before starting the next trial . The reward probabilities for each of the four second-step actions ( two in each second-step state ) , vary over time as reflecting Gaussian random walks on the range 0 . 25–0 . 75 ( Fig 1C ) . Rewards obtained ( or not ) at the second step modify the subjects estimates of the values of the second-step states , which are themselves the outcomes of the first step actions . On trials with rare transitions , the second-step state whose value is changed by obtaining or not a reward is normally reached from the first-step action that was not chosen . This suggests that a model-based agent which understands the true mapping between first-step actions and second-step states will behave differently from a model-free agent which does not use this knowledge . Model-based and model-free control can indeed be dissociated by evaluating how the events on one trial , specifically the transition ( common or rare ) and outcome ( rewarded or not ) , affect the probability of repeating the same choice at the first step on subsequent trials . Three sorts of analysis of these effects are in common use . The simplest is to look directly at the probability of repeating the first-step choice on just the next trial–this is called the ‘stay’ probability . A model-free strategy in which the value of the chosen first step action is updated directly by the trial outcome produces a pattern in which the subject tends to stay following rewarded and switch following non-rewarded trials , with no effect of transition ( Fig 1E ) . By contrast a model-based strategy in which the subject understands the transition structure linking the first step actions to second-step states produces a pattern of stay probabilities which show a transition x outcome interaction , i . e . rewards increase stay probability following common transitions and decrease stay probability following rare transitions ( Fig 1F ) . A second , more sophisticated version of this analysis is to perform multiple logistic regression of the probability of choice on one trial based on facets of choice and outcomes on one or more previous trials . RL algorithms imply that events can have an impact multiple trials into the future; this analysis can test this . We will also see that using extra regressors can alleviate potential confounds in the differentiation of MB and MF strategies . Finally , a third analysis is to fit RL models to behaviour using likelihood-based methods , and to compare directly their quality of fit . There is strong evidence that human subjects who have been explicitly told in advance about the transition structure and drifting reward probabilities , gamely pursue model based strategies , potentially integrating them with MF influences [7 , 8 , 34] . Given the unique set of attractive features offered by the two-step task , versions suitable for animal subjects would be desirable , and several groups are currently pursuing work in this direction ( Miller at al . Soc . Neurosci . Abstracts 2013 , 855 . 13 , Groman et al . Soc . Neurosci . Abstracts 2014 , 558 . 19 , Miranda et al . Soc . Neurosci . Abstracts 2014 756 . 09 , Akam et al . Cosyne Abstracts 2015 , II-15 ) . However , an informal observation , that we formalize below , is that the stochasticity of the conventional version of the task means that even optimised model-based strategies perform little better than chance level and do not outperform simple model-free strategies . Animal subjects are less tolerant when complex strategies have only limited advantages , and often switch to strategies such as always choosing the same option , or alternation , which obtain rewards at chance level with minimal cognitive effort . It will therefore likely prove necessary to increase the contrast between good and bad options in order to use the task with animal subjects , and our understanding is this is being done in the current crop of animal studies . Here we consider a stripped down version of the task which substantially improves the payoff for model-based strategies relative to chance level and model-free control . We show that seemingly innocuous changes to the task induce correlations between events which can allow model-free RL to masquerade as model-based . We first show that correlation between action values at the start of trials and the subsequent trial events can cause the stay probability analysis , when applied to the behaviour of purely model-free agents , to exhibit the transition-outcome interaction classically interpreted as indicative of model-based RL . We further show that a previously proposed modification to the analysis [34] accurately corrects for these correlations . A second , and more pernicious , issue arises from the correlation between where rewards are obtained ( second-step state a or b ) , and the expected value of choosing action A or B at the first step . We explore the behaviour of two agents which exploit this correlation . The first uses the trial outcome and location on the previous trial as a discriminative stimulus for the state of the world , using model-free RL to learn separate values for actions A and B following each combination of outcome ( rewarded or not ) and second-step state ( a or b ) reached on the previous trial . The agent develops a fixed mapping from one trial’s events to the next trial’s choice ( e . g . reward in state a ➔ choose action A ) , that generates behaviour that would be assessed as being model-based by either classical or improved stay probability analysis . The representation underlying the second agent makes explicit the latent or hidden state of the world–i . e . which second-step state has higher reward probability . The agent infers this hidden state by observing where it obtains rewards , and uses a fixed mapping from its estimate of the latent state to action . This agent produces behaviour which is qualitatively very similar to that of a model-based agent . Both agents outperform classical model-free strategies in terms of the fraction of rewarded trials; this provides an incentive for the acquisition of these alternative representations via the ample statistical evidence available particularly to over-trained animal subjects of the correlations that underpin them . These strategies can also in principle generate seemingly model-based behaviour on the original version of the task used in the human literature , and may play a role in the automatization of apparently model-based control recently reported to occur with extended training on the original task [42] .
Behaviour simulated from the Q ( 1 ) agent on the reduced version of the task showed a strikingly different pattern of stay probabilities from that seen in the original task ( Fig 2G , repeated for convenient comparison in Fig 3A ) . Stay probabilities showed a clear interaction between transition and outcome . A logistic regression analysis predicting stay probability as a function of outcome , transition , and transition-outcome interaction confirmed that transition-outcome interaction predicted stay probability ( P < 10−10 , t-test for non-zero predictor loading ) ( Fig 2A ) , and this predictive relationship held true over a wide range of agent parameter values ( S1A Fig ) . This result is counter-intuitive because by construction , the action values and hence choice probabilities of the Q ( 1 ) agent are unaffected by whether a common or rare transition occurred . The difference in stay probability between trials with the same outcome but different transitions therefore cannot be accounted for by a difference in the action value update that occurred on that trial , as the update is identical irrespective of the transition . Instead , the reason why the action values of the chosen and non-chosen option are ( on average ) different following trials with the same outcome but different transitions must be that the action values at the start of the trial are ( on average ) different . This can indeed be seen ( Fig 2B ) ; the mean difference between the action values for the chosen and not chosen option at the start of the trial was larger for common-rewarded and rare-not rewarded trials than for common-not rewarded and rare-rewarded trials . Why are action values at the start of the trial correlated with subsequent trial events , specifically the transition-outcome interaction ? There are two steps in the argument . First , the difference in action values between chosen and not-chosen action is on average larger for trials where the agent chooses the correct action , i . e . that which commonly leads to the state with high reward probability , than for trials where the agent choses the incorrect option . When the difference in action values is small , the agent has little evidence that one option is better than the other , and is more likely to choose the incorrect action . Additionally , due to the stochastic softmax decision rule the agent sometimes chooses the action with lower subjective value , and such ‘exploratory’ choices are more likely to be incorrect . Second , choosing the correct , rather than incorrect , action changes the probabilities of observing different combinations of trial events . Rewarded common transitions and unrewarded rare transitions are more likely to occur following a correct action than they are to occur following an incorrect action . Conversely , rewarded rare transitions and unrewarded common transitions are more likely to occur following an incorrect action . To summarise; the difference in action values going into the trial correlates with the probability of choosing the correct option . Whether the agent chooses the correct option determines the probability of observing each combination of subsequent trial events . Therefore when trials are divided into groups by outcome and transition , the action values at the start of the trial show a transition-outcome interaction ( Fig 2B ) , which is then also observed for the stay probabilities ( Fig 1G ) , even though the agent did not use any information about the transition in its action value update . This effect is not restricted to block based reward probabilities; it can also be observed when reward probabilities change as random walks ( S2A and S2B Fig ) , or with static fixed reward probabilities of 0 . 8 / 0 . 2 in states a / b ( S2F and S2G Fig ) . When reward probabilities in the two second-step states are fixed and equal ( S2H and S2I Fig ) , the Q ( 1 ) agent shows no transition-outcome interaction as action values at the start of the trial differ only through stochastic fluctuations in the experienced outcomes and are therefore not correlated with subsequent trial events . Data simulated from the Q ( 1 ) agent on the original version of the task does in fact show a significant ( P = 0 . 01 ) albeit very small positive loading on the transition-outcome interaction predictor ( S3B Fig ) due to the mechanism outlined above . The effect is radically weaker than in the reduced task because the greater stochasticity in the state transitions and reward delivery in the original task greatly reduce the strength of correlation between action values at the first step and subsequent trial events . As this effect is so weak in the original task we do not consider it to have any implication for the stay probability analyses in the existing human literature . It is possible to modify the logistic regression analysis of stay probabilities to prevent differences in action values at the start of the trial from appearing as a spurious loading on the transition-outcome interaction predictor . This can be done by including an additional ‘correct’ predictor which captures the tendency of the agent to repeat correct choices , as originally suggested in [34] . Including this additional predictor completely removed loading on the transition-outcome interaction predictor for the Q ( 1 ) agent ( P = 0 . 67 , t-test for non-zero predictor loading ) ( Fig 2C; repeated for convenient comparison in Fig 3C ) , correctly revealing that only the trial outcome affected the agent’s subsequent choice . For the model-based agent this extended logistic regression analysis showed positive loading on the transition-outcome interaction predictor ( P < 10−12 ) ( Fig 3G ) reflecting the true importance of this interaction to the action value update used by the agent . Including the correct predictor did reduce loading on the interaction predictor for the model-based agent by 32 . 3% , indicating that trial start action values also contributed to the pattern of stay probabilities for this agent . The addition of a correct predictor works because the correlation between actions values at the start of a trial and the subsequent transition-outcome interaction is entirely mediated by the correlation between these action values and whether the agent chose the correct action on that trial . Explicitly including a predictor for repeating correct choices absorbs the variance due to action values at the trial start which would otherwise be absorbed by the transition-outcome interaction predictor due to correlation between these two predictors ( Fig 2D ) . Including the correct predictor reduced , but failed to completely remove , loading on the transition-outcome interaction predictor for the Q ( 1 ) agent simulated on the reduced task version with random walk reward probabilities ( P < 10−3 , t-test for non-zero predictor loading ) ( S2D Fig ) . We hypothesised that the correct predictor failed to correctly compensate for trial start action values because it did not reflect the magnitude of the difference in reward probabilities between the two second-step states . We therefore tried using a continuous valued correct predictor whose magnitude was given by this difference . Including this predictor completely removed loading from the transition-outcome interaction predictor ( P = 0 . 78 , t-test for non-zero predictor loading ) ( S2E Fig ) . An alternative way of differentiating model-based and model-free strategies is a lagged logistic regression analysis which examines the effect on choice probability of trial events at different lags relative to the current trial ( Miller at al . Soc . Neurosci . Abstracts 2013 , 855 . 13 ) . Fig 3D and 3H show a lagged logistic regression analysis for Q ( 1 ) and model-based agents . The analysis evaluated how different combinations of outcome and transition predict that the agent will repeat the same choice a given number of trials in the future . For example , the ‘rewarded , rare’ predictor at lag -2 captures the extent to which receiving a reward following a rare transition predicted that the agent will choose the same action two trials later . This analysis is therefore an extension of the classical stay probability analysis to include the effect of earlier trials . For the Q ( 1 ) agent ( Fig 3D ) , obtaining a reward predicted that the agent will repeat the same choice irrespective of the transition , with a smoothly decreasing predictive weight at increasing lag . For the model-based agent ( Fig 3H ) , rewarded-common transitions and non-rewarded rare transitions predicted the agent will repeat the same choice , while rewarded-rare and non-rewarded common transitions predict the agent will not repeat the same choice , again with the predictive weight smoothly decreasing with increasing lag . Various other factors have been suggested as influencing strategies , including eligibility traces for MF algorithms , the possibilities of continual learning of the transition probabilities , and also outcome- and transition-independent perseveration . We also considered the effects of all of these on the statistics of choice . Although a Q ( 1 ) agent is typically used to illustrate model-free behaviour on the two-step task , it represents one end of a spectrum of model-free agents differentiated by the extent to which the action value update at the first step depends on either the trial outcome or second-step action values . This spectrum is parameterized by the eligibility trace parameter conventionally called λ . The update used by the Q ( 1 ) agent depends only on the trial outcome and not at all on the values of the second-step state ( or second-step actions in the original two-step task [7] ) . At the other end of the spectrum is the Q ( 0 ) agent which updates the value of the first step action based only on the value of the second-step state , with no direct influence of the trial outcome . The value of the second-step state is then updated based on the trial outcome . The behaviour of a Q ( 0 ) agent on the simplified two-step task is shown in Fig 3I–3L , and on the original task in S3I–S3L Fig . The behaviour on the reduced task of model-free agents which use mixtures of the Q ( 1 ) and Q ( 0 ) updates are shown in S4 Fig . The one trial back extended logistic regression analysis for the Q ( 0 ) agent ( Fig 3K ) shows positive loading on the transition and outcome predictors and negative loading on the transition-outcome interaction predictor . Loading on the transition-outcome interaction predictor in the extended logistic regression analysis distinguishes the model-based agent from model-free agents across the range of values of λ , none of which shows positive loading on this predictor . The lagged logistic regression for the Q ( 0 ) agent shows a complex pattern in which the predictive weight of each combination of trial events does not decay smoothly at increasing lags . It is typically assumed that subjects on the two-step task understand that the transition probabilities linking the first step actions to second-step states are fixed , and hence do not update their estimates of these based on the transitions they experience trial to trial . As this assumption may not be valid for subjects who do not have prior information about the task structure , we evaluated the behaviour of a model-based agent which learned the transition matrix online by updating its estimate of the transition probabilities for the chosen action on each trial based on the experienced transition ( S5 Fig ) . With a low transition learning rate , such that the estimates of the transition probabilities averaged over many prior trials , the behaviour of the agent was similar to that of the model-based agent with fixed transition probabilities ( S5A–S5D Fig ) . At higher transition learning rates , loading on the transition-outcome interaction predictor decreased , while loading on the outcome and transition predictors increased ( S5G Fig ) . At high transition learning rates where the agent’s estimate of the transition probabilities was dominated by the most recently experienced transition , loading on both the outcome and transition predictors was substantially higher than that on the transition-outcome interaction predictor ( S5L Fig ) . Human subjects typically show a perseveration bias on the two-step task [7 , 8] , i . e . a tendency to repeat first step choices independent of the trial events . We therefore tested how a perseveration bias affected behaviour on the reduced task for Q ( 1 ) and model-based agents ( S6 Fig ) . Perseveration bias increased stay probability ( S6A and S6E Fig ) and loading on the stay predictor ( S6C and S6G Fig ) , but did not change the characteristic pattern of positive loading on the outcome predictor for the Q ( 1 ) agent and the transition-outcome interaction predictor for the model-based agent ( S6C and S6G Fig ) . We have so far considered only agents whose state representation corresponds to that used by the experimenter to define the task . However , identifying those states that are relevant for behaviour is a substantial component of the real control problem faced by organisms and there is no guarantee that when faced with a decision task , subjects will adopt the same state representation conceived by the experimenter . In the two-step task there is an underlying latent state that is relevant to behaviour–whether the reward probabilities are higher in state a or b . This induces correlation between where rewards are obtained and the true expected value of first step actions . It turns out that model-free agents that exploit these correlations or even attempt to learn this full latent structure , can produce behaviour similar to that of a model-based agent without using the prospective action evaluation that is the hallmark of classical model-based RL . We first consider a simple way of exploiting the correlations . The two-step task has a circular structure in which subjects cycle repeatedly through the decision state , second-step states and trial outcomes . This repeating structure provides opportunities for subjects to learn predictive relationships between events on one trial and the actions that are likely to lead to reward on the subsequent trial . One such predictive relationship is that the location where reward is obtained on one trial predicts which choice on the next trial is likely to lead to reward . That is , if a reward is obtained in state a , the reward probability is higher for choosing action A on the subsequent trial , while if reward is obtained in state b the reward probability is higher for choosing action B on the subsequent trial . Note that this predictive relationship holds true across reversals in which second-step state has higher reward probability . The locations where reward is obtained , and conversely where non-rewards are obtained , can therefore , in principle , be used as discriminative stimuli to guide choice on the next trial . We therefore considered the behaviour of a ‘reward-as-cue’ agent which uses the location of reward as a discriminative stimulus for the state of the world . Specifically , the reward-as-cue agent treated the choice between actions A and B as occurring in one of 4 distinct states on each trial , defined by whether a reward or non-reward was obtained in state a or b at the end of the previous trial . The agent used model-free RL to learn independent values of actions A and B in each of these 4 states . Like the Q ( 1 ) agent , the reward-as-cue agent updated the value of the chosen action dependent on reward prediction error between its current estimate of the action value and the trial outcome , without using the action value at the second step in the update . The agent learned action values which produced the strategy of choosing action A following rewards in state a , action B following rewards in state b , action B following no reward in state a and action A following no reward in state b . This corresponds to a strong stay probability transition-outcome interaction ( Fig 3M–3O ) . Unlike the other agents considered so far , the reward-as-cue agent does not adapt to changes in the reward probabilities across blocks through changes in its action values . Rather , the action values are stable across blocks and reflect a fixed mapping between where reward is obtained and which action should be taken on the next trial . It is plausible that over-trained animals could learn to use the location of reward as a discriminative stimulus to guide choice on the next trial , as animals straightforwardly learn to use discriminative sensory stimuli of various sorts as cues for the best action to take next [44–47] . Once learnt , this strategy would be minimally cognitively demanding as it is essentially a fixed stimulus-response habit with only a limited demand on working memory . However , although the reward-as-cue agent gives behaviour on the one trial back stay probability analysis which is qualitatively similar to that of a model-based agent , it shows a very different pattern of loadings in the lagged logistic regression analysis ( Fig 3P ) . Rather than the smooth drop off of predictive weight with increasing lag observed for the model-based agent , only the previous trial events are predictive of the reward-as-cue agent’s behaviour . For all the agents in Fig 3 , we chose parameters determined by a maximum likelihood fit to the behaviour of the model-based agent , so that they would all have comparable average behaviour; see materials and methods ) . For the reward-as-cue agent , this suggested a very low learning rate ( 0 . 003 ) . If , instead , we chose parameters for all agents that maximized the fraction of trials that are rewarded , the reward-as-cue agent outperformed both Q ( 1 ) and Q ( 0 ) model-free agents ( Fig 4B ) . As noted , the reward-as-cue strategy works because there is in fact a latent , unobservable state of the world that is important to the decision problem–whether the reward probability is higher in state a or b . The location where reward is obtained is correlated with , and hence informative about , this latent state , and therefore can be utilised as a discriminative stimulus to guide behaviour . However , because the reward-as-cue strategy uses only the most recent reward as a discriminative stimulus , it is far from optimal . We therefore evaluated the behaviour of a different agent we term ‘latent-state’ which understands that the world is always in one of two latent states , one in which the reward probability is high in state a and low in state b , and the other in which the reward probability is high in state b and low in state a . At the end of each trial the latent-state agent performed a Bayesian update of its estimate of the probabilities that world is in each latent state based on the observed trial events . In updating the probabilities the agent also assumed that there is a small probability ( the inverse of the mean block length ) that the state of the world switches between trials . This amounts to the assumption that the block lengths are exponentially distributed , rather than being of fixed length , as generally employed . We did not explicitly model the learning of action values in each of these latent states , but rather assumed asymptotic behaviour in which the agent chose action A with high probability in the latent state where state a had high reward probability and action B with high probability in the latent state where state b had high reward probability . The behaviour of the latent-state agent looked qualitatively very similar to that of the model-based agent . The one trial back stay probability analyses showed a transition-outcome interaction ( Fig 3Q–3T ) . As for the model-based agent ( Fig 3H ) , the lagged logistic regression analysis for the latent-state agent ( Fig 3T ) showed a tendency to repeat choices that were followed by rewarded common and non-rewarded rare transitions , and to not repeat choices that were followed by non-rewarded common and rewarded rare transitions , with a gradually decreasing predictive weight at increasing lag . However , the behaviour of the latent-state and model-based agents could be discriminated using model fitting , with data simulated by the model-based agent being fit with higher likelihood by the model-based agent ( Fig 5C ) and data simulated by the latent-state agent being fit with higher likelihood by the latent-state agent ( Fig 5E ) . Data simulated from either latent-state or model-based agents was better fit by both of these agents than by either the Q ( 0 ) or Q ( 1 ) model-free agents . All agents on the reduced task had the same number of parameters , so model comparison using data likelihood directly , without correction for model complexity , is appropriate . With parameters optimized to maximize reward , the latent-state agent achieved performance that was not significantly different from the model-based agent ( Fig 5B ) . Finally , we evaluated the behaviour of agents using the reward-as-cue and latent-state strategies on the original version of the task ( S3M–S3T Fig ) . The reward-as-cue strategy produced a weak but significant ( P = 0 . 01 ) transition-outcome interaction effect on stay probability ( S3M and S3O Fig ) . The transition-outcome interaction is much weaker than that observed for the reduced task because the location where rewards are obtained is more weakly correlated with the expected value of the first step actions–obtaining a reward in state a is in fact only weakly correlated with a high expected value of action A . For the latent-state strategy we assumed the agent believed that at any point in time , reward probability in one second-step state is 0 . 625 ( the 75th percentile of the range of reward probabilities ) and in the other 0 . 375 ( the 25th percentile ) . This agent produced behaviour that was qualitatively very similar to the model-based agent ( S3Q–S3T Fig ) . No significant difference was observed in the performance ( fraction of trials rewarded ) achievable by any of the different strategies considered on the original task version ( Fig 5A ) . As in the reduced task , data simulated from each agent was fit with higher likelihood ( Fig 5F–5J ) , and lower BIC score ( S7 Fig ) , by that agent than by any of the other agents .
We have provided a detailed analysis of the performance of a number of different RL strategies on variants of the two-step task . Since in the original task , complex and taxing strategies only garner modestly more reward than simple ones , it might seem attractive to alter the task to enhance the discrimination . We showed some dangers inherent in this idea , in that induced correlations can make discrimination harder . We also generalized this analysis to more complicated model-free strategies . In particular , we identified two ways in which behaviour on the two-step task could , under certain conditions , be incorrectly identified as arising from prospective model-based evaluation of actions . The first issue is with the stay probability analysis commonly used as a metric of subjects’ strategies . We showed that rather than reflecting only the action value update occurring on a given trial , which is distinct for model-based and model-free action evaluation , stay probabilities can also be affected by action values at the start of the trial . This can cause the behaviour of a model-free agent to exhibit a stay probability transition-outcome interaction , which is classically interpreted as a signature of model-based behaviour . The second issue is the existence of alternative strategies which use different state representations from the basic states that define the task structure and produce behaviour which is similar to that of a model-based agent though not dependent on prospective evaluation of the outcome of actions . The possibility that purely model-free agents can exhibit a transition-outcome interaction effect on stay probability has been discussed in two prior studies using the original two-step task [12 , 34] . Both studies suggested this can occur when the reward probabilities at the second step change slowly , with [12] further stating that a large initial difference in reward probabilities between the two second-step states contributes to the effect . Our analysis clarifies the mechanism by demonstrating that it is due to correlation between action values at start of the trial , specifically the difference in action values between the chosen and not chosen action , and subsequent trial events . The effect is very weak in the original task where both the state transitions and reward delivery are highly stochastic , and hence decorrelate action values and subsequent trial events . Our results show the effect can be strong even when reward probabilities change rapidly , as the reduced task version with random walks showed a strong effect ( S2A–S2E Fig ) even though the standard deviation of random walk step sizes was 4 times larger than in the original task . The effect of trial start action values can be corrected for in a logistic regression analysis of stay probabilities by including a predictor which captures the tendency to choose the action which was correct on the previous trial , i . e . to repeat correct choices . Such a modification to the regression analysis was proposed in [34] and our results demonstrate its efficacy . We suggest this modified stay probability analysis will be a useful tool for evaluating the influence of trial events on subsequent choice in task variants where the classical stay probability analysis gives misleading results . One cost of using this additional predictor is that , as it is correlated with the transition-outcome predictor ( Fig 2E ) , the relative loadings on these two predictors will be sensitive to fluctuations in the data , potentially requiring larger datasets to achieve reliable results . The second issue we have identified with the two-step task is that , due to its repeating structure , subjects could , in principle , learn to exploit correlations between where rewards are obtained and the expected value of first step actions , to produce behavioural strategies that look similar to model-based behaviour but do not use prospective evaluation of actions . One simple strategy which we termed ‘reward-as-cue’ learns a fixed mapping between events on one trial and choice on the next trial ( e . g . reward in state a → choose action A ) . Notably , it outperformed classical model-free strategies in terms of acquiring reward . The attraction of this strategy from the point of view of the subject is that once learned it requires no further updating of action values to adjust to changes in reward probability in the two second-step states , and hence can be fully automatized into a stimulus-response habit . Though this strategy produces a strong stay probability transition-outcome interaction , it can be distinguished from model-based behaviour as only the most recent trial influences choice . However behaviour strikingly similar to that of a model-based agent was generated by a more sophisticated strategy we termed ‘latent-state’ , which uses the location of recent rewards as a discriminative stimulus for which of two latent states the world is in ( high reward probability in state a , or high reward probability in state b ) , and follows a fixed mapping from the latent state of the world to choice . On the large simulated datasets used in this study , behaviour simulated from latent-state and model-based agents could be differentiated by model-comparison , and this probably represents the best approach to doing so in experimental data . Data simulated from the latent-state agent was fit with higher likelihood ( Fig 5E and 5J ) and lower BIC score ( S7E Fig ) by this agent than by the model-based agent , and vice versa . Though the differences were small , particularly on the original task , they were highly significant ( P < 10−5 ) . However , several factors will make this discrimination more difficult when working with experimental data . Firstly; the size of experimental datasets is typically substantially smaller , reducing the resolution of model comparison approaches . Secondly , the quantitative details of fitted models are unlikely to exactly match subject’s strategies . Thirdly , subject’s behaviour may be generated by a mixture of interacting control systems using different strategies . Whether latent-state and model-based strategies can be discriminated using model comparison in a given behavioural dataset is ultimately an empirical question . Is it plausible that subjects could learn latent-state type strategies in the two-step task ? Many paradigms for humans and animals show evidence of aspects of this . It is apparent in probabilistic reversal learning tasks , in which humans [48] and monkeys [49] learn that there are in fact two distinct latent states of the world and use inference about the current latent state to guide their behaviour . Further , the huge wealth of tasks involving integration of noisy sensory evidence such as random dot motion discrimination and the Poisson clicks auditory discrimination task [44 , 46] . Take the former . Here , the latent state concerns which of two directions of motion is more prevalent in the input . Noisy sensory evidence is accumulated to draw this conclusion . In our task , the latent state is which of two states ( a or b ) is associated with a higher prevalence of reward . Noisy evidence , in the form of actual rewards , can be accumulated to draw an equivalent conclusion . Certainly there are important differences between these tasks; the timescale of integration is longer in the two-step and spans multiple trials , the discriminative stimuli in the two-step are themselves rewards , and the subjects take an active role in sampling the two information streams . However the inferential commonality is striking . Both the reward-as-cue and latent-state strategies ( termed collectively ‘extended-state’ strategies ) work by exploiting the regularity in the task structure that the location where rewards are obtained correlates with which first step action has higher reward probability . Evidence for this regularity accrues slowly as it is only across multiple reversals in the reward probabilities that the correlation becomes apparent . It therefore seems probable that if subjects do learn to exploit this regularity , the strategy would only arise after extended experience with the task . In the original version of the task used typically in the human literature , subjects do a total of ~200 trials . The limited number of trials performed , and the fact that human subjects have been trained to understand the true task structure—presumably priming the use of a model-based strategy—both argue against the possibility that the apparently model-based behaviour reported in the bulk of the human literature in fact arises from extended-state strategies . Indeed , it is only after substantial additional training [42] that apparently model-based human two-step behaviour becomes resistant to inference from cognitive load from a demanding secondary task performed in parallel [11] . This training might lead to the creation of an extended-state strategy in which prospective model-based evaluation is replaced by a process of latent state inference with static state-action mappings , and thereby apparent automatization . Latent state strategies go beyond classical model-free RL and are interesting in their own right . Indeed , although they do not use a model which predicts future state given chosen action , which following [3] we take as the definition of model-based RL , the latent state representation is a form of world model which allows the agent to approximate the behaviour generated by planning without the computational costs of simulating behavioural trajectories . In this respect it is similar to the successor representation [50] , which generalises between actions based on the similarity of their experienced successor states , and can also approximate planning at reduced computational cost . Strategies like this illustrate the observation that the distinction between model-based and model-free RL is perhaps better thought of as a spectrum than a binary classification [51] . Nonetheless , the distinction between strategies that do and do not utilise a prospective model for predicting the future state given the chosen action is of interest , and we therefore suggest that in the design and interpretation of versions of tasks like the two-step , the possibility subjects may utilise extended-state strategies should be considered . This is of particular importance for versions intended for animal subjects , since the extensive training that typically precedes recordings or manipulations provides ample opportunity for task regularities to be learnt . Further , adaptations of the task to create sufficient contrast between good and bad options to offer sufficient incentive can provide stronger statistical evidence for the regularity that underpins extended-state strategies . Various options exist to minimise the probability that apparently model-based behaviour is in fact due to such strategies . One would be to avoid overtraining subjects , limiting the total number of trials they perform . However , this precludes generating very large behavioural datasets to better quantify the effect of manipulations or the relationship between behaviour and neural activity . A second possibility is to accept that it may be difficult to disambiguate extended-state from classical model-based strategies purely from behaviour , and use neural data to try and disambiguate the strategy used by subjects . A final potential option is to modify the two-step task to introduce reversals into the transition matrix which maps the first step choice to second-step state . In this task variant , not only does the reward probability in each second-step state change over time , but the action which must be chosen to reach a given second-step state also changes . Model-based control that performs incremental learning of the current transition probabilities ( one of the variants discussed above ) , can adjust in a straightforward manner to this change; one could even imagine coupling simple latent state inference for just the transition structure ( as in conventional probabilistic reversal learning ) to model-based RL . However , the task modification substantially increases the complexity of pure latent state strategies . Reversals in the transition matrix break the fixed predictive relationship in the original task between where reward is obtained and which action at the first step is likely to lead to reward . To solve this version through a fixed mapping from an inferred latent state to action requires latent states that are non-linear combinations of where rewards have been obtained and which actions have led to which states . The possibility we have identified here for model-free strategies to masquerade as model-based mirrors proposals that apparently model-free behaviour on the two-step task may in fact be due to model-based selection applied to action sequences [12] . Though very different in their underlying mechanisms , both indicate the complexity of cleanly dissociating the contribution of different learning strategies to behaviour . The two-step task latent state strategy provides an example of how agents may turn a planning problem into a set of automatized state-response mappings if there is a limited set of relevant states of the world , each with their own appropriate response . Even if the planning problem is large , with a great diversity of possible solutions , e . g . navigating from home to work , with experience the decision may be automatized to a mapping from a small number of relevant states of the world , e . g . is it rush hour , to a set of options which are known to work best in each condition . Such automatization is more sophisticated than stimulus-response habits as typically envisioned; the states of the world that evoke the response may be high level abstractions rather than directly observable stimuli , and the responses may be action sequences , or options in the hierarchical RL formalism [52] . However , as cached state–action mappings learnt through a history of reinforcement , such strategies have commonalities with classical habits and may be learnt using similar model-free RL algorithms applied to higher level state and action representations , perhaps instantiated in cortical-basal ganglia loops involving higher level cortices and associative and limbic striatal sub-regions . These considerations bring to the forefront the question of what state representations are learned and used [53 , 54] , something known to be central to the speed with which agents learn to solve decision problems .
All simulations and analysis were conducted in Python . Full code used to produce the paper figures is included in supplementary material ( S1 Code ) . For each agent , 10 sessions of length 10000 trials were simulated . All trials were included in analyses . Where errorbars are used these show standard error of the mean across session . All tasks used in the paper shared the common structure that on each trial an initial choice between two actions , termed action A and action B , led probabilistically to one of two states , termed state a and state b ( see diagrams; Fig 1A and 1B ) . Action A normally lead to state a and action B normally lead to state b , but with fixed probability on each trial , a rare transition could occur such that action A lead to state b and action B to state a . The following variants of the two-step task were used in the simulations: In describing the action value updates used by the different agents we use the following variables: Q ( s1 , a1 ) : The value of the first step action chosen on the trial . Q ( s2 , a2 ) : The value of the second-step action chosen on the trial . r: The trial outcome ( 1 for reward , 0 for non-reward ) . α: The agent’s learning rate . All agents used a softmax decision rule with inverse temperature parameter T to determine choice probabilities as a function of action values except for the first step choice of the latent state agent . The update rules used by the agents were as follows: The parameter values of the model-based agent on both tasks were set to: α = 0 . 5 , T = 5 To ensure that average behaviour for the different agents was comparable , the parameters of the other agents were set by maximum likelihood fitting to data simulated from the model-based agent . This resulted in the following agent parameters: To evaluate the performance of the different agents in Fig 4 , agent parameter values were optimised using Powell’s method [55] . To reduce fluctuations in the objective function due to stochastic task and agent behaviour , the random seed was set to the same value at the start of every simulation in a given optimisation run . Each optimisation run was repeated 10 times from randomised initial parameter values to avoid local maxima . To prevent overestimation of performance due to overfitting to the specific pattern of behaviour generated by a given random seed , once parameters had been found which maximised performance for a given random seed , performance was evaluated with these parameters but a different random seed and this value was taken . For each agent , performance was evaluated for 10 sessions each of 10000 trials , with a different random seed used during the optimisation for each session . For those agents with only two parameters we separately optimised the performance using a brute force grid search . Performance evaluated using the Powell and grid search optimisation methods did not differ significantly for any agent . Values reported in the paper are from the Powell optimisation . For the Reward-as-cue agent we used the performance of a deterministic reward-as-cue strategy which choose option A following reward in state a , option B following reward in state b , choose option A following non-reward in state b , option B following non-reward in state a . In all logistic regression analyses , the dependent variable was the subject’s choice , coded as stay vs switch , such that positive values of the predictor promote staying with the previous choice . Predictors used in the analysis took the following values as a function of trial events: Stay: +1 for all trials . Outcome: +0 . 5 for rewarded trials , -0 . 5 for non-rewarded trials . Transition: +0 . 5 for common transition trials , -0 . 5 for rare transition trials . Transition-outcome interaction: +0 . 5 for common transition rewarded and rare transitions non-rewarded trials , -0 . 5 for rare transition rewarded and common transition non-rewarded trials . Correct—binary: +0 . 5 for choosing option which led commonly to state with higher reward probability , -0 . 5 for choosing option which led commonly to state with lower reward probability . In the original task the higher reward probability of the two actions available in each second step state was taken as the states reward probability . Correct-continuous: Difference between reward probability in the state commonly reached from the chosen action and reward probability in the state commonly reached from the not-chosen action . Rewarded-common: +0 . 5 for rewarded trials with common transition , 0 otherwise . Rewarded-rare: +0 . 5 for rewarded trials with rare transition , 0 otherwise . Non-rewarded common: +0 . 5 for non-rewarded trials with common transition , 0 otherwise . Non-rewarded rare: +0 . 5 for non-rewarded trials with rare transition , 0 otherwise .
|
Planning is the use of a predictive model of the consequences of actions to guide decision making . Planning plays a critical role in human behaviour , but isolating its contribution is challenging because it is complemented by control systems which learn values of actions directly from the history of reinforcement , resulting in automatized mappings from states to actions often termed habits . Our study examined a recently developed behavioural task which uses choices in a multi-step decision tree to differentiate planning from value-based control . We compared various strategies using simulations , showing a range that produce behaviour that resembles planning but in fact arises as a fixed mapping from particular sorts of states to action . These results show that when a planning problem is faced repeatedly , sophisticated automatization strategies may be developed which identify that there are in fact a limited number of relevant states of the world each with an appropriate fixed or habitual response . Understanding such strategies is important for the design and interpretation of tasks which aim to isolate the contribution of planning to behaviour . Such strategies are also of independent scientific interest as they may contribute to automatization of behaviour in complex environments .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task
|
The cellular PI3K/Akt and/or MEK/ERK signaling pathways mediate the entry process or endosomal acidification during infection of many viruses . However , their roles in the early infection events of group A rotaviruses ( RVAs ) have remained elusive . Here , we show that late-penetration ( L-P ) human DS-1 and bovine NCDV RVA strains stimulate these signaling pathways very early in the infection . Inhibition of both signaling pathways significantly reduced production of viral progeny due to blockage of virus particles in the late endosome , indicating that neither of the two signaling pathways is involved in virus trafficking . However , immunoprecipitation assays using antibodies specific for pPI3K , pAkt , pERK and the subunit E of the V-ATPase co-immunoprecipitated the V-ATPase in complex with pPI3K , pAkt , and pERK . Moreover , Duolink proximity ligation assay revealed direct association of the subunit E of the V-ATPase with the molecules pPI3K , pAkt , and pERK , indicating that both signaling pathways are involved in V-ATPase-dependent endosomal acidification . Acidic replenishment of the medium restored uncoating of the RVA strains in cells pretreated with inhibitors specific for both signaling pathways , confirming the above results . Isolated components of the outer capsid proteins , expressed as VP4-VP8* and VP4-VP5* domains , and VP7 , activated the PI3K/Akt and MEK/ERK pathways . Furthermore , psoralen-UV-inactivated RVA and CsCl-purified RVA triple-layered particles triggered activation of the PI3K/Akt and MEK/ERK pathways , confirming the above results . Our data demonstrate that multistep binding of outer capsid proteins of L-P RVA strains with cell surface receptors phosphorylates PI3K , Akt , and ERK , which in turn directly interact with the subunit E of the V-ATPase to acidify the late endosome for uncoating of RVAs . This study provides a better understanding of the RVA-host interaction during viral uncoating , which is of importance for the development of strategies aiming at controlling or preventing RVA infections .
Group A rotavirus ( RVA ) , a species of the Rotavirus genus in the Reoviridae family , is recognized as a major pathogen that causes severe acute dehydrating diarrhea in young children and in a wide variety of young animals [1 , 2] . RVA infections are responsible for approximately 200 , 000 deaths every year in children under the age of 5 years [3] . RVA is composed of a triple-layered particle ( TLP ) , which surrounds eleven genomic segments of double-stranded RNA ( dsRNA ) [1 , 2] . RVAs enter the cell by a complex multistep process in which different domains of the RVA surface proteins , including the VP8* and VP5* domains of the spike VP4 protein , and the VP7 protein , interact with different cell surface receptors [4 , 5] . Several lines of evidence indicate that most RVAs enter the cell by clathrin-mediated endocytosis [4 , 6 , 7] , although some RVAs , including rhesus rotavirus ( RRV ) , are known to enter the cell through a clathrin- and caveolin-independent pathway [4 , 8 , 9] . Following virus uptake , RVAs travel to different endosomal compartments before uncoating and release of a double-layered particle ( DLP ) into the cytosolic space . Uncoating and release of DLP are triggered either in the maturing endosome ( ME ) [early-penetrating ( E-P ) viruses] or in the late endosome ( LE ) [late-penetrating ( L-P ) viruses] depending on the process requirements , which differ between RVA strains [9–12] . For RVA uncoating to deliver DLP into cytoplasm , the environment of the RVA-containing endosomes has to change , for instance , by endosomal acidification , a drop in calcium concentration , the exchange of membrane components , the formation of additional intraluminal vesicles , or the acquisition of lysosomal components [4 , 13] . Among these mechanisms , endosomal acidification plays a crucial role in RVA uncoating of L-P strains such as the human strain Wa , the porcine strain TFR-1 and the bovine strain UK , but not in uncoating of E-P strains such as RRV [4 , 11 , 14] . The vacuolar-H+ ATPase ( V-ATPase ) proton pump is a multi-subunit membrane protein complex , which is found in the membranes of intracellular organelles and in the plasma membrane of certain specialized cells . It is composed of a peripheral V1 domain ( consisting of subunits A to H ) , mediating the hydrolysis of ATP , and a membrane-bound V0 domain ( consisting of subunits a , c , c' , c" , d , and e ) , translocating protons across the membrane . It maintains the acidification of endosomes , lysosomes , phagosomes and Golgi-derived secretory vesicles [15] . Recently , the V-ATPase has been found to be involved in cell entry of viruses by maintaining the acidic pH within the endosome necessary for viral genome release [16] . The acidic milieu of LE constitutes a critical precondition that induces uncoating of a wide variety of enveloped and non-enveloped viruses including L-P strains [4 , 17] . The phosphoinositide 3-kinase/protein kinase B [known as Akt] ( PI3K/Akt ) signaling pathway regulates a wide range of cellular processes such as mitogenic signaling , cell survival , proliferation , apoptosis , and cytoskeleton remodeling [18 , 19] . The mitogen-activated protein-extracellular signal-regulated kinase/extracellular-regulated kinase ( MEK/ERK ) signaling cascade mediates a large variety of processes including cell adhesion , cell cycle progression , cell migration , cell survival , differentiation , metabolism , proliferation , and transcription [20] . Many viruses , including DNA and RNA viruses , employ the PI3K/Akt and/or MEK/ERK pathways to facilitate different stages of their life cycle [18–21] . With regard to the initial stages of viral infection , the signaling pathways or their molecules participate in virus entry and/or uncoating . For example , these signaling pathways mediate the viral entry process of many viruses such as hepatitis C virus ( HCV ) [22] , Ebola virus [23] , and human rhinovirus serotype 2 [24] , whereas influenza A virus ( IAV ) employs both PI3K and ERK to mediate V-ATPase-dependent endosomal acidification for fusion [25] . Activation of the PI3K signaling pathway has been found to play a crucial role in the increased yield of infectious RVAs by improving the adhesion and survival of infected cells [26] . The heat shock protein-90 ( Hsp90 ) , a molecular chaperone , is also known to modulate RVA replication during the late stage of the virus life cycle through activation of the PI3K/Akt signaling pathway [27] . The RVA-induced cellular apoptosis observed during the early stage of infection is inhibited by the RVA non-structural protein 1 via activation of the PI3K/Akt and NF-κB prosurvival pathways [28–30] . In addition , RVA activates the COX-2/PGE2 axis by modulating the MEK/ERK pathway which promotes RVA replication [31] . While the PI3K/Akt and/or MEK/ERK signaling cascades have been found to play different roles in the entry and/or uncoating of various viruses , their role in RVA entry and uncoating has remained elusive . Here , we demonstrate that the immediate early activation of the molecules PI3K , Akt , and ERK is important for endosomal acidification and uncoating of the L-P strains DS-1 and NCDV . Inhibition of both cascades blocked the release of both DS-1 and NCDV strains from the LE with a reduction in the fluorescence intensity of the CMFDA pH probe . Moreover , the direct interaction of phosphorylated PI3K , Akt , and ERK molecules with the subunit E of the V-ATPase V1 domain in response to early RVA infection emphasize the importance of RVA-induced PI3K , Akt , and ERK early activation as signaling mediators of the V-ATPase-stimulated endosomal acidification required for RVA uncoating .
To explore whether the PI3K/Akt and MEK/ERK signaling pathways are activated during immediate early RVA infection , monkey kidney epithelial MA104 cells and human intestinal epithelial Caco-2 cells were mock infected or infected with the human RVA strain DS-1 or the bovine RVA strain NCDV at a MOI of 10 for the indicated time points . Each mock- or RVA-infected cell lysate was subjected to Western blot analysis with antibodies specific for PI3K , phosphorylated PI3K ( pPI3K ) , Akt , phosphorylated Akt ( pAkt ) , ERK , and phosphorylated ERK ( pERK ) . In comparison with mock-inoculated cells , increased phosphorylation of the signaling molecules PI3K , Akt , and ERK was detected in RVA-infected cells as early as 2 min after virus inoculation , with a maximum at 5 min that was sustained at high level until 15 min , and declined afterwards ( Fig 1 and S1 Fig ) . In addition , both signaling pathways were reactivated at 4 h post-infection ( hpi ) and remained active up to 12 hpi ( Fig 1 and S1 Fig ) . We next determined whether the simian RVA strain RRV , which is considered an E-P strain [10 , 12] , similarly upregulates pPI3K , pAkt , and pERK during entry in MA104 and Caco-2 cells . Interestingly , the RRV strain activated both signaling pathways at 30 and 60 min post-infection ( mpi ) in MA104 and Caco-2 cells ( S2 Fig ) , respectively , with activation patterns similar to those previously reported [26] . We next checked whether phosphorylation of Akt and ERK is mediated by the upstream signaling molecules PI3K and MEK , and whether the PI3K/Akt and MEK/ERK pathways influence each other during immediate early RVA infection . MA104 and Caco-2 cells were pretreated with inhibitors specific for PI3K ( wortmannin ) and MEK ( U0126 ) . Each inhibitor specifically and efficiently reduced the levels of the corresponding downstream molecule in virus-infected cells in comparison with the levels in mock-treated and virus-inoculated cells ( Fig 1C and S1C Fig ) . Furthermore , knockdown of PI3K p85α and MEK by transfection with specific siRNAs resulted in a reduction in pAkt and pERK , respectively , following RVA infection ( Fig 1D and S1D Fig ) . Our results suggest that , in contrast to the simian RVA E-P strain RRV [10 , 12] , both the human and animal RVA strains DS-1 and NCDV concomitantly and independently activate the signaling pathways PI3K/Akt and MEK/ERK during immediate early RVA infection . Since MA104 cells are reasonably transfectable , highly permissive for many RVA strains , and have been widely used in the study of RVA entry [12] , we used this cell line in the rest of the experiments . Activation of the PI3K/Akt and MEK/ERK pathways during immediate early RVA infection might be involved in the RVA life cycle and blocking these pathways might eventually influence virus replication [26–31] . To further investigate whether these signaling pathways are involved in the RVA life cycle , we pretreated cells for 1 h with wortmannin , followed by RVA inoculation and incubation for 8 h , and we then checked the infectivity of the viral progeny . Compared to the mock control , pretreatment with wortmannin reduced total viral RNA in a dose-dependent manner ( S3A Fig ) , resulting in a dose-dependent gradual decline in virus-infected cells ( S3B and S3C Fig ) , viral protein expression ( S3D Fig ) and infectivity of viral progeny ( S3E Fig ) . Likewise , pretreatment of MA104 cells with U0126 reduced the infectivity of the progeny of both human and animal RVA strains ( S4 Fig ) . The reduction of the RVA life cycle was not due to cytotoxicity of the inhibitors used as no apparent effect of the chemicals was found on MA104 cell viability ( S5 Fig ) . Taken together , these data suggest that immediate early induction of both PI3K/Akt and MEK/ERK signaling pathways could play a role in one or more steps of the RVA replication cycle . The above results indicated that the activation of the PI3K/Akt and MEK/ERK pathways detected during the immediate early phase of RVA infection might be involved in RVA entry . Before examining the involvement of these pathways in RVA entry , we first analyzed endosomal trafficking of the human DS-1 strain and the bovine NCDV strain; the former is already known to use both early endosomes ( EEs ) and LEs [10] . In MA104 cells transfected with scrambled siRNA , both the human and the bovine RVA strains labeled with Alexa Fluor 594 ( AF594 ) were mainly localized in the perinuclear area ( Fig 2A ) . However , silencing of an EE marker Ras-related protein 5 ( Rab5 ) , or a LE marker Rab7 by transfection with specific siRNAs trapped the AF594-labeled viruses in the periphery of the cytoplasm ( Fig 2A ) . Blocking of intracellular trafficking by these siRNAs resulted in a reduction of total viral RNA , virus-infected cell numbers , viral protein expression , and infectivity of the viral progeny in comparison with mock-treated , virus-infected cells ( Fig 2B–2E ) . Our results are consistent with previous studies showing that L-P RVAs , such as the human DS-1 and the bovine NCDV strains used in this study , enter the cell through both EEs and LEs [4 , 10] . To further confirm the above results and to study the dynamics of entry of both strains , we examined the colocalization of viral particles ( using anti-VP8* antibody ) with the EE marker early endosome antigen 1 ( EEA1 ) and the LE marker lysosome-associated membrane protein-2 ( LAMP2 ) at different time points . Colocalization of DS-1 and NCDV viral particles with EEA1 gradually increased , reaching a maximum at 60 mpi and decreasing thereafter ( Fig 3A and 3B ) . Colocalization of both viral particles with LAMP2 was found to increase gradually starting at 60 mpi ( Fig 3C and 3D ) . Interestingly , the time required to attain the maximum signal was found to be different for each strain . The maximum colocalization signal of NCDV particles with LAMP2 was achieved at 80 mpi , and it started to decline at 90 mpi ( Fig 3D ) . However , DS-1 had a maximum colocalization with LAMP2 at 90 mpi ( Fig 3C ) , suggesting that the uncoating time was between 80 and 90 mpi for NCDV and between 90 and 100 mpi for DS-1 . The loss of VP8* staining at 120 mpi ( Fig 3 ) suggested that decapsidation of the virions , i . e . uncoating , had already occurred . The above results showed that both strains reached the LEs and that the uncoating process was completed within 120 mpi . Therefore , we next assessed whether the uncoating process of both strains are mediated by acidification of LEs . For this purpose , we employed chloroquine and ammonium chloride ( NH4Cl ) which inhibit endosomal acidification . As expected , the spots of VP8* antigens colocalized with LAMP2 had disappeared in the cytoplasm at 120 mpi ( Fig 4A ) . However , both chemical inhibitors retained both strains in LAMP2-positive LEs in the cytoplasm ( Fig 4A ) . As a consequence of virus trapping in LEs , total viral RNA , virus-infected cell numbers , viral protein expression , and infectivity of viral progeny were significantly reduced in comparison with mock-treated , virus-infected cells ( Fig 4B–4E ) . These data suggested that both strains were uncoated in the acidified LE . The above data implied that PI3K/Akt and MEK/ERK signaling pathways might be involved in the RVA entry process . Next , we investigated which step ( s ) of the RVA entry process was influenced by these signaling pathways . To check whether both signaling pathways are involved in virus trafficking from EEs to LEs , MA104 cells were preincubated with either wortmannin or U0126 , infected with DS-1 or NCDV for 30 min at 4°C , washed , and shifted to 37°C for the indicated time points . Subsequently , colocalization of both strains with EEA1 or LAMP2 was assessed by confocal microscopy . As expected , none of the RVA strains were detected by immune-fluorescence assay ( IFA ) using an antibody specific for the VP8* protein in mock-treated cells after 2 hpi since uncoating of these strains had already occurred at these time points ( Fig 5 ) . After pretreatment with either of the two chemicals , the fluorescence pattern of the DS-1 and NCDV VP8* antigens was still present but it did not colocalize with EEA1 even after 6 hpi ( Fig 5A and 5B ) . However , neither of the chemicals affected the colocalization of viral particles with LAMP2 , even when the incubation time was extended until 6 hpi ( Fig 5C and 5D ) . We further confirmed these results by examining the effect of PI3K p85α and MEK specific siRNAs on colocalization of RV particles with EEA1 and LAMP2 . As shown in Fig 5E and 5F , silencing of either PI3K p85α or MEK also trapped RVA particles in the LEs . Taken together , these data indicate that the PI3K/Akt and MEK/ERK signaling pathways do not influence trafficking of the L-P RVA strains DS-1 and NCDV from the EEs to the LEs . Since both wortmannin and U0126 blocked the release of DS-1 and NCDV viral particles from the LE ( Fig 5 ) , both signaling pathways could be involved in acidification of the LEs for uncoating and release of DLPs . Therefore , we first examined deposition of the cell tracker CMFDA pH probe for monitoring endosomal acidification . The fluorescence intensity of the CMFDA-positive signal markedly increased in DS-1- and NCDV-infected cells compared with mock-infected control cells ( Fig 6A ) . Like the inhibitory effect of chloroquine on endosomal acidification , pretreatment of MA104 cells with wortmannin or U0126 significantly eliminated the CMFDA fluorescent signal in DS-1- or NCDV-infected cells ( Fig 6A ) . Since the E-P RVA strain RRV does not use endosomal acidification for uncoating [4 , 7 , 11 , 14] , a basal level of CMFDA fluorescence could be seen in RRV-infected MA104 cells regardless of pretreatment with wortmannin or U0126 ( S6A Fig ) . The CMFDA-positive structures were found to colocalize with LAMP2 in mock-treated , DS-1- or NCDV-infected cells ( Fig 6B ) . However , the intensity of the CMFDA-positive signal decreased with loss of LAMP2 colocalization in virus-infected cells treated with inhibitors for endosomal acidification ( chloroquine ) , PI3K ( wortmannin ) , or MEK ( U1026 ) ( Fig 6B ) . Since trafficking of the E-P RVA strain RRV during entry into MA104 cells is restricted to the EE compartment ( 12 ) , colocalization of RRV with EEA1 or LAMP2 was not observed in MA104 cells at 3 hpi after pretreatment with either PI3K ( wortmannin ) or MEK inhibitors ( U0126 ) because uncoating of the RRV strain had already occurred at this time point ( S6B Fig ) . To confirm the above results , we next investigated whether replacement of culture medium with acidic buffer in cells treated with either of the two inhibitors restores virus release from LEs . In mock- or dimethyl sulfoxide ( DMSO ) -treated cells incubated with the strains DS-1 or NCDV for 2 h in medium at neutral pH , the immunofluorescence staining of viral VP8* of both NCDV and DS-1 strains disappeared , indicating that the virus was already uncoated ( Fig 6C ) . In contrast , MA104 cells treated with chloroquine , wortmannin or U0126 showed colocalization of the viral particles with LAMP2 even after 2 h incubation ( Fig 6C ) . Interestingly , acidic replenishment of the culture medium induced evanishment of viral particles from the LEs in chemical- or inhibitors-treated cells at 2 hpi ( Fig 6D ) . Collectively , these findings suggest that the PI3K/Akt and MEK/ERK signaling pathways are involved in acidification of the LEs for promoting the release of RVA DLPs into the cytoplasm . Since the acidification mechanism of the endosomal environment could be reliant on the V-ATPase [17] , we next investigated whether activation of the signaling molecules PI3K , Akt , and ERK could mediate endosomal acidification through direct interaction with the V-ATPase upon L-P RVA infection . To assess this , MA104 cells were either mock-infected or infected with the DS-1 or NCDV strains , and cell lysates were immunoprecipitated with antibodies specific for the subunit E of the V1 domain of the V-ATPase , pPI3K , pAkt or pERK . The antibody specific for the subunit E pulled down pPI3K , pAkt and pERK in the immunoprecipitated cell lysates ( Fig 7A ) . Immunoprecipitation using antibodies specific for pPI3K , pAkt or pERK precipitate the subunit E of the V1 domain of the V-ATPase , confirming the above results ( Fig 7B–7D ) . As a negative control , the cell lysates were immunoprecipitated with an irrelevant antibody against Na+/K+-ATPase B1 protein and then the immunoprecipitated proteins were evaluated by Western blot analysis . The results showed that antibody against Na+/K+-ATPase B1 protein did not interact with V-ATPase , pPI3K , pAkt , or pERK ( S7A and S7C Fig ) . To further confirm the IP results , the reaction mixture containing each antibody against V-ATPase E subunit , pPI3K , pAkt , or pERK , and cell lysate was incubated with secondary antibodies specific for each primary antibodies . Afterward , the immune complexes were captured by incubation with A- or G-agarose beads and the immunoprecipitated proteins were evaluated by Western blot analysis . The results showed that all three molecules ( pPI3K , pAkt , and pERK ) interacted with V-ATPase ( S7B and S7C Fig ) . To corroborate the above immunoprecipitation results , the interaction between the subunit E of the V1 domain of the V-ATPase and pPI3K , pAkt , and pERK was further evaluated in RVA-infected cells using Duolink proximity ligation assay ( PLA ) . In this assay , the signal from the interaction of two proteins in close proximity ( 40 nm or less ) is easily visible as a distinct fluorescent spot [32] . The assay was able to identify the subunit E of the V1 domain of the V-ATPase and pPI3K , pAkt , or pERK as partners in MA104 cells infected with either the DS-1 or the NCDV strains , as indicated by the red dots ( Fig 8 ) . In contrast , the E-P RVA strain RRV , which does not require endosomal acidification [4 , 7 , 11 , 14] , did not induce any positive signal in MA104 cells ( Fig 8 ) . As a negative control , the irrelevant antibody against Na+/K+-ATPase B1 protein did not interact with V-ATPase , pPI3K , pAkt , and pERK in the mock- or infected cells with RVA strains , DS-1 , NCDV , or RRV ( S8 Fig ) . Taken together , these results indicate that , during immediate early infection of the L-P strains DS-1 and NCDV , pPI3K , pAkt , and pERK directly interact with the subunit E of the V1 domain of the V-ATPase , resulting in late endosomal acidification . Internalization of ligand-activated receptors can initiate many cellular signaling events [4] . Different domains of the RVA surface proteins interact with different cell surface molecules including sialic acid ( SA ) or histo-blood group antigens ( HBGAs ) , integrins and a heat shock cognate ( hsc70 ) protein [5 , 33–37] . Since the PI3K/Akt and MEK/ERK pathways were found to be activated during immediate early RVA infection , interaction of RVA surface proteins with its cellular receptors and/or coreceptors could trigger the activation of these cascades . To address which surface viral protein ( s ) could induce the activation of both signaling pathways , we used purified VP8* , VP5* , and VP7 of the RVA strains DS-1 and NCDV . Addition of 10 μg/ml of VP8* , VP5* , and VP7 proteins resulted in phosphorylation of PI3K and Akt as early as 2 min post-treatment , becoming obvious at 5 min post-treatment ( Fig 9 ) . Moreover , phosphorylation of ERK was detected 5 min after treatment with VP8* , VP5* and VP7 of either strain ( Fig 9 ) . However , none of the recombinant VP8* , VP5* , or VP7 proteins from the E-P RVA strain RRV could induce the activation of either of the two signaling cascades ( S9 Fig ) , supporting that neither of the two signaling pathways are involved in entry of the RRV strain . To further confirm the above results , MA104 cells were pretreated for 1 h at 37°C with either wortmannin or U0126 , incubated with each surface domain of the two RVA strains for 5 min , and analyzed for phosphorylation of Akt and ERK by Western blot analysis . The expression levels of pAkt and pERK were found to be reduced following wortmannin and U0126 pretreatment , respectively ( S10 Fig ) . Sodium periodate ( NaIO4 ) can remove the VP8*-binding cell surface carbohydrate moieties , terminal SAs , and HBGAs without altering proteins or membranes . In particular , SAs can be removed by pretreatment with 1 mM NaIO4 , whereas neutral glycan structures such as HBGAs can be eliminated by pretreatment with 10 mM NaIO4 [38 , 39] . Therefore , we pretreated MA104 cells with 1 mM NaIO4 for the SA-dependent NCDV strain , and with 10 mM NaIO4 for the HBGA-dependent DS-1 strain . Removal of SAs and HBGAs inhibited both signaling pathways ( S11A Fig ) . We next examined whether VP5*- and VP7-induced early activation of both signaling pathways could be reduced by pre-incubation with antibodies specific for the αVβ3 integrin at the VP7 CNP site [34 , 36] , and for Hsc70 at the VP5* KID site [40] . The results showed that depletion of the cellular receptors reduced VP5*- and VP7-induced early activation of both signaling pathways ( S11B and S11C Fig ) . These data further support that the RVA outer capsid proteins VP4-VP8* and VP4-VP5* domains as well as VP7 can induce phosphorylation of signaling molecules PI3K , Akt , and ERK during entry of L-P RVA strains . To ensure that induction of these signaling molecules was indeed triggered by the outer capsid proteins but not by trypsin , pro-inflammatory cytokines , and/or other contaminants from the virus preparation , we first treated MA104 cells with 10 μg crystal trypsin and analyzed the activation of both signaling pathways . No significant changes were observed in the expression levels of pPI3K , pAkt , or pERK in mock- or crystal trypsin-treated cells ( S12 Fig ) . We next infected the cells with the two purified 4’-aminomethyltrioxsalen hydrochloride ( psoralen ) -UV-inactivated strains , or with cesium chloride ( CsCl ) -purified TLPs of the two strains , or we transfected the cells with DLPs of the two strains . As shown in Fig 10 , MA104 cells infected with psoralen-UV-inactivated strains or CsCl-purified TLPs of the two strains exhibited activation of pPI3K , pAkt , and pERK during the immediate phase . In contrast , these signaling molecules could not be activated in RVA DLPs-lipofected cells ( Fig 10 ) . Moreover , recombinant NSP1 and VP6 proteins of DS-1 and NCDV strains were used to rule out effects of other RVA proteins . No activation of the PI3K/Akt and MEK/ERK signaling pathways was seen in NSP1- or VP6-incubated MA104 cells ( S13 Fig ) . Taken together , our results indicate that sequential multistep interactions of the outer capsid proteins of both L-P strains with their corresponding cellular receptors activate the PI3K/Akt and MEK/ERK signaling pathways .
Host defense systems have developed to eliminate invading viruses at different stages of virus infection [41 , 42] . However , viruses , in turn , have evolved numerous strategies to disarm the host antiviral responses including evasion of host innate and adaptive immunities , and hijacking of the host cell machinery including a variety of host cell signaling pathways that modulate the intracellular environment at different stages of their life cycle [41 , 42] . In addition to the strategies of RVA to antagonize the host innate immunity via the viral NSP1 and VP3 [28–30 , 43–46] , RVA also hijacks and alters host cellular signaling pathways during its life cycle [26 , 27 , 31 , 41] . Among them , the PI3K/Akt and MEK/ERK signaling pathways have attracted much interest due to their multifaceted roles modulating virus entry , replication , assembly , and release [18–21] . Here , we demonstrate that the direct interaction of phosphorylated PI3K , Akt , and ERK molecules with the subunit E of the V-ATPase V1 domain in response to early infection of L-P RVA strains stimulates endosomal acidification , which is required for uncoating of TLP and delivering of DLP into the cytoplasm to continue the virus life cycle . Our findings extend our current understanding of the mechanisms of RVA entry and uncoating , and could contribute to devising useful new strategies for developing anti-RVSs drugs for treatment of the RVA infection . L-P RVA strains such as the human strains Wa and DS-1 , the porcine strain TFR-1 , and the bovine strain UK are known to require late endosomal acidification for TLP uncoating and delivery of DLP into cytoplasm [4 , 7 , 10] . In agreement with the results of a recent report [10] , we found that the human strain DS-1 and the bovine strain NCDV used in this study were L-P strains that required LE acidification . Both strains colocalized with the LE marker LAMP2 and their entry and infectivity depended on Rab7 as well as on endosomal acidification . However , to date , the molecular mechanism ( s ) driving LE acidification for uncoating of L-P strains remains largely unknown . The V-ATPase , with energy harnessed from ATP hydrolysis by phosphotransferases such as PI3K , Akt , and ERK targeted in this study , is known to translocate protons from the cytoplasmic to the luminal side of the membrane , resulting in luminal acidification including that of LE [15] . In the present study , the PI3K/Akt and MEK/ERK cascades were activated during the immediate early infection of both neuraminidase ( NA ) -sensitive DS-1 and NA-insensitive NCDV strains . Moreover , co-immunoprecipitation experiments using antibodies specific for pPI3K , pAkt , pERK , and the subunit E of the V1 domain of the V-ATPase , immunoprecipitated the V-ATPase or its counter partners , the signaling molecules pPI3K , pAkt , and pERK . Using the Duolink technology , we further proved that the subunit E of the V1 domain of the V-ATPase directly interacts with the signaling molecules pPI3K , pAkt , and pERK following RVA infection . Our data indicate that pPI3K , pAkt , and pERK induced during the immediate early infection of the L-P strains used in this study directly interact with V-ATPase to produce a proton gradient by ATP hydrolysis . These results are also supported by the finding that acidic replenishment of the medium restored uncoating of the L-P strains in cells pretreated with inhibitors specific for the PI3K/Akt and MEK/ERK signaling pathways . Our results are partially consistent with findings that influenza A virus-induced early activation of PI3K and ERK mediates V-ATPase-dependent endosomal acidification [25] . The PI3K/Akt and/or MEK/ERK signaling pathways are involved in entry of many viruses [22–24] . However , the interaction of host cell signaling pathways with L-P RVA strains for continuing their journey to the LE remains elusive . In this study , the L-P RVA strains DS-1 and NCDV could not be detected by antibody against the VP8* domain in the LEs at 120 mpi , suggesting that uncoating of these strains had been completed at this time point . The PI3K/Akt and MEK/ERK pathways were activated by these strains as early as 5 mpi . Moreover , inhibition of these signaling pathways by pretreatment with PI3K ( wortmannin ) or MEK ( U0126 ) inhibitors markedly reduced infectivity , suggesting a possible involvement of these signaling pathways in entry of L-P strains . However , inhibition of both signaling pathways did not trap the incoming virus particles in the EEs . Instead , both L-P strains were retained in the LEs for as long as 6 hpi by these inhibitors . These data demonstrate that neither of the two signaling pathways is involved in trafficking of L-P RVAs , such as the DS-1 and NCDV strains , from the cell surface to LEs . Binding of viral ligand ( s ) to host cell surface receptor ( s ) can activate signaling pathways through the plasma membrane , promoting virus uptake or penetration into the cells [1] . RVAs initiate the infection by a complex multistep process in which the VP8* and VP5*domains of the VP4 capsid protein , as well as the VP7 capsid protein interact with different cell surface receptors [4 , 5] . Here , we demonstrate that the VP4-VP8* and VP4-VP5* domains , and the VP7 transiently activate the PI3K/Akt and MEK/ERK pathways during entry of the L-P strains DS-1 and NCDV . Inhibitors specific for each signaling pathway blocked the expression of both signaling pathways activated by the outer capsid proteins , further confirming that the outer capsid proteins of RVAs can activate both the PI3K/Akt and the MEK/ERK signaling pathways . This possibility was bolstered by the observation that psoralen-UV-irradiated RVAs , which were noninfectious but preserved the structural and immunological functions [47 , 48] , as well as CsCl-purified RVA TLPs , but not DLPs lipofection or trypsin treatment , could also activate the PI3K/Akt and MEK/ERK pathways . The immediate early activation of the PI3K/Akt and MEK/ERK pathways by DS-1 and NCDV was unlikely due to other proteins since the NSP1 and VP6 proteins failed to induce phosphorylation of PI3K , Akt , or ERK during the time of RVA entry . Our data indicate that the outer capsid proteins of RVAs , VP4-VP8* and VP4-VP5* domains , and VP7 , can induce phosphorylation of PI3K , Akt , and ERK signaling molecules during the entry phase of L-P RVA strains , which in turn directly interact with the subunit E of the V1 domain of the V-ATPase to produce a proton gradient by ATP hydrolysis and subsequently acidify the LE for uncoating of RVAs . There have been a considerable number of reports showing that activation of the PI3K/Akt and MEK/ERK signaling pathways can occur at multiple steps in the virus life cycle [18–21] . Activation of the PI3K/Akt signaling cascade has also been reported at different time points during the RVA life cycle [26–31] . Among these reports , Halasz and colleagues reported an RVA-induced sequential activation of the Akt signaling molecule in RVA-infected cells [26] . PI3K-dependent Akt phosphorylation was observed at 1 hpi , and it was sustained until 3 to 4 hpi in simian RRV strain-infected human intestinal epithelial Caco-2 and HT-29 cells , as well as in monkey kidney epithelial MA104 cells [26] . As confirmed in this study , the phosphorylated form of Akt molecule could not be detected at earlier time points during infection of RRV strain [26] . The simian RVA strain RRV does not require a deep journey to LEs since it is an E-P strain [12] . In addition , acidification of the EEs is not necessary for uncoating of this E-P strain [4 , 11 , 14] . These biological properties of E-P strains could make them be released DLP from EEs in to cytosol within 10 mpi [49] . Therefore , unlike L-P strains such as the DS-1 and NCDV strains used in this study , RRV strain does not need immediate early-activation of both PI3K/Akt and MEK/ERK signaling pathways for uncoating in acidified LEs . Rather , simian RVA RRV-induced early activation of the PI3K/Akt signaling pathway occurring from 1 hpi to 3–4 hpi , coinciding with the period for RRV-induced cell adhesion , plays a key role in the increased yield of infectious RRV particles by increasing the adhesion and survival of infected cells [26] . Following findings that RVA-induced early apoptosis in MA104 and HT29 cells was protected by RVA activation of PI3K [30] , it becomes clear that the direct interaction between the RVA NSP1 protein and the p85 subunit of PI3K suppresses RVA-induced cellular apoptosis to facilitate viral growth [28 , 29] . This interaction , in turn , activates the PI3K-dependent Akt signaling molecule from 2 to 12 hpi [28] . Hsp90 , a molecular chaperone primarily involved in protein folding during protein synthesis , increases RVA replication through upregulation of the PI3K-dependent Akt signaling molecule during RVA protein synthesis , specifically from 4 to 12 hpi for the SA11 strain and from 2 to 6 hpi for the KU strain [27] . Hsp90 is also known to contribute to viral replication either by modulating cellular signaling pathways or by direct interactions with viral proteins such as the RNA dependent RNA polymerase of influenza A and vesicular stomatitis viruses , the reverse transcriptase of hepatitis B virus , and NSP2/3 protein of hepatitis C virus [27 , 50–55] . Similarly , we observed a second activation phase of these signaling molecules from 4 to 12 hpi in both MA104 and Caco-2 cells , which could be triggered during and/or after RVA protein synthesis . RVA-induced immediate early activation of the PI3K and/or Akt signaling molecules observed in cells infected with the L-P strains DS-1 and NCDV used in the present work had not been examined in the previous studies [27–30] . Since activation of the PI3K-dependent Akt molecule observed in the previous studies seems to occur during and/or after RVA protein synthesis , including synthesis of the NSP1 protein , the molecular mechanism of PI3K/Akt signaling pathway activation observed during entry of RVAs in the present study could be distinct from that observed in RVA-induced early apoptosis and RVA protein synthesis [27–30] . In summary , this study demonstrates that the L-P RVA strains DS-1 and NCDV phosphorylate the signaling molecules PI3K , Akt , and ERK which , in turn , directly interact with the subunit E of the V1 domain of the V-ATPase to produce a proton gradient by ATP hydrolysis and subsequently acidify the LE for uncoating of RVAs ( Fig 11 ) . Activation of the signaling molecules PI3K , Akt , and ERK seems to be induced by binding of outer capsid proteins , VP4-VP8* and VP4-VP5* domains , and VP7 , to known cell surface receptors . However , the PI3K/Akt and MEK/ERK signaling pathways induced during the immediate early infection of L-P strains were not involved in virus trafficking from the EEs to the LEs . This study provides a better understanding of the RVA-host interaction at the cell signaling level , which is of fundamental importance for developing strategies for the control and prevention of RVA infections .
Monkey kidney MA104 cells obtained from the American Type Culture Collection ( ATCC , Manassas , VA , USA ) were grown in alpha minimal essential medium ( α-MEM ) ( Welgene , Daegu , South Korea ) supplemented with 10% fetal bovine serum ( FBS ) , 100 U/ml penicillin , and 100 μg/ml streptomycin . Human intestinal Caco-2 cells ( ATCC ) were grown in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% FBS , 100 U/ml penicillin , and 100 μg/ml streptomycin . Spodoptera frugiperda ovarian cells ( Sf9 cells ) purchased from Gibco ( Fort Worth , Texas , USA ) were cultured at 27°C in SF-900 II SFM media containing 10% FBS , 100 U/ml penicillin , 100 μg/ml streptomycin , lipid medium supplement , and 0 . 1% pluronic acid solution ( Sigma Aldrich , St . Louis , MO , USA ) . The human RVA DS-1 ( G2P1B[4] ) and bovine RVA NCDV ( G6P6[1] ) strains were purchased from the ATCC , and the rhesus RVA strain RRV was kindly provided by Professor Susana López ( Instituto de Biotecnología , Cuernavaca , Morelos , México ) . Both strains were preactivated with 10 μg/ml crystalized trypsin ( Cat . No . 27250–018 , Gibco ) , and propagated in MA104 cells as previously described [56] . RVA TLPs and DLPs were purified by CsCl isopycnic gradients as previously described [57] . Virus titers were determined by cell culture immunofluorescence ( IF ) assay using monoclonal antibodies ( Mabs ) specific for RVA VP6 , and are expressed as fluorescence focus units per milliliter ( FFU/ml ) . Chloroquine , NH4Cl , psoralen and NaIO4 were purchased from Sigma-Aldrich . Wortmannin ( PI3K inhibitor ) and U0126 ( MEK inhibitor ) were obtained from Invivogen ( San Diego , CA , USA ) . AF594 succinimidyl ester was purchased from Molecular Probes ( Bedford , MA , USA ) . Wortmannin , U0126 , and AF594 were dissolved in DMSO , while chloroquine , NH4Cl , and psoralen were dissolved in distilled water to prepare stock solutions . Before each use on cell monolayers , these chemicals were freshly diluted to the desired concentration with free media . The cytotoxic effects of the chemicals and their solvents were tested using the 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) assay as previously described [38] . All the chemicals were used at concentrations that were not toxic to the cells . AF488-labeled phalloidin , cell tracker green 5-chloromethylfluorescein diacetate ( CMFDA ) , and SlowFade Gold antifade reagent with 4’ , 6-diamidino-2-phenylindole ( DAPI ) were obtained from Molecular Probes . Protein A-agarose and protein G plus-agarose were purchased from Santa Cruz ( Dallas , Texas , USA ) . All the siRNAs were purchased from Santa Cruz and the sequences are presented in S2 Table . Specific rabbit polyclonal antibodies against Akt , pAkt at the active site serine 473 , PI3K p85 regulatory subunit , pPI3K at the p85 regulatory subunit tyrosine 458 and at the p55 regulatory subunit tyrosine 199 , Rab5 , ERK1/2 , and pERK1/2 at the threonine 202 and tyrosine 204 were purchased from Cell Signaling ( Beverly , Massachusetts , USA ) . Goat anti-V-ATPase E subunit ( ATP6E ) and rabbit anti-glyceraldehyde 3-phosphate dehydrogenase ( GAPDH , FL-335 ) polyclonal antibodies were from Santa Cruz . Mouse monoclonal and rabbit polyclonal antibodies against Na+/K+-ATPase B1 protein were purchased from Santa Cruz . Rabbit anti-Hsc70 polyclonal antibody was obtained from GeneTex ( Irvine , CA , USA ) and mouse anti-αVβ3 Mab was from Millipore ( Temecula , CA , USA ) . Rabbit anti-Rab7 and mouse LAMP2 Mabs were purchased from Abcam ( Cambridge , MA , USA ) . Mouse anti-EEA1 Mab was obtained from BD Transduction Laboratories ( Lexington , KY , USA ) . Mouse anti-RVA VP6 Mab was purchased from Median Diagnostic ( Chuncheon , South Korea ) , and hyperimmune rabbit sera raised against the VP8* domain of the DS-1 and NCDV strains were produced in this study . Secondary antibodies included horseradish peroxidase ( HRP ) -conjugated goat anti-rabbit IgG ( Cell Signaling ) , HRP-conjugated goat anti-mouse IgG ( Ab Frontier , Seoul , South Korea ) , HRP-conjugated donkey anti-goat IgG , fluorescein isothiocyanate ( FITC ) -conjugated anti-rabbit IgG , FITC-conjugated anti-mouse IgG ( Santa Cruz ) , AF594-conjugated donkey anti-rabbit IgG , AF647-conjugated goat anti-rabbit IgG , AF594-conjugated goat anti-mouse IgG , and AF647-conjugated goat anti-mouse IgG ( Life Technologies , Eugene , OR , USA ) . AF594-labeling of DS-1 or NCDV strains purified by CsCl density-gradient centrifugation was performed as previously described [58] . Briefly , purified RVA TLPs ( 10 mg at 1 mg ml-1 ) in 0 . 1 M sodium bicarbonate buffer ( pH 8 . 3 ) was labeled with one-tenth-fold-molar concentration of AF594 succinimidyl ester ( 1 mg at 1 mg ml-1 in DMSO . Each reaction was mixed thoroughly by vortexing for 30 s and incubated for 1 h at room temperature with continuous stirring . This fluorophore reacts exclusively with free amines , resulting in a stable carboxamide bond , and contains a seven-atom aminohexanoyl spacer ( X ) , which allows higher degree of labeling without functional perturbance of the virus [58] . The labeled virus was repurified by CsCl density-gradient centrifugation , dialysed against virion buffer , and stored in 2 μg aliquots at -20°C [58] . Analysis of sodium dodecyl sulfate-polyacrylamide gel ( SDS-PAGE ) -separated AF594 labeled viral particles by Coomassie blue staining and Western blotting showed that the label was exclusively coupled to the viral protein . The number of RVA particles was examined as described previously [59 , 60] . Briefly , CsCl-purified AF594-RVA particles were mixed with an equal volume of a suspension of 120 nm latex beads ( Sigma Aldrich ) . The mixture was then applied to the grids . The grids were stained with 3% phosphotungstic acid ( PTA ) at pH 7 , for 3 min , at room temperature and observed under a JEM-2100F transmission electron microscope ( Jeol , Peabody , MA , USA ) . The virus particles were counted along with the beads in at least 10 randomly chosen squares on the grid . The total virus count was calculated by multiplying the ratio of the virus particle number to the latex particle number by the known latex particle concentration per ml . Genetic inactivation of cell culture-derived RVAs was performed using psoralen and long-wave UV light exposure as described elsewhere [47 , 48] . Briefly , 2 ml of virus suspension was mixed with psoralen at 20 μg/ml in a petri dish and incubated at 4°C for 15 min . Virus was then exposed to UV light at 366 nm for 40 min . The effectiveness of psoralen-UV inactivation was demonstrated by the lack of detectable viral antigen in an immunofluorescence assay in MA104 cells infected with psoralen-UV-treated RVAs . MA104 or Caco-2 cells were grown in 6- or 12-well plates or in 8-well chamber slides to the desired confluency , washed twice with phosphate-buffered saline ( PBS , pH 7 . 4 ) , and mock-treated or pretreated with working concentrations of the chemicals for 1 h at 37°C . Treatment with NaIO4 was conducted for 30 min at 4°C [38 , 39] , and antibodies were incubated for 2 h at 37°C [34] . Inhibitory chemicals and antibodies were used at the following concentrations: chloroquine ( 100 μM ) , NH4Cl ( 100 mM ) , wortmannin ( 10 nM and 1 μM ) , U0126 ( 0 . 25 μM and 50 μM ) , NaIO4 ( 1 mM and 10 mM ) , rabbit anti-Hsc70 polyclonal antibody ( 10 μg/ml ) , and anti-αVβ3 Mab ( 10 μg/ml ) . After washing the cells twice with PBS , the cells were infected with AF594-labeled or mock-labeled virus or treated with each purified viral protein independently , and then used for assessing signaling pathways and for measuring virus infectivity and titer by IF assay , genome copy number by RT-qPCR , and protein expression by Western blot as described below . MA104 or Caco-2 cells cultured in 8-well chamber slides or 12-well culture plates to 70–80% confluency were transfected with siRNA ( 80 pmol of scrambled control siRNA and siRNAs against Rab5 and Rab7 , or 40 nmol of siRNAs against PI3K p85α and MEK ) using the Lipofectamine 2000 ( Invitrogen , Carlsbad , California , USA ) following the manufacturer’s instructions . To optimize knockdown efficiency , a second transfection was carried out 24 h after the first transfection . Cells treated in parallel were evaluated by Western blot analysis to ensure effective knockdown of each target protein . After confirming knockdown of each target protein , the cells were infected with AF594-labeled or mock-labeled virus and then used for assessing signaling pathways and for measuring virus infectivity and titer by IF assay , genome copy number by RT-qPCR , and protein expression by Western blot analysis as described below . Transfection of DLPs was performed using Lipofectamine 2000 ( Invitrogen ) as described previously [46] . Briefly , DLPs ( 10 μg/ml ) were diluted in opti-MEM and incubated with a mixture of Lipofectamine in opti-MEM for 20 min at room temperature . One hundred microliters of this mixture was added to the cells for 1 h at 37°C . After removal of the lipofection mixture , the medium was replaced with MEM and cells were incubated for the indicated times . Recombinant VP8* domain of the RVA strains DS-1 , NCDV and RRV were cloned , expressed and purified as described previously [37] . Briefly , the cDNAs encoding the VP8* domain with a cysteine peptide were cloned into the expression vector pGEX-4T-1 ( glutathione S-transferase [GST]-gene fusion system ) ( GE Healthcare Life Sciences , Piscataway , NJ , USA ) . After sequence confirmation , the recombinant GST-VP8* fusion proteins were expressed in Escherichia coli ( E . coli ) strain BL21 . Expression of each domain was induced with isopropyl-β-D-thiogalactopyranoside ( IPTG; 0 . 2 mM ) at room temperature overnight . RVA GST-VP8* fusion proteins were purified using the Pierce GST spin purification kit ( Pierce , IL , USA ) according to the manufacturer’s protocol . Recombinant RVA NSP1 and VP6 proteins were also expressed and purified as mentioned before [61–62] . The full genome amplicons of NSP1 and VP6 were cloned into plasmids pET28a and pPROEX HTc , respectively , digested with NcoI and XhoI , and the resulting constructs were verified by sequencing . The NSP1 and VP6-containing plasmids were expressed in E . coli BL21 . Protein expression was induced with IPTG ( 1 mM ) at room temperature overnight . The His-tagged NSP1 and VP6 proteins were purified using Ni-NTA agarose ( Qiagen , Valencia , CA , USA ) , and the proteins were eluted with 500 mM imidazole . The concentration of the purified RVA VP8* domain , and NSP1 and VP6 proteins was determined by measuring the absorbance at 280 nm . Recombinant VP5* and VP7 proteins are usually insoluble , difficult to express and purify , or toxic to the cell if expressed in E . coli [35 , 63–66] . Since baculovirus has been successfully used as an expression system for the production of RVA proteins in the past [66–70] , we expressed recombinant VP5* and VP7 proteins in baculovirus-infected Sf9 cells using the Bac-to-Bac Baculovirus expression system ( Invitrogen ) according to the manufacturer’s instructions . Briefly , the complete sequence of the VP5* domain and the VP7 gene of DS-1 , NCDV , and RRV strains were amplified by RT-PCR with primers specific for each domain or gene ( S1 Table ) [66 , 69] . Subsequently , the amplified fragments tagged with polyhistidine were subcloned into the donor plasmid pFastBac1 . Recombinant baculovirus was generated by transformation of the recombinant pFastBac1 plasmid into DH10Bac E . coli to produce recombinant Bacmid DNA , which was then transfected into Sf9 cells using Cellfectin II reagent ( Invitrogen ) . Recombinant VP5* and VP7 were expressed in baculovirus-transformed Sf9 insect cells at 27°C and harvested 5–7 days post-infection . After clarification by centrifugation , the polyhistidine-tagged proteins were purified using Ni-NTA agarose ( Qiagen , Valencia , CA , USA ) according to the manufacturer’s protocol . The concentration of purified RVA VP5* domain and VP7 protein was determined by measuring the absorbance at 280 nm . Each purified protein was used at 10 μg/ml to test whether they could activate target signaling pathways by Western blot analysis as described below [35] . Subconfluent MA104 cells grown in 8-well chamber slides , pretreated with or without the chemicals of interest or siRNA , were washed twice with PBS . Then , AF594-labeled DS-1 ( 595 particles/cell ) or NCDV ( 790 particles/cell ) were allowed to bind to the cells for 30 min at 4°C , followed by incubation at 37°C to allow entry . Cells were washed extensively with cold PBS , and fixed with 4% paraformaldehyde in PBS for 15 min at room temperature . For colocalization with early and late endosomes markers , mock- , chemical- or siRNA-treated MA104 cells were independently infected with the RVA strains DS-1 and NCDV . The cells were then fixed with 4% paraformaldehyde in PBS for 15 min at room temperature , and permeabilized by addition of 0 . 2% Triton X-100 in PBS for 10 min at room temperature . Next , the cells were washed with PBS containing 0 . 1% new born calf serum ( PBS-NCS ) and incubated at 4°C overnight with Mabs against EEA1 and LAMP2 , and polyclonal antibody against RVA VP8* domain ( 1:100 dilution ) , or Mabs against Rab5 and Rab7 ( 1:100 dilution ) . After washing twice with PBS-NCS , the cells were incubated with AF647-conjugated goat anti-mouse ( 1:100 dilution ) or anti-rabbit IgG ( 1:100 dilution ) antibodies for 1 h at room temperature . Immediately after washing with PBS-NCS , the cells were incubated with AF488-labeled phalloidin ( 10 units ) ( Invitrogen ) for 15 min at room temperature for cytoskeleton staining and washed with PBS . Finally , chambers were mounted with SlowFade Gold antifade reagent containing 1 x DAPI solution ( Molecular Probes ) for nucleus staining , and the infected cells were observed with a LSM 510 confocal microscope and analyzed using the LSM software ( Carl Zeiss ) . Low-pH rescue experiment was performed as described previously [11] . Briefly , infected cells were incubated with either neutral ( pH 7 . 2 ) or acidic ( pH 5 ) citrate buffers for 5 min at 37°C . After washing three times with PBS , the cells were incubated in serum-free medium for 2 h at 37°C . Subsequently , the cells were fixed , permeabilized and stained with anti-RVA VP8* and anti-LAMP2 antibodies as described above . To determine virus infectivity , genome copy number , and protein expression , confluent monolayers of MA104 cells grown in 6- or 12-well plates or 8-well chamber slides were pretreated with or without various inhibitors or transfected with or without siRNAs as described above . MA104 cells were then independently infected with trypsin-preactivated DS-1 and NCDV strains ( 10 μg/ml crystalized trypsin ) at a MOI of 10 FFU/cell . Virus inocula were removed after 1 h of infection , and cells washed twice with PBS were used for determining the virus infectivity and titer by IF assay , genome copy number by RT-qPCR , and protein expression by Western blot analysis as described below . To determine virus infectivity after pretreatment with chemicals or siRNAs , an IF assay was performed as described previously with minor modifications [38] . Briefly , MA104 cells grown in 8-well chamber slides , pretreated with or without chemicals or transfected with or without siRNAs were infected with trypsin-preactivated DS-1 and NCDV strains ( 10 μg/ml crystalized trypsin ) at a MOI of 10 FFU/cell . Virus inocula were removed after 1 h of infection , and cells were washed twice with PBS . Cells were incubated for 8 h with medium containing 1 μg/ml crystalized trypsin , fixed with 4% paraformaldehyde for 15 min at room temperature , and permeabilized by addition of 0 . 2% Triton X-100 for 10 min at room temperature before being washed with PBS . The chamber slides were then incubated with Mabs against the RVA VP6 protein at 4°C overnight . Subsequently , cells were washed 3 times with PBS , and FITC-conjugated secondary antibodies were added . After washing with PBS-NCS , the nuclei were stained with DAPI , and cells were examined by confocal microscopy . Infected cells and total DAPI-stained cells were counted , and were scored for RVA VP6 expression . After image analysis with Zeiss LSM image browser ( Oberkochen , Germany ) , infected cells were counted as positive for viral antigen if they had a fluorescence intensity at least three times that of the uninfected controls . The percentage of positive cells was then normalized to that of the untreated control . To determine the RVA titer after pretreatment with chemicals or siRNAs , the IF assay was performed . Briefly , MA104 cells grown in 8-well chamber slides pretreated with or without chemicals or transfected with or without siRNA were independently infected with trypsin-preactivated DS-1 and NCDV strains ( 10 μg/ml crystalized trypsin ) for 8 h . After three times of freeze-thaw cycles , a ten-fold dilution of each sample was used to infect in triplicate confluent MA104 cells grown in 96-well plates . After an adsorption period of 1 h at 37°C , the virus inocula were removed and the cells were washed with PBS . Subsequently , the infection was continued at 37°C for 16 h with medium containing 1 μg/ml crystalized trypsin . The cells were fixed with 80% cold acetone . After washing with PBS ( pH 7 . 4 ) , each well was incubated with Mab against the RVA VP6 protein at 4°C overnight . Subsequently , cells were washed 3 times with PBS , and FITC-conjugated secondary antibodies were added . After washing with PBS ( pH 8 . 0 ) , the nuclei were stained with DAPI . The viral titer was expressed as FFU/ml . To assess the expression levels of each viral protein and target cellular protein under the conditions described above , antibodies specific for each protein were used for Western blot analysis . Briefly , MA104 cells grown in 6- or 12-well plates and treated with factors including chemicals , siRNAs , and viruses , were washed three times with cold PBS and lysed using cell extraction buffer containing 10 mM Tris/HCl pH 7 . 4 , 100 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1 mM NaF , 20 mM Na2P2O7 , 2 mM Na3VO4 , 1% Triton X-100 , 10% glycerol , 0 . 1% SDS , and 0 . 5% deoxycholate ( Invitrogen ) for 30 min on ice . To determine signaling molecules induced by recombinant VP8* and VP5* domains as well as recombinant VP7 protein , GST-VP8* , His-VP5* , and His-VP7 ( 10 μg/ml ) were independently incubated with confluent MA104 cells grown in 6-well plates for 30 min at 4°C , shifted to 37°C for the indicated times , washed , and lysed as described above . Lysates were spun down by centrifugation at 12 , 000×g for 10 min at 4°C and the supernatants were analyzed for total protein content with a BCA protein assay kit ( Thermo Scientific , Waltham , MA , USA ) . Samples were resolved by SDS-PAGE and transferred onto nitrocellulose membranes ( GE Healthcare Life Sciences ) . The membranes were blocked for 1 h at room temperature with Tris-buffered saline containing 5% skimmed milk before they were incubated overnight at 4°C with the indicated primary antibodies . The bound antibodies were developed by incubation with a HRP-labeled secondary antibody and the immunoreactive bands were detected by enhanced chemiluminescence ( ECL ) ( Dogen , Seoul , South Korea ) using a Davinch-K Imaging System ( Youngwha Scientific Co . , Ltd , Seoul , South Korea ) . Immunoprecipitation of each target protein was performed as previously described [25] . Briefly , MA104 cells grown in 6-well plates were independently infected with DS-1 and NCDV strains at a MOI of 10 FFU/cell , or mock-infected , and incubated for the indicated time points at 37°C . Afterwards , the cells were washed and lysed as described above . Cell lysates were pre-cleared by incubation with protein A- or G-agarose beads for 30 min at 4°C . Subsequently , the pre-cleared cell lysates were incubated with antibodies against the V-ATPase E subunit , pPI3K , pAkt , and pERK , or with an irrelevant antibody against Na+/K+-ATPase B1 protein overnight at 4°C . The immune complexes were captured by incubation with A- or G- agarose beads for 1 h at 4°C , and the immunoprecipitated proteins were then evaluated by Western blot analysis as described above . To rule out entrapment of any of the V-ATPase , pPI3K , pAkt , or pERK during immunoprecipitation , the pre-cleared cell lysates described above were incubated with antibodies against the V-ATPase E subunit , pPI3K , pAkt , and pERK . Instead of capturing by incubation with A- or G-agarose beads , secondary antibodies specific for each primary antibody were added into each reaction mixture . The immune complexes were then captured by incubation with A- or G-agarose beads and the immunoprecipitated proteins were evaluated by Western blot analysis as described above . In situ interactions of pPI3K , pAkt , and pERK with the V-ATPase were detected with the Duolink PLA kit ( Sigma-Aldrich ) as described elsewhere [32] . Briefly , RVA-infected MA104 cells grown in 8-well chamber slides were fixed with 4% paraformaldehyde in PBS for 15 min and permeabilized by addition of 0 . 2% Triton X-100 for 10 min at room temperature . The cells were then incubated with Duolink blocking solution in a pre-heated humidity chamber for 30 min at 37°C followed by incubation with primary antibodies , goat anti-V-ATPase E subunit , and rabbit anti-pPI3K , pAkt or pERK antibodies , or incubation with the control irrelevant rabbit or mouse antibodies against Na+/K+-ATPase B1 protein , and the above primary antibodies , overnight at 4°C . After washing twice in Duolink washing buffer A for 5 min , the cells were incubated with secondary antibodies conjugated with oligonucleotides ( PLA probes anti-rabbit MINUS and anti-goat PLUS ) for 1 h in a pre-heated humidity chamber at 37°C . Unbound PLA probes were removed by washing twice in Duolink washing buffer A for 5 min , and then the Duolink ligation solution was applied to the slides for 30 min in a pre-heated humidity chamber at 37°C followed by washing in Duolink washing buffer A twice for 2 min . The Duolink amplification-polymerase solution was applied to the slides in a dark pre-heated humidity chamber for 100 min at 37°C . The slides were then washed twice in 1x Duolink washing buffer B for 10 min followed by washing for 1 min with 0 . 01x Duolink washing buffer B . The cells were then mounted using Duolink in situ mounting medium with DAPI and observed with a LSM 510 confocal microscope ( Carl Zeiss ) . PLA signals were recognized as red fluorescent spots . To quantify the genome copy numbers of RVA , real-time RT-PCR was carried out as described previously with minor modifications [71] . MA104 cells grown in 12-well plates were pretreated with or without the indicated concentration of chemicals or transfected with or without siRNAs as described above . The cells were then infected with the strains DS-1 and NCDV at a MOI of 10 FFU/cell for 1 h . Next , the unbound viruses were removed by washing the cells with PBS . At 8 hpi , the infected cell cultures were washed twice with PBS , harvested by freezing and thawing three times , and cell debris was spun down at 2 , 469×g for 10 min at 4°C . The supernatants along with the remaining bulk samples were collected and stored at -80˚C until used . Total RNA was extracted using RNeasy kit ( Qiagen ) following the manufacturer’s instructions . The viral genome copy number was determined by one-step SYBR Green real-time RT-PCR using primer pairs specific for the DS-1 VP6 or NCDV VP6 genes ( S1 Table ) . Each reaction mixture in a total volume of 20 μl contained 4 μl of RNA template ( 1μg ) , 10 μl SensiFast SYBR Lo-ROX One step mixture ( Bioline , Quantace , London , UK ) , 0 . 8 μl each of 10 μM forward and reverse primers , 0 . 2 μl of reverse transcriptase , 0 . 4 μl of RiboSafe RNase inhibitor , and 3 . 8 μl of RNase-free water . Real-time RT-PCR was performed using a Rotor-Gene Real-Time Amplification system ( Corbett Research , Mortlake , Australia ) with the following conditions: reverse transcription was carried out at 50°C for 30 min , followed by activation of the hot-start DNA polymerase at 95°C for 10 min and 40 cycles of three steps of 95°C for 15 s , 50°C for 30 s , and 72°C for 20 s . Quantitation of viral RNA was carried out using a standard curve derived from serial 10-fold dilutions of complementary RNA ( cRNA ) generated by reverse transcription of in vitro transcribed control RNA ( RVA VP6 gene ) . The threshold was automatically defined in the initial exponential phase , reflecting the highest amplification rate . A direct relationship between cycle number and the log concentration of RNA molecules initially present in the RT-qPCR reaction was used to calibrate the crossing points resulting from the amplification curves of the samples . The pH probe cell tracker green CMFDA was used to visualize intracellular acidic compartments as described previously [72] . After chemical treatment and virus infection , MA104 cells were washed with PBS and incubated with cell tracker CMFDA working solution ( 10 μM ) for 30 min at 37°C , which was then replaced with serum-free media and incubated for another 30 min at 37°C . Afterwards , fixation and permeabilization were performed as described above . To check colocalization with late endosome marker , the cells were incubated with anti-LAMP2 antibody and then examined by confocal microscope as described above . Statistical analyses were performed on triplicate experiments by One-Way ANOVA using GraphPad Prism software version 5 . 03 ( GraphPad Software Inc . , La Jolla , CA , USA ) . P values of less than 0 . 05 were considered statistically significant . Figures were generated using Adobe Photoshop CS6 and Prism 5 version 5 . 03 .
|
Viral particles must transport their genome into the cytoplasm or the nucleus of host cells to initiate successful infection . Knowledge of how viruses may pirate host cell signaling cascades or molecules to promote their own replication can facilitate the development of antiviral drugs . Group A rotavirus ( RVA ) is a major etiological agent of acute gastroenteritis in young children and the young of various mammals . RVA enters cells by a complex multistep process . However , the cellular signaling cascades or molecules that facilitate these processes are incompletely understood . Here , we demonstrate that infection with late-penetration RVA strains results in phosphorylation of PI3K , Akt , and ERK signaling molecules at an early stage of infection , a process mediated by the multistep binding of RVAs outer capsid proteins . Specific inhibitors for PI3K/Akt and MEK/ERK signaling pathways trap the viral particles in late endosome , and acidic replenishment restores and releases them . Moreover , the RVA-induced phosphorylated PI3K , Akt , and ERK directly interact with the subunit E of the V-ATPase proton pump , required for endosomal acidification and RVA uncoating . Understanding how RVA-induced early activation of cellular signaling molecules mediates the V-ATPase-dependent endosomal acidification required for uncoating of viral particles opens up opportunities for targeted interventions against rotavirus entry .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"gene",
"regulation",
"immunology",
"microbiology",
"signal",
"inhibition",
"immunoprecipitation",
"antibodies",
"research",
"and",
"analysis",
"methods",
"erk",
"signaling",
"cascade",
"small",
"interfering",
"rnas",
"immune",
"system",
"proteins",
"infectious",
"diseases",
"proteins",
"gene",
"expression",
"viral",
"packaging",
"rotavirus",
"infection",
"viral",
"replication",
"precipitation",
"techniques",
"biochemistry",
"rna",
"signal",
"transduction",
"cell",
"biology",
"nucleic",
"acids",
"virology",
"physiology",
"genetics",
"signaling",
"molecules",
"biology",
"and",
"life",
"sciences",
"viral",
"diseases",
"non-coding",
"rna",
"cell",
"signaling",
"signaling",
"cascades"
] |
2018
|
Activation of PI3K, Akt, and ERK during early rotavirus infection leads to V-ATPase-dependent endosomal acidification required for uncoating
|
Segment 7 of influenza A virus produces up to four mRNAs . Unspliced transcripts encode M1 , spliced mRNA2 encodes the M2 ion channel , while protein products from spliced mRNAs 3 and 4 have not previously been identified . The M2 protein plays important roles in virus entry and assembly , and is a target for antiviral drugs and vaccination . Surprisingly , M2 is not essential for virus replication in a laboratory setting , although its loss attenuates the virus . To better understand how IAV might replicate without M2 , we studied the reversion mechanism of an M2-null virus . Serial passage of a virus lacking the mRNA2 splice donor site identified a single nucleotide pseudoreverting mutation , which restored growth in cell culture and virulence in mice by upregulating mRNA4 synthesis rather than by reinstating mRNA2 production . We show that mRNA4 encodes a novel M2-related protein ( designated M42 ) with an antigenically distinct ectodomain that can functionally replace M2 despite showing clear differences in intracellular localisation , being largely retained in the Golgi compartment . We also show that the expression of two distinct ion channel proteins is not unique to laboratory-adapted viruses but , most notably , was also a feature of the 1983 North American outbreak of H5N2 highly pathogenic avian influenza virus . In identifying a 14th influenza A polypeptide , our data reinforce the unexpectedly high coding capacity of the viral genome and have implications for virus evolution , as well as for understanding the role of M2 in the virus life cycle .
Influenza A virus ( IAV ) is a genetically diverse pathogen of global significance , responsible for seasonal epidemics and sporadic pandemics in humans , as well as outbreaks in domestic animals . Its primary reservoir is wild birds , but it can infect a wide range of vertebrate species . For these reasons , there is the need to develop better therapeutics and vaccines [1] . Current vaccines target the surface glycoproteins haemagglutinin ( HA ) and neuraminidase ( NA ) , but these proteins are subject to antigenic change , necessitating regular updating of the vaccine to ensure a good antigenic match to the circulating strains . Next generation influenza vaccines seek to induce broader or ‘universal’ protection against conserved epitopes; for example , the ‘stalk’ region of HA or the ectodomain of the matrix 2 ion channel protein ( M2 ) [2] , [3] . The IAV genome consists of eight segments of negative sense , single stranded RNA ( vRNA ) , each encapsidated into ribonucleoproteins ( RNPs ) by the viral RNA dependent RNA polymerase and multiple copies of the viral nucleoprotein ( NP ) . Upon infection , incoming RNPs are imported into the nucleus , where the vRNA is transcribed to give positive sense mRNA , and also cRNA , which acts as a replication intermediate . The approximately 13 kb genome has so far been demonstrated to encode up to 13 proteins [4] , [5] . Segments 1 , 4 , 5 and 6 each encode a single protein: PB2 , HA , NP and NA respectively . However , segments 2 , 3 , 7 and 8 have additional protein coding capacity . Segments 2 and 3 , whose primary protein products are the polymerase proteins PB1 and PA respectively , additionally produce PB1-F2 , PB1-N40 and PA-X proteins from single mRNA species by leaky ribosomal scanning and translation termination-reinitiation in the case of segment 2 and +1 ribosomal frameshifting for segment 3 [4]–[7] . In segments 7 and 8 , protein coding capacity is expanded by differential mRNA splicing . For segment 8 , a single spliced species has been described , producing NS2/NEP , while NS1 is produced from the unspliced transcript [8] , [9] . Segment 7 mRNA splicing is more complex , as three spliced transcripts have been described ( designated mRNAs 2–4 ) in addition to the unspliced mRNA1 [10]–[12] . Unspliced mRNA1 gives rise to M1 protein . The spliced mRNAs use a common 3′-splice acceptor ( SA ) site , but use different 5′-splice donor ( SD ) sites ( Fig . 1A ) . To date , only mRNA2 has been demonstrated to encode a protein: the M2 ion channel [13] . mRNA3 is produced from the most 5′-proximal SD , and is proposed to negatively regulate segment 7 protein expression during early infection [14] , a non-essential function for virus growth in tissue culture [15] , [16] . More recently , mRNA4 has been shown to be produced by the A/WSN/33 ( WSN ) strain of virus [12] , [15] , [17] . It hypothetically encodes an internally deleted form of the M1 protein ( “M4”; Fig . 1A ) but this protein has not been detected [12] . M2 is a 97 aa integral membrane protein , functional as a homotetramer , with multiple important roles during the virus lifecycle [18] , [19] . Each monomer consists of a 24 aa N-terminal ectodomain , a transmembrane α-helix and a ∼50 aa cytoplasmic domain that contains a membrane proximal amphipathic alpha helix [20] , [21] . M2 has proton channel activity , which is important for acidification of the interior of the virion upon entry [22]–[24] . In some strains of virus , proton conductance plays an additional role in modulating the pH of the Golgi compartment to prevent premature activation of the HA fusion apparatus [22] , [25] . The cytoplasmic tail of M2 also has roles in virus assembly , budding and morphogenesis [26]–[32] . The function of the ectodomain is less well described , although along with the transmembrane domain , it likely plays a role in directing the membrane topology of M2 [33] , [34] . It may also be important for incorporation of the protein into virions [35] . Nevertheless , the ectodomain is highly conserved amongst virus strains and this has made it an attractive candidate for a universal influenza vaccine [2] , [3] . Surprisingly , it has been possible to generate M2 null viruses , either by introduction of stop codons or by mutating the splice donor site , although these viruses are highly attenuated [15] , [36]–[39] . Here , we describe a pseudoreversion mechanism of a virus with a mutated mRNA2 SD site , which reveals a new aspect of IAV biology . After serial passage , we identified a single mutation that upregulated mRNA4 expression without restoring M2 synthesis . Instead , mRNA4 encodes an M2 variant with an alternative ectodomain , designated here M42 , which nevertheless functionally complements M2 , in vitro and in vivo . Furthermore , we present evidence that certain strains of IAV , most notably those responsible for the 1983 Pennsylvania outbreak of highly pathogenic avian influenza ( HPAI ) , normally express M42 . Our data extend the known IAV proteome and have implications for virus evolution and vaccine design .
Previously , we used reverse genetics to create an A/PR/8/34 ( PR8 ) virus with synonymous mutations to the mRNA 2 SD sequence . This virus , ( M1 V7-T9 , hereafter named V7-T9 ) , did not produce detectable levels of M2 and was highly attenuated in tissue culture [37] . To better understand the role of M2 in the virus life cycle , we studied the mechanism by which V7-T9 could regain fitness upon serial passage . WT and V7-T9 viruses were subjected to six rounds of serial passage via low multiplicity infections of MDCK cells . At each round , outputs were titred by plaque and HA assay . Before serial passage ( “P0” ) , the input V7-T9 virus replicated to a plaque titre 400-fold lower than the WT and had an HA titre 100-fold lower ( Fig . 2A ) . However , on serial passage it regained fitness rapidly , producing similar plaque and HA titres to WT virus within two passages . As a further test of fitness recovery , the plaque areas of the WT and V7-T9 viruses were measured before and after serial passage . Prior to serial passage , V7-T9 displayed a small plaque phenotype ( [37]; Fig . 2B ) . However , after passage six ( P6 ) , its average plaque area had increased over four-fold and was no longer significantly different from that of the WT virus ( Fig . 2B ) . To test if the regained fitness resulted from restoration of M2 expression , we examined infected cell lysates from the original and serially passaged versions of the WT and V7-T9 viruses by western blotting for M1 and M2 . All infected cells showed abundant M1 expression , confirming infection ( Figs . 2C ) . Cells infected with the WT virus isolates also contained a polypeptide recognised by the M2 ectodomain-specific 14C2 monoclonal antibody , but as before [37] , cells infected with the original V7-T9 virus did not; a phenotype that remained unchanged in the serially passaged isolate ( Fig . 2C , lanes 2–5 ) . However , 14C2 antibody recognition is restricted to an epitope encompassing residues 4 to 16 of M2 [40]–[42] . When a polyclonal antibody raised against the entire M2 protein , G74 [43] , was used , the original V7-T9 virus still did not show any reactivity ( Fig . 2C ) . However , the P6 V7-T9 virus produced detectable amounts of a G74-reactive polypeptide of similar electrophoretic mobility to that of M2 ( Fig . 2C , compare lanes 4 and 5 ) , suggesting that it now expressed some M2 polypeptide , albeit with different antigenicity to the WT protein . To further investigate M2 expression by the P6 V7-T9 virus , we examined cells infected with passaged or unpassaged WT and mutant viruses by indirect immunofluorescence for NP ( to identify infected cells ) and M2 , using the two M2-specific sera . All infected cells stained strongly for NP , confirming similar levels of infection ( Fig . 3; in red ) . Consistent with the western blot data , WT virus infected cells also stained strongly with both 14C2 and G74 anti-M2 antibodies , showing the expected predominant staining of apical and lateral membranes [44] , while neither isolate of the V7-T9 virus reacted with the 14C2 monoclonal ( Fig . 3; in green or separate channel in grey ) . Also consistent with the western blot data , the unpassaged V7-T9 virus did not stain above background levels with the G74 antiserum , but the P6 isolate showed clear reactivity . However , the staining pattern was markedly different to that shown by WT virus , with prominent perinuclear staining and some staining of lateral membranes ( Fig . 3B ) . Overall , these data suggested that the serially passaged M2 null virus had regained fitness by expressing a variant form of M2 that no longer reacted with the ectodomain-specific antibody . Following serial passage , segment 7 of both the P6 WT and V7-T9 viruses was sequenced . No changes were detected in the WT virus compared to the reference sequence ( GenBank accession EF467824 ) . The P6 V7-T9 virus retained the original mutations that destroyed the mRNA2 SD site , indicating pseudoreversion to recover WT growth properties rather than true reversion . It also contained a single additional change , not seen in the original V7-T9 virus , of a U to A substitution at nucleotide 148 ( U148A; mRNA sense , Fig . S1 ) . This change is in the M2 intron and is silent in M1 . However , the change would be predicted to improve the mRNA4 SD consensus ( Fig . S1 ) , from AG/GUU to AG/GUA [45] . As previously noted , mRNA4 is predicted to encode a 54 aa internally deleted version of M1 [12] , from the first AUG on the transcript ( Fig . 1B ) . Notably , the Kozak consensus [46] of AUG1 is not optimal , lacking a G at position 4 ( Figs . 1B , S1 ) and in the context of segment 2 , an intermediate strength initiation context AUG1 is known to permit translation initiation at downstream codons by a leaky ribosomal scanning mechanism [4] , [6] , [7] . Inspection of the segment 7 sequence showed another AUG starting at position 114 in frame 2 ( Fig . 1B ) . The predicted protein product from this AUG would have a variant ectodomain compared to M2 , but would be identical from amino acid 10 onwards . The predicted size of the protein product would be 99 amino acids , compared to 97 for M2 ( Fig . 1C ) . Accordingly , we hypothesized that the U148A change induced pseudoreversion of the V7-T9 virus via upregulation of mRNA4 , to produce a variant M2 protein ( designated here “M42” ) from AUG2 via leaky ribosomal scanning . To test this , we used reverse genetics to first ask whether the U148A change was sufficient to restore WT growth properties to the V7-T9 virus . Initially , a PR8 V7-T9+U148A virus was generated , along with WT , V7-T9 and a virus with only the U148A change . Viruses were rescued by transfecting bidirectional plasmids [47] into 293T cells , amplified by one passage in MDCK cells and plaque titred . WT PR8 grew to approximately 7×108 PFU/ml and formed large plaques , whereas V7-T9 had a small plaque phenotype and was attenuated by approximately 3 log10 ( Fig . 4A ) , consistent with previous observations [37] . Introduction of the single U148A mutation into the background of an otherwise WT virus did not alter virus growth properties . However , when the change was added to the V7-T9 background , the double mutant grew to an average of 5×108 PFU/ml and produced normal-sized plaques ( Fig . 4A ) , confirming that the U148A mutation was necessary and sufficient to restore WT growth properties . To further test the M42 hypothesis , we introduced two mutations that would be expected to block production of the predicted novel polypeptide: either by removing its AUG codon ( U115C ) , or by destroying the mRNA4 SD site ( G145A ) . Each of these mutations was made on the background of WT segment 7 , as well as with the V7-T9 , U148A or V7-T9+U148A mutations . On a WT background , a virus with only the U115C mutation grew normally and produced plaques indistinguishable from the WT virus ( Fig . 4A ) . When the U115C mutation was combined with the U148A change , the resulting virus grew slightly less well than WT ( an average relative titre of 0 . 44 [n = 4] ) and displayed a small plaque phenotype . Addition of U115C to the V7-T9 mutant also had only a minor effect on growth relative to the parent virus . In contrast , its addition to the V7-T9+U148A background reversed the positive effect of the U148A mutation , resulting in a virus that grew poorly ( to less than104 PFU/ml ) and produced small plaques . Similarly , the G145A mutation had no effect on virus growth as a single mutation or when combined with the U148A change . However , in 3 independent attempts , it was not possible to rescue a virus with V7-T9 , U148A and G145A mutations , suggestive of a lethal phenotype . These data indicated that pseudoreversion of the M2-null virus required mRNA4 and also AUG2 . As an additional genetic test of the M42 hypothesis , we introduced a premature stop codon ( K70* ) into the distal region of the M2 ORF that would be common to both M2 and M42 polypeptides , but outside the M1 ( or hypothetical M4 ) coding region . As a control K70 was also substituted for tryptophan ( K70W ) , a similar sequence change but known to be compatible with M2 function [29] . It was not possible to rescue a virus with V7-T9+U148A+K70stop , although the V7-T9+U148A+K70W mutant grew comparably to WT and V7+U148A viruses . Together , the genetic data are consistent with the hypothesis that the U148A change restores growth of an M2-deficient virus by upregulating expression of mRNA4 , allowing expression of an M2 variant from AUG2 of the transcript . To provide biochemical evidence for the M42 hypothesis , we next examined segment 7 mRNA splicing by the panel of viruses in 293T cells . The V7-T9+U115C+U148A virus grew to insufficient titres to allow high multiplicity synchronous infections and the V7-T9+G145A+U148A virus could not be rescued , so the U115C+U148A and G145A+U148A viruses were used as proxies to analyse the effects of the U115C and G145A mutations on mRNA expression . Following infection , total RNA was extracted and reverse transcriptase-primer extension reactions were performed using a single primer capable of distinguishing segment 7 mRNAs 1–4 [17] . Separate primers specific for segment 7 vRNA and cellular 5S rRNA were also included as controls for virus infection and RNA recovery respectively . The levels of 5S rRNA were equivalent between samples ( Fig . 4B ) , demonstrating equal loading . vRNA levels were also comparable between infected samples , suggesting that all infections had proceeded successfully . The levels of unspliced mRNA1 were also similar between the viruses . However , large differences in the levels of spliced mRNAs 2 and 4 were apparent . In cells infected with the WT virus , the unspliced transcript predominated , but abundant levels of mRNA2 ( for M2 ) were also present ( Fig . 4B , lane 1; quantification in Fig . 4C ) . In contrast , mRNAs 3 and 4 formed minor species that were only visible on long exposure ( primary data not shown , but see Fig . 4C for quantification ) . As expected , mRNA2 was not detected in viruses containing the V7-T9 mutation ( Fig . 4B , lanes 5 and 6 ) . Importantly , and as predicted , the U148A mutation , either alone or on a V7-T9 background , strongly upregulated production of mRNA4 ( compare lanes 1 , 3 and 6 ) . This effect was blocked when the mRNA4 SD was destroyed with a G145A change ( lane 7 ) . Interestingly , the changes in levels of mRNAs 2 and 4 were partly reciprocal . Loss of the mRNA2 SD site in the V7-T9 virus was associated with weak upregulation of mRNA4 ( compare lanes 1 and 5 ) , while improvement of the mRNA4 SD by the U148A change in an otherwise WT background led to around a three-fold drop in mRNA2 levels ( compare lanes 1 and 3 ) . Addition of the U115C change to the U148A virus caused a further decrease in mRNA2 levels , but left mRNA4 levels unaltered ( compare lanes 3 and 4 ) . Overall , these data supported the proposed mechanism of pseudoreversion involving increased production of mRNA4 . Next , we analysed segment 7 protein expression from the mutant viruses by western blotting for M1 , and for M2 using 14C2 and G74 antisera . Lysates from the primary reverse genetics transfections in 293T cells were used , because V7-T9+G145A or V7-T9+G145A+U148A viruses could not be obtained . Virus polypeptides in these lysates will therefore come from several sources: from RNA Pol II transcription of the bidirectional plasmid , from viral transcription in cells where active RNPs have been reconstituted by transfection , and from spread of viable virus through the cell culture . To control for purely plasmid-mediated expression , lysates from a transfection where PB2 was omitted were probed . In this sample , levels of M1 and M2 were below the limit of detection , although they were readily visualised when all eight plasmids were transfected ( Fig . 4D , compare lanes 1 and 2 ) . This suggested that under these conditions , the major signal came from viral gene expression . When mutant and WT transfections were compared , M1 levels were broadly similar between samples , but there was more variation in M2 levels . As expected , 14C2 reactivity was only detected from viruses with an intact mRNA2 SD ( lanes 2–6 ) and was absent from all of the V7-T9 family of viruses ( lanes 7–12 ) . G74 reactivity was also readily detectable in all samples from viruses able to make mRNA2 . However , in the absence of mRNA2 , it was only detectable in the V7-T9+U148A transfected lysates ( lane 10 ) . Significantly , this was dependent on the presence of both elevated mRNA4 levels and segment 7 AUG2 , as addition of either or the G145A or U115C mutations ablated its expression ( compare lanes 10 , 11 and 12 ) . Next , to prove the existence of the M42 polypeptide , we raised a specific antibody against a peptide corresponding to the predicted novel ectodomain of PR8 M42 . To validate the serum , we tested it against transfected M42 and M2 , both fused to GFP . M42-GFP , M2-GFP or GFP alone were transfected into 293T cells and the resulting cell lysates were probed with anti-M42 and anti-M2 14C2 or G74 . Samples were also probed with anti-GFP and tubulin antisera , to confirm expression of the GFP polypeptides and equal sample loading respectively ( Fig . 5A ) . The anti-M42 serum detected M42-GFP with a high degree of specificity over M2-GFP ( compare lanes 1 and 2 ) . Conversely , the 14C2 antibody was specific for M2-GFP , while as expected , anti-G74 detected both M42 and M2-GFP . A preimmune bleed from the rabbits immunized with the M42 peptide did not react with either M42-or M2 GFP . To further probe the immunological cross-reactivity between M2 and M42 , we tested a polyclonal antiserum raised against the entire M2 ectodomain , anti-M2e [48] . This reacted strongly with M2-GFP and only weakly with M42-GFP , confirming the novel antigenicity of the M42 ectodomain . Having generated a specific M42 antibody , we investigated expression of the protein in cells infected with the panel of WT and mutant viruses . Western blot analysis of MDCK cell lysates using anti-tubulin sera demonstrated equivalent loading of all samples while anti-M1 and anti-NP sera showed equal levels of infection except in mock infected cells ( Fig . 5B ) . WT , U115C , G145A and G145A+U148A viruses expressed abundant quantities of a polypeptide that reacted with 14C2 and G74 anti-M2 sera ( Fig . 5B , lanes 2–5 and 7 ) . In contrast , the V7-T9 virus did not produce detectable levels of any M2-related polypeptide ( lane 8 ) . However , concomitant upregulation of mRNA4 on this background by the addition of the U148A mutation led to abundant synthesis of an anti-M42 reactive polypeptide ( lane 9 ) , confirming our hypothesis of a novel M2-related polypeptide . The U148A mutation also led to synthesis of readily detectable amounts of the M42 polypeptide when introduced into an otherwise WT background ( lane 5 ) . M42 reactivity was however lost on this background by mutation of AUG2 with the U115C change , or by mutation of the mRNA4 SD using G145A ( lanes 6 and 7 ) . Consistent with the mRNA abundance data , overall amounts of M2 were reduced by the U148A mutation , as judged by 14C2 and G74 staining ( compare lanes 1 and 5 ) . In addition , double staining the same blot with the mouse 14C2 antibody in red and the rabbit M42 antibody in green allowed the creation of a merged image ( Fig . 5B , top panel ) that illustrates the similar molecular weights of the M2 and M42 polypeptides , as well as their changing relative abundance in response to mutations to SD sites of mRNAs 2 and 4 . Immunofluorescent staining of P6 V7-T9-infected cells suggested that the two forms of M2 localised differently within infected cells ( Fig . 3B ) . To test if this truly reflected a difference in behaviour of M42 compared to M2 , we infected cells with the relevant recombinant viruses and examined M2 and M42 localisation by immunofluorescence . In WT virus infected cells , M2 protein localized to the plasma membrane ( visible as staining of lateral membranes in single optical slices through the midline of the cells ) as well as internally , often in a perinuclear position ( Fig . 6A ) . Double staining for M42 however , only produced background levels of fluorescent signal , similar to mock infected or V7-T9 infected cells . In contrast , cells infected with the V7-T9+U148A virus did not stain for M2 but stained strongly with anti-M42 , with the M42 signal largely present in a perinuclear structure . Confirming that the two proteins did indeed localize differently in infected cells , when cells infected with the U148A virus ( which expresses both proteins; Fig . 5B ) were examined , the two polypeptides displayed limited colocalisation in the perinuclear region , but were largely in separate regions of the cell ( Fig . 6A ) . Double staining of cells infected with the V7-T9+U184A virus with anti-M42 sera and a variety of markers for the cellular exocytic pathway showed good co-localisation with GM130 ( Fig . 6B and data not shown ) , indicating that M42 was largely resident in the cis-Golgi apparatus . To examine intracellular trafficking of M42 further , we created a plasmid encoding an M42-red fluorescent protein fusion ( M42-mCherry ) and examined the localization of the chimaeric protein in living cells , in comparison to a simultaneously transfected M2-GFP fusion . Both polypeptides co-localised in discrete cytoplasmic puncta and at the plasma membrane , but while the intensity of GFP and mCherry fluorescence was similar in the cytoplasmic dots , there was clearly less of the M42-mCherry protein on the plasma membrane at steady state ( Fig . 6C ) . Examination of time lapse films showed that the cytoplasmic puncta showed the expected pattern of movement for intracellular vesicles ( Videos S1 , S2 , S3 ) . Overall therefore , we conclude that like M2 , M42 enters the exocytic pathway , but its altered ectodomain affects intracellular trafficking of the protein , resulting in a lower proportion resident at the plasma membrane . The marked difference in intracellular localization between M2 and M42 was surprising , given that the two proteins were apparently interchangeable with respect to virus replication in MDCK cells ( Fig . 4A ) . We therefore tested the effect of modulating M42 expression on virus pathogenicity , using the murine infection model . When either BALB/c or C57BL/6 strain mice were infected with 100 PFU of WT PR8 virus , they lost weight rapidly ( Figs . 7A , B ) , showing average peak weight losses of around 20% and substantial amounts of mortality ( Fig . 7C ) . High titres of virus were also recoverable from the lungs of infected C57BL/6 mice at days 2 and 4 p . i . , dropping somewhat at day 7 ( Fig . 7D ) . In contrast , mice infected with the same titer of the M2-null V7-T9 virus showed minimal weight loss , few signs of illness or virus replication and no mortality . However , upregulation of mRNA4 synthesis via U148A on the V7-T9 background substantially increased virus replication and pathogenicity in terms of virus titres andweight loss , although the overall mortality was less than observed with WT virus . Conversely , increasing M42 expression by adding the U148A change on the background of a WT virus still able to express M2 had the opposite effect , decreasing the severity of weight loss and overall mortality , although lung titres were not affected . Removal of the M42 AUG codon with the U115C mutation had little effect in BALB/c mice but led to slightly delayed weight loss and decreased mortality in C57BL/6 mice . Overall therefore , altering the balance between M2 and M42 expression modulated virus pathogenicity , but a virus that only expressed M42 still caused significant disease . The work described above demonstrated that mRNA4 encodes a biologically significant polypeptide that can compensate for loss of M2 expression . The question therefore arose as to whether this might apply to other strains of IAV . The two requisites for M42 expression are production of mRNA4 and the possession of an AUG codon in the appropriate reading frame . mRNA4 was originally discovered in the WSN strain of virus but was not detected in the A/Udorn/72 ( Udorn ) strain , a difference proposed to result from a single nucleotide difference in the sequences immediately surrounding the splice site: AG/GUU in WSN versus AG/GCU in Udorn ( [12]; see Fig . S1 , which shows an alignment of the viruses used or discussed in this work , in addition to the consensus sequences of the major virus subtypes that have infected humans this century ) . To test this prediction , we compared mRNA4 synthesis in a panel of viruses with either GUA , GUU or GCU at the 5′-end of the mRNA4 intron . In agreement with the quality of match with the consensus SD sequence , mRNA4 was not detectable in the two viruses with a GCU sequence: human H3N2 Udorn and H1N1 A/USSR/77 ( Fig . 8A , lanes 6 and 7 ) , while it was most abundant in the PR8 U148A mutant ( GUA; lane 2 ) . The prediction was also partially supported when mRNA4 synthesis was examined in viruses with a GUU sequence immediately downstream of the SD site . Intermediate quantities of mRNA 4 were detected in RNA from WSN and Cambridge PR8 virus infected cells ( lanes 4 and 5; note lower overall amounts of segment 7 mRNAs with the latter virus ) . Curiously however , mRNA4 was not seen from reverse genetics PR8 ( compare lanes 3–5 ) . This was despite segment 7 of this virus only differing from Cambridge PR8 and WSN at two nucleotide positions in the 5′-240 nucleotides , with neither change located near the mRNA4 SD sequence ( Fig . S1 and data not shown ) . There were also notable differences in the amount of mRNA3 produced by the viruses , with WSN and Udorn making abundant quantities , A/USSR/77 rather less and all three PR8 viruses making very little ( Fig . 8A ) . Thus mRNA4 production is predictable by examination of the SD consensus sequence , with GUA>GUU>>GCU , although other unidentified sequence polymorphisms also play a role . We therefore used this information to interrogate Genbank for IAV segment 7 sequences likely to express M42 . As of October 2011 , over three-quarters of the 20 , 236 viruses for which useful segment 7 sequence was available contain the M42 AUG ( data not shown ) . An imperfect initiation context of the M1/M2 AUG codon ( which is likely to be necessary to allow leaky ribosomal scanning ) is a very highly conserved feature of IAV ( only 3 of 17256 sequences covering the M1 AUG have an optimal G at position +4 ) . However , the majority ( ∼80% ) of viruses are unlikely to produce substantial amounts of mRNA4 , as they possess an unfavourable AG/GCU or otherwise non-canonical SD sequence . In 1998 , Shih and colleagues identified 8 viruses likely to make appreciable amounts of mRNA4 [12] . Now , with an increased number of sequences available as well as a better understanding of the sequence elements necessary for expression of a third biologically active protein from segment 7 , we identified around two dozen viruses likely to express M42 ( Table 1 ) , by virtue of containing an AUG codon at positions 114–116 and an mRNA4 SD sequence of AG/GU ( U/A/G ) . These mostly fell into three partially overlapping groups: isolates from the early 20th Century ( human isolates from the 1930s and two classic fowl plague highly pathogenic avian influenza ( HPAI ) viruses ) , viruses that had been adapted to replicate in mice ( the WS family , two H3N2 isolates and PR8 ) and a set of HPAI isolates , mostly from the USA 1983 outbreak [49] . The latter H5N2 grouping seemed the most likely to express large amounts of M42 because of their AG/GUA mRNA4 SD sequence . In addition , the H5N2 outbreak spread widely , persisted for several years and resulted in the culling of 17 , 000 , 000 poultry [50] , making it an important group of non laboratory-derived viruses , even if represented on Genbank by relatively few sequenced isolates . We therefore tested whether the AG/GUA mRNA4 SD consensus of the H5N2 viruses was biologically significant . For biosafety reasons , we used reverse genetics to create a PR8 reassortant ( MPenn ) with segment 7 from A/chicken/Pennsylvania/10210/1986 ( Penn ) as well as various mutant derivatives with alterations to the mRNA2 or 4 SD sequences or the M42 AUG codon ( Fig . S1 ) , and then analysed their segment 7 mRNA expression profiles . Analysis of viral RNA synthesis showed that , as predicted by the MPenn mRNA4 SD sequence , mRNA4 was the predominant species made from segment 7 , accumulating to markedly higher levels than either the unspliced transcript or spliced mRNAs 2 and 3; a reversal of the ratios seen with the ‘prototype’ mRNA 4-expressing virus , WSN , where mRNA4 was the least abundant species ( Fig . 8B , compare lanes 2 and 10 ) . Mutations to the mRNA2 and mRNA4 SD sequences had the expected effects . Destruction of the mRNA2 SD sequence by a G52C change reduced mRNA2 accumulation to below detectable levels ( lane 3 ) . Removal of the mRNA4 splice site with the G145A change blocked detectable synthesis of mRNA4 with , as before , the side effect of upregulating mRNA2 and mRNA3 production ( lane 5 ) . Mutations that weakened the mRNA4 SD consensus ( A148G/U or C ) dramatically reduced mRNA4 accumulation whilst simultaneously improving synthesis of mRNAs 2 and 3 ( lanes 6–9 ) . Also as expected , these changes were specific to segment mRNA , as the levels of segment 7 vRNA and segment 5 mRNA and cRNA were much more consistent between viruses ( Fig . 8B ) . Next the impact of these changes on virus growth were assessed . The WT MPenn reassortant virus grew well , reaching titres of around 107 PFU/ml ( Fig . 8C ) . Abolition of mRNA2 expression ( G52C ) had no effect on virus replication; in contrast to the attenuation seen when M2 synthesis was blocked in other virus strains [15] , [36]–[39] . Similarly , mutations predicted to block M42 expression by destroying its AUG codon ( U115C ) or mRNA4 production ( G145A ) had no effect on virus growth . A similar lack of effect on virus titres were seen from the mutations that attenuated mRNA4 production: A148G , A148U and A148C . However , double mutations targeting both M2 and M42 production were deleterious to virus growth . Viruses lacking the M42 AUG codon or mRNA4 SD sequence could not be rescued ( in 3 attempts ) in combination with the G52C mRNA 2 SD knockout ( Fig . 8C ) . Moderate downregulation of mRNA4 production by an A148G change in the absence of mRNA 2 synthesis led to a virus that grew to high titres but with a small plaque phenotype , while a more severe downregulation of mRNA4 synthesis with an A148U change resulted in an additional phenotype of very poor growth . Overall , these data indicate that the A/chicken/Pennsylvania/10210/1986 segment 7 expresses two functionally redundant versions of the viral ion channel , either one of which is sufficient to support replication in cultured cells . Thus M42 expression is not peculiar to laboratory adapted viruses but is likely to have been a feature of a major group of HPAI viruses that circulated for four years in North America .
Here , we demonstrate expression of a 14th IAV polypeptide; a variant form of the M2 protein with an alternative ectodomain , encoded by a distinct segment 7 mRNA . This novel protein , M42 , can functionally replace M2 and support efficient replication in tissue culture cells and pathogenicity in an animal host , despite showing clear phenotypic differences with respect to its intracellular localization . We have not directly tested M42 for proton conductance but the PR8 MUd virus engineered to express M42 rather than M2 retained amantadine sensitivity ( data not shown ) , providing indirect evidence that the protein retains this function , as expected from its identical transmembrane domain sequence to M2 . The ability of M42 to support efficient virus replication despite its inefficient transport to the plasma membrane is interesting in light of current theories regarding the role of M2 in membrane scission [32] . Like the three other IAV “accessory” genes that were discovered long after the virus genome was sequenced ( PB1-F2 , PB1-N40 and PA-X; [4]–[6] ) , M42 is clearly non-essential for virus replication , as long as sufficient M2 is expressed . Unlike the additional proteins expressed from the P protein genes , M42 expression is likely to be restricted to a minority of IAV strains under normal conditions , as a result of the suboptimal SD sequence of mRNA4 . Examination of the consensus sequences for the major subtypes of IAV that have infected humans in the last century showed that ( in consensus , with occasional exceptions ) all possess ( ed ) a weak mRNA4 SD sequence ( GCU at the intron boundary ) of the type found in Udorn ( Fig . S1 ) . However , all these viruses except the current 2009 swine-origin pandemic virus also contain the M42 AUG codon as well as an imperfect Kozak consensus around the M1/M2 AUG codon ( Fig . S1 ) , suggesting the potential for M42 expression should mRNA4 expression be present . This perhaps argues that there are environments in which it is advantageous for IAV to shift the balance of segment 7 splicing away from the normal mRNA2/M2 route to increased mRNA4/M42 . In this respect it is noteworthy that increased mRNA4 synthesis has been selected for on at least three , probably four , occasions on different virus backgrounds during adaptation to growth in mice ( Table 1 ) . Also , given the Golgi-biased localisation of M42 , it is tempting to draw a link between the requirement for pH-modulation of the Golgi during intracellular transport of HA molecules with polybasic cleavage sites [25] , [51] and the overrerpresentation of HPAI viruses in the list of those likely to express M42 . We also speculate that the altered antigenicity of the M42 ectodomain might provide the virus with a route to escape selection pressure imposed by a vaccine directed against the M2e sequence , given that in many viruses , a single nucleotide change would be predicted to alter the balance of splicing towards M42 . The viruses in which we can be reasonably confident of M42 expression represent a very small minority ( ∼0 . 2% ) of the available sequences . However , there are two further considerations that may render M42 expression more widespread in IAV than our conservative prediction in Table 1 . Firstly , we do not yet fully understand what controls segment 7 splicing . A sizeable number of viruses ( around 15%; mostly from avian hosts ) have an mRNA4 SD sequence of AG/GCA . An A at position+3 clearly promotes more efficient use of the splice site when position +2 is U but it remains to be determined if it is sufficient to override a C at +2 . Furthermore , the differences in relative splicing seen between PR8 and WSN viruses make it clear that sequence elements outside of the core consensus splice sites affect their use; these sequences are identical in the two viruses but their splicing patterns are very different . Analysis of a 7+1 PR8:WSN reassortant indicates that the difference is intrinsic to segment 7 ( HW , PD , unpublished data ) but we have not yet identified the sequence determinants . Secondly , there are many precedents for cell-type dependence of alternative splicing in cellular mRNAs [52] so it may be that in some host species and/or cell types , M42 expression is present in a wider array of IAV strains . Further experiments are required to test these hypotheses .
Animal experiments were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals under the auspices of an NIH Animal Care and Use Committee-approved animal study protocol . The protocol was approved by the NIAID Animal Care and Use Committee ( Permit Number LID-6E ) . All efforts were made to minimize suffering . Madin-Darby Canine Kidney , 293T human embryonic kidney and A549 human lung adenocarcinoma cells were cultured according to standard procedures [53] . A reverse genetics clone of influenza PR8 and its M2-null derivative , V7-T9 have been previously described [37] , [47] . A clone of A/chicken/Pennsylvania/10210/1986 ( GU052748 ) with the UTRs derived from A/chicken/Pennsylvania/1/1983 ( CY015074; where a complete segment sequence was available ) was synthesized by Genscript and cloned into pDUAL reverse genetics vector [47] . Further mutants were made using the 8 bidirectional promoter plasmid system described in [47] , following oligonucleotide-directed mutagenesis to introduce the desired changes , as indicated in Fig . S1 . Primer sequences are available upon request . Non-recombinant Cambridge lineage PR8 virus , A/USSR/77 and A/WSN/33 viruses were obtained from the University of Cambridge Division of Virology's collection of viruses . A/Udorn/72 was a gift from Professor Compans [54] . M2 and M42-GFP tagged expression constructs were produced by cloning the coding sequences of the respective proteins into the KpnI/AgeI sites of pEGFP-N1 ( Clontech ) . An M42-mCherry fusion was made by substituting the EGFP open reading frame with mCherry . Purchased monoclonal antisera were against ß-tubulin ( clone YL1/2: AbD-Serotec ) , GM130 ( Clone 610822; BD Transduction Laboratory ) , GFP ( clone JL8 , Clontech ) and anti-influenza M2 ( Clone 14C2 , Abcam ) . Further anti-M2 reagents of a goat polyclonal ( G74 ) raised against the whole protein and a mouse polyclonal raised against the M2 ectodomain ( M2e ) were the generous gifts of Drs . Alan Hay and Xavier Saelens , respectively . Rabbit polyclonal anti-M1 ( A2917 ) and anti-NP ( A2915 ) have been previously described [55] , [56] . Affinity purified anti-M42 specific serum was purchased from Genscript . Rabbits were immunized with a peptide corresponding to the N-terminal 16 amino acids of the protein , MSLQGRTPILRPIRNE ( where unique sequences compared to M2 are underlined ) . Recombinant viruses were rescued by 8 plasmid transfection into 293T cells followed by amplification in MDCK cells as previously described [37] . In some cases , stocks were further amplified by growth in day 10–12 embryonated eggs , also as described [37] . Tissue culture cells were infected by allowing virus to adsorb for 30–60 min in serum free medium . For synchronous analyses of viral RNA and protein synthesis , infections were carried out at an MOI of 3–10 . For analyses of virus growth , infections were initiated at low multiplicity and cells overlaid with serum free medium supplemented with 1 µg/ml trypsin ( Worthington Biochemicals ) and 0 . 14% bovine serum albumin . Serial passages were performed by infecting 3×106 MDCK cells at an MOI of 0 . 01 . At 48 h p . i . , the medium was clarified and 10 µl ( of 5 ml ) used to infect fresh MDCK cells . This procedure was repeated a further five times . Plaque assays were carried out in MDCK cells using an Avicel overlay followed by staining with toluidine blue [37] , [57] . Plaque areas were measured from scanned images using an oval selection marquee in the program Image J [58] and calibrated with respect to the area of a 6-well dish . HA assays were performed using 1% chicken red blood cells in 96 well plates according to standard procedures [37] . Infection of C57BL/6J or BALB/c mice ( strains 664 and 1026 , JAX Mice and Services ) was carried out under animal BSL3 conditions at the National Institutes of Health . Groups of five 9–10 week old female mice were infected intranasally with 100 PFU of virus in 50 µl DMEM under oxygenated isoflurane anesthesia . Mice were individually identified and weighed daily; mice losing 25% or more of their initial body weight were euthanised . Three mice on days 2 and 7 and four mice on day 4 postinfection were euthanised and lungs collected for weight-normalized homogenization and MDCK plaque titration . Total cellular RNA was extracted using Trizol ( Sigma ) and individual RNA species detected using radiolabelled reverse transcriptase primer extension followed by urea-PAGE and autoradiography as previously described [17] , [59] . The primer GAAGGCCCTCCTTTCAGTCC , which targeted nucleotides 885–904 in mRNA sense , was used to detect segment 7 mRNA . Quantitation was performed using Fujifilm imaging plates and a Fujifilm FLA-5000 fluorescent image analyser . Data was analysed using AIDA software ( Raytest ) . SDS-PAGE followed by western blotting was performed according to standard procedures . Blots were developed using infrared fluorescent secondary antibodies and imaged using a LiCor Biosciences Odyssey platform . Cells were stained for immunofluorescence after formaldehyde fixation using primary followed by Alexa-fluor conjugated secondary antibodies ( Invitrogen ) as previously described [60] and imaged on Zeiss LSM510 , Leica SPE or TCS-NT confocal microscopes . Live cell imaging was performed in a temperature-controlled hood and CO2-independent medium as previously described [53] .
|
Influenza A virus is a pathogen capable of infecting a wide range of avian and mammalian hosts , causing seasonal epidemics and pandemics in humans . In recent years , the unexpected coding capacity of the virus has begun to be unravelled , with the identification of three more protein products ( PB1-F2 , PB1-N40 and PA-X ) on top of the 10 viral proteins originally identified 30 years ago . Here , we identify a 14th primary translation product , made from segment 7 . Previously established protein products from segment 7 include the matrix ( M1 ) and ion channel ( M2 ) proteins . M2 , made from a spliced transcript , has multiple roles in the virus lifecycle including in entry and budding . In a laboratory setting , it is possible to generate M2 deficient viruses , but these are highly attenuated . However , upon serial passage a virus lacking the M2 splice donor site quickly recovered wild type growth properties , without reverting the original mutation . Instead we found a compensatory single nucleotide mutation had upregulated another segment 7 mRNA . This mRNA encoded a novel M2-like protein with a variant extracellular domain , which we called M42 . M42 compensated for loss of M2 in tissue culture cells and animals , although it displayed some differences in subcellular localisation . Our study therefore identifies a further novel influenza protein and gives insights into the evolution of the virus .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"biochemistry",
"public",
"health",
"and",
"epidemiology",
"immunology",
"biology",
"molecular",
"cell",
"biology",
"public",
"health"
] |
2012
|
Identification of a Novel Splice Variant Form of the Influenza A Virus M2 Ion Channel with an Antigenically Distinct Ectodomain
|
The rise of resistance together with the shortage of new broad-spectrum antibiotics underlines the urgency of optimizing the use of available drugs to minimize disease burden . Theoretical studies suggest that coordinating empirical usage of antibiotics in a hospital ward can contain the spread of resistance . However , theoretical and clinical studies came to different conclusions regarding the usefulness of rotating first-line therapy ( cycling ) . Here , we performed a quantitative pathogen-specific meta-analysis of clinical studies comparing cycling to standard practice . We searched PubMed and Google Scholar and identified 46 clinical studies addressing the effect of cycling on nosocomial infections , of which 11 met our selection criteria . We employed a method for multivariate meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate/1000 patient days of both total infections by 4 . 95 [9 . 43–0 . 48] and resistant infections by 7 . 2 [14 . 00–0 . 44] . This positive effect was observed in most pathogens despite a large variance between individual species . Our findings remain robust in uni- and multivariate metaregressions . We used theoretical models that reflect various infections and hospital settings to compare cycling to random assignment to different drugs ( mixing ) . We make the realistic assumption that therapy is changed when first line treatment is ineffective , which we call “adjustable cycling/mixing” . In concordance with earlier theoretical studies , we find that in strict regimens , cycling is detrimental . However , in adjustable regimens single resistance is suppressed and cycling is successful in most settings . Both a meta-regression and our theoretical model indicate that “adjustable cycling” is especially useful to suppress emergence of multiple resistance . While our model predicts that cycling periods of one month perform well , we expect that too long cycling periods are detrimental . Our results suggest that “adjustable cycling” suppresses multiple resistance and warrants further investigations that allow comparing various diseases and hospital settings .
The emergence and spread of antibiotic resistance threatens our ability to treat bacterial infections and is a substantial danger for public health world-wide [1] . Resistant strains are especially prevalent in hospitals , where the high usage of antibiotics facilitates emergence and spread of resistant strains . Globally , 8% of hospital stays result in nosocomial infections [1] . It has been estimated that 70% of these are caused by single- or multiple-resistant bacteria [2] . Compared to infections by susceptible bacteria , those caused by resistant strains often increase mortality , morbidity and costs [3] . While treatment can be tailored to the pathogen and its resistance profile once cultures are available , treatment typically needs to be initiated immediately . This treatment phase is called empirical therapy . In single hospitals or wards , population-wide empirical treatment of patients can be coordinated , and several such strategies have been proposed to fight resistance [4]–[10] . Here , we focus on the comparison of two strategies on which most clinical and theoretical studies have focused so far: The first is “cycling” i . e . scheduled changes of the predominant antibiotic in a whole ward or hospital . The second is “mixing” i . e . the random assignment of patients to different antibiotics , such that at any given time point multiple antibiotics are employed in approximately equal proportions . Mixing has been seen as the strategy closest to the current usage patterns in most wards [5] . Theoretical models predict that , when different antibiotics are employed at comparable average frequencies , mixing should outperform cycling since the pathogen is subject to greater environmental heterogeneity when transmitted from host to host [5] , [6] . Clinical studies addressing the general usefulness of cycling have come to contradictory results . Not only has no clear pattern emerged from these studies , but also some studies report divergent outcomes for different pathogen species . A qualitative meta-analysis [10] has argued that cycling could be beneficial for preserving drug susceptibility in Pseudomonas aeruginosa . However , neither a quantitative pathogen specific meta-analysis , nor a theoretical explanation of potential benefits of cycling is available to date . Despite the difficulties to exclude confounders in the clinical setting and the often-criticized study designs [11] , it may therefore be worth to re-evaluate both clinical studies and theoretical models . Specifically , it is important to elucidate whether inherent characteristics of the pathogen or the host populations may lead to these different outcomes . Here , we perform a quantitative and pathogen-specific meta-analysis of clinical studies comparing cycling to standard treatment regimens . We furthermore develop an epidemiological model tailored to the situation in hospital wards . We design this model such that it can easily describe a multitude of infectious diseases . Furthermore , we aim at a model structure that allows parameterization with observed clinical parameters . Earlier theoretical studies assumed that patients remained on the prescribed drugs until leaving the hospital ( “strict cycling”/“strict mixing” ) . Here , we make the realistic assumption that empirical therapy is automatically changed when ineffective ( “adjustable cycling”/“adjustable mixing” ) . For the sake of clarity , we will refer to any clinical cycling schedules as “clinical cycling” , because clinical reality is likely different from these two extremes . We compare the results of our meta-analysis with the predictions of our theoretical model and highlight common observations that may explain the divergence between earlier studies .
Here , we define clinical cycling as repeated rotations of at least two antibiotics in the same order . We performed a literature search ( see methods ) to identify studies meeting these criteria . For performing a quantitative meta-analysis , we required the following additional criteria: i ) a baseline period in the same ward or comparison to simultaneously recorded data from a ward in the same hospital , ii ) no other infection control measures introduced in the observation period and iii ) unprocessed data on the number of isolates . As explained above , we chose the number of total isolates and resistant isolates per patient day as primary endpoints . Additionally , we collected data on mortality as a secondary endpoint . To be able to link resistance evolution to the used antibiotics and to compare the results to our epidemiological model , we only included resistance against the scheduled antibiotics . To account for multiple resistance , we summed over the number of isolates against each of the employed drugs ( later referred to as weighted incidence rate of resistant infections ) . Moreover , we extracted data on mortality as a secondary endpoint to ascertain that cycling has no unexpected detrimental effects . We identified 46 studies [15]–[59] , of which 11 were eligible [45]–[54] , [59] , i . e . fulfilled our criteria and provided all needed data ( Figure S1 , Table S1 and methods ) . One of these [51] reported outcomes from independent wards , which we report separately . Table S2 lists all data extracted from these studies . For all endpoints , cycling performed significantly better in univariate analyses , also after adjustment for multiple testing ( Table S3 ) . However , the three endpoints are correlated ( not independent ) , such that univariate meta-analyses on each individual endpoint is inferior to a multivariate meta-analysis on those three endpoints simultaneously . We employed a multivariate analysis framework ( methods and supplementary materials p . 9 ) , which revealed significant reductions in the weighted incidence rate of resistant isolates from 27 to 20 isolates/1000 patient days ( p = 0 . 037 ) as well as in the total incidence rate from 30 to 25 isolates/1000 patient days ( p = 0 . 03 , Figure 1A ) . We found a pronounced correlation ( p = 0 . 00059 , p = 0 . 028 after Benjamini-Hochberg correction for multiple testing ) between the total incidence rate and level of resistance against the cycled drugs during the baseline period as measured in average number of resistances per isolate ( Figure 1B ) . At low levels of baseline resistance , clinical cycling reduced the total incidence rate of resistant infections substantially . Because of the enormous biological differences between the various bacterial pathogens that may cause hospital-acquired infections , we repeated our analysis for single pathogen groups and species ( Figure 1C ) . While clinical cycling remains overall beneficial , its success strongly depended on the pathogen species . Differences in antibiotic consumption and import of resistance into the hospital are strong confounders when comparing strategies to fight resistance . It has e . g . been argued that conducting a study per se might alter prescription behavior and thus reduce antibiotic usage and increase antibiotic heterogeneity [60] . We collected data on overall antibiotic heterogeneity during baseline and clinical cycling ( in this case measured over all periods ) , antibiotic usage and whether the study controlled for imported pathogens . We obtained very similar estimates for the effect of clinical cycling ( Table 1 ) when adjusting for these three confounders in a multivariate meta-regression . A sensitivity analysis of the results can be found in the supplementary material ( text S1 , †table S4 , S5 ) . We used our epidemiological model to investigate whether we could find theoretical explanations for the results of our meta-analysis . Unlike previous work , our model distinguishes between asymptomatically colonized and symptomatically infected patients . In particular , we make the realistic assumption that treatment is adapted if an asymptomatically colonized patient progresses to symptomatic disease . For instance , patients receiving drug A are switched to drug B when their condition deteriorates . In clinical practice , it may be impossible to adhere to the current treatment regimen under all circumstances . To accommodate for variable adherence , we include patients treated with neither of the scheduled drugs as well as patients treated with both drugs simultaneously . We also consider two transmission modes: delayed transmission via contaminated surfaces and direct transmission . Furthermore , we consider a stochastic and a deterministic version , which describe small populations ( i . e . single wards ) and large populations ( i . e . entire hospitals ) , respectively . We employed our model to address how the benefit of “adjustable cycling” changes with the period length . Figure 2 gives an overview of the dynamics during different period lengths . For the extremes of the screened period range , our findings are in accordance with previous studies ( Figure 3 , S2 , and S3 ) . For periods below 5 days , there is no difference between “adjustable cycling” and “adjustable mixing” . Intuitively , this makes sense because for cycling periods below the average length of stay ( in our standard setting 6 . 8 days ) , at a given time point the patients in the ward will have started their therapy in different phases of the cycling regime , and will therefore be treated with different drugs . Thus , “adjustable cycling” leads to a similar heterogeneity in drug use as “adjustable mixing” . For very long periods , “adjustable cycling” performs worse than “adjustable mixing” . This is because cycling with long periods is almost equivalent to strict mono-therapy , leading to high frequencies of the currently favored single-resistant strain ( Figure 3D , S2 , and S3 ) . However , we find for most parameter settings that “adjustable cycling” outperforms “adjustable mixing” for a range of intermediate periods ( Figure S4 ) . The success of “adjustable cycling” might be attributable to extinction of strains that are resistant to the antibiotic that is currently unused . Surprisingly , adjustable cycling performs worse in stochastic models , falsifying that hypothesis ( Figure 3 , S2 , and S3 ) . Our findings are in contrast to earlier studies that employed deterministic models [5] , [6] , which have argued that cycling always performs worse than mixing . In particular , it was argued that the disadvantage of cycling is monotonically increasing with the cycling period [5] . We tested which of our model assumptions changes the predictions so fundamentally . Unlike previous work , our model distinguishes between asymptomatically colonized and symptomatically infected patients . In particular , we make the realistic assumption that treatment is adapted if an asymptomatically colonized patient progresses to symptomatic disease . For instance , patients receiving prophylaxis with drug A are switched to drug B upon progression . We compared our chosen endpoints as well as the prevalence of different genotypes either with ( Figure 3 A and C ) or without ( Figure 3 B and D ) this adjustment of treatment . When assuming that there was no progression from colonization to symptomatic infection , the number of colonized patients raised monotonically with the period ( Figure 3 B ) as observed by Bergstrom et al . [5] . Why does an adjustment of therapy make “adjustable cycling” effective ? The most pronounced difference is that the prevalence of singly resistant pathogens is lower with the adjustments ( Figure 3 C and D ) . This is because the treatment switch slows the rise of those pathogens , which are resistant to the current treatment during a particular cycling period . At the same time , a substantial amount of resistant infections is washed out by asymptomatic carriers leaving the hospital . Due to this fast decline and the slow rise of resistance , a previously restricted drug can be successfully re-employed in the next cycling period . As mentioned above , one aim of this study was to investigate whether we could find theoretical explanations for potentially divergent recommendations depending on hospital settings or differences in pathogen biology . To this end , we screened a very large parameter space ( see material and methods and text S1 ) . We defined the optimal period as the period most successfully reducing inappropriate treatment without leading to an increased prevalence of symptomatic infections ( Figure S4 ) . A sensitivity analysis describing how the optimal period can be found in the supplementary material ( Figure S5 , S6 , and S7 ) . In our model , “adjustable cycling” with a fixed period of 30 days was often successful and rarely clearly disadvantageous . Despite enormous improvements in some settings when individually adjusting the period for each setting , the optimal period only performed 1 . 6% ( median ) better than a fixed period of 30 days . A detailed analysis of the influence of specific disease and hospital characteristics is given in the text S1 . In accordance with the results of our meta-regression , adjustable cycling is especially advantageous if multiple resistance has not risen to high frequencies yet but is likely to rise further .
The question when to use cycling or mixing has been controversially debated [10] , and clinical and theoretical studies came to different conclusions [5]–[7] , [9] , [10] , [61] . There are two possible explanations for this divergence between theory and clinical observations and potentially between different pathogens: We might not have sufficient data on population-wide resistance emergence . Alternatively , specific settings or differences in pathogen biology might not have been adequately captured in theoretical models so far . We employed a method for a quantitative multivariate random-effects meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate for both total infections and resistant infections . Our findings remain robust in uni- and multivariate metaregressions . We investigated the influence of 60 factors that might affect study outcome by meta-regressions . Regressions with a single potential confounder are simplifications and might bias our results . However , if the results of the meta-regressions and the mathematical model are in concordance , it is justified to suggest that the tested factors influence the success of cycling . Indeed , we found that clinical cycling reduced the number of total infections when the pathogens isolated during the baseline period had a low average number of resistance genes to the drugs employed in the cycling regimen . This is in line with our and previous theoretical results arguing that cycling is effective in preventing the evolution of multiple resistance , but that there is little difference once multiple resistance is wide-spread [5] , [6] , [62] . In our analysis , we assume that the baseline period most closely resembles “adjustable mixing” . However , physicians might tend to prescribe drugs that both cover a broad spectrum of pathogens and which they are familiar with . Additionally , they may be asked to use the substance that has the lowest cost . These restrictions may lead to a predominance of one drug . Furthermore , there is only one clinical study simultaneously comparing mixing and cycling [63] . Finally , the implementation of a study might alter prescribing behavior [60] . We examined these confounders by adjusting for different AHIs ( antibiotic heterogeneity indices ) [28] , as well as the total volume of antibiotic consumption ( measured in daily defined doses , DDD ) . These adjustments did not generally change the outcome . Another potential confounder may be differences in the influx of resistant pathogens into the hospital . Some of the studies we analyzed controlled for this confounder . Additional adjustment for the differences in these two study groups further widened the confidence interval such that all differences became statistically insignificant , while the general trend towards a positive effect of clinical cycling remains . Although adjustment leads to less clear results , we would expect that confounders are comparable for all considered pathogens and confounders therefore do not explain the divergent findings for different pathogens . Moreover , the adjusted endpoints are not uniformly shifted towards a less favorable outcome . This indicates that the observed success of clinical cycling is not solely attributable to confounders that were most criticized [11] . Despite testing for publication bias ( data not shown ) , we cannot exclude that unsuccessful studies were not published . However , we chose a new composite outcome ( weighted incidence rate of resistance ) , making publication bias for this particular measure less likely . Nosocomial infections can be caused by a large variety of bacterial pathogens . We therefore repeated our analysis for important pathogen groups and bacterial species . Again , the overall effect of clinical cycling was beneficial , especially in reducing resistance . Surprisingly , we observed large differences between different pathogens . The detrimental effect of clinical cycling regarding infections caused by enterococci might be partially explained by the fact that one study used linezolid and vancomycin for their regimen , leading to outbreaks of vancomycin-resistant enterococci ( VRE ) in the vancomycin periods [54] . Also from previous theoretical studies as well as our theoretical results , we expect that cycling favors singly-resistant pathogens . The study design of many clinical studies had been criticized before [11] . Based on the available studies that fulfill our selection criteria , our meta-analyses consistently show that clinical cycling is beneficial . To strengthen this finding , we used mathematical modeling to investigate the underlying mechanisms that explain our results . This theoretical epidemiological model specifically addresses the situation in hospitals and can be adapted to a multitude of infectious diseases . Importantly , our model allows adjusting ineffective treatment . These “adjustable strategies” are different from the strict cycling and mixing modeled in previous theoretical work , but is likely to be closer to clinical reality . The flexibility of our model enables us to identify the optimal period for a large parameter-range for several settings . This is essential for elucidating the influence of pathogen biology on optimal treatment strategies . Our model includes many of the characteristics of hospital wards that were not considered in previous models . However , we made simplifications that are discussed below . These simplifications were necessary because screening a large parameter space would become impossible with increasing model complexity . Importantly , for parameter settings corresponding to those in earlier models we come to the same conclusions . We assume that resistance always fully protects from the effects of the antibiotics and neglect any within-host dynamics . We only model a single hospital ward and assume that the composition of incoming patients is constant . In our modeling framework , the susceptible state is a result of previous antibiotic therapy . It indicates that a patient's microflora has been disturbed to a degree that other strains can easily invade . Despite the large numbers of bacteria in the microflora in colonized patients , their numbers likely decline when treated with an antibiotic for which they are susceptible . This process would be most accurately described by a continuous decline of infectiousness , which may never reach zero . However , to keep our model tractable , we assume that the bacterial load in the microflora is reduced to an extent that transmission of a strain susceptible to the used antibiotics is negligible compared to the infectious pressure by fully colonized or infected patients . Furthermore , we assume that the mutation rates to resistance are constant . With plasmid-borne resistance , the rate of resistance acquisition depends on the abundance of both donor- and acceptor strains . Thus , our model reflects chromosomal resistance more accurately than plasmid-borne resistance . However , mutation rates have a negligible influence once resistance is brought in by incoming patients . We therefore do not expect that the results of our model would change substantially when taking different modes of resistance acquisition into account . Naturally , there is an enormous biological diversity in all pathogens that cause nosocomial infections . Thus , we would expect differences in the speed of resistance emergence and spread . Here , we focus on the question , which salient properties of these bacteria determine which treatment strategy will be most successful . One important factor we identify in all our analyses is the rate of emergence of multiple resistance . In our general meta-analysis we found that the baseline prevalence of resistance strongly affects the success of cycling . Consistent with these results , we observe in our model that “adjustable cycling” can suppress the emergence of multiple resistance . This is the case when multiple resistance is not present in incoming patients , but would emerge de novo in the ward during “adjustable mixing” , i . e . with high mutation rates in the stochastic model and more generally in the deterministic model . The fact that “adjustable cycling” is even more effective in a deterministic model indicates that extinction events during the off-periods play only a minor role and cannot explain potential advantages of cycling . This is in contrast to making use of extinction in informed switching , where treatment is switched depending on current resistance frequencies [64] . Unsurprisingly , these results only hold when the single-resistant strains have a competitive advantage over the double-resistant strain in each cycling period . The optimal period depends on the emergence of double-resistant strains and the generation time ( time between the infection of a patient and the transmission to the next patient ) . These factors are not always known , but a period length of 30 days performed well in nearly all settings . When in doubt , a shorter period seems to be more beneficial , because there is no difference between “adjustable cycling” and “adjustable mixing” when the period length is shorter than the generation time , while too long periods are equal to treating with only one drug . Despite the lack of correlation between number of used drugs and study outcome in the meta-analysis , it would also be interesting to develop theoretical models with more than two drugs . From previous theoretical studies [6] , we would expect that cycling improves as more drugs are included , because resistance against a specific drug would decline to lower levels until this drug is reintroduced . Most importantly , the findings of our meta-analysis agree well with our theoretical results . Both the meta-analysis as well as the theoretical model shows that cycling is beneficial if there is emerging or a low influx of double-resistance . Thus , our model incorporates an important , previously disregarded factor that changes treatment recommendations . Clearly , more pathogen-specific studies of larger scales are needed to answer in which pathogens cycling is beneficial .
We assess the outcome for a timeframe of ten years to account for the fact that the expected time for the availability of new broad-spectrum antibiotics is in the range of a decade . We performed all analyses with a stochastic and a deterministic version , which describe small populations ( i . e . single wards ) and large populations ( i . e . entire hospitals ) , respectively . In preliminary analyses , we found that transmission mode and the proportion of incoming resistant strains were the factors that lead to the greatest changes in model predictions . Therefore , we chose a total of six standard scenarios . We consider two transmission modes: delayed transmission via contaminated surfaces and direct transmission . Both transmission modes were analyzed for three settings: either i ) no pre-existing resistance in the community , ii ) pre-existing single-resistance or iii ) both single and double-resistance pre-exist . For these six standard scenarios , each of the 22 model parameters was varied over a clinically relevant range ( Table S6 ) , while all other parameters were kept at default values . All periods were chosen such that we evaluate the success at 3600 days exactly at the end of a period where the second antibiotic ( B ) was employed . For all six settings , we screened the combination of all default values by varying the period length over all integer divisors of 1800 ( i . e . 1800 , 900 , 600 , … , 2 , 1 ) . When varying single parameters in each of the six standard settings , we chose a subset between 5 and 360 days ( Figure S4 ) . The model we use in this study is based on a model we used previously [64] , [72] . We consider a compartmental epidemiological model that aims at describing a single hospital ward ( for an overview over the parameterization see Table S6 ) . We assume that two broad-spectrum antibiotics are available for empirical treatment . We will refer to these as drug A and B . Accordingly , we follow four genotypes ( Figure S8 ) : wild type ( sensitive to both drugs ) , resistant to A , resistant to B , and resistant to both drugs . Resistance can be acquired via mutations , which occur at rates μa , μb and μab , the subscript denotes the drug against which resistance is acquired . The parameter μasym describes μb relative to μa , while keeping the resulting μab constant . For simplicity , we make the assumption that there is no cross-resistance , meaning there is no additional selection pressure for A-resistance or B-resistance other than by drug A and B , respectively . Patients are classified as being protected ( P , e . g . intact microflora ) , susceptible ( S ) , colonized ( C; i . e . asymptomatic carriers ) , or infected ( I; i . e . symptomatic carriers ) ( Figure 4 ) . In this context the susceptible state is a result of previous antibiotic therapy . It indicates that a patient's microflora has been disturbed to a degree that other strains can easily invade . Furthermore , we assume that the bacterial load in the microflora is reduced such that transmission of a strain susceptible to the used antibiotics ceases . We assume that both mortality and morbidity only differ in symptomatically infected patients , the additional mortality in these patients is given by the parameter d , the reduced likelihood of leaving the hospital when infected by the parameter rI . With long-term treatment , protected patients may proceed to the susceptible compartment after a time tcl , P . We consider two transmission modes; either immediate transmission ( also appropriate for transmission without a time-lag via health care workers ) or delayed transmission . The latter occurs via a pathogen reservoir outside the patients ( E ) , which describes most appropriately environmental contamination . It may also describe the dynamics resulting from the transient colonization of health care workers , although these are not modeled explicitly . Patients are first asymptomatically colonized and may then progress after a time tp . The time to clearance tcl when treated appropriately is the same for both colonized and infected patients . The compartments C and I are divided in subcompartments according to the carried genotype ( wt , A- , B- or AB-res ) . We assume a fixed number of beds ( 20 ) in the hospital ward . As soon as a bed is free , new patients are admitted within a day , resulting in an average population size of ∼17 patients per ward ( 85% occupancy ) . The composition of the incoming patients regarding colonization and resistance status is assumed to be constant over the observed timeframe . These frequencies are described using the parameters in table S6 , section 2 . The proportion of patients carrying resistant and double-resistant strains is given by pres and pab , respectively , the relative proportions of A- and B- resistance are given by pasym ( if this is 0 . 5 , both strains are found at equal frequencies ) . To follow treated patients , all compartments are subdivided according to the treatment status ( Figure 5 ) . Since we only investigate resistance to drug A and B , we do not take any other drugs into account . Infected patients are treated per default according to the current treatment strategy ( “adjustable mixing” or “adjustable cycling” ) as soon as they enter the hospital or progress to the infected compartment . Here , we consider “adjustable strategies” , i . e . we assume that in patients that progress while they are treated , the treatment is switched . Furthermore , we assume that susceptible patients cannot be infected with a strain when treated with a drug the pathogen is susceptible for . A certain fraction of patients that are not symptomatically infected with the considered pathogen may also receive treatment with the scheduled drugs . The frequency of such treatment , which we call here prophylaxis , is given by fp , and describes the number of asymptomatic patients receiving the currently scheduled therapy . In addition , some patients may receive both drugs , this is denoted by fp , AB . Figure S9 gives the average treatment frequency during one strategy . Around seven of seventeen patients ( 41% ) receive either of the scheduled drugs at any point in time . Once an infected patient is assigned to a drug , he remains on this treatment until he leaves the hospital unless the treatment is inappropriate ( for example treatment with drug A for an A-resistant infection ) , in which case it is switched with a rate s . The biology of different infectious diseases is described with the parameters of table S6 , section 3 . These determine a ) how fast de novo resistance may arise in individual patients , b ) the costs of these resistance mutations , which are assumed to lower transmission probability , c ) the rates with which patients can recover after treatment , d ) the increase in mortality by the disease and finally e ) how fast colonized patients become symptomatic . We consider both a deterministic and a stochastic version of the model described above . The deterministic model is implemented by numerically solving the ordinary differential equations ( ODEs ) that correspond to figures 4 , 5 and S8 . The stochastic model is derived from these ODEs by considering transition between compartments as stochastic events according to the Gillespie algorithm . All codes are available upon request .
|
The rise of antibiotic resistance is a major concern for public health . In hospitals , frequent usage of antibiotics leads to high resistance levels; at the same time the patients are especially vulnerable . We therefore urgently need treatment strategies that limit resistance without compromising patient care . Here , we investigate two strategies that coordinate the usage of different antibiotics in a hospital ward: “cycling” , i . e . scheduled changes in antibiotic treatment for all patients , and “mixing” , i . e . random assignment of patients to antibiotics . Previously , theoretical and clinical studies came to different conclusions regarding the usefulness of these strategies . We combine meta-analyses of clinical studies and epidemiological modeling to address this question . Our meta-analyses suggest that cycling is beneficial in reducing the total incidence rate of hospital-acquired infections as well as the incidence rate of resistant infections , and that this is most pronounced at low baseline levels of resistance . We corroborate our findings with theoretical epidemiological models . When incorporating treatment adjustment upon deterioration of a patient's condition ( “adjustable cycling” ) , we find that our theoretical model is in excellent accordance with the clinical data . With this combined approach we present substantial evidence that adjustable cycling can be beneficial for suppressing the emergence of multiple resistance .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"organismal",
"evolution",
"medicine",
"and",
"health",
"sciences",
"infectious",
"disease",
"epidemiology",
"microbiology",
"bacterial",
"diseases",
"emerging",
"infectious",
"diseases",
"population",
"modeling",
"microbial",
"evolution",
"population",
"biology",
"infectious",
"disease",
"control",
"bacterial",
"pathogens",
"infectious",
"diseases",
"medical",
"microbiology",
"epidemiology",
"evolutionary",
"modeling",
"microbial",
"pathogens",
"infectious",
"disease",
"modeling",
"microbial",
"ecology",
"ecology",
"biology",
"and",
"life",
"sciences",
"computational",
"biology",
"evolutionary",
"biology"
] |
2014
|
Cycling Empirical Antibiotic Therapy in Hospitals: Meta-Analysis and Models
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.